Interesting detail

“Communism deprives no one of the power to appropriate products in society” → Under communism, people can still use and enjoy things — you still get to “appropriate” (take for yourself) the things you need or want: food, clothes, a home, etc.

It doesn’t mean everyone loses personal possessions. You still get the products of society — you just don’t own them at the expense of others., cant' exploit other peoples' work to get rich

Wage labour means working for someone else in exchange for a wage (money) — instead of directly producing things you own yourself.

So, under capitalism:

You don’t own the factory, tools, or land you work with.

You sell your labour power (your ability to work) to someone who does own them — the capitalist.

In return, you get a wage (enough to live on, ideally), but the value you produce is usually greater than your wage.

DOI: 10.3390/ijms21103727

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bdscstockkeepers

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1093/genetics/iyae181

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/j.cmet.2025.09.015

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

I know from personal experience that when people actually listen, it's really helpful for me because I feel like I'm being validated and I feel seen.

I know therapists use paraphrasing a lot to make sure they're getting the right information, and to make sure that it's clear, I know paraphrasing is also a great way to put things into perspective for people. I will talk with my mom and she's really good at paraphrasing things for me to clear things up.

Clarifying questions are important especially when you work as a team. You can't clear things up if there's no question for the clarification.

Some people who have certain neurodivergences can have issues recognizing the need for feedback during the listening stages of a conversation, I know I personally struggle with this often.

Most times, the brain takes what we do remember to patch it up and make it into a full story that we remember.

You can also narrow down your research by choosing a specific time period for your sources like the past 10 or 20 years

Decide what type of sources you’re going to use whether that’s articles, scholarly journals, textbooks, or primary sources.

Writing a research paper can be intimidating, but it gives you the chance to explore and deeply understand a topic.

Instead of trying to cover everything, narrow down your focus to a specific subtopic, doing this will help your research process

It is advising you to carefully consider the type of sources you use and ensuring that they are high quality and relevant.

asking questions to make sure you are doing your assignment correctly.

make sure you are able to get the data you want and need.

Breaking the paper into steps like picking a topic, make an outline, research a little at a time, write a rough draft these steps will make it less intimidating.

I liked how they talked about how figurative language can be straight forward or be more complicated because it lets you use it in different ways. If your writing a light hearted poem or even a funny poem you might use figurative language in a more simple obvious way. But when your writing about a more serious topic you might make it more complicated.

I would agree being more specific helps your reader picture what your picturing and helps get the story across more

I would agree the most effective poems should express your feelings and tell your story. Telling your readers how to feel about something will make them not want to read your work anymore and your poem will feel unauthentic.

This is very important when writing a simple poem. You want to make sure your story is still coming across the way you want to share it and your readers aren't getting distracted or overwhelmed by too many details. So only focus on the ones you think matter the most

My middle school english teacher did something like with my class every Friday. She would pick and object and we would have to write about what we think it's emotion is and why. Along with are they smiling or frowning. Then we would have to write a poem about it and we could choose to share or not. It was a great way to help build our creativity and got us thinking outside the box.

The specific writing that poetry is, is very healing. It helps express emotions in a way that only our individual brains can think of. Every one can write down I'm sad but it's the way everyone would describe their sadness in a unique way. When someone might say it feels like pouring rain another person might say it's like getting hit by a bus. The same thing can be true about happiness too. No one would describe their happiness the same way. Someone might say It feels like the sun is shinning just for me, when another person might say it feels like I picked up a lucky penny. It's a healing way to let our subconscious come out and speak

can you have this if you’re not able to cloister them on a goat farm though

hm. I believe this as itself but distrust the meta

how often do you find someone being reflective about this! neat

they though it was too strict and there was an uproar OOP-

ALL CREDIT TO BRENERS LEE FOR WEB DESIGN and just making technology better !!!

love how he was into coding at a young age!!!

first web page was published on August 16th,1991 by Berners lee.

HTTP (Hyper Text Transfer Protocol) for communication between servers and clients

Gives an idea what will be in the essay and will help structure your essay

Thesis statement is the main argument and is where you state the paper’s intentions

make sure i revise or ask a peer for help.

pinpoint the topic with a broad subject

is this not what noCAPTCHA reCAPTCHA does? let you in if norm, if not then it presents the image-based reCAPTCHA

how much can you push if you dont the answer yourself?

this guy is spitting. i think that education should be a place where different ideas can be challenged and discussed by all. because that gives the lesson strength. you can indoctrinate any old idea by suppressing all other points of view, but a lesson that can prove its worth by going up against opposing arguments is one that is truly sound and worthy of teaching.

i take it here that by relativism he means the idea that everyone should tolerate the ideas of others even when the ideas of morality clash. I also agree with this because of the paradox of tolerance.

Not only was coal going to be used to extensively increase industrial growth speed but it also marked the end of traditional heating of that time in favor for coal heating, combined with the fact that in the founding fathers eyes, they had inexhaustible coal reserves.

at the end, it shows us a chart of what is the most reliable - post-hoc detection is the best

it will be harder to detect Ai genereated content without computter help

basically a nonprofit organization to keep the internet running

ways to make watermarking a regular thing

page is not available? i wonder why looks like it had been under biden when published - "biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/"

both credible sources

one of the biggest companies working off of their own AI-model in oder to make a seamless watermark

certain words in a certain order show if text is AI-written

why would the developers not cooperate?

actual government PDFs and websites which talk about what is being made/done about AI

at lot of what im focusing on

In principle do you reckon it would be possible to use a random masking approach (like in models like ESM2) for this problem? Currently one (entire) side of a region informs the models prediction on the focal window, while in principle the most informative regions are both to the immediate left/right of the focal window. Random masking as a strategy could allow the model to leverage this information bidirectionally, but technically could be more challenging.

We greatly enjoyed reading this preprint for our internal journal club. This seems like a very principled and useful application of the transformer architecture in biology.

What do the different colors correspond to here (the figure is missing a legend)? You state in the text that the black line is the expectation, only describe one more - the inference limit, though it's unclear what color this is. I suspect this is the single line in red, whereas the estimated approximate posterior distribution from cxt is in blue?

connects with "walking and the wâhkôhtowin imagination" by dwayne donald. how does our environment impact our thought process? walking/reading outside, nature as rejuvenation, a refresh

Combining the key ideas, findings, and arguments from other published sources to create a new understanding topic.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

_Below we address all the comments by the reviewers. However, the figures that were used in our response are unfortunately not displayed in this format. _

Reviewer #1

Evidence, reproducibility and clarity

Thanks to the development of Ribo-Seq, translational buffering has been reported in the database of published Ribo-Seq and matched RNA-Seq, Rao et al. attempt to understand the mechanism underlying translational buffering of mRNA variation across diverse materials. Although the authors' report provides a step forward in our understanding of translational buffering, this reviewer found a series of concerns in this paper. These points could be tackled to improve the reliability of their findings, the strength of their main message, and the global understandability of the paper.

Major comments: 1. This paper heavily relies on the reference 18. However, this paper was not properly stated (no page or journal number); the study in Bioinformatics is nowhere to be found on the website, despite being out in 2024 apparently. Either title is wrong (yet a biorxiv can be found). This reviewer guessed that the reference 18 may be accepted. However, without a proper reference, this paper could not be judged since nearly all the parts of this work have been based on the reference 18. Also, the Ribobase data used in this manuscript comes from this reference, so it had better be well defined, especially when another Ribobase data set seems to be available online: http://www.bioinf.uni-freiburg.de/~ribobase/index.html

We apologize for the citation issue. This citation by Liu et al , 2024 (18) was a preprint from BioRxiv. This manuscript is now published in Nature Biotechnology. The reference has been updated in the revised version of the manuscript. The reference number in revised manuscript is Liu et al, 2025 (23).

In the Discussion, the authors mentioned "TE is based on a compositional regression model (18) rather than the commonly applied approach of using a logarithmic ratio of ribosome occupancy to mRNA abundance." This important information should be mentioned in the early section of the manuscript. Related to this, there are other published methods for exploring change in translation efficiency (e.g., 10.1093/bioinformatics/btw585; 10.1093/nar/gkz223) that could also be suitable in this context. It is not entirely clear if their approach is better than before. Again, the improper reference to 18 made our assessment of this work difficult.

We apologize and acknowledge the impact of the citation issue on this point. In Liu et al (2025), we have provided a comparison between our approach and the log-ratio strategy. We also agree that additional context was needed within the current study. Hence, we have now included more detailed information about the TE calculations in the initial results section (line 94).

As noted by the reviewer, several other methods have been developed previously for measuring changes in translation efficiency. These methods are designed to be used in cases of paired designs where there is a treatment or manipulation that is assayed along with controls. While these methods are highly valuable in assessing differential TE, they are unable to accommodate the type of meta-analyses described in our study. In particular, we do not report changes/differential TE with respect to a control sample but instead focus on the coordinated patterns of TE across experiments. We now note this important distinction in the manuscript in the discussion section (line 494).

The paper mainly relies on detecting a set of buffered genes using mRNA-TE correlation and MAD ratios (Ribo-Seq/RNA-Seq). While the concept seems sound, the authors should ensure that this method is reliable. Several controls could be used to confirm this. First, if any studies in humans or mice have described a set of genes as buffered, it would be worth checking for overlap between the authors' set of 'TB high' genes and the previously established list. Furthermore, the authors could use packages explicitly developed for translational buffering detection, such as annota2seq (https://academic.oup.com/nar/article/47/12/e70/5423604?login=true). Not all of the data used by the authors may be suitable for such packages, but the authors could at least partially use them on some of their datasets and see whether the buffered genes reported by these packages match their predictions.

We thank the reviewer for this constructive suggestion. To the best of our knowledge, no prior study in humans or mice has systematically analyzed translational buffering across a wide range of conditions. As a result, defining a gold-standard set for benchmarking is currently not feasible.

While packages such as anota2seq have proven highly valuable for identifying buffering effects in controlled experimental designs (e.g., comparing a treatment to a matched control), they are not readily applicable to the type of large-scale meta-analysis we present here.Our study integrates ribosome profiling and RNA-seq data across diverse datasets and conditions, which lies outside the design scope of such tools.

The most relevant point of comparison to our work is Wang et al. 2020 Nature, which examined a related but distinct form of translational buffering across species for a given tissue. We now present the overlap of genes identified as buffered in our study vs Wang et al. 2020. The details are presented in the reviewer's comment 5-2.

The threshold of 'TB high' or 'TB low' (top and bottom 250) is somewhat arbitrary. Why not top 100 or 500? The authors should provide a rationale for this choice. Also, they could include a numeric measure of buffering (the sum of the two rankings is probably suitable for this purpose). Several of the authors' explorations are suitable for numerical quantification (GO enrichment can be turned into GSEA, and the boxplot can be shown as correlations)

Thanks for these suggestions. We agree that the threshold used to define TB high and low are somewhat subjective. We ensure that changing this cutoff as suggested is easily achievable with the provided R script. These can be used to reproduce all of the reported analyses of translational buffering with different cutoffs.

To further assess whether our conclusions are robust to the selection of these thresholds, we tested several different values to define the TB high and TB low groups. As an example, we show here that the effect on protein variation and association of intrinsic features like the UTR lengths with the buffering potential of genes for different thresholds (i.e. if the TB high = top 100 or TB high = top 200) remain similar to the current cutoff of 250. However, if we increase the cutoff of TB high to 2000 and TB low to top 2000-4000 , the difference between the various features is diminished (Figure A& B). Further, protein variation (human cancer cell line and tissue) also becomes more similar across the three categories, possibly indicating a reduced regulatory potential of genes as their rank increases (Figure C& D).Our analyses reveal that highly ranked genes show associations with particular features, indicating an underlying hierarchy in translational buffering potential. This point is now discussed in the manuscript (line 177).

Legend: Effect of different thresholds on . A. Length features B. Median RNA expression C. Protein variation in human cancer cell line and D. on Primary human tissues

In response to the reviewer's suggestion of presenting data using numerical quantitation, we incorporated several additional inclusions in the manuscript.

1. We now report association of CDS / UTR length with translational buffering as a function of their translational buffering rank with highly ranked genes showing associations with particular features, indicating an underlying hierarchy in translational buffering potential (Sup Fig 3 A-B) Ii. We now include scatter plots which show that highly ranked genes have lower variation at the protein level in both cancer cell line and primary tissues (Sup Fig. 6 A-C).

Iii. We have now carried out modified GO enrichment analyses. Specifically, Gene Ontology enrichment analysis was performed for the TB high genes in humans and mouse using the clusterProfiler R package. Lists of TB high genes in human or mouse were analyzed against the Gene Ontology (GO) database using the enrichGO() function, with the organism-specific annotation database (org.Hs.eg.db for human or org.Mm.eg.db for mouse) as reference. Gene identifiers were supplied as gene symbols, and all genes in the current study were used as the background universe. Enrichment was carried out for the Biological Process (BP) ontology, with significance assessed by the hypergeometric test. P-values were adjusted for multiple testing using the Benjamini–Hochberg method, and terms with an adjusted p-value Legend: Gene Ontology (GO) enrichment analysis of the TB high gene set, performed with the clusterProfiler R package. Enriched GO Biological Process terms are shown after redundancy reduction using clusterProfiler::simplify. Each dot represents a GO term, with dot size indicating the number of genes associated with the term and color reflecting the adjusted p-value (Benjamini–Hochberg correction). Only the top non-redundant terms are displayed.

• *

Additionally, we performed Gene set enrichment analysis using the list of genes ordered according to their RNA-TE correlation. Hence lower ranks have lower RNA-TE correlations. The GSEA plots show significantly enriched Gene Ontology Biological Process (GO:BP) terms at the lower ranks of the ordered gene list. Together, these analyses further emphasize the observation that genes involved in macromolecular complexes are translationally buffered.

• *

Legend: Curves represent the enrichment score (ES) across the ranked gene list, with vertical bars indicating the positions of pathway-associated genes. The enrichment was identified using the gseGO() function from clusterProfiler.

Several of the statements of the authors in the Introduction or Discussion sections are not entirely true regarding the literature on the topics, or lack major papers on the topic, and therefore, they are a bit misleading. Among others, here are some:

We thank the reviewer for the suggestions and now have been incorporated in the revised manuscript, accordingly.

5-1 "In addition, genetic differences arising from aneuploidy, cell type differences or variability observed in the natural population can further determine the amplitude of variation (4-7). The effect of mRNA variation under these conditions is mostly reflected at the protein levels (2, 4-8).". Several recent or more ancient papers suggest that mRNA variation coming from aneuploidy, natural genetic variation, or CNV is buffered or not well reflected at the protein level: DOI: 10.1038/s41586-024-07442-9 DOI: 10.1073/pnas.2319211121 DOI: 10.1016/j.cels.2017.08.013 DOI: 10.15252/msb.20177548

We agree that mRNA variation coming from aneuploidy, natural genetic variation, or CNV is buffered or not well reflected at the protein level for some genes. This point has now been revised in the introduction. We have incorporated all the suggested literature into the revised manuscript (line 38).

5-2: The authors should also consider mentioning these studies and softening their initial statement. "Similarly, translational buffering of certain genes have been reported in mammalian cells, specifically under estrogen receptor alpha (ERα) depletion conditions (16).". Translational buffering has been deeply explored in mammalian tissues and even across several mammalian species in this study (DOI: 10.1038/s41586-020-2899-z). In this, the authors also provide a nice exploration of the gene characteristics that are associated with translational buffering. The authors should mention it and compare the study's findings to theirs ultimately.

We thank the reviewer for this suggestion. We have now cited the recommended study in the revised manuscript (line 65). Here, we provide a comparison of its findings with ours. While this related work offers important insights into translational buffering, its focus is on buffering across species within a given tissue, whereas our study emphasizes buffering across conditions, cell types, and treatments within a species. Despite this difference in focus, the comparison is highly informative, and we now highlight both the similarities and distinctions between the two studies in the relevant section of the revised manuscript.

Wang et al. calculate the variation at the transcriptome level vs at the translatome level and is represented as delta ∆ value for each gene. A lower value represents lower variation at the ribosome occupancy level than at the mRNA levels across various species. We classified the genes in the Wang et al study as TB high, TB low genes or others as identified in the current study while indicating the calculated delta ∆ from Wang et al. Many of the genes with a lower delta value (are delta ∆ Legend: A. Dot plot to highlight the delta value of all genes in the Wang et al study (also present in RiboBase) which are further grouped as TB high, low or others in (A) brain and (B) liver.

5-3: "Differences in species evaluated and statistical methods have resulted in conflicting interpretations (13, 28).". These conflicting results have been previously discussed in reviews on the topic that would be worth mentioning: DOI: 10.1016/j.cell.2016.03.014 DOI: 10.1038/s41576-020-0258-4

We have added these reviews at the appropriate location of the manuscript.

1. In addition to the p-values stated in the main text, the authors should annotate their plots when they find significant differences between groups to greatly facilitate the visual interpretation of the graphs.

We have now annotated many of the relevant graphs with p-values to facilitate visual interpretation, adding them where space and figure design allow.

Based on the data of Figure 4D, apparently, ribosome occupancy was not buffered even in high TB sets. The authors may argue that translational buffering may not cope with such a strong mRNA reduction. In that case, how big a difference in mRNA level does the buffering system adjust in protein synthesis? The authors should test gradual gene knockdown and/or overexpression and conduct Ribo-Seq/RNA-Seq to survey the buffering range.

We appreciate the reviewer’s suggestion regarding the experiment to determine the buffering range.To understand this for multiple genes, we attempted a series of knockdowns using CRISPR/gRNA approach using a MutiCas12a approach. We targeted 8 buffered and 2 non-buffered genes using a 10-plex crRNA along with 10-plex gRNA serving as a negative control (Figure below). The fold change at the mRNA level of the targeted gene was within the variation range observed in replicates for other non-targeted genes. The challenge in performing a gradual knockdown is the subtle changes in RNA expression falls within the margin of error of estimation, making it difficult to understand the clear implications of the mRNA levels on buffering. Hence, the precise experimental manipulation of mRNA expression levels that would be conducive to translational buffering remains highly technically challenging. As noted in our manuscript (Figure 4D), the conventional approaches for manipulation of transcript abundance lead to larger changes than typically observed as a result of natural variation.

*Legend: Validation of translational buffering by targeted knockdown of genes. A. The scatter plot shows the coefficient of variation of mRNA and ribosome occupancy between HEK293T cells targeted with sgRNA of different efficiencies. The genes indicated in blue are buffered and those in green are non buffered genes. B. The plot shows the fold change in mRNA abundance and ribosome occupancy as compared to cells that were infected with non-targeting crRNA array control (ratio of cpm in test vs control). Each color represents a gene and each point of a gene represents cells targeted by one of the four CRISPR arrays. *

"differential transcript accessibility model" could not be functional if mRNA is reduced beyond the accessible pool (i.e., less than the threshold, all the mRNAs are translated without buffering). The authors should carefully reconsider this model and the effective range of mRNAs.

We agree with the reviewer that according to the 'differential transcript accessibility model,' transcripts with abundances below a certain threshold should be completely accessible to the translational pool. Further, this could also be true for the other model, wherein initiation rate cannot increase beyond a particular threshold for transcripts of very low abundance. However, our observation from our haploinsufficiency analysis (Figure 4 B& C) and siRNA knockdown analysis from RiboBase (Figure 4 D) suggests that buffering might be possible within a given range of transcript abundance. Testing the buffering range by serial knockdowns might help in determining the threshold at which transcripts exhibit buffering. However, due to the challenges of serial knockdown as discussed above, makes this analysis difficult with Ribosome profiling and matched RNA-seq approach. An alternative approach could involve imaging translating and non-translating mRNA of buffered genes in different cells, which may help distinguish the two models. However, this falls outside the scope of the manuscript.

Minor comments:

1. Some figures are of poor quality as they seem to have points outside of the panel representations... Like Figure 3C, one point is out of the square, same for Figure 4E. Similarly, on figure 5F, some outliers seem to be clearly cut from the figure (maybe not, but then the author should put a larger space between the end of the figure and the max y points). Same for panel S2D and S6D, this does not sound so rigorous.

We agree and apologize for this issue. The axes of the figures have been annotated appropriately to indicate the presence of outliers in the figures.

1. There are several typos or weird sentences. Here are some (but maybe not all): 2-1: [...]with lower sums corresponding to higher final ranks. "two rankings". Based on these final ranks[...] 2-2: For each dataset, median absolute deviation (MAD) "i" protein abundance was calculated across samples 2-3: [...]neighbor method implemented in the MatchIT package (38) Differences in protein[...] a point is missing here. 2-4: Additionally a second dataset providing predictions of haploinsufficiency (pHaplo score) and triplosensitivity (pTriplo score) for all autosomal genes (25) was used to asses the distribution of these score"S" across buffered and non-buffered gene sets . There is a missing "s" at "score" and there is a space between the last word and the final point.

The necessary corrections have been incorporated in the revised version of the manuscript.

1. In the "Lymphoblastoid cell line data analysis:" section, this reviewer wonders why the authors used a different method to calculate buffering compared to before.

The main reason is the limited sample of the lymphoblastoid cell line data. In our larger analyses, we could use median absolute deviation as a robust metric of dispersion across heterogeneous samples. However, given the smaller dataset in that study we decided CV would be a better indicator of dispersion. To evaluate the potential for translational buffering of genes from RiboBase, we used two metrics. The first was the negative correlation between translation efficiency and RNA abundance across samples. The second metric relied on the ratio of variation in ribosome occupancy to variation in RNA levels. Given the limited sample size of the lymphoblastoid cell line dataset, we used the coefficient of variation (CV) instead of the median absolute deviation (MAD), as the data in this study were normalized using counts per million (CPM) rather than the centered log-ratio (clr) normalization used in RiboBase. This CV ratio allowed us to assess the effect of natural variation in RNA abundance on ribosome occupancy.

1. "Samples which had R2 less than 0.2 were removed as the residuals calculated for these samples could be unreliable". These samples for which the correspondence between RNA-Seq and Ribo-Seq is low wouldn't be the ones most impacted by translational buffering? Is it sure that the authors are not missing something here?

We agree with the reviewer that genes that show translational buffering may not conform to linear relationships between the two parameters. However, the proportion of genes exhibiting this buffering effect is not expected to significantly influence the overall regression fit. Instead, we hypothesized that low quality samples or truly different relationships between the two parameters can make this relationship nonlinear, rendering it unsuitable for linear regression analysis for calculation of TE.

To address these possibilities, we first analysed a commonly used proxy for data quality. Given the characteristic movement of ribosomes across mRNAs, periodicity of sequencing reads is a useful metric to assess whether reads are randomly fragmented, as in RNA-seq, or specifically represent ribosome-protected footprints. For this, we compared two groups: samples that were removed (~30) and those retained for analysis. We plotted the distribution of periodicity scores for all samples in both groups. For the calculation of periodicity scores, first the percentage of reads mapped to the dominant frame position across the dynamic ribosome footprint read length range was calculated for each sample. The periodicity score was calculated by taking the weighted sum of these dominant percentages, with weights based on the total read counts at each length.

The results indicate that the removed samples did not have lower periodicity scores, suggesting that their quality in terms of periodicity was comparable to the retained samples.

        To assess the second possibility, we checked if the study involved major perturbations, which may skew the relationship towards non linearity. The 30 samples that were removed came from 14 unique studies, 18 of which involved perturbation which possibly affected either of the two parameters. In addition to the genetic/pharmacological perturbations specific to the study, the overall conditions of the cells during an experiment could influence this relationship. Another point to note is that many of the filtered-out samples are HeLa and HEK293T cells, which show a normal relationship between ribosome occupancy and RNA abundance for the majority of cases.

        These considerations suggest that removing these samples is most appropriate, as their inclusion could bias the TE calculations.

For Figure 4B and 4C, the authors should provide statistical tests and p-values to confirm the observed trends.

The haploinsufficiency and triplosensitivity analyses are now supported by a chi-squared test. The details of the statistical test are now mentioned in the text and the p-values have been noted on the respective figures.

In Figure 2A, the "all genes" color doesn't correspond to the point color.

The color in the figure has been modified in the revised version of the manuscript.

1. "To understand if codon usage patterns are[...]". This comes slightly out of the blue. The authors could maybe explain why codon usage should be explored for translational buffering. The authors should cite recent key works in the fields: DOI: 10.1016/j.celrep.2023.113413 DOI: 10.1101/2023.11.27.568910

We would like to thank the reviewer for their suggestion. The references have been incorporated in the revised version of the manuscript. We have now explained why codon usage could be a contributor in determining the translational buffering potential (line 190).

"The change in each metric was calculated by subtracting the mean value in the control samples from that in the knockdown samples. This yielded the differential mRNA abundance and ribosome occupancy resulting from gene knockdown.". This looks statistically weak. The authors should consider using more robust methods like DESeq.

We thank the reviewer for the suggestion. We reanalyzed the selected studies using edgeR and the modified figure is included in the revised version of the manuscript (Figure 4D). The conclusion after this analysis remains essentially the same. In particular, translational buffering is ineffective when mRNA abundance is perturbed drastically. Additionally, the limited number of experiments with direct perturbation of buffered genes limit the generalizability of this observation. This limitation is included in the result section (line 342).

Legend: Scatter plot represents log2 fold change in RNA abundance and ribosome occupancy. Each point represents a gene and the fold change in its RNA and ribosome occupancy with respect to their controls. The line represents the line of equivalence. Buffered genes do not show less change in ribosome occupancy upon reduction in their RNA levels than other genes.

1. "Genes in the buffered gene set had a higher codon adaptation index than the non-buffered set, indicating that candidates in the buffered gene set are relatively well expressed due to the presence of a higher proportion of the codons observed in highly expressed genes". What do the authors mean by "relatively well expressed"? Abundantly expressed? This sentence and the causality under it is unclear and should be modified or better explained.

We thank the reviewer for pointing out the lack of clarity in the sentence. We have now quantitatively measured the CAI in the three categories and modified the sentence to better explain the rationale in the revised version (line 183). “To understand if codon usage patterns are associated with translational buffering, we next analyzed codon properties across buffered and non-buffered human gene sets. The codon adaptation index quantifies how closely a gene’s codon usage aligns with that of highly expressed genes. Genes in the buffered gene set had a higher codon adaptation index than the non-buffered set. Specifically, 28.4% of TB high genes, 14% of TB low genes and 9.3% of genes in the other category fall within the top decile (>90th percentile) of codon adaptation index.”

The panel 4D is unclear. Is one point associated with one gene? Or is it the average of several genes? If it's one point for one gene, it is important to clearly state it because the number of cases is therefore quite low, especially for the TB high and low.

Each point and line are associated with a single gene. This is now clarified in the legend of the figure (line 364). The number of genes in this analysis is limited to the available ribosome profiling data with gene knockdown experiments.

1. In Figure 2J, GGU (Gly), AAG (Lys), and ACU (Arg) provide negative effects on prediction, although these were enriched in the high TB set (Figure 2E). This contradiction should be explained.

While this appears to be a seeming contradiction, it is in line with what we expected. In particular, the objective of Figure 2J is to illustrate the features that predict the mRNA–TE correlation of genes, as identified using a LGBM model. The Spearman correlation shown reflects the relationship between each feature and the mRNA–TE correlation values. A negative correlation for codons such as GGU (Gly), AAG (Lys), and ACU (Thr) suggests that enrichment of these codons is associated with lower mRNA–TE correlation. This is in agreement with our observation in Figure 2E which suggests that high TB genes are enriched in these codons. In contrast, transcript size exhibits a positive correlation, indicating that shorter transcripts tend to have lower mRNA–TE correlation values.

Given that the choice of colors is a potential source of confusion, we have revised the text (line 230) and the figure (& legend) to try to clarify this relationship.

The subtitle of "Translationally buffered genes exhibit variable association kinetics with the translational machinery in response to mRNA variation" sounds unfair to this reviewer. Since the authors did not work on kinetics directly, the use of this word is misleading.

We agree and revised the subtitle to “The association of translationally buffered genes with the translational machinery varies in response to changes in mRNA abundance"

1. The explanation of Figure 5A "We next explored the potential mechanisms that may give rise to translational buffering. Specifically, we considered two non-mutually exclusive models by which mRNA abundance might be decoupled from ribosome occupancy. In the first, the "differential transcript accessibility model", mRNA abundance determines the fraction of transcripts that are accessible to the translational pool. In this scenario, an increase in mRNA abundance would be accompanied by a proportionally smaller increase in the fraction of transcripts entering the translating pool for buffered genes, compared to non-buffered genes. In the second, the "initiation rate model", the rate of translation initiation per transcript scales inversely with mRNA abundance. Under this model, the proportion of mRNA entering the translational pool would be comparable across buffered and non-buffered genes (Fig 5A)." is hard to understand. The authors should rewrite for a better understanding of the readers.

This section has been rewritten in the revised version of the manuscript. The text now reads as

“We next explored the potential mechanisms that may give rise to translational buffering. Specifically, we considered two non-mutually exclusive models by which mRNA abundance might be decoupled from ribosome occupancy. In the first, the “differential transcript accessibility model”, mRNA abundance determines the fraction of transcripts that are accessible to the translational pool. In this scenario, an increase in mRNA abundance would be accompanied by a proportionally smaller increase in the fraction of transcripts entering the translating pool for buffered genes, compared to non-buffered genes. In the second, the “initiation rate model”, the rate of translation initiation per transcript scales inversely with mRNA abundance. Under this model, as mRNA abundance increases, translation initiation on each transcript is reduced, thereby lowering the number of ribosomes per transcript. However, this mechanism allows a proportional increase in transcripts entering the translational pool for buffered genes, similar to non-buffered genes”

Significance

Thanks to the development of Ribo-Seq, translational buffering has been reported in various works. However, the systematic investigation has remained challenging. Employing the database of published Ribo-Seq and matched RNA-Seq, Rao et al. attempt to understand the mechanism underlying translational buffering of mRNA variation across diverse materials. A group of mRNAs whose expression variance is buffered at the translation level was comprehensively surveyed in humans and mice. The authors found a series of features in the translationally buffered genes, including high GC contents in the 5′ UTR, optimal codon usage, and mRNA length. The depletion or increase of one allele of the genes in the group may be particularly detrimental to cells. The authors' report provides a step forward in our understanding of translational buffering, appealing to the broad scientific community in basic and applied biology. However, this reviewer found a series of concerns in this paper, including clarity in the methods, experimental validation, referring the earlier works, etc. These points could be tackled to improve the reliability of their findings, the strength of their main message, and the global understandability of the paper.

We thank the reviewer for noting the significance of the work and for their constructive feedback.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Rao and colleagues present a comprehensive analysis of translational buffering in human and mouse by mining 1515 matched ribosome profiling and RNAseq datasets from diverse tissues and cell lines. They define translational buffering as genes whose TE is negatively correlated with mRNA abundance across conditions, and further identify candidates by comparing median absolute deviations of ribosome occupancy versus mRNA levels. The authors find a conserved set of buffered genes enriched for components of multiprotein complexes, demonstrate that buffered genes exhibit lower protein variability and greater dosage sensitivity, and propose two non-mutually exclusive mechanistic models (differential accessibility and initiation rate modulation). Finally, they perform complementary fractionation experiments in HEK293T cells to support these models.

These findings propose a novel, conserved mechanism of translational buffering that tunes gene expression in mouse and human, showing how intrinsic sequence features and cellular context cooperate to stabilize protein output across diverse conditions. However, further evidence is required to fully support the authors conclusions, particularly direct validation of the proposed models of buffering.

We thank the reviewer for their positive assessment and thoughtful suggestions that we address below.

Below are my main concerns:

1. The choice of the top 250 genes by spearman correlation and MAD ratio as "TB high" seems arbitrary. The authors should justify these cut offs (via permutation analysis or FDR control) and show that conclusions are robust to different thresholds.

We agree that the threshold used to define TB high and TB low is somewhat subjective, and we now clearly acknowledge this in the discussion section (line 485). We now provide an R script that reproduces all analyses of translational buffering, where changing this cutoff to higher or lower values is straightforward.

To ensure the robustness of our conclusions, we evaluated several thresholds for defining TB high and TB low. We observed that the conclusions hold within a reasonable range of values (100-250). For example, the effects on protein variation and the association of intrinsic features such as UTR lengths with buffering potential remain consistent when TB high is defined as the top 100 or the top 200 genes, compared with the current cutoff of 250. In contrast, when we define TB high as the top 2000 and TB low as ranks 2000–4000, the difference between the various features is diminished (Figure A& B). Further, protein variation (human cancer cell line and tissue) also becomes more similar across the three categories, possibly indicating a reduced regulatory potential of genes as their rank increases (Figure C& D). Our results show that highly ranked genes consistently associate with specific features, suggesting an underlying hierarchy in translational buffering potential.

Legend: Effect of different thresholds on . A. Length features B. Median RNA expression C. Protein variation in human cancer cell line and D. on Primary human tissues

The modified compositional regression approach for TE and imputation of missing values are central to the study, but details are relegated to supplemental methods. The manuscript would benefit from a clear comparison of this method against standard log-ratio TE estimates, including sensitivity analyses to missing-data imputation strategies

We thank the reviewer for the feedback. We have now added further description of the modified compositional regression and the imputation strategy in the results section (line 94). Comparison to standard log-ratio TE estimates and their limitations has already been detailed in Liu et al. 2025, Nature Biotechnology. Therefore, in the current manuscript we specifically focus on the effect of the imputation strategy.

        Specifically, the modified imputation slightly improved concordance between the set of genes that are identified to be translationally buffered using the negative RNA-TE relationship or using RNA -Ribosome occupancy correlation (0.91 to 0.94). Further, we assessed the correlation between TE and protein abundance as measured by mass spectrometry from seven human cell lines (A549, HEK293, HeLa, HepG2, K562, MCF7 and U2OS). The protein measurements were obtained from PaxDb. The new imputation strategy slightly increased mean correlation between the TE and proteome abundance as compared to naive strategy. It specifically showed improved correlation for HepG2, A549 and HeLa cell lines. 3507 genes were used for this analysis that were common between PaxDb, Liu et al., 2005 and the current study.

Legend: Proteomics vs TE correlation of cell types without or with imputation strategy. Spearman correlation between compositional TE calculated as calculated by Liu et al., 2025 from 68 samples from 11 studies (HEK293), 86 samples from 10 studies (HeLa), 58 samples from four studies (U2OS), 29 samples from five studies (A549), five samples from two studies (MCF7), seven samples from two studies (K562) and 10 samples from two studies (HepG2) or from the current study. 57 samples from 10 studies (HEK293), 82 samples from 9 studies (HeLa), 58 samples from four studies (U2OS), 29 samples from five studies (A549), 5 samples from two studies (MCF7), one samples from one studies (K562) and 9 samples from two studies (HepG2) . 3507 genes were used for this analysis that were common between Paxdb, Liu et al., 2005 and the current study.

Human data are derived mainly from immortalized cell lines, whereas mouse data are from primary tissues. Pooling these heterogeneous sources may conflate cell type-specific regulation with intrinsic buffering. The authors should either stratify analyses by context or demonstrate buffering signatures remain consistent within more homogeneous subsets

We thank the reviewer for the suggestion and agree that heterogeneity could potentially mask cell type-specific buffering effects. The TB-high genes we report are those that show consistent and robust expression across diverse contexts. However, unlike RNA-seq datasets, the current number of ribosome profiling samples per cell type is still limited, and a more comprehensive assessment of context-specific buffering will require larger datasets that will accumulate over time.

Nonetheless, we have stratified the analysis by cellular context. Specifically, we grouped samples of the same cell-type and repeated the buffering analysis. We provide a new table listing TB-ranks of genes for the five cell types with the largest sample sizes as a table in github.

https://github.com/CenikLab/Translational-buffering/blob/Translational-Buffering/combined_tables.xlsx

As an additional control, we compared buffering patterns between related and unrelated cell lines. For example, the correlation of TB ranks between related cell lines HEK293T (n = 98) and HEK293 (n = 57) is higher (0.46) than between either and an unrelated cell line, HeLa (n = 82). Similarly, the correlation between two liver cell lines, Huh7 (n = 39) and HepG2 (n = 9), is higher (0.20) than between Huh7 and a similarly sampled but unrelated lymphoblastoid cell line (LCL, n = 9; correlation = 0.05). While these analyses suggest that cell type-specific patterns may exist, their exploration is currently limited by sample size, as detecting buffering requires substantial variability in mRNA expression. We now highlight this as a limitation in the Discussion section (line 573).

*Legend: Spearman correlation between TB ranks of different pairs of cell lines. The first set indicates comparison with HEK293T. The second set indicates comparison between liver cells (HepG2 and Huh 7). *

The HEK293T fractionation experiments offer preliminary support for both the "accessibility" and "initiation" models, but only slope analyses are shown. To validate these models, the authors should perform targeted reporter assays (dual luciferase constructs with 5′UTR swaps) or manipulations of initiation factors (eIF4E knockdown) to directly test how transcript abundance alters initiation rates versus pool entry

We thank the reviewer for suggesting experiments to validate the proposed models. In the luciferase reporter experiments, constructs bearing the endogenous UTRs from non-buffered genes would be expected to result in expression that is proportional to transcript abundance. In contrast, swapping a 5’ UTR from buffered genes would mitigate this effect of translation buffering via “initiation rate model” depending on the 5 UTR sequence of transcript. However, as outlined below, this experiment has important caveats:

1. Role of coding sequence: Such assays primarily test the contribution of the 5′UTR and do not address potential cooperative effects between the 5′UTR and the coding sequence (CDS). Thus, if 5′UTRs fails to recapitulate translational buffering, it would be unclear whether the buffering requires coordinated action of the 5′UTR and CDS or whether the gene in question simply does not conform to the initiation-rate model.
2. Sensitivity of measurements: Reporter-based measurements often rely on RT-qPCR to quantify expression changes. While suitable for large fold-changes, small shifts may fall within the assay’s technical margin of error, limiting the interpretability of the results. iii. Gene-to-gene variability: Buffered and non-buffered transcripts likely span a wide range of intrinsic initiation rates. Selecting only a few “representative” transcripts for 5′UTR swapping could yield results that are not broadly generalizable.

Similarly, knockdown of general initiation factors will likely impact on both buffered and non-buffered genes, which could limit the ability to distinguish the effect of transcript abundance on translational buffering via either of the proposed models. We envision an alternative future approach that would involve single molecule imaging translating and non-translating mRNAs of buffered and non-buffered genes under varying abundance conditions in a physiological context. Such experiments are likely the most suitable for disentangling the contributions of accessibility versus initiation. While we find this an exciting direction for future work, it lies beyond the scope of the present manuscript.

The conclusion that buffering reduces protein variability relies on mass-spec comparisons, but ribosome occupancy does not always reflect functional protein output (due to elongation stalling or co-translational degradation). Incorporating orthogonal measures, such as pulse-labeling or western blots for key buffered versus non-buffered genes, would strengthen the link between buffering and proteome stability

We agree with the reviewer’s concern and have been acknowledged as a limitation in the discussion section. To address this with orthogonal approaches, we carried out several additional experiments. Specifically, we identified a study from RiboBase (GSE132703) that exhibited significant variation in FUS transcript (a translationally buffered gene) abundance across conditions—namely HEK293T wild type, LARP1A single knockout (SKO), and LARP1A/B double knockout (DKO) using their RNA-seq data. We reached out to the authors of the study and obtained these knockout cell lines. We reanalyzed RNA abundance under the different conditions by RT-qPCR and assessed protein levels by Western blot. Despite observing differences in RNA abundance, FUS protein levels did not exhibit corresponding change at the protein level.

We also selected a non-buffered gene; DNAJC6, that also showed RNA-level differences. However, the change in RNA expression was not consistent at the protein level. Some caveats of Western blot is its limited sensitivity which may prevent detection of subtle changes and that the measurements are steady-state protein levels which cannot resolve whether differences arise from altered synthesis or degradation.

*Legend : Validation of buffering gene by western blot: A. Plot showing the RNA abundance and ribosome occupancy of buffered gene ; FUS and non buffered genes; DNAJC6 with variation in HEK293T-wild type, LARP1A single knockout and LARP1A/B double knockout. B. Validation of the RNA seq data by qPCR. C. Western Blot showing the FUS, DNAJC6 and Actin in wild type and different mutants. D. Bar plot showing the quantification of western blot. *

        In addition to this targeted analysis , we performed quantitative mass spectrometry to evaluate the effect of mRNA variation at the protein level at global scale.

LC MS/MS analysis was performed on the above samples in triplicates at the Proteomics facility of the University of Texas. A total of 4,048 proteins were identified using a peptide confidence threshold of 95% and a protein confidence threshold of 99%, with a minimum of two peptides required for identification. Total precursor intensities for all peptides of a protein was summed and was used for protein quantification using DEP (Differential Enrichment of Proteomics Analysis) Package, in Bioconductor, R (https://rdrr.io/bioc/DEP/man/DEP.html). DEP was used for variance normalization and statistical testing of differentially expressed proteins. As expected LARP1 protein was identified in the control cells but not in the single or double knockouts.

We then plotted the fold change in RNA as determined by edgeR analysis of RNA-seq from (Philippe et al. 2020) and the fold change in protein abundance from our mass spectrometry data. We observed that genes in the TB high group show reduced changes at the protein level compared to TB low or others as determined by the linear regression analysis in both single and double LARP1 KO mutants. This finding is consistent with our findings that buffered genes show lower variation in the protein abundance in response to change in mRNA expression.

Legend: Scatter plot showing the log2fold change in the RNA and protein levels as determined by RNA seq from (Philippe et al. 2020) or mass spectroscopy. Differential analysis of RNA was done using the edgeR package and the DEP (Differential Enrichment of Proteomics Analysis) Package *was used for mass spectrometry analysis. Only genes with an FDR We have not included this data in the manuscript given the deviation of the approach from our original analysis, but we are happy to reconsider the inclusion of this data to supplement our proteomic analysis.

While the LGBM modeling shows modest predictive power of sequence features alone, the manuscript stops short of exploring what cellular factors might drive context dependence. Integrating public datasets on RNA-binding protein expression or mTOR pathway activity across samples could illuminate trans-acting determinants of buffering and move beyond correlative sequence analyses,

We thank the reviewer for this suggestion. To investigate potential trans-acting determinants of buffering, we focused on 1,394 human RBPs as classified by Hentze et al. (2018), reasoning that some of these factors may facilitate translational buffering. Specifically, we examined correlations between the RNA expression of each RBP and the TE of all other genes across samples. p-values were corrected using the Bonferroni procedure. For each RBP, we then performed a Fisher’s exact test to assess whether the number of significant correlations was enriched among buffered versus non-buffered genes.

This analysis revealed that the expression levels of many RBPs are significantly enriched for either positive or negative correlations with the TE of buffered genes. In particular, we note that RNA expression of many buffered RBPs is enriched for negative correlations with the TE of other buffered transcripts. These results suggest that, rather than considering translational buffering in isolation for each transcript, buffering effects may be coordinated at the translational level and influenced by shared trans-acting factors such as RBPs. Network-based approaches have been valuable for RNA co-expression and are only now being applied to TE covariation. However, the correlative nature of these analyses limits causal inference. For example, although many ribosomal proteins appear to influence the buffering of other ribosomal proteins, they themselves may be regulated by a non-ribosomal RBP—so the apparent effects could reflect upstream regulatory influences. This analysis is now included as a supplementary figure (Sup. Fig. 5) of the revised manuscript.

Legend: A scatter plot of odds ratio log₂ of number of significant correlations (RNA abundance of RBPs ::TE of genes) and the p value from fisher test. The vertical dashed line represents the threshold odds ratio, above which RBPs exhibit a higher number of significant correlations with buffered genes. P values were corrected using Bonferroni procedure* and the horizontal dashed line represents the adjusted p value cutoff. *

Reviewer #2 (Significance (Required)):

Overall, this manuscript leverages an unprecedented compendium of matched ribosome profiling and RNAseq datasets across human cell lines and mouse tissues, combined with improved TE estimation, to robustly catalog genes exhibiting translational buffering, a clear methodological and conceptual strength. The main limitations stem from heterogeneous sample sources, largely correlative analyses, and a lack of targeted mechanistic validation. Compared to prior yeast focused studies, it fills a key gap by demonstrating conservation of buffering in mammals and linking it to dosage sensitivity and protein stability, representing a conceptual advance in understanding post-transcriptional homeostasis and a methodological step forward in TE analysis. This work will interest researchers in RNA biology, gene expression regulation, systems biology, and cancer proteomics, as well as those studying dosage-sensitive pathways and translational control. My expertise is on translational control in cancer.

We thank the reviewer for noting the broader significance of the work and for their constructive feedback.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

In this manuscript, Rao and colleagues present a comprehensive analysis of translational buffering in human and mouse by mining 1515 matched ribosome profiling and RNAseq datasets from diverse tissues and cell lines. They define translational buffering as genes whose TE is negatively correlated with mRNA abundance across conditions, and further identify candidates by comparing median absolute deviations of ribosome occupancy versus mRNA levels. The authors find a conserved set of buffered genes enriched for components of multiprotein complexes, demonstrate that buffered genes exhibit lower protein variability and greater dosage sensitivity, and propose two non-mutually exclusive mechanistic models (differential accessibility and initiation rate modulation). Finally, they perform complementary fractionation experiments in HEK293T cells to support these models.

These findings propose a novel, conserved mechanism of translational buffering that tunes gene expression in mouse and human, showing how intrinsic sequence features and cellular context cooperate to stabilize protein output across diverse conditions. However, further evidence is required to fully support the authors conclusions, particularly direct validation of the proposed models of buffering. Below are my main concerns:

1. The choice of the top 250 genes by spearman correlation and MAD ratio as "TB high" seems arbitrary. The authors should justify these cut offs (via permutation analysis or FDR control) and show that conclusions are robust to different thresholds
2. The modified compositional regression approach for TE and imputation of missing values are central to the study, but details are relegated to supplemental methods. The manuscript would benefit from a clear comparison of this method against standard log-ratio TE estimates, including sensitivity analyses to missing-data imputation strategies
3. Human data are derived mainly from immortalized cell lines, whereas mouse data are from primary tissues. Pooling these heterogeneous sources may conflate cell type-specific regulation with intrinsic buffering. The authors should either stratify analyses by context or demonstrate buffering signatures remain consistent within more homogeneous subsets
4. The HEK293T fractionation experiments offer preliminary support for both the "accessibility" and "initiation" models, but only slope analyses are shown. To validate these models, the authors should perform targeted reporter assays (dual luciferase constructs with 5′UTR swaps) or manipulations of initiation factors (eIF4E knockdown) to directly test how transcript abundance alters initiation rates versus pool entry
5. The conclusion that buffering reduces protein variability relies on mass-spec comparisons, but ribosome occupancy does not always reflect functional protein output (due to elongation stalling or co-translational degradation). Incorporating orthogonal measures, such as pulse-labeling or western blots for key buffered versus non-buffered genes, would strengthen the link between buffering and proteome stability
6. While the LGBM modeling shows modest predictive power of sequence features alone, the manuscript stops short of exploring what cellular factors might drive context dependence. Integrating public datasets on RNA-binding protein expression or mTOR pathway activity across samples could illuminate trans-acting determinants of buffering and move beyond correlative sequence analyses

Significance

Overall, this manuscript leverages an unprecedented compendium of matched ribosome profiling and RNAseq datasets across human cell lines and mouse tissues, combined with improved TE estimation, to robustly catalog genes exhibiting translational buffering, a clear methodological and conceptual strength. The main limitations stem from heterogeneous sample sources, largely correlative analyses, and a lack of targeted mechanistic validation. Compared to prior yeast focused studies, it fills a key gap by demonstrating conservation of buffering in mammals and linking it to dosage sensitivity and protein stability, representing a conceptual advance in understanding post-transcriptional homeostasis and a methodological step forward in TE analysis. This work will interest researchers in RNA biology, gene expression regulation, systems biology, and cancer proteomics, as well as those studying dosage-sensitive pathways and translational control. My expertise is on translational control in cancer.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Thanks to the development of Ribo-Seq, translational buffering has been reported in various works. However, the systematic investigation has remained challenging. Employing the database of published Ribo-Seq and matched RNA-Seq, Rao et al. attempt to understand the mechanism underlying translational buffering of mRNA variation across diverse materials. Although the authors' report provides a step forward in our understanding of translational buffering, this reviewer found a series of concerns in this paper. These points could be tackled to improve the reliability of their findings, the strength of their main message, and the global understandability of the paper.

Major comments:

1. This paper heavily relies on the reference 18. However, this paper was not properly stated (no page or journal number); the study in Bioinformatics is nowhere to be found on the website, despite being out in 2024 apparently. Either title is wrong (yet a biorxiv can be found). This reviewer guessed that the reference 18 may be accepted. However, without a proper reference, this paper could not be judged since nearly all the parts of this work have been based on the reference 18. Also, the Ribobase data used in this manuscript comes from this reference, so it had better be well defined, especially when another Ribobase data set seems to be available online: http://www.bioinf.uni-freiburg.de/~ribobase/index.html
2. In the Discussion, the authors mentioned "TE is based on a compositional regression model (18) rather than the commonly applied approach of using a logarithmic ratio of ribosome occupancy to mRNA abundance." This important information should be mentioned early section of the manuscript. Related to this, there are other published methods for exploring change in translation efficiency (e.g., 10.1093/bioinformatics/btw585; 10.1093/nar/gkz223) that could also be suitable in this context. It is not entirely clear if their approach is better than before. Again, the improper reference to 18 made our assessment of this work difficult.
3. The paper mainly relies on detecting a set of buffered genes using mRNA-TE correlation and MAD ratios (Ribo-Seq/RNA-Seq). While the concept seems sound, the authors should ensure that this method is reliable. Several controls could be used to confirm this. First, if any studies in humans or mice have described a set of genes as buffered, it would be worth checking for overlap between the authors' set of 'TB high' genes and the previously established list. Furthermore, the authors could use packages explicitly developed for translational buffering detection, such as annota2seq (https://academic.oup.com/nar/article/47/12/e70/5423604?login=true). Not all of the data used by the authors may be suitable for such packages, but the authors could at least partially use them on some of their datasets and see whether the buffered genes reported by these packages match their predictions.
4. The threshold of 'TB high' or 'TB low' (top and bottom 250) is somewhat arbitrary. Why not top 100 or 500? The authors should provide a rationale for this choice. Also, they could include a numeric measure of buffering (the sum of the two rankings is probably suitable for this purpose). Several of the authors' explorations are suitable for numerical quantification (GO enrichment can be turned into GSEA, and the boxplot can be shown as correlations)
5. Several of the statements of the authors in the Introduction or Discussion sections are not entirely true regarding the literature on the topics, or lack major papers on the topic, and therefore, they are a bit misleading. Among others, here are some:

5-1 "In addition, genetic differences arising from aneuploidy, cell type differences or variability observed in the natural population can further determine the amplitude of variation (4-7). The effect of mRNA variation under these conditions is mostly reflected at the protein levels (2, 4-8).". Several recent or more ancient papers suggest that mRNA variation coming from aneuploidy, natural genetic variation, or CNV is buffered or not well reflected at the protein level:

DOI: 10.1038/s41586-024-07442-9 DOI: 10.1073/pnas.2319211121 DOI: 10.1016/j.cels.2017.08.013 DOI: 10.15252/msb.20177548

5-2: The authors should also consider mentioning these studies and softening their initial statement. "Similarly, translational buffering of certain genes have been reported in mammalian cells, specifically under estrogen receptor alpha (ERα) depletion conditions (16).". Translational buffering has been deeply explored in mammalian tissues and even across several mammalian species in this study (DOI: 10.1038/s41586-020-2899-z). In this, the authors also provide a nice exploration of the gene characteristics that are associated with translational buffering. The authors should mention it and compare the study's findings to theirs ultimately.

5-3: "Differences in species evaluated and statistical methods have resulted in conflicting interpretations (13, 28).". These conflicting results have been previously discussed in reviews on the topic that would be worth mentioning: DOI: 10.1016/j.cell.2016.03.014 DOI: 10.1038/s41576-020-0258-4 6. In addition to the p-values stated in the main text, the authors should annotate their plots when they find significant differences between groups to greatly facilitate the visual interpretation of the graphs. 7. Based on the data of Figure 4D, apparently, ribosome occupancy was not buffered even in high TB sets. The authors may argue that translational buffering may not cope with such a strong mRNA reduction. In that case, how big a difference in mRNA level does the buffering system adjust in protein synthesis? The authors should test gradual gene knockdown and/or overexpression and conduct Ribo-Seq/RNA-Seq to survey the buffering range. 8. "differential transcript accessibility model" could not be functional if mRNA is reduced beyond the accessible pool (i.e., less than the threshold, all the mRNAs are translated without buffering). The authors should carefully reconsider this model and the effective range of mRNAs.

Minor comments:

1. Some figures are of poor quality as they seem to have points outside of the panel representations... Like Figure 3C, one point is out of the square, same for Figure 4E. Similarly, on figure 5F, some outliers seem to be clearly cut from the figure (maybe not, but then the author should put a larger space between the end of the figure and the max y points). Same for panel S2D and S6D, this does not sound so rigorous.
2. There are several typos or weird sentences. Here are some (but maybe not all):

2-1: [...]with lower sums corresponding to higher final ranks. "two rankings". Based on these final ranks[...]

2-2: For each dataset, median absolute deviation (MAD) "i" protein abundance was calculated across samples

2-3: [...]neighbor method implemented in the MatchIT package (38) Differences in protein[...] a point is missing here.

2-4: Additionally a second dataset providing predictions of haploinsufficiency (pHaplo score) and triplosensitivity (pTriplo score) for all autosomal genes (25) was used to asses the distribution of these score"S" across buffered and non-buffered gene sets . There is a missing "s" at "score" and there is a space between the last word and the final point. 3. In the "Lymphoblastoid cell line data analysis:" section, this reviewer wonders why the authors used a different method to calculate buffering compared to before. 4. "Samples which had R2 less than 0.2 were removed as the residuals calculated for these samples could be unreliable". These samples for which the correspondence between RNA-Seq and Ribo-Seq is low wouldn't be the ones most impacted by translational buffering? Is it sure that the authors are not missing something here? 5. For Figure 4B and 4C, the authors should provide statistical tests and p-values to confirm the observed trends. 6. In Figure 2A, the "all genes" color doesn't correspond to the point color. 7. "To understand if codon usage patterns are[...]". This comes slightly out of the blue. The authors could maybe explain why codon usage should be explored for translational buffering. The authors should cite recent key works in the fields: DOI: 10.1016/j.celrep.2023.113413 DOI: 10.1101/2023.11.27.568910 8. "The change in each metric was calculated by subtracting the mean value in the control samples from that in the knockdown samples. This yielded the differential mRNA abundance and ribosome occupancy resulting from gene knockdown.". This looks statistically weak. The authors should consider using more robust methods like DESeq. 9. "Genes in the buffered gene set had a higher codon adaptation index than the non-buffered set, indicating that candidates in the buffered gene set are relatively well expressed due to the presence of a higher proportion of the codons observed in highly expressed genes". What do the authors mean by "relatively well expressed"? Abundantly expressed? This sentence and the causality under it is unclear and should be modified or better explained. 10. The panel 4D is unclear. Is one point associated with one gene? Or is it the average of several genes? If it's one point for one gene, it is important to clearly state it because the number of cases is therefore quite low, especially for the TB high and low. 11. In Figure 2J, GGU (Gly), AAG (Lys), and ACU (Arg) provide negative effects on prediction, although these were enriched in the high TB set (Figure 2E). This contradiction should be explained. 12. The subtitle of "Translationally buffered genes exhibit variable association kinetics with the translational machinery in response to mRNA variation" sounds unfair to this reviewer. Since the authors did not work on kinetics directly, the use of this word is misleading. 13. The explanation of Figure 5A "We next explored the potential mechanisms that may give rise to translational buffering. Specifically, we considered two non-mutually exclusive models by which mRNA abundance might be decoupled from ribosome occupancy. In the first, the "differential transcript accessibility model", mRNA abundance determines the fraction of transcripts that are accessible to the translational pool. In this scenario, an increase in mRNA abundance would be accompanied by a proportionally smaller increase in the fraction of transcripts entering the translating pool for buffered genes, compared to non-buffered genes. In the second, the "initiation rate model", the rate of translation initiation per transcript scales inversely with mRNA abundance. Under this model, the proportion of mRNA entering the translational pool would be comparable across buffered and non-buffered genes (Fig 5A)." is hard to understand. The authors should rewrite for a better understanding of the readers.

Significance

Thanks to the development of Ribo-Seq, translational buffering has been reported in various works. However, the systematic investigation has remained challenging. Employing the database of published Ribo-Seq and matched RNA-Seq, Rao et al. attempt to understand the mechanism underlying translational buffering of mRNA variation across diverse materials. A group of mRNAs whose expression variance is buffered at the translation level was comprehensively surveyed in humans and mice. The authors found a series of features in the translationally buffered genes, including high GC contents in the 5′ UTR, optimal codon usage, and mRNA length. The depletion or increase of one allele of the genes in the group may be particularly detrimental to cells. The authors' report provides a step forward in our understanding of translational buffering, appealing to the broad scientific community in basic and applied biology. However, this reviewer found a series of concerns in this paper, including clarity in the methods, experimental validation, referring the earlier works, etc. These points could be tackled to improve the reliability of their findings, the strength of their main message, and the global understandability of the paper.

staining the meaning out of our words

Reading the sample critique above really helped me understand what a "good" critique looks like, which is something I feel like is rarely focused on in other courses I have taken. I like the idea of critique as a conversation more than just a required Canvas comment. It's much easier to misunderstand ideas if you are not able to ask the designers any questions– I feel I have misunderstood other people's ideas and that my own ideas have been misunderstood when there is no communication. This has also shown me how I can improve my own critiques if I really engage with the idea I am critiquing and ask the designer questions about their idea.

Cite as:

Nature Briefing, 2025. Question of the week: should science make room for a “speculation box” in the literature? Nature Briefing (Friday 10 October 2025). https://purl.org/INRMM-MiD/z-GNFZY4SI

.

see the World in a Grain of Sand not through the eye

never doubting what you see and lift those

shimmering silver twines framing translucent veils

Joy & Woe are woven fine

A Clothing for the soul divine

Under every grief & pine

Runs a joy with silken twine

shimmering silver translucent veils and silken twines

t is right it should be so

Man was made for Joy & Woe

And when this we rightly know

Thro the World we safely go

Me pregunto si algún gobierno o agencia ya ha reconocido el Antropoceno y si ya ha tenido influencia en la política pública o si ha sido de forma más indirecta su influencia

Aqui se decribe qué es lo que tiene en mente hacer, me recuerda a Arjun Appadurai y la vida social de las cosas y a Latour con las traducciones, translations

McKinsey prepared the first marginal abatement cost curve for a Swedish utility in 2007.

Went to jail because he wanted to resign.

Fresh Not Frozen connections

东华金龙？wayfair conspiracy?

wdtm

Need to be Brave to create such a movement

U mean People-first as Universal Users

more universal than the Universal machines as they are the ones who created those

Brave browser passes 100 million monthly active users

JavaScript Mozilla

This formula is similar to the pH formula of a weak acid given its H+ concentration. This likely means due to the logarithmic nature that a hypothetical titration curve could be plotted with similarly its H+ concentration to that of its OH- concentration (logarithmic relationship).

This formula will tell you the amount of A- Ions in solution, note that this formula is for a monoprotic weak acid (less it also applies to di+)?

so they're irreversible?

I wonder about her ideas on the border dispute with Guyana.

McKinsey Abatement Cost Curve showing what are the most cost effective responses to reduce the emissions of CO2 and Green House Gases, it has been widely used in global public policy. All these interventions and actions cost less than 60 euros per ton of CO2

BAD: (VIDEO)The video does not have subtitles at all. Potentially bad for impaired of hearing.

GOOD: Well organized and contrasted.

Development of productive forces: As society progresses, the tools, machinery, and ways of producing things (productive forces) improve.

Problem stage: Eventually, there comes a point where these productive forces—like advanced machinery or money systems—stop helping most people. Instead of being useful, they start causing harm. In other words, what used to help society produce wealth now can be destructive.

Emergence of an oppressed class: At this stage, a large group of people ends up bearing all the hard work and suffering, but they don’t enjoy the benefits of society. This group is pushed out of power and comfort and becomes opposed to the other, more privileged classes.

Revolutionary consciousness: This oppressed majority realizes that society is unfair and that the only solution is a big change—a revolution. They develop a “communist consciousness,” meaning they understand the need to transform society for everyone’s benefit. Some members of other classes might also come to understand this situation and support the cause.

accept old generations didn't plan capitlaidsm to allow for future plansd eg, of communism

By this, Marx means that the traditional economic functions of the family—like producing goods, passing down property, and sustaining the household—become less central or obsolete.

Families no longer control production; capital and labor markets dominate.

Social reproduction (raising children, caring for the elderly) may still exist, but the economic basis of family power is reduced.

Essentially, the family is “superseded” as the primary unit of economic life.

note

pedagogical tool / model to understand the stock vs flow problem, too many of the public policy proposals to attend the Climate Warming focused on the reduction of emissions, not the removal of them. 30x30x30 is probably one exeption

Difference netween stock and flow of carbon in the atmosphere it is the stock of carbon what's the problem,. Is not a flow problem, is a stock problem. And this model is something that needs to be changed in the mental models of climate change.

This is what I was saying the other day, does the words, concepts or debates (particularly those coming from Soc Sci) about anthropocene and climate change actually mean something. I think there's a great gap between discussions and action. To reach actual soluctions is necesarry to be ready to bring some technical solutions on the ground, to engage directly with engineers biologists agriculturalists foresters.

Hay una gran misconception acerca de cómo funciona el clima, no basta solo con disminuir las emisiones gases de efecto invernadero, sino que es necesario mitigar los efectos con la remoción de estos gases. Ahora mismo, la emisión de GHG es dos veces mayor a la que anualmente nuestro planeta puede absorber

relating modes of resolving conflict (e.g., narrowing vs widening the focus of attention) to changing environmental demands

Large tribal impact to investigate.

I need to understand the tribes and culture of Yemen.

This line appears to be an inversion of the story of Lazarus, mirroring much of the language used to describe Lazarus’s revival. However, rather than going from dead to living, The Waste Land inverts the story, taking what was once alive and making it die, the opposite of the Lazarus story. Jesus says in John, “he that believeth in me, though he were dead, yet shall he live.” (John 11:25). In direct contradiction to this language, Eliot writes, “he who was living is now dead / we who were living are now dying.” Eliot twists the biblical language, making this section of the poem stand out when contrasted with John. However, the idea of the living dying is not a shocking one, generally understood as a natural development in the lives of human beings. The radical message is actually the one coming out of the bible, that the dead can live again, as exemplified through Lazarus, and then Jesus. The second line in Eliot takes both the first line and the biblical reference and shapes them into a new thought, one that directly addresses a collective “we,” claiming not that the living are “now dead” but instead that they are “now dying.” The use of the word “now” suggests that the poem is describing the moment when the living went from living to dying. This is odd, given the fact that every moment of life is a step closer to death, and in that sense, the living are always dying. Eliot could be, through his inversion and twisting of the language of the resurrection, suggesting a sort of apocalypse, the end of the world as we know it. His emphasis on the dying nature of life could also be referencing the circumstances of industrialization and World War I, of great change that seems to invert the practices and realities that previously seemed so reliable.

This line stood out to me due to its connection with the title "What the Thunder said," and similar connotation to the Gospel of John. This line appears after a somewhat odd repetition of a lack of water within the land. Rather, the speaker is left in a desolate landscape of "only rock." One may think that this baren image would also prompt a stillness of silence in nature. However, Eliot is quick to point out the presence of loud booms of thunder in my highlighted line. In particular, the thunder is "dry and sterile," therefor connecting to the state of the land; the rocky terrain is indeed also dry due to the emphasized absence of water and also sterile as a result. In The Gospel of John (line 29), thunder holds a contrasting purpose. 29] The people therefore, that stood by, and heard it, said that it thundered: others said, An angel spake to him. Therefore, the voice of God in John is expressed through thunder, showing the great force of divinity over the world. However, Eliot's vivid descriptions of the thunder in his wasteland could not be more different. The thunder is "dry and sterile." and in my opinion, lacks the religious importance evident in John, In connection the title, my reading of this line suggests that Eliot does not believe the thunder is saying anything (What the Thunder Said). Instead, we are trapped in a dry and sterile land mass with no divine connection to guide us out.

The presence of circles/cycles within "Death by Water" stood out to me as a symbol of a universal connection of shared pain and one's ultimate path to death. This section of the poem depicts the body of Phlebas the Phoenician drifting in the ocean two weeks (or a fortnight) after his death. He is taken by the power of nature as seen through the currents control of his limbs, guiding him towards the "whirlpool," (318). The circular motion prompted from this line ultimately connects to the action of "turning the wheel" (320) in the final stanza. The whirlpool and wheel, both round in shape, connect to the circle of life and the passage of time. Everyone, despite how "handsome" or "tall" they are will eventually reach their inescapable culmination in life. Additionally, this is the first time in "Death by Water" where the reader is addressed to as "you," therefore striking a more personal note. Furthermore, the inevitability of the cycles in life is also clear in the preceding line "Gentile or Jew." According to The Oxford Dictionary, a Gentile is a person who it not Jewish. Therefore, by stating "Gentile or Jew" Eliot asserts that anyone, no matter their religion, background, appearance, etc, moves through life in accordance to the circular trajectory. The idea of universal life experiences is evident in Corinthians 10 as well. In fact, it is taken one step further through the connection of bodies/spirits, as seen in the line below: [13] For by one Spirit we were all baptized into one body -- Jews or Greeks, slaves or free -- and all were made to drink of one Spirit. [14] For the body does not consist of one member but of many. This section highlights a divine connection for baptized individuals. I find this particularly interesting given one is usually baptized towards the beginning of their life. However, Eliot chose to present Phlebas well after his passing. In turn, Eliot could be asserting the power of death and an afterlife--a fitting idea alongside the ongoing decay of The Wasteland.

eLife Assessment

This manuscript characterizes a mutated clone of RNA polymerase I in yeast, referred to as SuperPol, to understand the mechanisms of RNA polymerase I elongation and termination. The authors present convincing evidence that demonstrates the existence of premature termination in Pol I transcription. Overall, the characterization of this RNA pol I offers important insights into the regulation of ribosomal RNA transcription and its potential application in cancer pharmacology.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #1 (Public review):

Summary:

The study characterises an RNA polymerase (Pol) I mutant (RPA135-F301S) named SuperPol. This mutant was previously shown to increase yeast ribosomal RNA (rRNA) production by Transcription Run-On (TRO). In this work, the authors confirm this mutation increases rRNA transcription using a slight variation of the TRO method, Transcriptional Monitoring Assay (TMA), which also allows the analysis of partially degraded RNA molecules. The authors show a reduction of abortive rRNA transcription in cells expressing the SuperPol mutant and a modest occupancy decrease at the 5' region of the rRNA genes compared to WT Pol I. These results suggest that the SuperPol mutant displays a lower frequency of premature termination. Using in vitro assays, the authors found that the mutation induces an enhanced elongation speed and a lower cleavage activity on mismatched nucleotides at the 3' end of the RNA. Finally, SuperPol mutant was found to be less sensitive to BMH-21, a DNA intercalating agent that blocks Pol I transcription and triggers the degradation of the Pol I subunit, Rpa190. Compared to WT Pol I, short BMH-21 treatment has little effect on SuperPol transcription activity, and consequently, SuperPol mutation decreases cell sensitivity to BMH-21.

Significance:

The work further characterises a single amino acid mutation of one of the largest yeast Pol I subunits (RPA135-F301S). While this mutation was previously shown to increase rRNA synthesis, the current work expands the SuperPol mutant characterisation, providing details of how RPA135-F301S modifies the enzymatic properties of yeast Pol I. In addition, their findings suggest that yeast Pol I transcription can be subjected to premature termination in vivo. The molecular basis and potential regulatory functions of this phenomenon could be explored in additional studies.

Our understanding of rRNA transcription is limited, and the findings of this work may be interesting to the transcription community. Moreover, targeting Pol I activity is an open strategy for cancer treatment. Thus, the resistance of SuperPol mutant to BMH-21 might also be of interest to a broader community, although these findings are yet to be confirmed in human Pol I and with more specific Pol I inhibitors in future.

Comments on revision:

The authors' response addressed all the points I raised adequately.

Reviewer #2 (Public review):

Summary:

This article presents a study on a mutant form of RNA polymerase I (RNAPI) in yeast, referred to as SuperPol, which demonstrates increased rRNA production compared to the wild-type enzyme. While rRNA production levels are elevated in the mutant, RNAPI occupancy as detected by CRAC is reduced at the 5' end of rDNA transcription units. The authors interpret these findings by proposing that the wild-type RNAPI pauses in the external transcribed spacer (ETS), leading to premature transcription termination (PTT) and degradation of truncated rRNAs by the RNA exosome (Rrp6). They further show that SuperPol's enhanced activity is linked to a lower frequency of PTT events, likely due to altered elongation dynamics and reduced RNA cleavage activity, as supported by both in vivo and in vitro data.

The study also examines the impact of BMH-21, a drug known to inhibit Pol I elongation, and shows that SuperPol is less sensitive to this drug, as demonstrated through genetic, biochemical, and in vivo approaches. The authors show that BMH-21 treatment induces premature termination in wild-type Pol I, but only to a lesser extent in SuperPol. They suggest that BMH-21 promotes termination by targeting paused Pol I complexes and propose that PTT is an important regulatory mechanism for rRNA production in yeast.

The data presented are of high quality and support the notion that 1) premature transcription termination occurs at the 5' end of rDNA transcription units; 2) SuperPol has an increased elongation rate with reduced premature termination; and 3) BMH-21 promotes both pausing and termination. The authors employ several complementary methods, including in vitro transcription assays. These results are significant and of interest for a broad audience.

Adding experiments in different growth conditions to support the claim of regulation by PTT (as the authors propose) will also be an important addition. The revisions further support the claim, with in particular the notion that increased elongation rate of superpol occurs at the expense of fidelity.

Significance:

These results are significant and of interest for a basic research audience.

Reviewer #3 (Public review):

In the manuscript "Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription", Azouzi and co-authors investigate the regulatory mechanisms of ribosomal RNA (rRNA) transcription by RNA Polymerase I (RNAPI) in the budding yeast S. cerevisiae. They follow up on exploring the molecular basis of a mutant allele of the second-largest subunit of RNAPI, RPA135-F301S, also dubbed SuperPol, that they had previously reported (Darrière et al, 2019), and which was shown to rescue Rpa49-linked growth defects, possibly by increasing rRNA production.

Through a combination of genomic and in vitro approaches, the authors test the hypothesis that RNAPI activity could be subjected to a premature transcription termination (PTT) mechanism, akin to what is observed for RNA Polymerase II (RNAPII). The authors demonstrate that SuperPol increased processivity "desensitizes" RNAPI to abortive transcription cycles at the expense of decreased fidelity. In agreement, SuperPol is shown to be resistant to BMH-21, a drug previously shown to impair RNAPI elongation.

Overall, this work expands the mechanistic understanding of the early dynamics of RNAPI transcription. The presented results are of interest for researchers studying transcription regulation, particularly those interested in RNAPI's transcription mechanisms and fidelity.

Strengths:

Overall, the experiments are performed with rigor and include the appropriate controls and statistical analyses. Conclusions are drawn from appropriate experiments. Both the figures and the text present the data clearly. The Materials and Methods section is detailed enough.

Weaknesses:

The biological significance of this phenomenon remains unaddressed and thus unclear. The lack of experiments to test a specific regulatory function (such as UTP-A loading checkpoint or other mechanisms) limit these termination events to possibly abortive actions of unclear significance.

Comments on revised version:

I appreciated the additional experiments and the other changes made by the authors in the revised version.

Author response:

The following is the authors’ response to the original reviews

General Statements:

In our manuscript, we demonstrate for the first time that RNA Polymerase I (Pol I) can prematurely release nascent transcripts at the 5' end of ribosomal DNA transcription units in vivo. This achievement was made possible by comparing wild-type Pol I with a mutant form of Pol I, hereafter called SuperPol previously isolated in our lab (Darrière at al., 2019). By combining in vivo analysis of rRNA synthesis (using pulse-labelling of nascent transcript and cross-linking of nascent transcript - CRAC) with in vitro analysis, we could show that Superpol reduced premature transcript release due to altered elongation dynamics and reduced RNA cleavage activity. Such premature release could reflect regulatory mechanisms controlling rRNA synthesis. Importantly, This increased processivity of SuperPol is correlated with resistance with BMH-21, a novel anticancer drugs inhibiting Pol I, showing the relevance of targeting Pol I during transcriptional pauses to kill cancer cells. This work offers critical insights into Pol I dynamics, rRNA transcription regulation, and implications for cancer therapeutics.

We sincerely thank the three reviewers for their insightful comments and recognition of the strengths and weaknesses of our study. Their acknowledgment of our rigorous methodology, the relevance of our findings on rRNA transcription regulation, and the significant enzymatic properties of the SuperPol mutant is highly appreciated. We are particularly grateful for their appreciation of the potential scientific impact of this work. Additionally, we value the reviewer’s suggestion that this article could address a broad scientific community, including in transcription biology and cancer therapy research. These encouraging remarks motivate us to refine and expand upon our findings further.

All three reviewers acknowledged the increased processivity of SuperPol compared to its wildtype counterpart. However, two out of three questions our claims that premature termination of transcription can regulate ribosomal RNA transcription. This conclusion is based on SuperPol mutant increasing rRNA production. Proving that modulation of early transcription termination is used to regulate rRNA production under physiological conditions is beyond the scope of this study. Therefore, we propose to change the title of this manuscript to focus on what we have unambiguously demonstrated:

“Ribosomal RNA synthesis by RNA polymerase I is subjected to premature termination of transcription”.

Reviewer 1 main criticisms centers on the use of the CRAC technique in our study. While we address this point in detail below, we would like to emphasize that, although we agree with the reviewer’s comments regarding its application to Pol II studies, by limiting contamination with mature rRNA, CRAC remains the only suitable method for studying Pol I elongation over the entire transcription units. All other methods are massively contaminated with fragments of mature RNA which prevents any quantitative analysis of read distribution within rDNA.  This perspective is widely accepted within the Pol I research community, as CRAC provides a robust approach to capturing transcriptional dynamics specific to Pol I activity. 

We hope that these findings will resonate with the readership of your journal and contribute significantly to advancing discussions in transcription biology and related fields.

Description of the planned revisions:

Despite numerous text modification (see below), we agree that one major point of discussion is the consequence of increased processivity in SuperPol mutant on the “quality” of produced rRNA. Reviewer 3 suggested comparisons with other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al., 2006). This comparison has already been addressed by the Schneider lab (Viktorovskaya OV, Cell Rep., 2013 - PMID: 23994471), which explored Pol II (rpb1-E1103G) and Pol I (rpa190-E1224G). The rpa190-E1224G mutant revealed enhanced pausing in vitro, highlighting key differences between Pol I and Pol II catalytic ratelimiting steps (see David Schneider's review on this topic for further details).

Reviewer 2 and 3 suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Pol I mutant with decreased rRNA cleavage have been characterized previously, and resulted in increased errorrate. We already started to address this point. Preliminary results from in vitro experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively. This could provide valuable insights into the mechanistic differences between SuperPol and the wild-type enzyme. SuperPol is the first pol I mutant described with an increased processivity in vitro and in vivo, and we agree that this might be at the cost of a decreased fidelity.

Regulatory aspect of the process:

To address the reviewer’s remarks, we propose to test our model by performing experiments that would evaluate PTT levels in Pol I mutant’s or under different growth conditions. These experiments would provide crucial data to support our model, which suggests that PTT is a regulatory element of Pol I transcription. By demonstrating how PTT varies with environmental factors, we aim to strengthen the hypothesis that premature termination plays an important role in regulating Pol I activity.

We propose revising the title and conclusions of the manuscript. The updated version will better reflect the study's focus and temper claims regarding the regulatory aspects of termination events, while maintaining the value of our proposed model.

Description of the revisions that have already been incorporated in the transferred manuscript:

Some very important modifications have now been incorporated:

Statistical Analyses and CRAC Replicates:

Unlike reviewers 2 and 3, reviewer 1 suggests that we did not analyze the results statistically. In fact, the CRAC analyses were conducted in biological triplicate, ensuring robustness and reproducibility. The statistical analyses are presented in Figure 2C, which highlights significant findings supporting the fact WT Pol I and SuperPol distribution profiles are different. We CRAC replicates exhibit a high correlation and we confirmed significant effect in each region of interest (5’ETS, 18S.2, 25S.1 and 3’ ETS, Figure 1) to confirm consistency across experiments. We finally took care not to overinterpret the results, maintaining a rigorous and cautious approach in our analysis to ensure accurate conclusions.

CRAC vs. Net-seq:

Reviewer 1 ask to comment differences between CRAC and Net-seq. Both methods complement each other but serve different purposes depending on the biological question on the context of transcription analysis. Net-seq has originally been designed for Pol II analysis. It captures nascent RNAs but does not eliminate mature ribosomal RNAs (rRNAs), leading to high levels of contamination. While this is manageable for Pol II analysis (in silico elimination of reads corresponding to rRNAs), it poses a significant problem for Pol I due to the dominance of rRNAs (60% of total RNAs in yeast), which share sequences with nascent Pol I transcripts. As a result, large Net-seq peaks are observed at mature rRNA extremities (Clarke 2018, Jacobs 2022). This limits the interpretation of the results to the short lived pre-rRNA species. In contrast, CRAC has been specifically adapted by the laboratory of David Tollervey to map Pol I distribution while minimizing contamination from mature rRNAs (The CRAC protocol used exclusively recovers RNAs with 3′ hydroxyl groups that represent endogenous 3′ ends of nascent transcripts, thus removing RNAs with 3’-Phosphate, found in mature rRNAs). This makes CRAC more suitable for studying Pol I transcription, including polymerase pausing and distribution along rDNA, providing quantitative dataset for the entire rDNA gene.

CRAC vs. Other Methods:

Reviewer 1 suggests using GRO-seq or TT-seq, but the experiments in Figure 2 aim to assess the distribution profile of Pol I along the rDNA, which requires a method optimized for this specific purpose. While GRO-seq and TT-seq are excellent for measuring RNA synthesis and cotranscriptional processing, they rely on Sarkosyl treatment to permeabilize cellular and nuclear membranes. Sarkosyl is known to artificially induces polymerase pausing and inhibits RNase activities which are involved in the process. To avoid these artifacts, CRAC analysis is a direct and fully in vivo approach. In CRAC experiment, cells are grown exponentially in rich media and arrested via rapid cross-linking, providing precise and artifact-free data on Pol I activity and pausing.

Pol I ChIP Signal Comparison:

The ChIP experiments previously published in Darrière et al. lack the statistical depth and resolution offered by our CRAC analyses. The detailed results obtained through CRAC would have been impossible to detect using classical ChIP. The current study provides a more refined and precise understanding of Pol I distribution and dynamics, highlighting the advantages of CRAC over traditional methods in addressing these complex transcriptional processes.

BMH-21 Effects:

As highlighted by Reviewer 1, the effects of BMH-21 observed in our study differ slightly from those reported in earlier work (Ref Schneider 2022), likely due to variations in experimental conditions, such as methodologies (CRAC vs. Net-seq), as discussed earlier. We also identified variations in the response to BMH-21 treatment associated with differences in cell growth phases and/or cell density. These factors likely contribute to the observed discrepancies, offering a potential explanation for the variations between our findings and those reported in previous studies. In our approach, we prioritized reproducibility by carefully controlling BMH-21 experimental conditions to mitigate these factors. These variables can significantly influence results, potentially leading to subtle discrepancies. Nevertheless, the overall conclusions regarding BMH-21's effects on WT Pol I are largely consistent across studies, with differences primarily observed at the nucleotide resolution. This is a strength of our CRAC-based analysis, which provides precise insights into Pol I activity.

We will address these nuances in the revised manuscript to clarify how such differences may impact results and provide context for interpreting our findings in light of previous studies.

Minor points:

Reviewer #1:

In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example: 

On page 14: "SuperPol accumulation is decreased (compared to Pol I)". 

On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph." 

We clarified and increased the global writing style according to reviewer comment.

There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references.  

This was corrected in this version of the manuscript.

In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA? 

Data are effectively normalized relative to the 5S rRNA, but the value for the 18S rRNA is arbitrarily set to 100%.

Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling. 

The presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. In the absence of these subunits (which can vary depending on the purification batch), Pol I pausing, cleavage and elongation are known to be affected. To strengthen our conclusion, we really wanted to show the subunit composition of the purified enzyme. This important control should be shown, but can indeed be shown in a supplementary figure if desired.

Y-axis is figure 7D is now correctly labelled

In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result? 

The 140 nt product is a degradation fragment resulting from trimming, which explains its lower accumulation in the absence of Rrp6. BMH21 significantly affects WT Pol I transcription but has also a mild effect on SuperPol transcription. As a result, the 140 nt product accumulates under these conditions.

Reviewer #2:

pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions of the rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates" 

According to reviewer, statement has been simplified in this version of the manuscript.

Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors. 

We agree that this is a very important discussion of our results. Transcription is a very dynamic process in which paused polymerase is easily detected using the CRAC assay. Elongated polymerases are distributed over a much larger gene body, and even a small amount of polymerase detected in the gene body can represent a very large rRNA synthesis. This point is of paramount importance and, as suggested by the reviewer, is now discussed in detail.

A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility? 

Reviewer suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. We thank Reviewer #2 to point it as in our opinion, this is an important point what should be added to the manuscript. We have now included new data (panels 5G, 5H and 5I) in the manuscript showing that SuperPol in vitro exhibits an increased error rate compared to the WT enzyme. From these results obtained in vitro, we concluded that SuperPol shows reduced nascent transcript cleavage, associated with more efficient transcript elongation, but to the detriment of transcriptional fidelity.

pp. 15 and 22: Premature transcription termination as a regulator of gene expression is welldocumented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research. 

According to reviewer suggestion, we referenced these studies.

p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement. 

This represents some unpublished data from our lab. KR and SuperPol are the only two known mutants resistant to BMH-21. We observed that resistance between both alleles is synergistic, with a much higher resistance to BMH-21 in the double mutant than in each single mutant (data not shown). Comparing their resistance mechanisms is a very important point that we could provide upon request. This was added to the statement.

p. 23: "The released of the premature transcript" - this phrase contains a typo 

This is now corrected.

Reviewer #3:

Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes. 

The precise objective of this experiment is to avoid the use of the Rrp6 mutant. Under these conditions, we prevent the accumulation of transcripts that would result from a maturation defect. While it is possible to conduct the experiment with the Rrp6 mutant, it would be impossible to draw reliable conclusions due to this artificial accumulation of transcripts.

Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly. 

We corrected Figure 1A.  

Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture. 

We addressed this point to make the figure more visually informative. In Northern Blot analysis, we use a TSS (Transcription Start Site) probe, which detects only transcripts containing the 5' extremity. Due to co-transcriptional processing, most of the rRNA undergoing transcription lacks its 5' extremity and is not detectable using this technique. We have the data, but it does not show any difference between Pol I and SuperPol. This information could be included in the supplementary data if asked.

"Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C. 

Thanks for pointing out this mistake. It has been corrected.

Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region. 

We agree that such an analysis would have been interesting. However, with the exception of the pre-rRNA starting at the transcription start site (TSS) studied here, any cleaved rRNA at its 5' end could result from premature termination and/or abnormal processing events. Exploring the production of other abnormal rRNAs produced by premature termination is a project in itself, beyond this initial work aimed at demonstrating the existence of premature termination events in ribosomal RNA production.

Figure 4: should probably be provided as supplementary material. 

As l mentioned earlier (see comments), the presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. This important control should be shown, but can indeed be shown in a supplementary figure if desired.

"While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected. 

We agree with this comment and we modified the text accordingly.

Figure 7D: the legend of the y-axis is missing as well as the title of the plot. 

Legend of the y-axis and title of the plot are now present.

The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.

This was discussed previously. See comment above.

Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b. 

Bibliography is now fixed

Description of analyses that authors prefer not to carry out:

Does SuperPol mutant produces more functional rRNAs ?

As Reviewer 1 requested, we agree that this point requires clarification.. In cells expressing SuperPol, a higher steady state of (pre)-rRNAs is only observed in absence of degradation machinery suggesting that overproduced rRNAs are rapidly eliminated. We know that (pre)rRNas are unable to accumulate in absence of ribosomal proteins and/or Assembly Factors (AF). In consequence, overproducing rRNAs would not be sufficient to increase ribosome content. This specific point is further address in our lab but is beyond the scope of this article.

Is premature termination coupled with rRNA processing 

We appreciate the reviewer’s insightful comments. The suggested experiments regarding the UTP-A complex's regulatory potential are valuable and ongoing in our lab, but they extend beyond the scope of this study and are not suitable for inclusion in the current manuscript.

this tethers back to the findings that 3-10% of MOOCs are actually completed — negative liberty unlocks the door, but how do learners un-learn the pedagogical suppositions of how education and learning function to actually utilize these educational tools?

5 critiques of OER movement chrome-extension://bjfhmglciegochdpefhhlphglcehbmek/pdfjs/web/viewer.html?file=file%3A%2F%2F%2FUsers%2Fprestontaylor%2FDownloads%2FFive%2520critiques%2520of%2520the%2520open%2520educational%2520resources%2520movement.pdf

eLife Assessment

In this study, the authors offer a theoretical explanation for the emergence of nematic bundles in the actin cortex, carrying implications for the assembly of actomyosin stress fibers. As such, the study is a valuable contribution to the field actomyosin organisation in the actin cortex. The theoretical work is solid and provides a rigorous theoretical framework to study active self-organisation in actomyosin systems, including qualitative comparison with experimental observations.

This means that problem-solving strategies help readers overcome confusion when they don’t understand a part of the text. It shows that good readers adjust their approach — like rereading or thinking deeper — to make sense of difficult ideas. These strategies train readers to be active and independent thinkers while reading. write rocel gomez pingol

This means that reading strategies are essential tools for better understanding what we read. By using techniques like skimming, summarizing, or asking questions, readers can remember and explain ideas more clearly. It reminds me that good reading isn’t just natural — it’s something we can improve through practice and strategy. write of rocel gomez pingol

This means reading is more than just recognizing words — it’s about understanding, thinking critically, and applying what we learn in real life. It shows that reading helps us reach goals, gain knowledge, and connect with others in society. Reading is not only a school skill but also an important lifelong ability for communication and growth.

This part means that students’ reading skills and understanding are influenced by their personal background and environment. Factors like gender, family income, and parents’ education can affect how students study and learn. It shows that education is not one-size-fits-all, and teachers need to consider these differences when guiding students in reading.

This part means that teachers should use different methods to improve students’ reading comprehension, not just one strategy. The Simple View of Reading reminds educators that reading involves many connected skills — like decoding, vocabulary, and understanding meaning. It shows that teaching reading needs a balanced and flexible approach to help all types of learners. write of rocel gomez pingol

This shows that even if a reader struggles with decoding words, they can still understand what they read by using context clues. It means that good readers use different strategies to make sense of texts, depending on their strengths. The model reminds me that reading is flexible — people can still succeed by balancing their weak and strong reading skills .write of rocel gomez pingol

This means that most students often use problem-solving techniques (like rereading or guessing meaning through context) when they struggle to understand texts. However, they only sometimes use support strategies (like asking for help or taking notes) and rarely use global strategies (like connecting the reading to real-world ideas). It also shows that family background and education can influence how students learn, which is an important reminder that reading strategies can vary based on personal and social factors. Rocel This sentence shows how reading comprehension is not just a school skill, but something important for success in life. Understanding what we read helps students do well in their studies, careers, and personal growth. It reminds me that improving comprehension can lead to lifelong learning and better opportunities. Write of Rocel Gomez Pingol

This means that most students often use problem-solving techniques (like rereading or guessing meaning through context) when they struggle to understand texts. However, they only sometimes use support strategies (like asking for help or taking notes) and rarely use global strategies (like connecting the reading to real-world ideas). It also shows that family background and education can influence how students learn, which is an important reminder that reading strategies can vary based on personal and social factors.

1. Or this study of Learners' Reading Strategies and Comprehension Skills seeks to unravel the complexities underlying successful reading comprehension.

1. Strategies that the learners employ to decode, interpret, and integrate information from texts.

1. defines as the reading level of the students is accurate, but when it comes to their reading comprehension level, they comprehend words not too easily,

1. This are the findings or the result in the study of Learner's Reading Strategies and Comprehension Skills

1. Or known as Philippine Informal Reading Inventory is a test that used to determine the level of the students' reading comprehension.

1. investigated the reading strategies and comprehension levels among the students

eLife Assessment

The study is useful for advancing spatial transcriptomics through its novel regression-based linear model (glmSMA) that integrates single-cell RNA-seq with spatial reference atlases, and its methodological framework is convincing. The approach demonstrates notable utility by enabling higher-resolution cell mapping across multiple biological systems and spatial platforms compared to existing tools.

Reviewer #1 (Public review):

Liu et al., present glmSMA, a network-regularized linear model that integrates single-cell RNA-seq data with spatial transcriptomics, enabling high-resolution mapping of cellular locations across diverse datasets. Its dual regularization framework (L1 for sparsity and generalized L2 via a graph Laplacian for spatial smoothness) demonstrates robust performance of their model. It offers novel tools for spatial biology, despite some gaps in fully addressing spatial communication.

The study presents a clear methodological framework that balances sparsity and smoothness, with parameter guidelines for different tissue contexts. It is commendable for its application to multiple spatial omics platforms, including both sequencing-based and imaging-based data, with results that can be generalized across both structured and less-structured tissues. After revision, there is a more transparent discussion of assumptions, including the correlation between expression and physical distance, and how performance may vary by tissue heterogeneity.

Limitations are modest - the spatial communication application is mentioned but not fully developed, and resolution reporting is primarily qualitative, which may limit direct comparability between datasets. The imaging-based validation is currently limited to simulated or lower-plex data, and expansion to high-plex datasets would further support platform versatility, although this is not essential to the core claims.

Overall, the manuscript delivers on its main objective, which is to present and validate a practical, flexible, and accurate framework for spatial mapping. The methods are clearly described, and the resource will be useful for researchers seeking to integrate single-cell and spatial datasets in diverse biological contexts.

Reviewer #2 (Public review):

Summary:

The author proposes a novel method for mapping single-cell data to specific locations with higher resolution than several existing tools.

Strengths:

The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus.

Comments on revised version:

The authors have sufficiently addressed all of my comments.

Reviewer #3 (Public review):

Summary:

The authors have provided a thorough and constructive response to the comments. They effectively addressed concerns regarding the dependence on marker gene selection by detailing the incorporation of multiple feature selection strategies, such as highly variable genes and spatially informative markers (e.g., via Moran's I), which enhance glmSMA's robustness even when using gene-limited reference atlases.

Furthermore, the authors thoughtfully acknowledged the assumption underlying glmSMA-that transcriptionally similar cells are spatially proximal-and discussed both its limitations and empirical robustness in heterogeneous tissues such as human PDAC. Their use of real-world, heterogeneous datasets to validate this assumption demonstrates the method's practical utility and adaptability.

Overall, the response appropriately contextualizes the limitations while reinforcing the generalizability and performance of glmSMA. The authors' clarifications and experimental justifications strengthen the manuscript and address the reviewer's concerns in a scientifically sound and transparent manner.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

Liu et al., present glmSMA, a network-regularized linear model that integrates single-cell RNA-seq data with spatial transcriptomics, enabling high-resolution mapping of cellular locations across diverse datasets. Its dual regularization framework (L1 for sparsity and generalized L2 via a graph Laplacian for spatial smoothness) demonstrates robust performance of their model and offers novel tools for spatial biology, despite some gaps in fully addressing spatial communication.

Overall, the manuscript is commendable for its comprehensive benchmarking across different spatial omics platforms and its novel application of regularized linear models for cell mapping. I think this manuscript can be improved by addressing method assumptions, expanding the discussion on feature dependence and cell type-specific biases, and clarifying the mechanism of spatial communication.

The conclusions of this paper are mostly well supported by data, but some aspects of model developmentand performance evaluation need to be clarified and extended.

We are thankful for the positive comments and have made changes following the reviewer's advice, as detailed below.

(1) What were the assumptions made behind the model? One of them could be the linear relationship between cellular gene expression and spatial location. In complex biological tissues, non-linear relationships could be present, and this would also vary across organ systems and species. Similarly, with regularization parameters, they can be tuned to balance sparsity and smoothness adequately but may not hold uniformly across different tissue types or data quality levels. The model also seems to assume independent errors with normal distribution and linear additive effects - a simplification that may overlook overdispersion or heteroscedasticity commonly observed in RNA-seq data.

Thank you for this comment. We acknowledge that the non-linear relationships can be present in complex tissues and may not be fully captured by a linear model. 

Our choice of a linear model was guided by an investigation of the relationship in the current datasets, which include intestinal villus, mouse brain, and fly embryo.There is a linear correlation between expression distance and physical distance [Nitzan et al]. Within a given anatomical structure, cells in closer proximity exhibit more similar expression patterns (Fig. 3c). In tissues where non-linear relationships are more prevalent—such as the human PDAC sample—our mapping results remain robust. We acknowledge that we have not yet tested our algorithm in highly heterogeneous regions like the liver, and we plan to include such analyses in future work if necessary.

Regarding the regularization parameters, we agree that the balance between sparsity and smoothness is sensitive to tissue-specific variation and data quality. In our current implementation, we explored a range of values to find robust defaults. Supplementary Figure 7 illustrates the regularization path for cell assignment in the fly embryo.  

The choice of L1 and L2 regularization parameters is crucial for balancing sparsity and smoothness in spatial mapping. 

For Structured Tissues (brain):

Moderate L1 to ensure cells are localized.

Small to moderate L2 to maintain local smoothness without blurring distinct regions.

For Less Structured (PDAC):

Slightly lower L1 to allow cells to be associated with multiple regions if boundaries are ambiguous.

Higher L2 to stabilize mappings in noisy or mixed regions.

(2) The performance of glmSMA is likely sensitive to the number and quality of features used. With too few features, the model may struggle to anchor cells correctly due to insufficient discriminatory power, whereas too many features could lead to overfitting unless appropriately regularized. The manuscript briefly acknowledges this issue, but further systematic evaluation of how varying feature numbers affect mapping accuracy would strengthen the claims, particularly in settings where marker gene availability is limited. A simple way to show some of this would be testing on multiple spatial omics (imaging-based) platforms with varying panel sizes and organ systems. Related to this, based on the figures, it also seems like the performance varies by cell type. What are the factors that contribute to this? Variability in expression levels, RNA quantity/quality? Biases in the panel? Personally, I am also curious how this model can be used similarly/differently if we have a FISH-based, high-plex reference atlas. Additional explanation around these points would be helpful for the readers.

Thank you for this thoughtful comment. The performance of our method is indeed sensitive to the number and quality of selected features. To optimize feature selection, we employed multiple strategies, including Moran’s I statistic, identification of highly variable genes, and the Seurat pipeline to detect anchor genes linking the spatial transcriptomics data with the reference atlas. The number of selected markers depends on the quality of the data. For highquality datasets, fewer than 100 markers are typically sufficient for prediction. To select marker genes, we applied the following optional strategies:

(1) Identifying highly variable genes (HVGs).

(2) Calculating Moran’s I scores for all genes to assess spatial autocorrelation.

(3) Generating anchor genes based on the integration of the reference atlas and scRNA-seq data using Seurat.

We evaluated our method across diverse tissue types and platforms—including Slide-seq, 10x Visium, and Virtual-FISH—which represent both sequencing-based and imaging-based spatial transcriptomics technologies. Our model consistently achieved strong performance across these settings. It's worth noting that the performance of other methods, such as CellTrek [Wei et al] and novoSpaRc [Nitzan et al], also depends heavily on feature selection. In particular, performance degrades substantially when fewer features are used. For fair comparison across different methods, the same set of marker genes was used. Under this condition, our method outperformed the others based on KL divergence (Fig. 2b, Fig. 5g). 

To assess the effect of marker gene quantity, we randomly selected subsets of 2,000, 1500, 1,000, 700, 500, and 200 markers from the original set. As the number of markers decreases, mapping performance declines, which is expected due to the reduction in available spatial information. This result underscores the general dependence of spatial mapping accuracy on both the number and quality of informative marker genes (Supplementary Fig. 10).

We do not believe that the observed performance is directly influenced by cell type composition. Major cell types are typically well-defined, and rare cell types comprise only a small fraction of the dataset. For these rare populations, a single misclassification can disproportionately impact metrics like KL divergence due to small sample size. However, this does not necessarily indicate a systematic cell type–specific bias in the mapping. We incorporated a high-resolution Slide-seq dataset from the mouse hippocampus to evaluate the influence of cell type composition on the algorithm’s performance [Stickels et al., 2020]. Most cell types within the CA1, CA2, CA3, and DG regions were accurately mapped to their original anatomical locations (Fig. 5e, f, g).

(3) Application 3 (spatial communication) in the graphical abstract appears relatively underdeveloped. While it is clear that the model infers spatial proximities, further explanation of how these mappings translate into insights into cell-cell communication networks would enhance the biological relevance of the findings.

Thank you for this valuable feedback. We agree that further elaboration on the connection between spatial proximity and cell–cell communication would enhance the biological interpretation of our results. While our current model focuses on inferring spatial relationships,  we may provide some cell-cell communications in the future.

(4) What is the final resolution of the model outputs? I am assuming this is dictated by the granularity of the reference atlas and the imposed sparsity via the L1 norm, but if there are clear examples that would be good. In figures (or maybe in practice too), cells seem to be assigned to small, contiguous patches rather than pinpoint single-cell locations, which is a pragmatic compromise given the inherent limitations of current spatial transcriptomics technologies. Clarification on the precise spatial scale (e.g., pixel or micrometer resolution) and any post-mapping refinement steps would be beneficial for the users to make informed decisions on the right bioinformatic tools to use.

Thank you for the comment. For each cell, our algorithm generates a probability vector that indicates its likely spatial assignment along with coordinate information. In our framework, each cell is mapped to one or more spatial spots with associated probabilities. Depending on the amount of regularization through L1 and L2 norms, a cell may be localized to a small patch or distributed over a broader domain (Supplementary Fig. 5 & 7). For the 10x Visium data, we applied a repelling algorithm to enhance visualization [Wei et al]. If a cell’s original location is already occupied, it is reassigned to a nearby neighborhood to avoid overlap. The users can also see the entire regularization path by varying the penalty terms. 

Nitzan M, Karaiskos N, Friedman N, Rajewsky N. Gene expression cartography. Nature. 2019;576(7785):132-137. doi:10.1038/s41586-019-1773-3

Wei, R. et al. (2022) ‘Spatial charting of single-cell transcriptomes in tissues’, Nature Biotechnology, 40(8), pp. 1190–1199. doi:10.1038/s41587-022-01233-1.

Stickels, R.R. et al. (2020) ‘Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-SEQV2’, Nature Biotechnology, 39(3), pp. 313–319. doi:10.1038/s41587-020-0739-1. 

Reviewer #2 (Public review):

Summary:

The author proposes a novel method for mapping single-cell data to specific locations with higher resolution than several existing tools.

Strengths:

The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus.

Weakness:

(1) Although the researchers claim that glmSMA seamlessly accommodates both sequencing-based and image-based spatial transcriptomics (ST) data, their testing primarily focused on sequencingbased ST data, such as Visium and Slide-seq. To demonstrate its versatility for spatial analysis, the authors should extend their evaluation to imaging-based spatial data.

Thank you for the comment. We have tested our algorithm on the virtual FISH dataset from the fly embryo, which serves as an example of image-based spatial omics data (Fig. 4c). However, such datasets often contain a limited number of available genes. To address this, we will conduct additional testing on image-based data if needed. The Allen Brain Atlas provides high-quality ISH data, and we can select specific brain regions from this resource to further evaluate our algorithm if necessary [Lein et al]. Currently, we plan to focus more on the 10x Visium platform, as it supports whole-transcriptome profiling and offers a wide range of tissue samples for analysis.

(2) The definition of "ground truth" for spatial distribution is unclear. A more detailed explanation is needed on how the "ground truth" was established for each spatial dataset and how it was utilized for comparison with the predicted distribution generated by various spatial mapping tools.

Thank you for the comment. To clarify how ground truth is defined across different tissues, we provided the following details. Direct ground truth for cell locations is often unavailable in scRNA-seq data due to experimental constraints. To address this, we adopted alternative strategies for estimating ground truth in each dataset:

10x Visium Data: We used the cell type distribution derived from spatial transcriptomics (ST) data as a proxy for ground truth. We then computed the KL divergence between this distribution and our model's predictions for performance assessment.

Slide-seq Data: We validated predictions by comparing the expression of marker genes between the reconstructed and original spatial data.

Fly Embryo Data: We used predicted cell locations from novoSpaRc as a reference for evaluating our algorithm.

These strategies allowed us to evaluate model performance even in the absence of direct cell location data. In addition, we can apply multiple evaluation strategies within a single dataset.

(3) In the analysis of spatial mapping results using intestinal villus tissue, only Figure 3d supports their findings. The researchers should consider adding supplemental figures illustrating the spatial distribution of single cells in comparison to the ground truth distribu tion to enhance the clarity and robustness of their investigation.

Thank you for the comment. In the intestinal dataset, only six large domains were defined. As a result, the task for this dataset is relatively simple—each cell only needs to be assigned to one of the six domains. As the intestinal villus is a relatively simple tissue, most existing algorithms performed well on it. For this reason, we did not initially provide extensive details in the main text.

(4) The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus. However, the original anatomical regions are not displayed, making it difficult to directly compare them with the predicted mapping results. Providing ground truth distributions for each tested tissue would enhance clarity and facilitate interpretation. For instance, in Figure 2a and  Supplementary Figures 1 and 2, only the predicted mapping results are shown without the corresponding original spatial distribution of regions in the mouse cortex. Additionally, in Figure 3c, four anatomical regions are displayed, but it is unclear whether the figure represents the original spatial regions or those predicted by glmSMA. The authors are encouraged to clarify this by incorporating ground truth distributions for each tissue.

Thank you for the comment. To improve visualization, we included anatomical structures alongside the mapping results in the next version, wherever such structures are available (e.g., mouse brain cortex, human PDAC sample, etc.). Major cell type assignments for the PDAC samples, along with anatomical structures, are shown in Supplementary Figure 9. Most of these cell types were correctly mapped to their corresponding anatomical regions.

(5) The cell assignment results from the mouse hippocampus (Supplementary Figure 6) lack a corresponding ground truth distribution for comparison. DG and CA cells were evaluated solely based on the gene expression of specific marker genes. Additional analyses are needed to further validate the robustness of glmSMA's mapping performance on Slide-seq data from the mouse hippocampus.

Thank you for the comment. The ground truth for DG and CA cells was not available. To better evaluate the model's performance, we computed the KL divergence between the original and predicted cell type distributions, following the same approach used for the 10x Visium dataset. We identified a higher-quality dataset for the mouse hippocampus and used it to evaluate our algorithm. Additionally, we employed KL divergence as an alternative strategy to validate and benchmark our results (Fig. 5e, f, g). Most CA cells, including CA1, CA2, and CA3 principal cells, were correctly assigned back to the CA region. Dentate principal cells were accurately mapped to the DG region (Fig. 5e, f).

(6) The tested spatial datasets primarily consist of highly structured tissues with well-defined anatomical regions, such as the brain and intestinal villus. Anatomical regions are not distinctly separated, such as liver tissue. Further evaluation of such tissues would help determine the method's broader applicability.

Thank you for the insightful comment. We agree that many spatial datasets used in our study are from tissues with well-defined anatomical regions. To address the applicability of glmSMA in tissues without clearly separated anatomical structures, we applied glmSMA to the Drosophila embryo, which represents a tissue with relatively continuous spatial patterns and lacks well-demarcated anatomical boundaries compared to organs like the brain or intestinal villus.

Despite this less structured spatial organization, glmSMA demonstrated robust performance in the fly embryo, accurately mapping cells to their correct spatial spots based on gene expression profiles. This result indicates that glmSMA is not strictly limited to highly structured tissues and can generalize to tissues with more continuous or gradient-like spatial architectures. These results suggest that glmSMA has broader applicability beyond highly compartmentalized tissues.

Lein, E., Hawrylycz, M., Ao, N. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007). https://doi.org/10.1038/nature05453

Reviewer #3 (Public review):

The authors aim to develop glmSMA, a network-regularized linear model that accurately infers spatial gene expression patterns by integrating single-cell RNA sequencing data with spatial transcriptomics reference atlases. Their goal is to reconstruct the spatial organization of individual cells within tissues, overcoming the limitations of existing methods that either lack spatial resolution or sensitivity.

Strengths:

(1) Comprehensive Benchmarking:

Compared against CellTrek and Novosparc, glmSMA consistently achieved lower Kullback-Leibler divergence (KL divergence) scores, indicating better cell assignment accuracy.

Outperformed CellTrek in mouse cortex mapping (90% accuracy vs. CellTrek's 60%) and provided more spatially coherent distributions.

(2) Experimental Validation with Multiple Real-World Datasets:

The study used multiple biological systems (mouse brain, Drosophila embryo, human PDAC, intestinal villus) to demonstrate generalizability.

Validation through correlation analyses, Pearson's coefficient, and KL divergence support the accuracy of glmSMA's predictions.

We thank reviewer #3 for their positive feedback and thoughtful recommendations.

Weaknesses:

(1) The accuracy of glmSMA depends on the selection of marker genes, which might be limited by current FISH-based reference atlases.

We agree that the accuracy of glmSMA is influenced by the selection of marker genes, and that current FISH-based reference atlases may offer a limited gene set. To address this, we incorporate multiple feature selection strategies, including highly variable genes and spatially informative genes (e.g., via Moran’s I), to optimize performance within the available gene space. As more comprehensive reference atlases become available, we expect the model’s accuracy to improve further.

(2) glmSMA operates under the assumption that cells with similar gene expression profiles are likely to be physically close to each other in space which not be true under various heterogeneous environments.

Thank you for raising this important point. We agree that glmSMA operates under the assumption that cells with similar gene expression profiles tend to be spatially proximal, and this assumption may not strictly hold in highly heterogeneous tissues where spatial organization is less coupled to transcriptional similarity.

To address this concern, we specifically tested glmSMA on human PDAC samples, which represent moderately heterogeneous environments characterized by complex tumor microenvironments, including a mixture of ductal cells, cancer cells, stromal cells, and other components. Despite this heterogeneity, glmSMA successfully mapped major cell types to their expected anatomical regions, demonstrating that the method is robust even in the presence of substantial cellular diversity and spatial complexity.

This result suggests that while glmSMA relies on the assumption of spatialtranscriptomic correlation, the method can tolerate a reasonable degree of spatial heterogeneity without a significant loss of performance. Nevertheless, we acknowledge that in extremely disorganized or highly mixed tissues where transcriptional similarity is decoupled from spatial proximity, the performance may be affected.

Missing conditions here IF it returns paymentCredentialsAvailable OR paymentCredentialStatusUnknown(use 2nd option only if you want customer to going into adding a new card journey, not recommended in initial 2 months due to increased friction)

eLife Assessment

This study provides a fundamental advancement in our understanding of trabecular meshwork cell diversity and its role in eye pressure regulation and glaucoma using multimodal single-cell analysis, spatial validation, and functional testing that go beyond the current state-of-the-art. The study demonstrates that mitochondrial dysfunction, specifically in one of three distinct cell subtypes (TM3), contributes to elevated IOP in a genetic mouse model of glaucoma carrying a mutation in the transcription factor Lmx1b. While the identification of TM3 cells as metabolically specialized is compelling, there is somewhat limited evidence linking mitochondrial dysfunction to the Lmx1b mutation in TM3 cells.

Reviewer #1 (Public review):

Summary:

This study provides a comprehensive single-cell and multiomic characterization of trabecular meshwork (TM) cells in the mouse eye, a structure critical to intraocular pressure (IOP) regulation and glaucoma pathogenesis. Using scRNA-seq, snATAC-seq, immunofluorescence, and in situ hybridization, the authors identify three transcriptionally and spatially distinct TM cell subtypes. The study further demonstrates that mitochondrial dysfunction specifically in one subtype (TM3) contributes to elevated IOP in a genetic mouse model of glaucoma carrying a mutation in the transcription factor Lmx1b. Importantly, treatment with nicotinamide (vitamin B3), known to support mitochondrial health, prevents IOP elevation in this model. The authors also link their findings to human datasets, suggesting the existence of analogous TM3-like cells with potential relevance to human glaucoma.

Strengths:

The study is methodologically rigorous, integrating single-cell transcriptomic and chromatin accessibility profiling with spatial validation and in vivo functional testing. The identification of TM subtypes is consistent across mouse strains and institutions, providing robust evidence of conserved TM cell heterogeneity. The use of a glaucoma model to show subtype-specific vulnerability-combined with a therapeutic intervention-gives the study strong mechanistic and translational significance. The inclusion of chromatin accessibility data adds further depth by implicating active transcription factors such as LMX1B, a gene known to be associated with glaucoma risk. The integration with human single-cell datasets enhances the potential relevance of the findings to human disease.

Weaknesses:

Although the LMX1B transcription factor is implicated as a key regulator in TM3 cells, its role in directly controlling mitochondrial gene expression is not fully explored. Additional analysis of motif accessibility or binding enrichment near relevant target genes could substantiate this mechanistic link. The therapeutic effect of vitamin B3 is clearly demonstrated phenotypically, but the underlying cellular and molecular mechanisms remain somewhat underdeveloped-for instance, changes in mitochondrial function, oxidative stress markers, or NAD+ levels are not directly measured. While the human relevance of TM3 cells is suggested through marker overlap, more quantitative approaches, such as cell identity mapping or gene signature scoring in human datasets, would strengthen the translational connection.

Overall, this is a compelling and carefully executed study that offers significant advances in our understanding of TM cell biology and its role in glaucoma. The integration of multimodal data, disease modeling, and therapeutic testing represents a valuable contribution to the field. With additional mechanistic depth, the study has the potential to become a foundational resource for future research into IOP regulation and glaucoma treatment.

Reviewer #3 (Public review):

Summary:

In this study, the authors perform multimodal single-cell transcriptomic and epigenomic profiling of 9,394 mouse TM cells, identifying three transcriptionally distinct TM subtypes with validated molecular signatures. TM1 cells are enriched for extracellular matrix genes, TM2 for secreted ligands supporting Schlemm's canal, and TM3 for contractile and mitochondrial/metabolic functions. The transcription factor LMX1B, previously linked to glaucoma, shows the highest expression in TM3 cells and appears to regulate mitochondrial pathways. In Lmx1bV265D mutant mice, TM3 cells exhibit transcriptional signs of mitochondrial dysfunction associated with elevated IOP. Notably, vitamin B3 treatment significantly mitigates IOP elevation, suggesting a potential therapeutic avenue.<br /> This is an excellent and collaborative study involving investigators from two institutions, offering the most detailed single-cell transcriptomic and epigenetic profiling of the mouse limbal tissues-including both TM and Schlemm's canal (SC), from wild-type and Lmx1bV265D mutant mice. The study defines three TM subtypes and characterizes their distinct molecular signatures, associated pathways, and transcriptional regulators. The authors also compare their dataset with previously published murine and human studies, including those by Van Zyl et al., providing valuable cross-species insights.

Strengths:

(1) Comprehensive dataset with high single-cell resolution

(2) Use of multiple bioinformatic and cross-comparative approaches

(3) Integration of 3D imaging of TM and SC for anatomical context

(4) Convincing identification and validation of three TM subtypes using molecular markers.

Weaknesses:

(1) Insufficient evidence linking mitochondrial dysfunction to TM3 cells in Lmx1bV265D mice: While the identification of TM3 cells as metabolically specialized and Lmx1b-enriched is compelling, the proposed link between Lmx1b mutation and mitochondrial dysfunction remains underdeveloped. It is unclear whether mitochondrial defects are a primary consequence of Lmx1b-mediated transcriptional dysregulation or a secondary response to elevated IOP. Although authors have responded to this, the manuscript is not sufficiently altered to address these points. I would like to suggest that authors tone down mitochondrial connection with Lmx1b from the title and abstract, and clearly discuss that these events are associated, and future work is needed to dissect the role of mitochondria in this pathway.<br /> Furthermore, the protective effects of nicotinamide (NAM) are interpreted as evidence of mitochondrial involvement, but no direct mitochondrial measurements (e.g., immunostaining, electron microscopy, OCR assays) are provided. It is essential to validate mitochondrial dysfunction in TM3 cells using in vivo functional assays to support the central conclusion of the paper. Without this, the claim that mitochondrial dysfunction drives IOP elevation in Lmx1bV265D mice remains speculative. Alternatively, authors should consider revising their claims that mitochondrial dysfunction in these mice is a central driver of TM dysfunction.

(2) Mechanism of NAM-mediated protection is unclear: The manuscript states that NAM treatment prevents IOP elevation in Lmx1bV265D mice via metabolic support, yet no data are shown to confirm that NAM specifically rescues mitochondrial function. Do NAM-treated TM3 cells show improved mitochondrial integrity? Are reactive oxygen species (ROS) reduced? Does NAM also protect RGCs from glaucomatous damage? Addressing these points would clarify whether the therapeutic effects of NAM are indeed mitochondrial.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

This study provides a comprehensive single-cell and multiomic characterization of trabecular meshwork (TM) cells in the mouse eye, a structure critical to intraocular pressure (IOP) regulation and glaucoma pathogenesis. Using scRNA-seq, snATAC-seq, immunofluorescence, and in situ hybridization, the authors identify three transcriptionally and spatially distinct TM cell subtypes. The study further demonstrates that mitochondrial dysfunction, specifically in one subtype (TM3), contributes to elevated IOP in a genetic mouse model of glaucoma carrying a mutation in the transcription factor Lmx1b. Importantly, treatment with nicotinamide (vitamin B3), known to support mitochondrial health, prevents IOP elevation in this model. The authors also link their findings to human datasets, suggesting the existence of analogous TM3-like cells with potential relevance to human glaucoma.

Strengths:

The study is methodologically rigorous, integrating single-cell transcriptomic and chromatin accessibility profiling with spatial validation and in vivo functional testing. The identification of TM subtypes is consistent across mouse strains and institutions, providing robust evidence of conserved TM cell heterogeneity. The use of a glaucoma model to show subtype-specific vulnerability, combined with a therapeutic intervention-gives the study strong mechanistic and translational significance. The inclusion of chromatin accessibility data adds further depth by implicating active transcription factors such as LMX1B, a gene known to be associated with glaucoma risk. The integration with human single-cell datasets enhances the potential relevance of the findings to human disease.

We thank the reviewers for their thorough reading of our manuscript and helpful comments.

Weaknesses:

(1) Although the LMX1B transcription factor is implicated as a key regulator in TM3 cells, its role in directly controlling mitochondrial gene expression is not fully explored. Additional analysis of motif accessibility or binding enrichment near relevant target genes could substantiate this mechanistic link. 

We show that the Lmx1b mutation induces mitochondrial dysfunction with mitochondrial gene expression changes but agree with the referee in that we do not show direct regulation of mitochondrial genes by LMX1B. Emerging data suggest that LMX1B regulates the expression of mitochondrial genes in other cell types [1, 2] making the direct link reasonable. Future work that is beyond the scope of the current paper will focus on sequencing cells at earlier timepoints to help distinguish gene expression changes associated with the V265D mutation from those secondary to ongoing disease and elevated IOP. Additional studies, including ATAC seq at more ages, ChIP-seq and/or Cut and Run/Tag (in TM cells) will be necessary to directly investigate LMX1B target genes.

As we studied adult mice, mitochondrial gene expression changes could be secondary to other disease induced stresses. Because we did not intend to say we have shown a direct link, we have now added a sentence to the discussion ensure clarity. 

Lines 932-934: “Although our studies show a clear effect of the Lmx1b mutation on mitochondria, future studies are needed to determine if LMX1B directly modulates mitochondrial genes in V265D mutant TM cells”

(2) The therapeutic effect of vitamin B3 is clearly demonstrated phenotypically, but the underlying cellular and molecular mechanisms remain somewhat underdeveloped - for instance, changes in mitochondrial function, oxidative stress markers, or NAD+ levels are not directly measured. 

We agree that further experiments towards a fuller mechanistic understanding of vitamin B3’s therapeutic effects are needed. Such experiments are planned but are beyond the scope of this paper, which is already very large (7 Figures and 16 Supplemental Figures).

(3) While the human relevance of TM3 cells is suggested through marker overlap, more quantitative approaches, such as cell identity mapping or gene signature scoring in human datasets, would strengthen the translational connection.

We appreciate the reviewer’s suggestion and agree that additional quantitative analyses will further strengthen the translational relevance of TM3 cells. It is not yet clear if humans have a direct TM3 counterpart or if TM cell roles are compartmentalized differently between human cell types. We are currently limited in our ability to perform these comparative analyses. Specifically, we were unable to obtain permission to use the underlying dataset from Patel et al., and our access to the Van Zyl et al. dataset was through the Single Cell Portal, which does not support more complex analyses (ex. cell identity mapping or gene signature scoring). Differences between human studies themselves also affect these comparisons. Future work aimed at resolving differences and standardizing human TM cell annotations, as well as cross species comparisons are needed (working groups exist and this ongoing effort supports 3 human TM cell subtypes as also reported by Van Zyl). This is beyond what we are currently able to do for this paper. We present a comprehensive assessment using readily available published resources.

Reviewer #2 (Public review):

Summary:

This elegant study by Tolman and colleagues provides fundamental findings that substantially advance our knowledge of the major cell types within the limbus of the mouse eye, focusing on the aqueous humor outflow pathway. The authors used single-cell and single-nuclei RNAseq to very clearly identify 3 subtypes of the trabecular meshwork (TM) cells in the mouse eye, with each subtype having unique markers and proposed functions. The U. Columbia results are strengthened by an independent replication in a different mouse strain at a separate laboratory (Duke). Bioinformatics analyses of these expression data were used to identify cellular compartments, molecular functions, and biological processes. Although there were some common pathways among the 3 subtypes of TM cells (e.g., ECM metabolism), there also were distinct functions. For example:

TM1 cell expression supports heavy engagement in ECM metabolism and structure, as well as TGFb2 signaling.

TM2 cells were enriched in laminin and pathways involved in phagocytosis, lysosomal function, and antigen expression, as well as End3/VEGF/angiopoietin signaling.

TM3 cells were enriched in actin binding and mitochondrial metabolism.

They used high-resolution immunostaining and in situ hybridization to show that these 3 TM subtypes express distinct markers and occupy distinct locations within the TM tissue. The authors compared their expression data with other published scRNAseq studies of the mouse as well as the human aqueous outflow pathway. They used ATAC-seq to map open chromatin regions in order to predict transcription factor binding sites. Their results were also evaluated in the context of human IOP and glaucoma risk alleles from published GWAS data, with interesting and meaningful correlations. Although not discussed in their manuscript, their expression data support other signaling pathways/ proteins/ genes that have been implicated in glaucoma, including: TGFb2, BMP signaling (including involvement of ID proteins), MYOC, actin cytoskeleton (CLANs), WNT signaling, etc.

In addition to these very impressive data, the authors used scRNAseq to examine changes in TM cell gene expression in the mouse glaucoma model of mutant Lmxb1-induced ocular hypertension. In man, LMX1B is associated with Nail-Patella syndrome, which can include the development of glaucoma, demonstrating the clinical relevance of this mouse model. Among the gene expression changes detected, TM3 cells had altered expression of genes associated with mitochondrial metabolism. The authors used their previous experience using nicotinamide to metabolically protect DBA2/J mice from glaucomatous damage, and they hypothesized that nicotinamide supplementation of mutant Lmx1b mice would help restore normal mitochondrial metabolism in the TM and prevent Lmx1b-mediated ocular hypertension. Adding nicotinamide to the drinking water significantly prevented Lmxb1 mutant mice from developing high intraocular pressure. This is a laudable example of dissecting the molecular pathogenic mechanisms responsible for a disease (glaucoma) and then discovering and testing a potential therapy that directly intervenes in the disease process and thereby protects from the disease.

Strengths:

There are numerous strengths in this comprehensive study including:

Deep scRNA sequencing that was confirmed by an independent dataset in another mouse strain at another university.

Identification and validation of molecular markers for each mouse TM cell subset along with localization of these subsets within the mouse aqueous outflow pathway.

Rigorous bioinformatics analysis of these data as well as comparison of the current data with previously published mouse and human scRNAseq data.

Correlating their current data with GWAS glaucoma and IOP "hits".

Discovering gene expression changes in the 3 TM subgroups in the mouse mutant Lmx1b model of glaucoma.

Further pursuing the indication of dysfunctional mitochondrial metabolism in TM3 cells from Lmx1b mutant mice to test the efficacy of dietary supplementation with nicotinamide. The authors nicely demonstrate the disease modifying efficacy of nicotinamide in preventing IOP elevation in these Lmx1b mutant mice, preventing the development of glaucoma. These results have clinical implications for new glaucoma therapies.

We thank the reviewer for these generous and thoughtful comments on the strengths of this study.

Weaknesses:

(1) Occasional over-interpretation of data. The authors have used changes in gene expression (RNAseq) to implicate functions and signaling pathways. For example: they have not directly measured "changes in metabolism", "mitochondrial dysfunction" or "activity of Lmx1b".

We thank the reviewer for this feedback. We did not intend to overstate and agree. Our gene expression changes support, but do not by themselves prove, metabolic disturbances. We had felt that this was obvious and did not want to clutter the text. We have revised the manuscript to clarify that our conclusions about metabolic changes and LMX1B activity are based on gene expression patterns rather than direct functional assays and have added EM data (see below under “Recommendations for the authors”).

We have also added the following to the results:

Lines 715-721: “Although the documented gene expression changes strongly suggest metabolic and mitochondrial dysfunction, they do not directly prove it. Using electron microscopy to directly evaluate mitochondria in the TM, we found a reduction in total mitochondria number per cell in mutants (P = 0.015, Figure 6G). In addition, mitochondria in mutants had increased area and reduced cristae (inner membrane folds) in mutants consistent with mitochondrial swelling and metabolic dysfunction (all P < 0.001 compared to WT, Figure 6G-H).”

More detailed EM and metabolic studies are underway but are beyond the scope of this paper.

(2) In their very thorough data set, there is enrichment of or changes in gene expression that support other pathways that have been previously reported to be associated with glaucoma (such as TGFb2, BMP signaling, actin cytoskeletal organization (CLANs), WNT signaling, ossification, etc. that appears to be a lost opportunity to further enhance the significance of this work.

We appreciate the reviewer’s suggestions for enhancing the relevance of our work, we had not initially discussed this due to length concerns. We have now incorporated some of this information into the manuscript (see below under “Recommendations for the authors”).

Reviewer #3 (Public review):

Summary: In this study, the authors perform multimodal single-cell transcriptomic and epigenomic profiling of 9,394 mouse TM cells, identifying three transcriptionally distinct TM subtypes with validated molecular signatures. TM1 cells are enriched for extracellular matrix genes, TM2 for secreted ligands supporting Schlemm's canal, and TM3 for contractile and mitochondrial/metabolic functions. The transcription factor LMX1B, previously linked to glaucoma, shows the highest expression in TM3 cells and appears to regulate mitochondrial pathways. In Lmx1bV265D mutant mice, TM3 cells exhibit transcriptional signs of mitochondrial dysfunction associated with elevated IOP. Notably, vitamin B3 treatment significantly mitigates IOP elevation, suggesting a potential therapeutic avenue.

This is an excellent and collaborative study involving investigators from two institutions, offering the most detailed single-cell transcriptomic and epigenetic profiling of the mouse limbal tissues-including both TM and Schlemm's canal (SC), from wild-type and Lmx1bV265D mutant mice. The study defines three TM subtypes and characterizes their distinct molecular signatures, associated pathways, and transcriptional regulators. The authors also compare their dataset with previously published murine and human studies, including those by Van Zyl et al., providing valuable crossspecies insights.

Strengths: 

(1) Comprehensive dataset with high single-cell resolution

(2) Use of multiple bioinformatic and cross-comparative approaches

(3) Integration of 3D imaging of TM and SC for anatomical context

(4) Convincing identification and validation of three TM subtypes using molecular markers.

We thank the reviewer for their comments on the strengths of this study.

Weaknesses:

(1) Insufficient evidence linking mitochondrial dysfunction to TM3 cells in Lmx1bV265D mice: While the identification of TM3 cells as metabolically specialized and Lmx1b-enriched is compelling, the proposed link between Lmx1b mutation and mitochondrial dysfunction remains underdeveloped. It is unclear whether mitochondrial defects are a primary consequence of Lmx1b-mediated transcriptional dysregulation or a secondary response to elevated IOP. Additional evidence is needed to clarify whether Lmx1b directly regulates mitochondrial genes (e.g., via ChIP-seq, motif analysis, or ATAC-seq), or whether mitochondrial changes are downstream effects.

We agree and refer the reviewer to our responses to the other referees including Reviewer 1, Comment 1 and Reviewer 2 comments 1 and 17. As noted there, these mechanistic questions are the focus of ongoing and future studies. We have revised the text where appropriate to ensure it accurately reflects the scope of our current data.

(2) Furthermore, the protective effects of nicotinamide (NAM) are interpreted as evidence of mitochondrial involvement, but no direct mitochondrial measurements (e.g., immunostaining, electron microscopy, OCR assays) are provided. It is essential to validate mitochondrial dysfunction in TM3 cells using in vivo functional assays to support the central conclusion of the paper. Without this, the claim that mitochondrial dysfunction drives IOP elevation in Lmx1bV265D mice remains speculative. Alternatively, authors should consider revising their claims that mitochondrial dysfunction in these mice is a central driver of TM dysfunction.

We again refer the reviewer to our other response including Reviewer 1, Comment 1 and Reviewer 2 comments 1 and 17.

(3) Mechanism of NAM-mediated protection is unclear: The manuscript states that NAM treatment prevents IOP elevation in Lmx1bV265D mice via metabolic support, yet no data are shown to confirm that NAM specifically rescues mitochondrial function. Do NAM-treated TM3 cells show improved mitochondrial integrity? Are reactive oxygen species (ROS) reduced? Does NAM also protect RGCs from glaucomatous damage? Addressing these points would clarify whether the therapeutic effects of NAM are indeed mitochondrial.

We refer the reviewer to our response to Reviewer 1, Comment 2.

(4) Lack of direct evidence that LMX1B regulates mitochondrial genes: While transcriptomic and motif accessibility analyses suggest that LMX1B is enriched in TM3 cells and may influence mitochondrial function, no mechanistic data are provided to demonstrate direct regulation of mitochondrial genes. Including ChIP-seq data, motif enrichment at mitochondrial gene loci, or perturbation studies (e.g., Lmx1b knockout or overexpression in TM3 cells) would greatly strengthen this central claim.

We refer the reviewer to our response to Reviewer 1, Comment 1.

(5) Focus on LMX1B in Fig. 5F lacks broader context: Figure 5F shows that several transcription factors (TFs)-including Tcf21, Foxs1, Arid3b, Myc, Gli2, Patz1, Plag1, Npas2, Nr1h4, and Nfatc2exhibit stronger positive correlations or motif accessibility changes than LMX1B. Yet the manuscript focuses almost exclusively on LMX1B. The rationale for this focus should be clarified, especially given LMX1B's relatively lower ranking in the correlation analysis. Were the functions of these other highly ranked TFs examined or considered in the context of TM biology or glaucoma? Discussing their potential roles would enhance the interpretation of the transcriptional regulatory landscape and demonstrate the broader relevance of the findings.

Our analysis (Figure 5F) indicates that Lmx1b is the transcription factor most strongly associated with its predicted target gene expression across all TM cells, as reflected by its highest value along the X-axis. While other transcription factors exhibit greater motif accessibility (Y-axis), this likely reflects their broader expression across TM subtypes. In contrast, Lmx1b is minimally expressed in TM1 and TM2 cells, which may account for its lower motif accessibility overall (motifs not accessible in cells where Lmx1b is not / minimally expressed).

Our emphasis on LMX1B is further supported by its direct genetic association with glaucoma. In contrast, the other transcription factors lack clear links to glaucoma and are supported primarily by indirect evidence. Nonetheless, we agree that the transcription factors highlighted in our analysis are promising candidates for future investigation. However, to maintain focus on the central narrative of this study, we have chosen not to include an extended discussion of these additional genes.

(6) In abstract, they say a number of 9,394 wild-type TM cell transcriptomes. The number of Lmx1bV265D/+ TM cell transcriptomes analyzed is not provided. This information is essential for evaluating the comparative analysis and should be clearly stated in the Abstract and again in the main text (e.g., lines 121-123). Including both wild-type and mutant cell counts will help readers assess the balance and robustness of the dataset.

We thank the reviewer for noticing this oversight and have added this value to the abstract and results section. 

Lines 41 and 696: 2,491 mutant TM cells.  

(7) Did the authors monitor mouse weight or other health parameters to assess potential systemic effects of treatment? It is known that the taste of compounds in drinking water can alter fluid or food intake, which may influence general health. Also, does Lmx1bV265D/+ have mice exhibit non-ocular phenotypes, and if so, does nicotinamide confer protection in those tissues as well? Additionally, starting the dose of the nicotinamide at postnatal day 2, how long the mice were treated with water containing nicotinamide, and after how many days or weeks IOP was reduced, and how long the decrease in the IOP was sustained.

Water intake was monitored in both treatment groups, and dosing was based on the average volume consumed by adult mice (lines 1017–1018, young pups do not drink water and so drug is largely delivered through mothers’ milk until weaning and so we do not know an accurate dose for young pups). Mouse health was assessed throughout the experiment through regular monitoring of body weight and general condition.

Depending on genetic context, Lmx1b mutations can cause kidney disease and impact other systems. Non-ocular phenotypes were not the focus of this study and were not characterized.

We added a comment to the method to clarify the NAM treatment timeline. NAM was administered continuously in the drinking water starting at P2 and maintained throughout the experiment. IOP was measured beginning at 2 months and then at monthly time points. NAM lessened IOP at 2 and 3 months. We terminated IOP assessment at 3 months.

Lines 1028-1029: “Treatment was started at postnatal day 2 and continued throughout the experiment.”

(8) While the IOP reduction observed in NAM-treated Lmx1bV265D/+ mice appears statistically significant, it is unclear whether this reflects meaningful biological protection. Several untreated mice exhibit very high IOP values, which may skew the analysis. The authors should report the mean values for IOP in both untreated and NAM-treated groups to clarify the magnitude and variability of the response.

We have added supplemental table 7 with the statistical information. Regarding the high IOP values observed in a subset of untreated V265D mutant mice, we consistently detect individual mutant eyes with IOPs exceeding 30 mmHg across independent cohorts and time points [3-5]. It is important to note that IOP is subject to fluctuation and in disease states such as glaucoma, circadian rhythms can be disrupted with stochastic and episodic IOP spikes throughout the day. This may be occurring in those untreated mice. This is also why we strive to use sample sizes of 40 or more. Additionally, we observe that some mutant eyes with IOPs measured within the normal range have anterior chamber deepening (ACD) - a persistent anatomical change associated with sustained or recurrent high IOP that stretches the cornea and may posteriorly displace the lens. This suggests mutant mice experience transient IOP elevations that are not always captured at a single time point due to the stochastic nature of these fluctuations. To account for this, we include ACD as an additional readout alongside IOP measurements. The reduction in ACD observed in NAM-treated mice provides independent evidence supporting the biological relevance of NAM-mediated IOP reduction.   

(9) Additionally, since NAM has been shown to protect RGCs in other glaucoma models directly, the authors should assess whether RGCs are preserved in NAM-treated Lmx1b V265D/+ mice. Demonstrating RGC protection would support a synergistic effect of NAM through both IOP reduction and direct neuroprotection, strengthening the translational relevance of the treatment.

We again thank the referee. We note the possibility of dual IOP protection and neuroprotection in the manuscript (lines 961–963). The goal of the present study, however, was to determine mechanisms underlying IOP elevation in patients with LMX1B variants. Therefore, we limited our focus to IOP elevation (LMX1B is expressed in the TM but not RGCs). Studies of the RGCs and optic nerve in V265D mutant mice treated with NAM take considerable effort but are underway. They will be reported in a subsequent manuscript. Initial data support protection, but that is a work in progress.  

Additionally, we recently reported a similar pattern of IOP protection to that reported here using pyruvate - in experiments where we analyzed the optic nerve as the focus of the study was assessment of pyruvate as a resilience factor against high genetic risk of glaucoma [4]. In that case, there was statistically significant protection from glaucomatous optic nerve damage, arguing for translational relevance again with a possible synergistic effect through both IOP reduction and direct neuroprotection.

(10) Can the authors add any other functional validation studies to explore to understand the pathways enriched in all the subtypes of TM1, TM2, and TM3 cells, in addition to the ICH/IF/RNAscope validation?

We agree with the reviewer on the importance of further functional validation of pathways active in TM cell subtypes that influence IOP. However, comprehensive investigation of the pathways active in subtypes need to be in future studies. It is beyond the scope of his already large paper.

(11) The authors should include a representative image of the limbal dissection. While Figure S1 provides a schematic, mouse eyes are very small, and dissecting unfixed limbal tissue is technically challenging. It is also difficult to reconcile the claim that the majority of cells in the limbal region are TM and endothelium. As shown in Figure S6, DAPI staining suggests a much higher abundance of scleral cells compared to TM cells within the limbal strip. Additional clarification or visual evidence would help validate the dissection strategy and cellular composition of the captured region.

We appreciate the reviewer’s suggestion and have added additional images to Figure S1 to show our limbal strip dissection. However, we clarify that we do not intend to suggest that TM and endothelial cells are the most abundant populations in these dissected strips.  When we say “are enriched for drainage tissues” we mean in comparison to dissecting the anterior segment as a whole. We have clarified this in the text. In fact, epithelial cells (primarily from the cornea) constituted the largest cluster in our dataset (Figure 1A). Additionally, to avoid misinterpretation, we generally refrain from drawing conclusions about the relative abundance of cell types based on sequencing data. Single-cell and single nucleus RNA sequencing results are sensitive to technical factors that alter cell proportions depending on exact methodological details. In our study, TM cells comprised 24.4% of the single-cell dataset and 11.8% of the single-nucleus dataset, illustrating the impact of methodological variability. 

Lines 163-164: “Individual eyes were dissected to isolate a strip of limbal tissue, which is enriched for TM cells in comparison to dissecting the anterior segment as a whole.”

Reviewer #1 (Recommendations for the authors):

To enhance the reproducibility and transparency of the findings presented in this study, we strongly recommend that the authors make all analysis scripts and computational tools publicly available.

We agree with the reviewer’s emphasis on transparency and are currently building a GitHub page to share our scripts. However, we did not develop any new tools for this study. All tools that we used are publicly available and provided in our methods section. All data will be available as raw data and through the Broad Institute’s Single Cell Portal.

Reviewer #2 (Recommendations for the authors):

The authors are to be commended for a well-written presentation of high-quality data, their comparisons of datasets (other mouse and human scRNAseq data), correlation with clinical glaucoma risk alleles, and curative therapy for the mouse model of Lmx1b glaucoma. There are several minor suggestions that the authors might consider to further improve their manuscript:

(1) Lines 42-43: Although their data strongly support the role of mitochondrial dysfunction in Lmx1b glaucoma, they might want to soften their conclusion "supports a primary role of mitochondrial dysfunction within TM3 cells initiating the IOP elevation that causes glaucoma".

With the inclusion of EM data supporting mitochondrial dysfunction in Lmx1b mutant TM cells, we have revised this sentence to more accurately reflect our findings.

Lines 42-44 (previously lines 42-43): “Mitochondria in TM cells of V265D/+ mice are swollen with a reduced cristae area, further supporting a role for mitochondrial dysfunction in the initiation of IOP elevation in these mice.”

(2) Figure 1: Why is the shape of the "TM containing" cluster in 1A so different than the cluster shown in 1B?

We isolated cells from the 'TM-containing' cluster and performed unbiased reclustering, which alters their positioning in UMAP space. The figure legend has been updated to clarify this point.

Lines 143-144 “A separate UMAP representation of the trabecular meshwork (TM) containing cluster following subclustering.”

(3) Line 160: change "data was" to "data were"

Corrected

(4) S4 Fig C: Please comment on why the Columbia and Duke heatmaps for TM3 are not as congruent as the heatmaps for TM1 and TM2.

We cannot definitively determine the reason for this. However, differences in tissue processing techniques between the Columbia and Duke preparations may contribute. Such variations have been shown to affect cellular transcriptomes in certain contexts. It is possible that TM3 cells are more susceptible to these effects than others. We have added a statement addressing this point to the figure legend.

Lines 238-240: “Because tissue processing techniques can alter gene expression [52], the heatmap variation between institutes likely reflects differences in processing techniques (Methods) and suggests that TM3 cells are more susceptible to these effects than other cell types.”

(5) S9 Fig: It is very difficult to see any staining for TM1 CHIL1 (2nd panel), TM2 End3 (2nd panel), and TM3 Lypd1 (both panels)

We apologize for the difficulty in visualizing these panels. To improve clarity, we have increased the brightness of all relevant marker signals, within standard bounds, to facilitate easier interpretation.

(6) Line 380: "are significantly higher"; since statistical analysis was not reported, please do not use "significantly"

Done

(7) The authors should consider discussing several of their findings that agree with published literature. For example:

Figure 3B: "Wnt protein binding" (PMID: 18274669), "TGFb "binding" (numerous references), "integrin binding" (work of Donna Peters), "actin binding"/"actin filament binding"/"actin filament bundle" (CLANs references)

S10 Fig c: "ossification" (work of Torretta Borres)

S11 Fig A: ID2/ID3 (PMID: 33938911); (B) BMP4 (PMID: 17325163)

S12 Fig A: MYOC in TM1 cells (numerous references)

We appreciate the reviewer’s diligent review and comments regarding these pathways. We have added a comment to the discussion regarding the agreement of these pathways.

Lines 855-858: In addition, the expression of genes that we document generally agrees with the literature. For example, the following genes and signaling molecules have been reported in TM cells, WNT signaling [78], TGF-β signaling [79-85], integrin binding [86-88], actin cytoskeletal networks [89], calcification genes [90, 91], and Myocilin [91-94].

(8) Line 541: was confocal microscopy used to measure the "3D shapes" of nuclei or was this done with a single image to determine sphericity?

This analysis was performed using confocal microscopy and 3D reconstructed models of the TM nuclei. We have added text to clarify this in the figure legend 

Lines 553-556: “To rigorously assess whether TM1 nuclei are more spherical, we analyzed their reconstructed 3D shapes from whole mounts images by confocal microscopy, comparing them to TM3 nuclei using the ‘Sphericity’ tool in Imaris.”

(9) Line 545: please add a close parentheses after "scoring 1"

Done

(10) S15 Fig: (A) There does not appear to be "good agreement" (line 653) between the datasets for TM1. (C) please provide a better explanation on how to interpret these "Confusion Matrix" results.

We understand the referee's concern, the patterns likely appear different to the referee due to limited sampling in snRNA-seq data. Based on our results, TM1 seems particularly susceptible, possibly because these cells do not tolerate the isolation process as well. Although we are confident that TM1 shows good agreement between the two techniques based on our experience, we have revised the language in the text to “generally” to reflect this nuance.

Lines 633-635 (previously line 653): The generated clusters and their marker genes generally agreed with our scRNA-seq analyses (Fig 5A-B, S15A Fig).

We have also added additional clarification for how to interpret the Confusion Matrix. 

Lines 669-672: “Colors indicate the fraction of cells identified in each ATAC cluster (row) which are also identified in each RNA cell type (columns), where darker colors represent stronger correspondence between RNA and ATAC clusters.”

(11) Line 676: The transition from discussing the sc/snRNAseq data to the work in Lmx1b mutant mice is quite abrupt and could use a better transition to introduce this metabolism work.

We have revised this transition for improved flow but prefer to keep all transitions brief due to the paper's length.

Lines 691-694 (previously line 676): To evaluate the utility of our new TM cell atlas, we used it to examine how Lmx1b mutations affect the TM cell transcriptome and to identify potential mechanisms underlying IOP elevation. We selected LMX1B because it causes IOP elevation and glaucoma in humans and was identified as a highly active transcription factor in our TM cell dataset.

(12) Lines 696-697: It appears counter-intuitive that upregulation of ubiquitin pathways would lead to proteostasis (proteosome protein degradation requires ubiquination).

We have clarified that the protein tagging pathway was significantly upregulated. However, polyubiquitin precursor itself was downregulated. In general, the statistical significance of the protein tagging pathway suggests perturbation of the system tagging proteins for degradation. We have clarified this in the text. 

Lines 711-714 (previously lines 696-697): “In addition, mutant TM3 cells showed an upregulation of protein tagging genes. However, there is a downregulation of the polyubiquitin precursor gene (Ubb, P = 4.5E-30), indicating a general dysregulation of pathways that tag proteins for degradation.”

(13) Line 715: Please justify why "perturbed metabolism" was chosen to pursue vs the other differentially expressed pathways

We chose to narrow our focus on TM3 cells because of the enrichment for Lmx1b expression.Most pathways identified in our analysis of TM3 cells implicate mitochondrial metabolism.Therefore, we chose to further explore this avenue. We clarified that perturbed metabolism was the strongest gene expression signature in the text. 

Lines 753-754 (previously line 715): “Our findings most strongly implicate perturbed metabolism within TM3 cells as responsible for IOP elevation in an Lmx1b glaucoma model.”

(14) Line 759: The authors clearly demonstrate that Lmx1b is most expressed in TM3 cells; however, they did not demonstrate that "Lmx1b was most active"

ATAC analysis showed that Lmx1b was most active in TM cells overall. We inferred its activity in TM3 because Lmx1b is most enriched in that subtype. This has been clarified in the text.

Lines 799-800 (previously line 759): “More specifically, we demonstrate that Lmx1b is the most active TM cell TF and is enriched in TM3 cells,…”

(15) Lines 830-835: Please include references documenting increased TGFβ2 concentrations in POAG aqueous humor and TM, effects of TGFβ2 on TM ECM deposition, and TGFβ2 induced ocular hypertension ex vivo and in vivo.

Done.

(16) Line 875: The authors provide no direct evidence for enhances "oxidative stress" in Lmx1b TM3 cells

The mitochondrial abnormalities and changed pathways support oxidative stress, but we have not directly tested this. Experiments are currently underway to evaluate its role, but these additional analyses are beyond the scope of this paper. We removed oxidative stress from the sentence.

Lines 920-922 (previously line 875): “Importantly, in heterozygous mutant V265D/+ mice, TM3 cells had pronounced gene expression changes that implicate mitochondrial dysfunction, but that were absent or much lower in other cells including TM1 and TM2.”

(17) Line 880: Similarly, the authors have not directly assessed effects on metabolism in TM3 cells; they only have shown changes in the expression of mitochondrial genes that may affect metabolism

We have no way to specifically isolating TM3 cells to test this. Future work is underway to test this more broadly in isolated TM cells but is beyond the scope of this is already large paper. Considering our gene expression data and the addition of supporting EM data, we have qualified the text.

Lines 930-931 (previously 880): “Our data extend these published findings by showing that inheritance of a single dominant mutation in Lmx1b similarly affects mitochondria in TM cells.”

(18) Line 892: What markers were used to detect "cell stress"?

We have revised the text. Although our RNA data show stress gene changes, characterization of these markers is beyond the scope of the current study and will be included in a subsequent paper.

Lines 945-948 (previously line 892): “However, these processes were not limited to TM3 cells or even to cell types that express detectable Lmx1b, suggesting that they are secondary damaging processes that are subsequent to the initiating, Lmx1b-induced perturbations in TM3 cells.”

Additional author driven change

While revising and reviewing our data, we identified a coding error that resulted in the WT and V265D mutant group labels being switched in Figure 6. Importantly, the significance of the differentially expressed genes (DEGs), the implicated biological pathways, and the interpretation of pathway directionality in the manuscript remain accurate. The only issue was the incorrect labeling in the figure. We have corrected the labels in Figure 6 to accurately reflect the data. As noted above, all data and code will be made available to ensure full reproducibility of our results.

References

(1) Doucet-Beaupre H, Gilbert C, Profes MS, Chabrat A, Pacelli C, Giguere N, et al. Lmx1a and Lmx1b regulate mitochondrial functions and survival of adult midbrain dopaminergic neurons. Proc Natl Acad Sci U S A. 2016;113(30):E4387-96. Epub 2016/07/14. doi: 10.1073/pnas.1520387113. PubMed PMID: 27407143; PubMed Central PMCID: PMCPMC4968767.

(2) Jimenez-Moreno N, Kollareddy M, Stathakos P, Moss JJ, Anton Z, Shoemark DK, et al. ATG8-dependent LMX1B-autophagy crosstalk shapes human midbrain dopaminergic neuronal resilience. J Cell Biol. 2023;222(5). Epub 2023/04/05. doi: 10.1083/jcb.201910133. PubMed PMID: 37014324; PubMed Central PMCID: PMCPMC10075225.

(3) Cross SH, Macalinao DG, McKie L, Rose L, Kearney AL, Rainger J, et al. A dominantnegative mutation of mouse Lmx1b causes glaucoma and is semi-lethal via LDB1mediated dimerization [corrected]. PLoS Genet. 2014;10(5):e1004359. Epub 2014/05/09. doi: 10.1371/journal.pgen.1004359. PubMed PMID: 24809698; PubMed Central PMCID: PMCPMC4014447.

(4) Li K, Tolman N, Segre AV, Stuart KV, Zeleznik OA, Vallabh NA, et al. Pyruvate and related energetic metabolites modulate resilience against high genetic risk for glaucoma. Elife. 2025;14. Epub 2025/04/24. doi: 10.7554/eLife.105576. PubMed PMID: 40272416; PubMed Central PMCID: PMCPMC12021409.

(5) Tolman NG, Balasubramanian R, Macalinao DG, Kearney AL, MacNicoll KH, Montgomery CL, et al. Genetic background modifies vulnerability to glaucoma-related phenotypes in Lmx1b mutant mice. Dis Model Mech. 2021;14(2). Epub 2021/01/20. doi: 10.1242/dmm.046953. PubMed PMID: 33462143; PubMed Central PMCID: PMCPMC7903917.

X

Explore why this is a struggle

eLife Assessment

This useful study investigates how intrinsically disordered domains can interact to dictate the sub-cellular localization of a major innate immune sensor termed cGAS. The data from various cellular and biochemical assays are mostly solid, but the main conclusions from these experiments need to be validated further. This paper is relevant to immunologists, especially those interested in cytosolic DNA-sensing pathways.

Reviewer #1 (Public review):

Summary:

This manuscript by the Yin group presents interesting findings that organelle-tethered intrinsically disordered "MEMCA" scaffolds, as exemplified by ZDHHC18 at the Golgi and MARCH8 at endosomes, enhance the engagement of cGAS with organelle-proximal condensates, thereby sequestering cGAS from cytosolic DNA sensing and negatively regulating innate immunity.

Strengths:

These findings suggest a previously unrecognized mechanism by which Golgi/endosomal IDR scaffolds modulate cGAS activity, with implications for antiviral defense and tumor immunology. The study is conceptually intriguing and potentially impactful.

Weaknesses:

While the manuscript addresses a novel aspect of cGAS regulation, additional mechanistic insights and targeted validations are needed to ensure robustness:

(1) How do ZDHHC18/MARCH8 enhance cGAS engagement? Do they act as bridges to form a ternary, membrane-tethered cGAS-DNA-MEMCA complex, or alter cGAS condensate properties allosterically?

(2) Is organelle cGAS capture selective? For instance, can other palmitoyltransferases/E3 ligases be substituted for ZDHHC18/MARCH8?

(3) Why does membrane association suppress cGAS enzymic activity, as dsDNA still resides in cGAS condensation?

Reviewer #2 (Public review):

Summary:

The authors found that cGAS, a DNA sensor, relocalizes to organelle membranes (ER, Golgi, endosomes) upon DNA stimulation, revealing spatial regulation of its activity. ZDHHC18 and MARCH8 recruit cGAS to Golgi/endosomes via intrinsically disordered regions (IDRs), driving phase-separated condensates. This sequestration of cGAS-dsDNA complexes suppresses innate immune signaling, uncovering a novel regulatory mechanism.

Strengths:

The work overall is very interesting. The authors provided molecular and biochemical evidence.

Weaknesses:

Overall, the work is very interesting. However, the quality of some of the data does need to be improved, and more experiments need to be performed.

The following points need to be addressed:

(1) In Figure S7, no direct binding between cGAS and MARCH8 or ZD18 IDR is observed, and the interaction only occurs after DNA stimulation. However, Figure 5 shows cGAS recruitment to ZD18 or MARCH8 IDR droplets, suggesting direct interactions. This apparent discrepancy should be clarified.

(2) The authors propose that recruiting cGAS to organelle membranes reduces its activity, as demonstrated by the FKBP experiment. However, ZD18 and MARCH8 also post-translationally modify cGAS. Do both mechanisms contribute to this effect, and can the authors test this?

(3) To demonstrate the functional importance of MEMCA, the authors should test IFN production or STING activation in cells.

(4) Does the IDR of MARCH8 or ZD18 influence the interaction between cGAS and DNA?

(5) Which region of cGAS does the IDR of MARCH8 or ZD18 interact with: the cGAS-CD or the cGAS-N-terminus?

(6) The in vitro LLPS experiments with cGAS, DNA, and ZD18/MARCH8 should be conducted under physiological conditions.

Reviewer #3 (Public review):

Summary:

In this study by Shi et al., the authors evaluate if cGAS is recruited to the membranes of intracellular organelles. Using a combination of biochemical fractionation and imaging techniques, the authors propose that upon recognition of DNA, cGAS translocates to various subcellular locations, including the golgi, endoplasmic reticulum, and endosomes. Mechanistically, the authors propose that upon localizing to the Golgi or endosome, cGAS binding to MARCH8 and ZDHHC18 prevents cGAS activity by incorporating cGAS and dsDNA into biomolecular condensates. However, in its current form, the study does not directly address this question.

Strengths:

The question of evaluating cGAS sub-cellular localization as a mechanism for controlling activity is interesting, and there is some evidence that cGAS is localized to sub-cellular organelle membranes.

Weaknesses:

(1) The well-established nuclear localization of cGAS is not adequately addressed in the cell lines used and is inconsistent with the findings.

(2) Previous studies have shown that ZDHHC18 and MARCH8 control cGAS activity, which detracts somewhat from the novelty.

(3) A lot of inconsistency in the cell lines and artificial expression systems used across the study.

(4) A key element missing is showing that in the absence of ZDHHC18 or MARCH8, the loss of endogenous cGAS localization to the various sub-cellular organelles increases cGAMP synthesis and downstream STING activation in primary cells. There is an over-reliance on artificial expression systems. An important experiment to validate the hypothesis would be to evaluate endogenous cGAS localization in MARCH8- and ZDHHC18-deficient primary cells. Further, there should be evaluation of endogenous STING responses in MARCH8- and ZDHHC18-deficient primary cells in tandem with the localization studies.

(5) There are a large number of grammatical errors throughout the manuscript which should be addressed.

Author response:

Below we outline our provisional responses to the major points raised in the public reviews, and our planned revisions:

(1) Mechanistic model of how ZDHHC18/MARCH8 engage the cGAS–DNA condensate (Reviewer #1 & #2

We will add a dedicated subsection and a working-model figure describing our current view: IDRs of ZDHHC18 (Golgi) and MARCH8 (endosomes) engage pre-formed cGAS–DNA condensates at organelle membranes, and thereby tune cGAS activity through PTMs. We will explicitly discuss bridge-like versus allosteric modes by perform additional LLPS experiment (e.g. FRAP assay) to detect any IDR-driven changes in condensate properties, and explain how these scenarios fit our data.

(2) Selectivity beyond ZDHHC18/MARCH8 (Reviewer #1)

We will expand the text to explain existing evidence indicating that, in addition to ZDHHC18 or MARCH8, other post-translational modification (PTM) enzymes and/or membrane-associated scaffolds may also modulate cGAS. We will summarize our current datasets that support this possibility and outline how this selectivity relates to organelle identity.

(3) Why membrane association suppresses cGAS activity (Reviewer #1)

We will provide a concise mechanistic rationale—integrating our published work—to explain how membrane-proximal sequestration can limit cGAS catalysis despite cGAS–DNA coexistence within condensates. Specifically, we will discuss (i) IDR-dependent changes in condensate properties, and (ii) PTMs by ZDHHC18/MARCH8 that allosterically reduce catalytic efficiency; we will clearly cross-reference our prior publications that bear on these points.

(4) Reconciling Fig. S7 (DNA-dependent binding) with Fig. 5 (recruitment to IDR droplets) (Reviewer #2)

We will add text to clarify experimental context and readouts to prove that there is no real contradiction between Fig. S7 and Fig. 5. In the experiment shown in Fig. 5, PEG (a macromolecular crowding agent) was added to the system, which facilitates the formation of IDR phase-separated droplets. Under these conditions, cGAS partitions into the IDR condensates, leading to the observed recruitment. In contrast, Fig. S7 examines the direct physical interaction between cGAS and the IDRs using biochemical pull-down assays and shows that no direct interaction occurs in the absence of DNA. These two results reflect different experimental contexts and are therefore not mutually exclusive.

(5) Planned additional tests to address specificity and mechanism (Reviewer #2)

DNA pull-down: to test whether IDRs alter cGAS–DNA affinity, we will compare cGAS binding to DNA with/without MEMCA IDRs (and with charged-residue mutants).

Domain mapping: to determine which region of cGAS engages MEMCA IDRs, we will map binding using cGAS N-terminus/core-domain truncations and key surface mutants.

Physiological in vitro LLPS: we will repeat cGAS–DNA–IDR LLPS assays under physiological buffer conditions and report partition coefficients, FRAP, and phase diagrams to ensure physiological relevance.

(6) Image clarity and data presentation (Reviewer #2):

We will improve image resolution, add zoomed-in insets with organelle markers, and provide more significant Cy5-ISD signal.

(7) Nuclear localization of cGAS and system considerations (Reviewer #3)

We will explicitly document the nuclear signal of cGAS observed in our confocal experiments, detail the cell lines and expression systems used. We will also clarify cGAS nuclear localization in the cell lines used.

(8) Endogenous validation and cell line consistency (Reviewer #3):

We will perform experiments in primary cells (knockout macrophages) to address the concern of relying on overexpression.

(9) Language and grammar (Reviewer #3):

We will thoroughly revise the manuscript for grammar and clarity.

Together, these planned revisions will strengthen the mechanistic basis of our findings and provide direct evidence for the physiological role of organelle-tethered IDRs in regulating cGAS activity.

eLife Assessment

Ruppert et al. investigated how activation of thermogenesis by cold exposure (CE) and methionine restriction (MetR) impacts health and leads to weight loss in mice. The authors provided valuable datasets showing that the responses to MR and CE are tissue-specific, while MR and CE affect beige adipose similarly. Although the study is descriptive, the data analyses are solid, with well-supported conclusions drawn from the findings.

Reviewer #1 (Public review):

Summary:

Activation of thermogenesis by cold exposure and dietary protein restriction are two lifestyle changes that impact health in humans and lead to weight loss in model organisms - here, in mice. How these affect liver and adipose tissues has not been thoroughly investigated side by side. In mice, the authors show that the responses to methionine restriction and cold exposure are tissue-specific, while the effects on beige adipose are somewhat similar.

Strengths:

The strength of the work is the comparative approach, using transcriptomics and bioinformatic analyses to investigate the tissue-specific impact. The work was performed in mouse models and is state-of-the-art. This represents an important resource for researchers in the field of protein restriction and thermogenesis.

Weaknesses:

The findings are descriptive, and the conclusions remain associative. The work is limited to mouse physiology, and the human implications have not been investigated yet.

Reviewer #2 (Public review):

Summary:

This study provides a library of RNA sequencing analysis from brown fat, liver, and white fat of mice treated with two stressors - cold challenge and methionine restriction - alone and in combination (interaction between diet and temperature). They characterize the physiologic response of the mice to the stressors, including effects on weight, food intake, and metabolism. This paper provides evidence that while both stressors increase energy expenditure, there are complex tissue-specific responses in gene expression, with additive, synergistic, and antagonistic responses seen in different tissues.

Strengths:

The study design and implementation are solid and well-controlled. Their writing is clear and concise. The authors do an admirable job of distilling the complex transcriptome data into digestible information for presentation in the paper. Most importantly, they do not overreach in their interpretation of their genomic data, keeping their conclusions appropriately tied to the data presented. The discussion is well thought out and addresses some interesting points raised by their results.

Weaknesses:

The major weakness of the paper is the almost complete reliance on RNA sequencing data, but it is presented as a transcriptomic resource.

Reviewer #3 (Public review):

Summary:

Ruppert et al. present a well-designed 2×2 factorial study directly comparing methionine restriction (MetR) and cold exposure (CE) across liver, iBAT, iWAT, and eWAT, integrating physiology with tissue-resolved RNA-seq. This approach allows a rigorous assessment of where dietary and environmental stimuli act additively, synergistically, or antagonistically. Physiologically, MetR progressively increases energy expenditure (EE) at 22{degree sign}C and lowers RER, indicating a lipid utilization bias. By contrast, a 24-hour 4 {degree sign}C challenge elevates EE across all groups and eliminates MetR-Ctrl differences. Notably, changes in food intake and activity do not explain the MetR effect at room temperature.

Strengths:

The data convincingly support the central claim: MetR enhances EE and shifts fuel preference to lipids at thermoneutrality, while CE drives robust EE increases regardless of diet and attenuates MetR-driven differences. Transcriptomic analysis reveals tissue-specific responses, with additive signatures in iWAT and CE-dominant effects in iBAT. The inclusion of explicit diet×temperature interaction modeling and GSEA provides a valuable transcriptomic resource for the field.

Weaknesses:

Limitations include the short intervention windows (7 d MetR, 24 h CE), use of male-only cohorts, and reliance on transcriptomics without complementary proteomic, metabolomic, or functional validation. Greater mechanistic depth, especially at the level of WAT thermogenic function, would strengthen the conclusions.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

SECTION A - Evidence, Reproducibility, and Clarity Summary The study investigates the neurodevelopmental impact of trisomy 21 on human cortical excitatory neurons derived from induced pluripotent stem cells (hiPSCs). Key findings include a modest reduction in spontaneous firing, a marked deficit in synchronized bursting, decreased neuronal connectivity, and altered ion channel expression-particularly a downregulation of voltage‐gated potassium channels and HCN1. These conclusions are supported by a combination of in vitro calcium imaging, electrophysiological recordings, viral monosynaptic tracing, RNA sequencing, and in vivo transplantation with two‐photon imaging.

Major Comments • Convincing Nature of Key Conclusions: The study's conclusions are generally well supported by a diverse set of experimental approaches. However, certain claims regarding the intrinsic properties of the excitatory network would benefit from further qualification. In particular, the assertion that reduced synchronization is solely attributable to altered ion channel expression might be considered somewhat preliminary without additional corroborative experiments.

1.1) We agree with the reviewer and now write in the abstract: 'Together, these findings demonstrate long-lasting impairments in human cortical excitatory neuron network function associated with Trisomy 21 .' And in the Introduction: 'Collectively, the observed changes in ion channel expression, neuronal connectivity, and network activity synchronization may contribute to functional differences relevant to the cognitive and intellectual features associated with Down syndrome.'

• One major limitation of the current experimental design is the reliance on predominantly excitatory neuronal cultures derived from hiPSCs. Although the authors convincingly demonstrate differences in network synchronization and connectivity between trisomic (TS21) and control neurons, the almost exclusive focus on excitatory cells limits the physiological relevance of the in vitro network. In the developing cortex, interneurons and astrocytes play crucial roles in modulating network excitability, synaptogenesis, and plasticity. Therefore, incorporating these cell types-either through co-culture systems or through directed differentiation protocols that yield a more heterogeneous neuronal population-could help to determine whether the observed deficits are intrinsic to excitatory neurons or are compounded by a lack of proper inhibitory regulation and glial support. 1.2) Thank you for this thoughtful comment. We agree that interneurons and astrocytes are crucial for network function. To clarify, astrocytes are generated in this culture system, as we previously reported in our characterisation of the timecourse of network development using this approach (Kirwan et al., Development 2025). However, our primary goal was to first isolate and define the cell-autonomous defects intrinsic to TS21 excitatory neurons, minimizing the complexity introduced by additional neuronal types. This focused approach was chosen also because engineering a stable co-culture system with reproducible excitatory/inhibitory (E/I) proportions is a significant undertaking that extends beyond the scope of this initial investigation, and has proven challenging to date for the field. By establishing this foundational phenotype, our work complements prior studies on interneuron and glial contributions. Future studies building on this work will be essential to dissect the more complex, non-cell-autonomous effects within a heterogeneous network. Importantly, since our initial submission, two highly relevant preprints have emerged-including a notable study from the Geschwind laboratory at UCLA (Vuong et al., bioRxiv, 2025; Risgaard et al., bioRxiv, 2025), as well as our own complementary study Lattke et al, under revision, that highlight widespread transcriptional changes in excitatory cells of the human fetal DS cortex, providing strong validation for our central findings. This convergence of results from multiple groups underscores the timeliness and importance of our work.

• Furthermore, the assessment of neuronal connectivity via pseudotyped rabies virus tracing, while innovative, has inherent limitations. The quantification of connectivity as a ratio of red-to-green fluorescence pixels may be influenced by differential viral infection efficiencies, variations in the expression levels of the TVA receptor, or even by the lower basal activity levels observed in TS21 cultures. Complementary approaches-such as electron microscopy for synaptic density analysis or functional connectivity measurements using multi-electrode arrays (MEAs)-could provide additional structural and functional insights that would validate the rabies tracing data. 1.3) Thank you for this constructive feedback. While we cannot formally exclude that TS21 cells might express the TVA receptor at lower levels due to generalized gene dysregulation, we infected all WT and TS21 cultures in parallel using identical virus preparations and titers to minimize technical variability. Crucially, we also addressed the potential confound of differential basal activity by performing the rabies tracing under TTX incubation (see Suppl. Fig. 7), which blocks network activity and ensures that viral spread reflects structural connectivity alone.

While complementary methods like EM or MEA could provide additional insight, they fall outside the scope of the current study. We are confident that our rigorous controls validate our use of the rabies tracing method to assess structural connectivity.

• Qualification of Claims: Some conclusions, particularly those linking specific ion channel dysregulation (e.g., HCN1 loss) directly to network deficits, might be better presented as preliminary. The authors could temper their language to indicate that while the evidence is suggestive, the mechanistic link remains to be fully established. 1.4) We have revised the text to more clearly indicate that the link between HCN1 dysregulation and network deficits is correlative and remains to be fully established. While our ex vivo recordings suggest altered Ih-like currents consistent with reduced HCN1 expression, we now present these findings as preliminary and hypothesis-generating, pending further functional validation. We write in the discussion: However, further targeted functional validation will be needed to confirm a causal link.

• Need for Additional Experiments: Additional experiments that could further consolidate the current findings include: o Inclusion of Inhibitory Neurons or Co-culture Systems: Incorporating interneurons or astrocytes would help determine whether the observed deficits are solely intrinsic to excitatory neurons. See 1.2 o Alternative Connectivity Assessments: Complementing the rabies virus tracing with electron microscopy or multi-electrode array (MEA) recordings would add structural and functional validation of the connectivity differences. See 1.3 o Extended Temporal Profiling: Monitoring network activity over a longer developmental window would clarify whether the observed deficits represent a delay or a permanent alteration in network maturation. 1.5) In vivo we were able to track the cells for up to five months post-transplantation supporting the interpretation of a permanent alteration.

• Reproducibility and Statistical Rigor: The methods and data presentation are largely clear, with adequate replication and appropriate statistical analyses. Nonetheless, a more detailed description of the experimental replicates, particularly regarding the viral tracing and in vivo transplantation studies, would enhance reproducibility. The availability of raw data and scripts for calcium imaging analysis would also further support independent verification. We thank the reviewer for these suggestions and we now provide a more detailed description of replicates. We also add the raw data.

Minor Comments • Experimental Details: Minor revisions could include clarifying the infection efficiency and expression levels of the viral constructs used in connectivity assays to rule out technical variability.

See 1.3

• Literature Context: The authors reference prior studies appropriately; however, integrating a brief discussion comparing their findings with alternative DS models (e.g., organoids or other hiPSC-derived systems) would improve contextual clarity. We thank the reviewer for this helpful suggestion. We have now added a brief discussion comparing our findings with those reported in alternative Down syndrome models, including brain organoids and other hiPSC-derived systems. This addition helps to contextualize our results within the broader field and highlights the unique strengths and limitations of our in vitro and in vivo xenograft approach. We write: 'Our findings align with and extend previous studies using alternative Down syndrome models, such as brain organoids and other hiPSC-derived systems. Organoid models have provided valuable insights into early neurodevelopmental phenotypes in DS, including altered interneuron proportions (Xu et al Cell Stem Cell 2019) but also suggest that variability across isogenic lines can overshadow subtle trisomy 21 neurodevelopmental phenotypes (Czerminski et al Front in Neurosci 2023). However, these systems often lack the structural complexity, vascularization, and long-term maturation achievable in vivo. By using a xenotransplantation model, we were able to assess the maturation and functional properties of human neurons within a physiologically relevant environment over extended time frames, offering complementary insights into DS-associated circuit dysfunction (Huo et al Stem Cell Reports 2018; Real et al., 2018).

• Presentation and Clarity: Figures are generally clear,.But the manuscript contains a minor labeling error. On page 13, the figure is erroneously labeled as "Fig6A", whereas, based on the context and corresponding data, it should be "Fig5A". I recommend that the authors correct this mistake to ensure consistency and avoid potential confusion for readers. Thank you for pointing this out. This has been corrected in the revised manuscript.

Reviewer #1 (Significance (Required)):

SECTION B - Significance • Nature and Significance of the Advance: The work offers a substantial conceptual advance by providing a mechanistic link between trisomy 21 and impaired neuronal network synchronization. Technically, the study integrates state-of-the-art imaging, electrophysiology, and transcriptomic profiling, thereby offering a multifaceted view of DS-related neural dysfunction. Clinically, the findings have the potential to inform future therapeutic strategies targeting network connectivity and ion channel function in Down syndrome.

We thank the reviewer for this very supportive comment.

• Context in the Existing Literature: The study builds on previous observations of altered network activity in DS patients and DS mouse models (e.g., altered EEG synchronization and reduced synaptic connectivity). It extends these findings to human-derived neuronal models, thus bridging a gap between clinical observations and molecular/cellular mechanisms. Relevant literature includes studies on DS neurodevelopment and the role of ion channels in synaptic maturation. • Target Audience: The reported findings will be of interest to researchers in neurodevelopmental disorders, Down syndrome, and ion channel physiology. Additionally, the study may attract the attention of those working on hiPSC-derived models of neurological diseases, as well as clinicians interested in the pathophysiology of DS. • Keywords and Field Contextualization: Keywords: Down syndrome, trisomy 21, neuronal connectivity, synchronized network activity, hiPSC-derived cortical neurons, ion channel dysregulation.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary The manuscript by Peter et al., reports on the neuronal activity and connectivity of iPSC-derived human cortical neurons from Down syndrome (DS) that is caused by caused by trisomy of the human chromosome 21 (TS21). Major points: Although the manuscript is potentially interesting, the results appear somehow preliminary and need to be corroborated by control experiments and quantifications of effects to fully sustain the conclusions. (1) The authors have not assessed the percentage of WT and TS21 cells that acquire a neuronal or glia identity in their cultures. Indeed, the origin of alterations in network activity and connectivity observed in TS21 neurons could simply derive from reduced number of neurons arising from TS21 iPSC. Alternatively, the same alteration in network activity and connectivity could derive from a multitude of other factors including deficits in neuronal development, neurite extension, or intrinsic electrophysiological properties. In the current version of the manuscript, none of these has been investigated. 2.1) We thank the reviewer for this thoughtful comment. In response, we included an in vivo characterization of cell-type proportions at the same time points where we observed network activity defects using in vivo calcium imaging (see Supplementary Fig. 6).

Previous work has identified several cellular and molecular phenotypes in human cells, postmortem tissue, and mouse models-including those mentioned by the reviewer. In this study, our focus was on investigating neural network activity, intrinsic electrophysiological properties both in vitro and in vivo, and preliminary bulk RNA sequencing. We have also independently measured cell proportions in the human fetal cortex and conducted a more extensive transcriptomic analysis of Ts21 versus control cells in a separate study (Lattke et al., under revision). We observed a reduction of RORB/FOXP1-expressing Layer 4 neurons in the human fetal cortex at midgestation, as well as increased GFAP+ cells, reduced progenitors and a non significant reduction of Cux2+ cells in late stage DS human cell transplants, along with a gene network dysregulation specifically affecting excitatory neurons (Lattke et al., under revision). Here, we provide complementary findings, demonstrating reduced excitatory neuron network connectivity in vitro and decreased neural network synchronised activity in both in vitro and in vivo models (see also 2.8). We agree with the reviewer that this could be for a number of reasons, both cell autonomous (channel expression and/or function) or non-autonomous (connectivity and/or network composition - as reflected in differences in proportions of SATB2+ neurons generated in TS21 cortical differentiations).

(2) Electrophysiological properties of TS21 and WT neurons at day 53/54 in vitro indicate an extremely immature stage of development (i.e. RMP between -36 and -27 mV with most of the cells firing a single action potential after current injection) in the utilized culture conditions: This is far from ideal for in vitro neuronal-network studies. Finally, reduced activity of HCN1 channels should be confirmed by specific recordings isolating or blocking the related current.

2.2) Thank you for this thoughtful comment. We have also conducted ex vivo electrophysiological recordings and found that the neurons exhibit relatively immature properties, consistent with the known slow developmental trajectory of human neuron cultures. In light of this and the absence of direct confirmatory evidence, we now refer to the observed reduction in HCN1 as preliminary.

Main points highlighting the preliminary character of the study. 1) In Figure 1 immunofluorescence images of the neuronal differentiation markers (Tbr1, Ctip2 and Tuj1) are showed. However, no quantification of the percentage of cells expressing these markers for WT and TS21 neurons is reported. On the other hand, simple inspection of the representative images clearly seams to indicate a difference between the two genotypes, with TS21 cultures showing lower number of cells expressing neuronal markers. This quantification should be corroborated by a similar staining for an astrocyte marker (GFAP, but not S100b since is triplicated in DS). This is an extremely important point since it is obvious that any change in the percentage of neurons (or the neuron/astrocyte ratio) in the cultures will strongly affect the resulting network activity (shown in Figure 2) and the connectivity (showed in Figure 4). Possibly, the quantification should be done at the same time points of the calcium imaging experiments.

2.3) See 2.1. We included an in vivo characterization of cell-type proportions at the same time points where we observed network activity defects using in vivo calcium imaging. (see Supplementary Fig. 6).

2) In Figure 2 the authors show some calcium imaging traces of WT and TS21 cultures at different time points. However, they again do not show any quantification of neuronal activity. A power spectra analysis is shown in Supplementary Figure 2, but only for WT cultures, while in Supplementary Figure 3 a comparison between WT and Ts21 power spectra is done, but only at the 50 day time point, while difference in synchrony are assessed at 60 days. At minimum, the author should include in main Figure 2 the quantification of the mean calcium event rate and mean event amplitude at the different time points and the power spectra analysis for both WT and TS21 cultures at the same timepoints.

2.4) We thank the reviewer for this comment. We now add the power spectra analysis in the main Figure 2 and quantification of the mean calcium burst rate and mean event amplitude in SuppFig. 4.

Of note, the synchronized neuronal activity is present in WT cultures at day 60, but totally lost at subsequent time-points (70 and 80 days). The results of this later time points are different from previous data from the same lab (Kirwan et al., 2015). How might these data be explained? It would be important to rule out any potential issues with the health of the culture that could explain the loss of neuronal activity.It would be beneficial to check cell viability at the different time points to exclude possible confounding factors ? A propidium staining or a MTT assay would strongly improve the soundness of the calcium data.

2.5) We thank the reviewer for this important observation. The difference from the findings reported in Kirwan et al., 2015 is due to the use of a different neuronal differentiation medium in the current study (BrainPhys versus N2B27). BrainPhys medium supports robust early network activity compared to N2B27 (onset before day 60 in BrainPhys, post-day 60 in N2B27), resulting in an earlier decline in synchrony at later stages (day 70-80 in BrainPhys, compared with day 90-100 in N2B27). Importantly, in our in vivo xenograft model, burst activity is sustained up to at least 5 months post-transplantation (mpt), indicating that the neurons retain the capacity for network activity over extended periods in a more physiological environment. We adapted the text accordingly.

3) In Figure 3 there is no quantification of the number and/or density of transplanted neurons for WT and TS21, but only representative images. As above, inspection of the representative images seems to show a decrease in cells labeled by the Tbr1 neuronal marker for TS21 cells. Moreover, the in vivo calcium imaging of transplanted WT and TS21 cells lacks most of the quantification normally done in calcium imaging experiments. Are the event rate and event amplitude different between WT and TS21 neurons ? The measure of neuronal synchrony by mean pixel correlation is not well explained, but it looks somehow simplistic. Neuronal synchrony can be more precisely measured by cross-correlation analysis or spike time tiling coefficients on the traces from single-neuron ROI rather than on all pixels in the field of view, as apparently was done here.

2.6) We thank the reviewer for these valuable points. We now include quantification of the number and density of transplanted neurons for both WT and Ts21 grafts in Extended Data Figure 5 (see 2.1).

Regarding the in vivo calcium imaging, we appreciate the reviewer's suggestion to include additional standard metrics. We have quantified the event rate in Real et al 2018. These analyses reveal that Ts21 neurons show a reduction in event rate.

We agree that our initial description of the synchrony analysis using mean pixel correlation was not sufficiently detailed. We have now clarified this in the Methods and Results, and we acknowledge its limitations. Importantly, we note that the reduced synchronisation is a highly consistent phenotype, observed across at least six independent donor pairs, different differentiation protocols, and both in vitro (and in two independent labs) and in vivo settings. As suggested, future studies using ROI-based approaches-such as cross-correlation or spike-time tiling coefficients-would provide a more refined characterization of synchrony at the single-neuron level (Sintes et al, in preparation). We now include this point in the discussion.

4) The results on reduced neuronal connectivity in Figure 3 look very striking. However, these results should be accompanied by control experiments to verify the number of neuronal cells and neurite extension in WT and Ts21 cultures. These two parameters could indeed strongly influence the results. As the cultures appear to grow in clusters, bright-field images and TuJ1 staining of the cultures will also greatly help to understand the degree of morphological interconnection between the clusters.

We now add Tuj1 staining in Supplementary figure 10.

5) The authors performed RNA-seq experiments on day 50 cultures. Why the authors do not show the complete differential gene expression analysis, but only a small subset of genes? A comprehensive volcano plot and the complete list of identified genes with logFC and FDR values would be helpful. If possible, comparison of the present data (particularly on KCN and HCN expression changes) with published and publicly available expression datasets of other human or human Down syndrome iPSC-derived neurons or human Down syndrome brains will greatly increase the soundness of the present findings. In addition, the gene ontology (GO) results are mentioned in the text, but are not presented. Showing the complete GO analysis for both up and downregulated genes will help the reader to better understand the RNA-seq results. Notably, the results shown in Supplementary Figure on GRIN2A and GRIN2B expression (with values of 300-700 counts versus 2000-4000 counts, respectively) clearly indicate that in both WT and TS21 cultures the NMDA developmental switch has not occurred yet at the 50 days timepoint.

We now show volcano plots in Supplementary Fig. 11.

6) The measure of hyperpolarization-activated currents shown in Figure 5 lack proper control experiments. First, the hyperpolarizing current in TS21 cells do not reach a steady-state as the controls. The two curves are therefore hard to compare. To exclude possible difference in kinetic activation, the authors should have prolonged the current injection period (1-2 seconds). Second, to ultimately prove that such currents are mediated by HCN channels in WT cells the authors should perform some control experiments with a specific HCN blocker. A good example of a suitable protocol, with also current blockers to exclude all other possible current contributions, is the one reported in Matt et al Cell. Mol. Life Sci. 68, 125-137 (2011).

2.7) We thank the reviewer for this detailed and helpful comment. We agree that to definitively identify the recorded currents as Ih, it would be necessary to isolate them pharmacologically using specific HCN channel blockers and appropriate controls, such as those described in Matt et al., Cell. Mol. Life Sci. Unfortunately, due to current constraints, we no longer have access to the animals used in this study and cannot allocate the necessary time or resources, we are unable to perform the additional experiments at this stage.

However, our goal here was to use electrophysiological recordings as an indication of altered HCN channel activity, which we then support with molecular evidence. We now emphasize this point more clearly in the revised manuscript.

7) The manuscript lacks information on the statistical analysis used. Also, the numerosity of samples is not clear. Were the dots shown in some graph technical replicates from a single neuronal induction or were all independent neuronal inductions or a mix of the two ? Please clarify.

We now clarify the numbers in the Figure legend.

8) The method section lacks important information to guarantee reproducibility. Just a few examples: • Only electrophysiology methods for slice are reported, but not for in vitro culture.

We now clarify these details in the methods.

• Details on Laminin coating is lacking. What concentration was used ? Was poly-ornithine or poly-lysine used before Laminin coating ? We now clarify these details in the methods.

• How long cells were switched to BrainPhys medium before calcium imaging ? We now clarify these details in the methods.

Minor point/typos etc.

Introduction • Page 4 line 6: in the line "Trisomy 21 in humans commonly results in a range in developmental and morphological changes in the forebrain ..." "in" could be replaced by "of". We have fixed this. • Page 5 line 2: please remove "an" before the word "another". We have fixed this. • Page 5 line 2: please replace "ecitatory" with "excitatory". We have fixed this typo.

Results • Page 10 line 25: The concept of "pixel-wise" appears for the first time in this section and could be better introduced to facilitate the understanding of the experiment. • In the "results" section, page 11 line 1 and 4, references are made to "Figure 4D" and "4F," but these figures do not appear to be present in the figure section. Upon reviewing the rest of the section, the data seem to refer to "Figure 3D" and "3E." We have fixed this. Discussion • Page 15 line 20: please replace "synchronised" with "synchronized". We have fixed this typo. • Page 16 line 11: please replace "T21" with "TS21". We have fixed this typo. Methods • Page 19 line 12: "Pens/Strep" has to be replaced by Pen/Strep. We have fixed this typo. • Page 20 line 20: "Tocris Biocience" has to be replaced by "Tocris Bioscience". We have fixed this typo. • Page 21 line 2: "Addegene" has to be replaced by "Addgene". We have fixed this typo. Figures • Figure 3: the schematic experimental design (Fig. 3A) could be enlarged to match the width of the images/graphs below. We have fixed this. • Figure 5: the reviewer suggests resizing/repositioning the graphs in Fig. 1A so that they match the width of those below. We have fixed this. • Figure S1D: In all the figures of the paper, the respective controls for the TS21 1 and TS21 2 lines are labelled as "WT1/WT2," while in these graphs, they are called "Ctrl1" and "Ctrl2." To ensure consistency throughout the paper, it is suggested to change the names in these graphs. We have fixed this. • Figure S4L: The graph is not very clear, especially regarding the significance reported at -50 pA, please modify the graphical visualization and/or add a legend in the caption. We have fixed this.

Reviewer #2 (Significance (Required)):

Nature and significance of the advance for the field. The results presented in the manuscript are potentially interesting and useful, but not completely novel (currents deregulation has already been highlighted in mouse models of Down Syndrome).

2.8) We thank the reviewer for this comment. While we agree that current deregulation has been observed in mouse models of Down syndrome, the novelty and significance of our study lie in demonstrating these alterations directly in human neurons using both in vitro and in vivo xenograft models.

This is a critical advance because the human cortex has distinct developmental and functional properties not fully recapitulated in mice. In fact, three recent studies have already highlighted significant defects mainly in excitatory neurons within the fetal human DS cortex (Vuong et al., bioRxiv, 2025; Risgaard et al., bioRxiv, 2025; Lattke et al, under revision). Our work builds directly on these observations by providing, for the first time, an electrophysiological and network-level characterization of these human-specific deficits.

Our findings thus provide translationally relevant insight that is not merely confirmatory but extends previous work by grounding it in a human cellular context.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary

The manuscript by Peter et al., reports on the neuronal activity and connectivity of iPSC-derived human cortical neurons from Down syndrome (DS) that is caused by caused by trisomy of the human chromosome 21 (TS21).

Major points:

Although the manuscript is potentially interesting, the results appear somehow preliminary and need to be corroborated by control experiments and quantifications of effects to fully sustain the conclusions.

(1) The authors have not assessed the percentage of WT and TS21 cells that acquire a neuronal or glia identity in their cultures. Indeed, the origin of alterations in network activity and connectivity observed in TS21 neurons could simply derive from reduced number of neurons arising from TS21 iPSC. Alternatively, the same alteration in network activity and connectivity could derive from a multitude of other factors including deficits in neuronal development, neurite extension, or intrinsic electrophysiological properties. In the current version of the manuscript, none of these has been investigated.

(2) Electrophysiological properties of TS21 and WT neurons at day 53/54 in vitro indicate an extremely immature stage of development (i.e. RMP between -36 and -27 mV with most of the cells firing a single action potential after current injection) in the utilized culture conditions: This is far from ideal for in vitro neuronal-network studies. Finally, reduced activity of HCN1 channels should be confirmed by specific recordings isolating or blocking the related current.

Main points highlighting the preliminary character of the study.

1) In Figure 1 immunofluorescence images of the neuronal differentiation markers (Tbr1, Ctip2 and Tuj1) are showed. However, no quantification of the percentage of cells expressing these markers for WT and TS21 neurons is reported. On the other hand, simple inspection of the representative images clearly seams to indicate a difference between the two genotypes, with TS21 cultures showing lower number of cells expressing neuronal markers. This quantification should be corroborated by a similar staining for an astrocyte marker (GFAP, but not S100b since is triplicated in DS). This is an extremely important point since it is obvious that any change in the percentage of neurons (or the neuron/astrocyte ratio) in the cultures will strongly affect the resulting network activity (shown in Figure 2) and the connectivity (showed in Figure 4). Possibly, the quantification should be done at the same time points of the calcium imaging experiments.

2) In Figure 2 the authors show some calcium imaging traces of WT and TS21 cultures at different time points. However, they again do not show any quantification of neuronal activity. A power spectra analysis is shown in Supplementary Figure 2, but only for WT cultures, while in Supplementary Figure 3 a comparison between WT and Ts21 power spectra is done, but only at the 50 day time point, while difference in synchrony are assessed at 60 days. At minimum, the author should include in main Figure 2 the quantification of the mean calcium event rate and mean event amplitude at the different time points and the power spectra analysis for both WT and TS21 cultures at the same timepoints.

Of note, the synchronized neuronal activity is present in WT cultures at day 60, but totally lost at subsequent time-points (70 and 80 days). The results of this later time points are different from previous data from the same lab (Kirwan et al., 2015). How might these data be explained? It would be important to rule out any potential issues with the health of the culture that could explain the loss of neuronal activity.It would be beneficial to check cell viability at the different time points to exclude possible confounding factors ? A propidium staining or a MTT assay would strongly improve the soundness of the calcium data.

3) In Figure 3 there is no quantification of the number and/or density of transplanted neurons for WT and TS21, but only representative images. As above, inspection of the representative images seems to show a decrease in cells labeled by the Tbr1 neuronal marker for TS21 cells. Moreover, the in vivo calcium imaging of transplanted WT and TS21 cells lacks most of the quantification normally done in calcium imaging experiments. Are the event rate and event amplitude different between WT and TS21 neurons ? The measure of neuronal synchrony by mean pixel correlation is not well explained, but it looks somehow simplistic. Neuronal synchrony can be more precisely measured by cross-correlation analysis or spike time tiling coefficients on the traces from single-neuron ROI rather than on all pixels in the field of view, as apparently was done here.

4) The results on reduced neuronal connectivity in Figure 3 look very striking. However, these results should be accompanied by control experiments to verify the number of neuronal cells and neurite extension in WT and Ts21 cultures. These two parameters could indeed strongly influence the results. As the cultures appear to grow in clusters, bright-field images and TuJ1 staining of the cultures will also greatly help to understand the degree of morphological interconnection between the clusters.

5) The authors performed RNA-seq experiments on day 50 cultures. Why the authors do not show the complete differential gene expression analysis, but only a small subset of genes? A comprehensive volcano plot and the complete list of identified genes with logFC and FDR values would be helpful. If possible, comparison of the present data (particularly on KCN and HCN expression changes) with published and publicly available expression datasets of other human or human Down syndrome iPSC-derived neurons or human Down syndrome brains will greatly increase the soundness of the present findings. In addition, the gene ontology (GO) results are mentioned in the text, but are not presented. Showing the complete GO analysis for both up and downregulated genes will help the reader to better understand the RNA-seq results. Notably, the results shown in Supplementary Figure on GRIN2A and GRIN2B expression (with values of 300-700 counts versus 2000-4000 counts, respectively) clearly indicate that in both WT and TS21 cultures the NMDA developmental switch has not occurred yet at the 50 days timepoint.

6) The measure of hyperpolarization-activated currents shown in Figure 5 lack proper control experiments. First, the hyperpolarizing current in TS21 cells do not reach a steady-state as the controls. The two curves are therefore hard to compare. To exclude possible difference in kinetic activation, the authors should have prolonged the current injection period (1-2 seconds). Second, to ultimately prove that such currents are mediated by HCN channels in WT cells the authors should perform some control experiments with a specific HCN blocker. A good example of a suitable protocol, with also current blockers to exclude all other possible current contributions, is the one reported in Matt et al Cell. Mol. Life Sci. 68, 125-137 (2011).

7) The manuscript lacks information on the statistical analysis used. Also, the numerosity of samples is not clear. Were the dots shown in some graph technical replicates from a single neuronal induction or were all independent neuronal inductions or a mix of the two ? Please clarify.

8) The method section lacks important information to guarantee reproducibility. Just a few examples: - Only electrophysiology methods for slice are reported, but not for in vitro culture. - Details on Laminin coating is lacking. What concentration was used ? Was poly-ornithine or poly-lysine used before Laminin coating ? - How long cells were switched to BrainPhys medium before calcium imaging ?

Minor point/typos etc.

Introduction

• Page 4 line 6: in the line "Trisomy 21 in humans commonly results in a range in developmental and morphological changes in the forebrain ..." "in" could be replaced by "of".
• Page 5 line 2: please remove "an" before the word "another".
• Page 5 line 2: please replace "ecitatory" with "excitatory"

Results

• Page 10 line 25: The concept of "pixel-wise" appears for the first time in this section and could be better introduced to facilitate the understanding of the experiment.
• In the "results" section, page 11 line 1 and 4, references are made to "Figure 4D" and "4F," but these figures do not appear to be present in the figure section. Upon reviewing the rest of the section, the data seem to refer to "Figure 3D" and "3E."

Discussion

• Page 15 line 20: please replace "synchronised" with "synchronized".
• Page 16 line 11: please replace "T21" with "TS21".

Methods

• Page 19 line 12: "Pens/Strep" has to be replaced by Pen/Strep.
• Page 20 line 20: "Tocris Biocience" has to be replaced by "Tocris Bioscience".
• Page 21 line 2: "Addegene" has to be replaced by "Addgene".

Figures

• Figure 3: the schematic experimental design (Fig. 3A) could be enlarged to match the width of the images/graphs below.
• Figure 5: the reviewer suggests resizing/repositioning the graphs in Fig. 1A so that they match the width of those below.
• Figure S1D: In all the figures of the paper, the respective controls for the TS21 1 and TS21 2 lines are labelled as "WT1/WT2," while in these graphs, they are called "Ctrl1" and "Ctrl2." To ensure consistency throughout the paper, it is suggested to change the names in these graphs.
• Figure S4L: The graph is not very clear, especially regarding the significance reported at -50 pA, please modify the graphical visualization and/or add a legend in the caption.

Significance

Nature and significance of the advance for the field. The results presented in the manuscript are potentially interesting and useful, but not completely novel (currents deregulation has already been highlighted in mouse models of Down Syndrome).

Work in the context of the existing literature. This work follows the line of evidence that characterizes Down Syndrome in human neurons (Huo, H.-Q. et al. Stem Cell Rep. 10, 1251-1266 (2018); Briggs, J. A. et al. Etiology. Stem Cells 31, 467-478 (2013)), both in vitro and in xenotransplanted mice, by corrborating some important findings already found in animal models (Stern, S., Segal, M. & Moses, E. EBioMedicine 2, 1048-1062 (2015); Cramer, N. P., Xu, X., F. Haydar, T. & Galdzicki, Z. Physiol. Rep. 3, e12655 (2015); Stern, S., Keren, R., Kim, Y. & Moses, E. http://biorxiv.org/lookup/doi/10.1101/467522 (2018) doi:10.1101/467522.

Audience. Scientists in the field of pre-clinical biomedical research, especially those working on neurodevelopmental disorders and iPSC-based non-animal models.

Field of expertise. In vitro electrophysiology, Neurodevelopmental disorders, Down Syndrome, ips cells.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary

The study investigates the neurodevelopmental impact of trisomy 21 on human cortical excitatory neurons derived from induced pluripotent stem cells (hiPSCs). Key findings include a modest reduction in spontaneous firing, a marked deficit in synchronized bursting, decreased neuronal connectivity, and altered ion channel expression-particularly a downregulation of voltage‐gated potassium channels and HCN1. These conclusions are supported by a combination of in vitro calcium imaging, electrophysiological recordings, viral monosynaptic tracing, RNA sequencing, and in vivo transplantation with two‐photon imaging.

Major Comments

• Convincing Nature of Key Conclusions: The study's conclusions are generally well supported by a diverse set of experimental approaches. However, certain claims regarding the intrinsic properties of the excitatory network would benefit from further qualification. In particular, the assertion that reduced synchronization is solely attributable to altered ion channel expression might be considered somewhat preliminary without additional corroborative experiments.
• One major limitation of the current experimental design is the reliance on predominantly excitatory neuronal cultures derived from hiPSCs. Although the authors convincingly demonstrate differences in network synchronization and connectivity between trisomic (TS21) and control neurons, the almost exclusive focus on excitatory cells limits the physiological relevance of the in vitro network. In the developing cortex, interneurons and astrocytes play crucial roles in modulating network excitability, synaptogenesis, and plasticity. Therefore, incorporating these cell types-either through co-culture systems or through directed differentiation protocols that yield a more heterogeneous neuronal population-could help to determine whether the observed deficits are intrinsic to excitatory neurons or are compounded by a lack of proper inhibitory regulation and glial support.
• Furthermore, the assessment of neuronal connectivity via pseudotyped rabies virus tracing, while innovative, has inherent limitations. The quantification of connectivity as a ratio of red-to-green fluorescence pixels may be influenced by differential viral infection efficiencies, variations in the expression levels of the TVA receptor, or even by the lower basal activity levels observed in TS21 cultures. Complementary approaches-such as electron microscopy for synaptic density analysis or functional connectivity measurements using multi-electrode arrays (MEAs)-could provide additional structural and functional insights that would validate the rabies tracing data.
• Qualification of Claims: Some conclusions, particularly those linking specific ion channel dysregulation (e.g., HCN1 loss) directly to network deficits, might be better presented as preliminary. The authors could temper their language to indicate that while the evidence is suggestive, the mechanistic link remains to be fully established.
• Need for Additional Experiments: Additional experiments that could further consolidate the current findings include:
  • Inclusion of Inhibitory Neurons or Co-culture Systems: Incorporating interneurons or astrocytes would help determine whether the observed deficits are solely intrinsic to excitatory neurons.
  • Alternative Connectivity Assessments: Complementing the rabies virus tracing with electron microscopy or multi-electrode array (MEA) recordings would add structural and functional validation of the connectivity differences.
  • Extended Temporal Profiling: Monitoring network activity over a longer developmental window would clarify whether the observed deficits represent a delay or a permanent alteration in network maturation.
• Reproducibility and Statistical Rigor: The methods and data presentation are largely clear, with adequate replication and appropriate statistical analyses. Nonetheless, a more detailed description of the experimental replicates, particularly regarding the viral tracing and in vivo transplantation studies, would enhance reproducibility. The availability of raw data and scripts for calcium imaging analysis would also further support independent verification.

Minor Comments

• Experimental Details:

Minor revisions could include clarifying the infection efficiency and expression levels of the viral constructs used in connectivity assays to rule out technical variability. - Literature Context:

The authors reference prior studies appropriately; however, integrating a brief discussion comparing their findings with alternative DS models (e.g., organoids or other hiPSC-derived systems) would improve contextual clarity. - Presentation and Clarity:

Figures are generally clear,.But the manuscript contains a minor labeling error. On page 13, the figure is erroneously labeled as "Fig6A", whereas, based on the context and corresponding data, it should be "Fig5A". I recommend that the authors correct this mistake to ensure consistency and avoid potential confusion for readers.

Significance

• Nature and Significance of the Advance:

The work offers a substantial conceptual advance by providing a mechanistic link between trisomy 21 and impaired neuronal network synchronization. Technically, the study integrates state-of-the-art imaging, electrophysiology, and transcriptomic profiling, thereby offering a multifaceted view of DS-related neural dysfunction. Clinically, the findings have the potential to inform future therapeutic strategies targeting network connectivity and ion channel function in Down syndrome. - Context in the Existing Literature:

The study builds on previous observations of altered network activity in DS patients and DS mouse models (e.g., altered EEG synchronization and reduced synaptic connectivity). It extends these findings to human-derived neuronal models, thus bridging a gap between clinical observations and molecular/cellular mechanisms. Relevant literature includes studies on DS neurodevelopment and the role of ion channels in synaptic maturation. - Target Audience:

The reported findings will be of interest to researchers in neurodevelopmental disorders, Down syndrome, and ion channel physiology. Additionally, the study may attract the attention of those working on hiPSC-derived models of neurological diseases, as well as clinicians interested in the pathophysiology of DS. - Keywords and Field Contextualization:

Keywords: Down syndrome, trisomy 21, neuronal connectivity, synchronized network activity, hiPSC-derived cortical neurons, ion channel dysregulation.

eLife Assessment

This interesting study adapts machine learning tools to analyze movements of a chromatin locus in living cells in response to serum starvation. The machine learning approach developed is useful, the experiments are well controlled, and the data are solid. The study would be greatly strengthened by testing key predictions made using perturbation experiments. This work will be of interest to those studying chromosome biology and gene expression patterns.

Reviewer #1 (Public review):

Summary:

Redchuk et al. explore the dynamic properties of chromatin upon serum starvation using machine learning approaches. They use CRISPR-tagging to visualize a region on chromosome 1 in human cells and show that in their system, chromosome 1, but not the previously reported chromosomes 10, 13, and X, undergo a change in radial position upon serum starvation. Live cell imaging showed a position change towards the periphery after serum starvation. They then apply a machine learning algorithm for the analysis of the imaging data, which reveals changes in nuclear area during serum starvation and longer displacements of the chromosome 1 locus near the nuclear periphery. Differential behavior of homologues is also reported.

Strengths:

(1) The study of chromatin dynamics is an interesting and important area of research.

(2) The use of machine learning approaches to analyze live cell imaging data is timely.

(3) With serum starvation, the authors use a simple, well-controllable model system.

Weaknesses:

(1) This study only provides limited new insight into chromatin dynamics.

(2) It was not immediately evident what the use of machine learning approaches added to this study. It appears that the main conclusions could have been reached by conventional analysis.

(3) There are several specific technical points:

a) It was not clear what the CRISRP-Sirius probes actually labelled. The chromosome 1 sgRNA sequence is provided, but I could not find information as to which region(s) of the chromosome are actually labelled (size, location, etc.).

b) The authors visualize a relatively small region of chromosome 1 but make conclusions regarding the entire chromosome. Additional probes on the same chromosome should be used.

Related to this point, the discussion of why the authors are unable to reproduce the prior findings of relocation of chromosomes 10, 13, and X is not satisfying. It would be worth comparing the FISH-based painting of entire chromosomes, which generated the results suggesting relocation of these chromosomes, with the point-labelling method used here.

c) The study lacks controls. Since in their hands chromosomes 10, 13, and X do not change position, they should be used as a negative control in all experiments demonstrating a shift in the location of chromosome 1.

d) I did not find information about the spatial or temporal resolution of the imaging modality. This is important to assess whether the observed changes in position, relative to time, are meaningful.

e) The authors analyze surprisingly early timepoints (up to 40 minutes) of serum starvation. Would these results look different if longer serum starvation timepoints of several hours were analyzed?

f) The authors can do a better job of explaining what the biological meaning of the various parameters (DistR, TDist, etc.) they measure is.

g) I did not understand the reasoning for the authors' conclusion of differential behavior of homologues. Please explain this better, or idealy use more direct labeling methods that identify the individual homologues.

h) In many figures, statistical analysis of the data is missing, including, but not limited to, Figures 1B, C, G, Figures 4, 5, 6.

i) No information is provided throughout the manuscript as to how many cells were analyzed in each experiment. This should be indicated in every figure legend.

Reviewer #2 (Public review):

Summary:

The study demonstrates that CRISPR-Sirius provides a powerful approach to investigating chromosome dynamics in living cells during environmental stress. By focusing on serum starvation, the authors show that this process induces global nuclear changes, including a reduction in nuclear area and increased morphological dynamism, while at the same time driving specific reorganization of chromosome 1. Chromosome 1 relocates toward the nuclear periphery and displays distinctive patterns of motion, maintaining overall motility but punctuated by occasional long-distance displacements, particularly near the nuclear envelope. Importantly, the analysis reveals that homologous copies of chromosome 1 do not behave uniformly: peripheral loci become more mobile and responsive to starvation, whereas central homologs remain comparatively stable, often associated with nucleolar subcompartments. By integrating live imaging with machine learning and explainable AI analysis, the study highlights the complexity of nuclear organization and provides valuable insights into how chromosome-specific and locus-specific responses to stress are orchestrated within the three-dimensional nuclear landscape.

Strengths:

The study uses live-cell imaging to investigate the dynamics of loci during starvation. Live-cell tracking and data interpretation are carried out using machine learning and AI models, which is a major strength.

Weaknesses:

The manuscript is at times difficult to follow, partly because the methodological descriptions are highly specialized, especially for non-expert biologists. In addition, the observations are not tested for a mechanistic basis. Experiments that could provide deeper insights are missing, for example, why chromosome 1 moves, why the peripheral homologue dislocates, or why a "long jump" is observed at the periphery even though the speed of the loci does not change. It is also unclear whether a displacement of 0.5 μm is functionally meaningful.

eLife Assessment

This study characterises motor and somatosensory cortex neural activity during naturalistic eating and drinking tongue movement in nonhuman primates. The data, which include electrophysiology, three-dimensional tracking of tongue movements, and nerve block manipulations, are valuable to neuroscientists and neural engineers interested in tongue use. Although the current analyses provide a solid description of single neuron activity in these areas, both the population level analyses and the characterisation of activity changes following nerve block could be improved.

Reviewer #1 (Public review):

Summary:

Hosack and Arce-McShane investigate how the 3D movement direction of the tongue is represented in the orofacial part of the sensory-motor cortex and how this representation changes with the loss of oral sensation. They examine the firing patterns of neurons in the orofacial parts of the primary motor cortex (MIo) and somatosensory cortex (SIo) in non-human primates (NHPs) during drinking and feeding tasks. While recording neural activity, they also tracked the kinematics of tongue movement using biplanar video-radiography of markers implanted in the tongue. Their findings indicate that many units in both MIo and SIo are directionally tuned during the drinking task. However, during the feeding task, directional turning was more frequent in MIo units and less prominent in SIo units. Additionally, in some recording sessions, they blocked sensory feedback using bilateral nerve block injections, which seemed to result in fewer directionally tuned units and changes in the overall distribution of the preferred direction of the units.

Strengths:

The most significant strength of this paper lies in its unique combination of experimental tools. The author utilized a video-radiography method to capture 3D kinematics of the tongue movement during two behavioral tasks while simultaneously recording activity from two brain areas. This specific dataset and experimental setup hold great potential for future research on the understudied orofacial segment of the sensory-motor area.

Weaknesses:

A substantial portion of the paper is dedicated to establishing directional tuning in individual neurons, followed by an analysis of how this tuning changes when sensory feedback is blocked. While such characterizations are valuable, particularly in less-studied motor cortical areas and behaviors, the discrepancies in tuning changes across the two NHPs, coupled with the overall exploratory nature of the study, render the interpretation of these subtle differences somewhat speculative. At the population level, both decoding analyses and state space trajectories from factor analysis indicate that movement direction (or spout location) is robustly represented. However, as with the single-cell findings, the nuanced differences in neural trajectories across reach directions and between baseline and sensory-block conditions remain largely descriptive. To move beyond this, model-based or hypothesis-driven approaches are needed to uncover mechanistic links between neural state space dynamics and behavior.

Reviewer #2 (Public review):

Summary:

This manuscript by Hosack and Arce-McShane examines the directional tuning of neurons in macaque primary motor (MIo) and somatosensory (SIo) cortex. The neural basis of tongue control is far less studied than, for example, forelimb movements, partly because the tongue's kinematics and kinetics are difficult to measure. A major technical advantage of this study is using biplanar video-radiography, processed with modern motion tracking analysis software, to track the movement of the tongue inside the oral cavity. Compared to prior work, the behaviors are more naturalistic behaviors (feeding and licking water from one of three spouts), although the animals were still head-fixed.

The study's main findings are that:

• A majority of neurons in MIo and a (somewhat smaller) percentage of SIo modulated their firing rates during tongue movements, with different modulation depending on the direction of movement (i.e., exhibited directional tuning). Examining the statistics of tuning across neurons, there was anisotropy (e.g., more neurons preferring anterior movement) and a lateral bias in which tongue direction neurons preferred that was consistent with the innervation patterns of tongue control muscles (although with some inconsistency between monkeys).<br /> • Consistent with this encoding, tongue position could be decoded with moderate accuracy even from small ensembles of ~28 neurons.<br /> • There were differences observed in the proportion and extent of directional tuning between the feeding and licking behaviors, with stronger tuning overall during feeding. This potentially suggests behavioral context-dependent encoding.<br /> • The authors then went one step further and used a bilateral nerve block to the sensory inputs (trigeminal nerve) from the tongue. This impaired the precision of tongue movements and resulted in an apparent reduction and change in neural tuning in Mio and SIo.

Strengths:

The data are difficult to obtain and appear to have been rigorously measured, and provide a valuable contribution to this under-explored subfield of sensorimotor neuroscience. The analyses adopt well-established methods especially from the arm motor control literature, and represent a natural starting point for characterizing tongue 3D direction tuning.

Weaknesses:

There are alternative explanations from some of the interpretations, but those interpretations are described in a way that clearly distinguishes results from interpretations, and readers can make their own assessments. Some of these limitations are described in more detail below.

One weakness of the current study is that there is substantial variability in some of the results between monkeys, including the tuning characteristics of primary somatosensory cortex neurons during drinking, and the effect of nerve block on tongue movements and the associated changes in single neuron tuning.

This study focuses on describing directional tuning using the preferred direction (PD) / cosine tuning model popularized by Georgopoulous and colleagues for understanding neural control of arm reaching in the 1980s. This is a reasonable starting point and a decent first order description of neural tuning. However, the arm motor control field has moved far past that viewpoint, and in some ways an over-fixation on static representational encoding models and PDs held that field back for many years. The manuscript benefit from drawing the readers' attention (perhaps in their Discussion) that PDs are a very simple starting point for characterizing how cortical activity relates to kinematics, but that there is likely much richer population-level dynamical structure and that a more mechanistic, control-focused analytical framework may be fruitful. A good review of this evolution in the arm field can be found in Vyas S, Golub MD, Sussillo D, Shenoy K. 2020. Computation Through Neural Population Dynamics. Annual Review of Neuroscience. 43(1):249-75. A revised version of the manuscript incorporates more population-level analyses, but with inconsistent use of quantifications/statistics and without sufficient contextualization of what the reader is to make of these results.

The described changes in tuning after nerve block could also be explained by changes in kinematics between these conditions, which temper the interpretation of these interesting results.

I am not convinced of the claim that tongue directional encoding fundamentally changes between drinking and feeding given the dramatically different kinematics and the involvement of other body parts like the jaw (e.g., the reference to Laurence-Chasen et al. 2023 just shows that there is tongue information independent of jaw kinematics, not that jaw movements don't affect these neurons' activities). I also find the nerve block results inconsistent (more tuning in one monkey, less in the other?) and difficult to really learn something fundamental from, besides that neural activity and behavior both change - in various ways - after nerve block (not at all surprising but still good to see measurements of).

The manuscript states that "Our results suggest that the somatosensory cortex may be less involved than the motor areas during feeding, possibly because it is a more ingrained and stereotyped behavior as opposed to tongue protrusion or drinking tasks". An alternative explanation be more statistical/technical in nature: that during feeding, there will be more variability in exactly what somatosensation afferent signals are being received from trial to trial (because slight differences in kinematics can have large differences in exactly where the tongue is and the where/when/how of what parts of it are touching other parts of the oral cavity)? This variability could "smear out" the apparent tuning using these types of trial-averaged analyses. Given how important proprioception and somatosensation are for not biting the tongue or choking, the speculation that somatosensory cortical activity is suppressed during feedback is very counter-intuitive to this reviewer. In the revised manuscript the authors note these potential confounds and other limitations in the Discussion.

Reviewer #3 (Public review):

Summary

In this study, the authors aim to uncover how 3D tongue direction is represented in the Motor (M1o) and Somatosensory (S1o) cortex. In non-human primates implanted with chronic electrode arrays, they use X-ray based imaging to track the kinematics of the tongue and jaw as the animal is either chewing food or licking from a spout. They then correlate the tongue kinematics with the recorded neural activity. They perform both single-unit and population level analyses during feeding and licking. Then, they recharacterize the tuning properties after bilateral lidocaine injections in the two sensory branches of the trigeminal nerve. They report that their nerve block causes a reorganization of the tuning properties and population trajectories. Overall, this paper concludes that M1o and S1o both contain representations of the tongue direction, but their numbers, their tuning properties and susceptibility to perturbed sensory input are different.

Strengths

The major strengths of this paper are in the state-of-the-art experimental methods employed to collect the electrophysiological and kinematic data. In the revision, the single-unit analyses of tuning direction are robustly characterized. The differences in neural correlations across behaviors, regions and perturbations are robust. In addition to the substantial amount of largely descriptive analyses, this paper makes two convincing arguments 1) The single-neuron correlates for feeding and licking in OSMCx are different - and can't be simply explained by different kinematics and 2) Blocking sensory input alters the neural processing during orofacial behaviors. The evidence for these claims is solid.

Weaknesses

The main weakness of this paper is in providing an account for these differences to get some insight into neural mechanisms. For example, while the authors show changes in neural tuning and different 'neural trajectory' shapes during feeding and drinking - their analyses of these differences are descriptive and provide limited insight for the underlying neural computations.

eLife Assessment

The authors describe an interesting approach to studying the dynamics and function of membrane proteins in different lipid environments. The important findings have theoretical and practical implications beyond the study of EGFR to all membrane signalling proteins. The evidence supporting the conclusions is convincing, based on the use of a nanodisk system to study membrane proteins in vitro, combined with state-of-the-art single-molecule FRET. The work will be of broad interest to cell biologists and biochemists.

Reviewer #1 (Public review):

Summary:

This work addresses a key question in cell signalling: how does the membrane composition affect the behaviour of a membrane signalling protein? Understanding this is important, not just to understand basic biological function but because membrane composition is highly altered in diseases such as cancer and neurodegenerative disease. Although parts of this question have been addressed on fragments of the target membrane protein, EGFR, used here, Srinivasan et al. harness a unique tool, membrane nanodisks, which allow them to probe full-length EGFR in vitro in great detail with cutting-edge fluorescent tools. They find interesting impacts on EGFR conformation in differently charged and fluid membranes, explaining previously identified signalling phenotypes.

Strengths:

The nanodisk system enables full-length EGFR to be studied in vitro and in a membrane with varying lipid and cholesterol concentrations. The authors combine this with single-molecule FRET utilising multiple pairs of fluorophores at different places on the protein to probe different conformational changes in response to EGF binding under different anionic lipid and cholesterol concentrations. They further support their findings using molecular dynamics simulations, which help uncover the full atomistic detail of the conformations they observe.

Weaknesses:

Much of the interpretation of the results comes down to a bimodal model of an 'open' and 'closed' state between the intracellular tail of the protein and the membrane. Some of the data looks like a bimodal model is appropriate, but its use is not sufficiently justified (statistically or otherwise) in this work in its current form. The experiments with varying cholesterol in particular appear to suggest an alternate model with longer fluorescent lifetimes. More justification of these interpretations of the central experiment of this work would strengthen the paper.

Reviewer #2 (Public review):

Summary:

Nanodiscs and synthesized EGFR are co-assembled directly in cell-free reactions. Nanodiscs containing membranes with different lipid compositions are obtained by providing liposomes with corresponding lipid mixtures in the reaction. The authors focus on the effects of lipid charge and fluidity on EGFR activity.

Strengths:

The authors implement a variety of complementary techniques to analyze data and to verify results. They further provide a new pipeline to study lipid effects on membrane protein function.

Weaknesses:

Due to the relative novelty of the approach, a number of concerns remain.

(1) I am a little skeptical about the good correlation of the nanodisc compositions with the liposome compositions. I would rather have expected a kind of clustering of individual lipid types in the liposome membrane, in particular of cholesterol. This should then result in an uneven distribution upon nanodisc assembly, i.e., in a notable variation of lipid composition in the individual nanodiscs. Could this be ruled out by the implemented assays, or can just the overall lipid composition of the complete nanodisc fraction be analyzed?

(2) Both templates have been added simultaneously, with a 100-fold excess of the EGFR template. Was this the result of optimization? How is the kinetics of protein production? As EGFR is in far excess, a significant precipitation, at least in the early period of the reaction, due to limiting nanodiscs, should be expected. How is the oligomeric form of the inserted EGFR? Have multiple insertions into one nanodisc been observed?

(3) The IMAC purification does not discriminate between EGFR-filled and empty nanodiscs. Does the TEM study give any information about the composition of the particles (empty, EGFR monomers, or EGFR oligomers)? Normalizing the measured fluorescence, i.e., the total amount of solubilized receptor, with the total protein concentration of the samples could give some data on the stoichiometry of EGFR and nanodiscs.

(4) The authors generally assume a 100% functional folding of EGFR in all analyzed environments. While this could be the case, with some other membrane proteins, it was shown that only a fraction of the nanodisc solubilized particles are in functional conformation. Furthermore, the percentage of solubilized and folded membrane protein may change with the membrane composition of the supplied nanodiscs, while non-charged lipids mostly gave rather poor sample quality. The authors normalize the ATP binding to the total amount of detectable EGFR, and variations are interpreted as suppression of activity. Would the presence of unfolded EGFR fractions in some samples with no access to ATP binding be an alternative interpretation?

eLife Assessment

In this valuable study, through carefully executed and rigorously controlled experiments, the authors challenged a previously reported role of the Death Receptor 6 (DR6/Tnfrsf21) in Wallerian degeneration (WD). Using two DR6 knockout mouse lines and multiple WD assays, both in vitro and in vivo, the authors provided convincing evidence that loss of DR6 in mice does not protect peripheral axons from WD after injury. Questions remain about whether this conclusion is generalizable to CNS axonal degeneration in disease models such as ALS, AD, and prion diseases. In addition, the authors need to provide information about the sex, age, and genetic background of their animal studies to allow readers to better assess the basis for inconsistencies from previous reports on the protective effects of DR6.

Reviewer #1 (Public review):

Summary:

The authors show that genetic deletion of the orphan tumor necrosis factor receptor DR6 in mice does not protect peripheral axons against degeneration after axotomy. Similarly, Schwann cells in DR6 mutant mice react to axotomy similarly to wild-type controls. These negative results are important because previous work has indicated that loss or inhibition of DR6 is protective in disease models and also against Wallerian degeneration of axons following injury. This carefully executed counterexample is important for the field to consider.

Strengths:

A strength of the paper is the use of two independent mouse strains that knock out DR6 in slightly different ways. The authors confirm that DR6 mRNA is absent in these models (western blots for DR6 protein are less convincingly null, but given the absence of mRNA, this is likely an issue of antibody specificity). One of the DR6 knockout strains used is the same strain used in a previous paper examining the effects of DR6 on Wallerian degeneration.

The authors use a series of established assays to evaluate axon degeneration, including light and electron microscopy on nerve histological samples and cultured dorsal root ganglion neurons in which axons are mechanically severed and degeneration is scored in time-lapse microscopy. These assays consistently show a lack of effect of loss of DR6 on Wallerian degeneration in both mouse strains examined.

Therefore, in the specific context of these experiments, the author's data support their conclusion that loss of DR6 does not protect against Wallerian degeneration.

Weaknesses:

The major weaknesses of this paper include the tone of correcting previously erroneous results and the lack of reporting on important details around animal experiments that would help determine whether the results here really are discordant with previous studies, and if so, why.

The authors do not report the genetic strain background of the mice used, the sex distributions of their experimental cohorts, or the age of the mice at the time the experiments were performed. All of these are important variables.

The DR6 knockout strain reported in Gamage et al. (2017) was on a C57BL/6.129S segregating background. Gamage et al. reported that loss of DR6 protected axons from Wallerian degeneration for up to 4 weeks, but importantly, only in 38.5% (5 out of 13) mice they examined. In the present paper, the authors speculate on possible causes for differences between the lack of effect seen here and the effects reported in Gamage et al., including possible spontaneous background mutations, epigenetic changes, genetic modifiers, neuroinflammation, and environmental differences. A likely explanation of the incomplete penetrance reported by Gamage et al. is the segregating genetic background and the presence of modifier loci between C57BL/6 and 129S. The authors do not report the genetic background of the mice used in this study, other than to note that the knockout strain was provided by the group in Gamage et al. However, if, for example, that mutation has been made congenic on C57BL/6 in the intervening years, this would be important to know. One could also argue that the results presented here are consistent with 8 out of 13 mice presented in Gamage et al.

Age is also an important variable. The protective effects of the spontaneous WldS mutation decrease with age, for example. It is unclear whether the possible protective effects of DR6 also change with age; perhaps this could explain the variable response seen in Gamage et al. and the lack of response seen here.

It is unclear if sex is a factor, but this is part of why it should be reported.

The authors also state that they do not see differences in the Schwann cell response to injury in the absence of DR6 that were reported in Gamage et al., but this is not an accurate comparison. In Gamage et al., they examined Schwann cells around axons that were protected from degeneration 2 and 4 weeks post-injury. Those axons had much thinner myelin, in contrast to axons protected by WldS or loss of Sarm1, where the myelin thickness remained relatively normal. Thus, Gamage et al. concluded that the protection of axons from degeneration and the preservation of Schwann cell myelin thickness are separate processes. Here, since no axon protection was seen, the same analysis cannot be done, and we can only say that when axons degenerate, the Schwann cells respond the same whether DR6 is expressed or not.

The authors also take issue with Colombo et al. (2018), where it was reported that there is an increase in axon diameter and a change in the g-ratio (axon diameter to fiber diameter - the axon + myelin) in peripheral nerves in DR6 knockout mice. This change resulted in a small population of abnormally large axons that had thinner myelin than one would expect for their size. The change in g-ratio was specific to these axons and driven by the increased axon diameter, not decreased myelin thickness, although those two factors are normally loosely correlated. Here, the authors report no changes in axon size or g-ratio, but this could also be due to how the distribution of axon sizes was binned for analysis, and looking at individual data points in supplemental figure 3A, there are axons in the DR6 knockout mice that are larger than any axons in wild type. Thus, this discrepancy may be down to specifics and how statistics were performed or how histograms were binned, but it is unclear if the results presented here are dramatically at odds with the results in Colombo et al. (2018).

Finally, it is important to note that previously reported effects of DR6 inhibition, such as protection of cultured cortical neurons from beta-amyloid toxicity, are not necessarily the same as Wallerian degeneration of axons distal to an injury studied here. The negative results presented here, showing that loss of DR6 is not protective against Wallerian degeneration induced by injury, are important given the interest in DR6 as a therapeutic target, but they are specific to these mice and this mechanism of induced axon degeneration. The extent to which these findings contradict previous work is difficult to assess due to the lack of detail in describing the mouse experiments, and care should be taken in attempting to extrapolate these results to other disease contexts, such as ALS or Alzheimer's disease.

Reviewer #2 (Public review):

Summary:

This manuscript by Beirowski, Huang, and Babetto revisits the proposed role of Death Receptor 6 (DR6/Tnfrsf21) in Wallerian degeneration (WD). A prior study (Gamage et al., 2017) suggested that DR6 deletion delays axon degeneration and alters Schwann cell responses following peripheral nerve injury. Here, the authors comprehensively test this claim using two DR6 knockout mouse models (the line used in the earlier report plus a CMV-Cre derived floxed ko line) and multiple WD assays in vivo and in vitro, aligned with three positive controls, Sarm1 WldS and Phr1/Mycbp2 mutants. Contrary to the prior findings, they find no evidence that DR6 deletion affects axon degeneration kinetics or Schwann cell dynamics (assessed by cJun expression or [intact+degenerating] myelin abundance after injury) during WD. Importantly, in DRG explant assays, neurites from DR6-deficient mice degenerated at rates indistinguishable from controls. The authors conclude that DR6 is dispensable for WD, and that previously reported protective effects may have been due to confounding factors such as genetic background or spontaneous mutations.

Strengths:

The authors employ two independently generated DR6 knockout models, one overlapping with the previously published study, and confirm loss of DR6 expression by qPCR and Western blotting.<br /> Multiple complementary readouts of WD are applied (structural, ultrastructural, molecular, and functional), providing a robust test of the hypothesis.

Comparisons are drawn with established positive controls (WldS, SARM1, Phr1/Mycbp2 mutants), reinforcing the validity of the assays.

By directly addressing an influential but inconsistent prior report, the manuscript clarifies the role of DR6 and prevents potential misdirection of therapeutic strategies aimed at modulating WD in the PNS. The discussion thoughtfully considers possible explanations for the earlier results, including colony-specific second-site mutations that could explain the incomplete penetrance of the earlier reported phenotype of only 36%.

Weaknesses:

(1) The study focuses on peripheral nerves. The manuscript frequently refers to CNS studies to argue for consistency with their findings. It would be more accurate to frame PNS/CNS similarities as reminiscences rather than as consistencies (e.g., line 205ff in the Discussion).

(2) The DRG explant assays are convincing, though the slight acceleration of degeneration in the DR6 floxed/Cre condition is intriguing (Figure 4E). Could the authors clarify whether this is statistically robust or biologically meaningful?

(3) In the summary (line 43), the authors refer to Hu et al. (2013) (reference 5) as the study that previously reported AxD delay and SC response alteration after injury. However, this study did not investigate the PNS, and I believe the authors intended to reference Gamage et al. (2017) (reference 10) at this point.

(4) In line 74ff of the results section, the authors claim that developmental myelination is not altered in DR6 mutants at postnatal day 1. However, the variability in Figure S2 appears substantial, and the group size seems underpowered to support this claim. Colombo et al. (2018) (reference 11) reported accelerated myelination at P1, but this study likewise appears underpowered. Possible reasons for these discrepancies and the large variability could be that only a defined cross-sectional area was quantified, rather than the entire nerve cross-section.

(5) The authors stress the data of Gamage et al. (2017) on altered SC responses in DR6 mutants after injury. They employed cJun quantification to show that SC reprogramming after injury is not altered in DR6 mutants. This approach is valid and the conclusion trustworthy. Here, the addition of data showing the combined abundance of intact and degenerated myelin does not add much insight. However, Gamage et al. (2017) reported altered myelin thickness in a subset of axons at 14 days after injury, which is considerably later than the time points analyzed in the present study. While, in the Reviewer's view, the thin myelin observed by Gamage et al. in fact resembles remyelination, the authors may wish to highlight the difference in the time points analyzed.

Reviewer #3 (Public review):

Summary:

The authors revisit the role of DR6 in axon degeneration following physical injury (Wallerian degeneration), examining both its effects on axons and its role in regulating the Schwann cell response to injury. Surprisingly, and in contrast to previous studies, they find that DR6 deletion does not delay the rate of axon degeneration after injury, suggesting that DR6 is not a mediator of this process.

Overall, this is a valuable study. As the authors note, the current literature on DR6 is inconsistent, and these results provide useful new data and clarification. This work will help other researchers interpret their own data and re-evaluate studies related to DR6 and axon degeneration.

Strengths:

(1) The use of two independent DR6 knockout mouse models strengthens the conclusions, particularly when reporting the absence of a phenotype.

(2) The focus on early time points after injury addresses a key limitation of previous studies. This approach reduces the risk of missing subtle protective phenotypes and avoids confounding results with regenerating axons at later time points after axotomy.

Weaknesses:

(1) The study would benefit from including an additional experimental paradigm in which DR6 deficiency is expected to have a protective effect, to increase confidence in the experimental models, and to better contextualize the findings within different pathways of axon degeneration. For example, DR6 deletion has been shown in more than one study to be partially axon protective in the NGF deprivation model in DRGs in vitro. Incorporating such an experiment could be straightforward and would strengthen the paper, especially if some of the neuroprotective effects previously reported are confirmed.

(2) The quality of some figures could be improved, particularly the EM images in Figure 2. As presented, they make it difficult to discern subtle differences.

eLife Assessment

In their study, Brown et. al. provide an important advance in understanding the architecture of the mycobacterial outer membrane. Using all-atom simulations of model mycomembranes, the work reports compelling structural insights into how α-mycolic acids and outer leaflet lipids (PDIM and PAT) shape membrane organisation. The work revealed membrane heterogeneity with ordered inner leaflets and disordered outer leaflets that provide a molecular explanation for the resilience of the mycobacterial envelope.

Reviewer #1 (Public review):

Disclaimer:

This reviewer is not an expert on MD simulations but has a basic understanding of the findings reported and is well-versed with mycobacterial lipids.

Summary:

In this manuscript titled "Dynamic Architecture of Mycobacterial Outer Membranes Revealed by All-Atom 1 Simulations", Brown et al describe outcomes of all-atom simulation of a model outer membrane of mycobacteria. This compelling study provided three key insights:<br /> (1) The likely conformation of the unusually long chain alpha-branched beta-methoxy fatty acids, mycolic acids in the mycomembrane, to be the extended U or Z type rather than the compacted W-type. (2) Outer leaflet lipids such as PDIM and PAT provide regional vertical heterogeneity and disorder in the mycomembrane that is otherwise prevented in a mycolic acid-only bilayer.<br /> (3) Removal of specific lipid classes from the symmetric membrane systems leads to significant changes in membrane thickness and resilience to high temperatures.

Strengths:

The authors take a step-wise approach in building the complexity of the membrane and highlight the limitations of each of the approaches. A case in point is the use of supraphysiological temperature of 333 K or even higher temperatures for some of the simulations. Overall, this is a very important piece of work for the mycobacterial field, and will help in the development of membrane-disrupting small molecules and provide important insights for lipid-lipid interactions in the mycomembrane.

Weaknesses:

(1) The authors used alpha-mycolic acids only for their models. The ratios of alpha, keto, and methoxy-mycolic acids are known in the literature, and it may be worth including these in their model. Future studies can be aimed at addressing changes in the dynamic behavior of the MOM by altering this ratio, but the inclusion of all three forms in the current model will be important and may alter the other major findings of the current study.

(2) The findings from the 14 different symmetric membrane systems developed with the removal of one complex lipid at a time are very interesting but have not been analysed/discussed at length in the current manuscript. I find many interesting insights from Figures S3 and S5, which I find missing in the manuscript. These are as follows:

a) Loss of PDIM resulted in reduced membrane thickness. This is a very important finding given that loss of PDIM can be a spontaneous phenomenon in Mtb cultures in vitro and that this is driven by increased nutrient uptake by PDIM-deficient bacilli (Domenech and Reed, 2009 Microbiology). While the latter is explained by the enhanced solute uptake by several PE/PPE transporter systems in the absence of PDIM (Wang et al, Science 2020), the findings presented by Brown et al could be very important in this context. A discussion on these aspects would be beneficial for the mycobacterial community.

b) I find it interesting that loss of PAT or DAT does not change membrane thickness (Figure S3). While both PAT and PDIM can migrate to the interleaflet space, loss of PDIM and PAT has a different impact on membrane thickness. It is worth explaining what the likely interactions are that shape membrane thickness in the case of the modelled MOM.

c) Figure S5: Is the presence of SGL driving PDIM and PAT to migrate to the inter-leaflet space? Again, a discussion on major lipid-lipid interactions driving these lipid migrations across the membrane thickness would be useful.

Reviewer #2 (Public review):

Summary:

The manuscript reports all-atom molecular dynamics simulations on the outer membrane of Mycobacterium tuberculosis. This is the first all-atom MD simulation of the MTb outer membrane and complements the earlier studies, which used coarse-grained simulation.

Strengths:

The simulation of the outer membrane consisting of heterogeneous lipids is a challenging task, and the current work is technically very sound.

The observation about membrane heterogeneity and ordered inner leaflets vs disordered outer leaflets is a novel result from the study. This work will also facilitate other groups to work on all-atom models of mycobacterial outer membrane for drug transport, etc.

Weaknesses:

Beyond a challenging simulation study, the current manuscript only provides qualitative explanations on the unusual membrane structure of MTb and does not demonstrate any practical utility of the all-atom membrane simulation. It will be difficult for the general biology community to appreciate the significance of the work, based on the manuscript in its current form, because of the high content of technical details and limited evidence on the utility of the work.

Major Points:

(1) The simulation by Basu et al (Phys Chem Chem Phys 2024) has studied drug transports through mycolic acid monolayers. Since the authors of the current study have all atom models of MTb outer membrane, they should carry out drug transport simulations and compare them to the outer membranes of other bacteria through which drugs can permeate. In the current manuscript, it is only discussed in lines 388-392. Can the disruption of MA cyclopropanation be simulated to show its effect on membrane structure ?

(2) In line 277, the authors mention about 6 simulations which mimic lipid knockout strains. The results of these simulations, specifically the outcomes of in silico knockout of lipids, are not described in detail.

(3) Figure 5 shows PDIM and PAT-driven lipid redistribution, which is a significant novel observation from the study. However, comparison of 3B and 3D shows that at 313K, the movement of the PDIM head group is much less. Since MD simulations are sensitive to random initial seeds, repeated simulations with different random seeds and initial structures may be necessary.

(4) As per Figure 1, in the initial structure, the head group of PAT should be on the membrane surface, similar to TDM and TMM, while PDIM is placed towardsthe interior of the outer membrane. However, Figure 5 shows that at t=0, PAT has the same Z position as PDIM. It will be necessary to provide Z-position Figures for TMM and TDM to understand the difference. Is it really dependent on the chemical structure of the lipid moiety or the initial position of the lipid in the bilayer at the beginning of the simulation?

Minor Point:

In view of the complexity of the system undertaken for the study, the manuscript in its current form may not be informative for readers who are not experts in molecular simulations.

eLife Assessment

This important study uses innovative microfluidics-based single-cell imaging to monitor replicative lifespan, protein localization, and intracellular iron levels in aging yeast cells. The evidence for the proposed role of Ssd1 and reduced nutrients for lifespan through limiting iron uptake is convincing, even though some mechanistic details remain unclear. This work will be of interest to cell biologists working on aging and iron metabolism.

Reviewer #1 (Public review):

Summary:

Overexpression of the mRNA-binding protein Ssd1 was shown before to expand the replicative lifespan of yeast cells, whereas ssd1 deletion had the opposite effect. Here, the authors provide evidence that Ssd1 acts via sequestration of mRNAs of the Aft1/2-dependent iron regulon. This restricts activation of the regulon and limits accumulation of Fe2+ inside cells, thereby likely lowering oxidative damage. The effects of Ssd1 overexpression and calorie restriction on lifespan are epistatic, suggesting that they might act through the same pathway.

Strengths:

The study is well-designed and involves analysis of single yeast cells during replicative aging. The findings are well displayed and largely support the derived model, which also has implications for the lifespan of other organisms, including humans.

Weaknesses:

The model is largely supported by the findings, however, they remain largely correlative at the same time. Whether the knockout of ssd1 shortens lifespan by increased intracellular Fe2+ levels has not been tested. The finding that increased Ssd1 levels form condensates in a cell-cycle-dependent manner is interesting, yet the role of the condensates in lifespan expansion remains untested and unlinked.

Reviewer #2 (Public review):

This manuscript describes the use of a powerful technique called microfluidics to elucidate the mechanisms explaining how overexpression (OE) of Ssd1 and caloric restriction (CR) in yeast extend replicative lifespan (RLS). Microfluidics measures RLS by trapping cells in chambers mounted to a slide. The chambers hold the mother cell but allow daughters to escape. The slide, with many chambers, is recorded during the entire process, roughly 72 hours, with the video monitored afterwards to count how many daughters each of the trapped mothers produces. The power of the method is what can be done with it. For example, the entire process can be viewed by fluorescence so that GFP and mCherry-tagged proteins can be followed as cells age. The budding yeast is the only model where bona fide replicative aging can be measured, and microfluidics is the only system that allows protein localization and levels to be measured in a single cell while aging. The authors do a wonderful job of showing what this combination of tools can do.

The authors had previously shown that Ssd1, an mRNA-binding protein, extends RLS when overexpressed. This was attributed to Ssd1 sequestering away specific mRNAs under stress, likely leading to reduced ribosomal function. It remained completely unknown how Ssd1 OE extended RLS. The authors observed that overexpressed, but not normally expressed, Ssd1 formed cytoplasmic condensates during mitosis that are resolved by cytokinesis. When the condensates fail to be resolved at the end of mitosis, this signals death.

It has become clear in the literature that iron accumulation increases with age within the cell. The transcriptional programs that activate the iron regulon also become elevated in aging cells. This is thought to be due to impaired mitochondrial function in aging cells, with increased iron accumulation as an attempt at restoring mitochondrial activity. The authors show that Ssd1 OE and CR both reduce the expression of the iron regulon. The data presented indicate that iron accumulation shortens RLS: deletion of iron regulon components extends RLS, and adding iron to WT cells decreases RLS, but not when Ssd1 is overexpressed or when cells are calorically restricted. Interestingly, iron chelation using BPS has no impact on WT RLS, but decreases the elevated RLS in CR cells and cells overexpressing Ssd1. It was not initially clear why iron chelation would inhibit the extended lifespan seen with CR and Ssd1 OE. This was addressed by an experiment where it was shown that the iron regulon is induced (FIT2 induction) when iron is chelated. Thus, the detrimental effects of induction of the iron regulon by BPS and iron accumulation on RLS cannot be tempered by Ssd1 OE and CR once turned on.

I did not find any weaknesses to be addressed in this paper. The draft was well-written, and the extensive experimentation was well-designed, performed, and controlled. However, I did make minor comments that I recommend the authors address:

(1) Why would BPS not reduce RLS in WT cells? The authors could test whether OE of FIT2 reduces RLS in WT cells.

(2) The authors should add a brief explanation for why the GDP1 promoter was chosen for Ssd1 OE.

(3) On page 12, growth to saturation was described as glucose starvation. This is more accurately described as nutrient deprivation. Referring to it as glucose starvation is akin to CR, which growing to saturation is not. Ssd1 OE formed condensates upon saturation but not in CR. Why do the authors think Ssd1 OE did not form condensates upon CR? Too mild a stress?

(4) The authors conclude that the main mechanism for RLS extension in CR and Ssd1 OE is the inhibition of the iron regulon in aging cells. The data certainly supports this. However, this may be an overstatement as other mutations block CR, such as mutations that impair respiration. The authors do note that induction of the iron regulon in aging cells could be a response to impaired mitochondrial function. Thus, it seems that the main goal of CR and Ssd1 OE may be to restore mitochondrial function in aging cells, one way being inactivation of the iron regulon. A discussion of how other mutations impact CR would be of benefit.

(5) The cell cycle regulation of Ssd1 OE condensates is very interesting. There does not appear to be literature linking Ssd1 with proteasome-dependent protein turnover. Many proteins involved in cell cycle regulation and genome stability are regulated through ubiquitination. It is not necessary to do anything here about it, but it would be interesting to address how Ssd1 condensates may be regulated with such precision.

(6) While reading the draft, I kept asking myself what the relevance to human biology was. I was very impressed with the extensive literature review at the end of the discussion, going over how well conserved this strategy is in yeast with humans. I suggest referring to this earlier, perhaps even in the abstract. This would nail down how relevant this model is for understanding human longevity regulation.

In conclusion, I enjoyed reading this manuscript, describing how Ssd1 OE and CR lead to RLS increases, using different mechanisms. However, since the 2 strategies appear to be using redundant mechanisms, I was surprised that synergism was not observed.

Reviewer #3 (Public review):

In this paper, the authors investigate how the RNA-binding protein Ssd1 and calorie restriction (CR) influence yeast replicative lifespan, with a particular focus on age-dependent iron uptake and activation of the iron regulon. For this, they use microfluidics-based single-cell imaging to monitor replicative lifespan, protein localization, and intracellular iron levels across aging cells. They show that both Ssd1 overexpression and CR act through a shared pathway to prevent the nuclear translocation of the iron-regulon regulator Aft1 and the subsequent induction of high-affinity iron transporters. As a result, these interventions block the age-related accumulation of intracellular free iron, which otherwise shortens lifespan. Genetic and chemical epistasis experiments further demonstrate that suppression of iron regulon activation is the key mechanism by which Ssd1 and CR promote replicative longevity.

Overall, the paper is technically rigorous, and the main conclusions are supported by a substantial body of experimental data. The microfluidics-based assays in particular provide compelling single-cell evidence for the dynamics of Ssd1 condensates and iron homeostasis.

My main concern, however, is that the central reasoning of the paper-that Ssd1 overexpression and CR prevent the activation of the iron regulon-appears to be contradicted by previous findings, and the authors may actually be misrepresenting these studies, unless I am mistaken. In the manuscript, the authors state on two occasions:

"Intriguingly, transcripts that had altered abundance in CR vs control media and in SSD1 vs ssd1∆ yeast included the FIT1, FIT2, FIT3, and ARN1 genes of the iron regulon (8)"

"Ssd1 and CR both reduce the levels of mRNAs of genes within the iron regulon: FIT1, FIT2, FIT3 and ARN1 (8)"

However, reference (8) by Kaeberlein et al. actually says the opposite:

"Using RNA derived from three independent experiments, a total of 97 genes were observed to undergo a change in expression >1.5-fold in SSD1-V cells relative to ssd1-d cells (supplemental Table 1 at http://www.genetics.org/supplemental/). Of these 97 genes, only 6 underwent similar transcriptional changes in calorically restricted cells (Table 2). This is only slightly greater than the number of genes expected to overlap between the SSD1-V and CR datasets by chance and is in contrast to the highly significant overlap in transcriptional changes observed between CR and HAP4 overexpression (Lin et al. 2002) or between CR and high external osmolarity (Kaeberlein et al. 2002). Intriguingly, of the 6 genes that show similar transcriptional changes in calorically restricted cells and SSD1-V cells, 4 are involved in iron-siderochrome transport: FIT1, FIT2, FIT3, and ARN1 (supplemental Table 1 at http://www.genetics.org/supplemental/)."

Although the phrasing might be ambiguous at first reading, this interpretation is confirmed upon reviewing Matt Kaeberlein's PhD thesis: https://dspace.mit.edu/handle/1721.1/8318 (page 264 and so on).

Moreover, consistent with this, activation of the iron regulon during calorie restriction (or the diauxic shift) has also been observed in two other articles:

https://doi.org/10.1016/S1016-8478(23)13999-9

https://doi.org/10.1074/jbc.M307447200

Taken together, these contradictory data might blur the proposed model and make it unclear how to reconcile the results.

.4 - web snarf via web.telegram.org

use Singlefile to save what is shown using web.telegram

to.do - need to use save selected and only select the main panel not the left tab with al the connections shown

hallucination?

the associated card or wallet

card details or change the payment method

DOI: 10.1371/journal.pone.0317516

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1101/2025.09.03.673952

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1038/s42003-025-08348-6

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

Could you please explain more about the bandwidth here? What is it? How high is sufficient?

t was frustrating because I’m from a city in southern China where we don’t make dumplings for New Year, yet I was forced to be the expert on all things Chinese. The author is right: this objectifies minority students. We’re not cultural dictionaries, we’re individuals with our own experiences.

I experienced the importance of cultural codes firsthand when I did a group project with American classmates. We were asked to present our research in a creative way, and I prepared a detailed poster with graphs and quotes, something common in my home country for academic presentations. But my teammates were confused: “Why not make a video or do a skit?” They explained that in U.S. classrooms, creative often means interactive or performative, not just visual.

True safety isn’t about silence; it’s about feeling heard.

Including marginalized perspectives isn’t about checking a bo, it’s about treating their work as seriously as the canon. Literature courses should integrate works from different ethnic groups throughout the semester, not as an afterthought. This way, we learn to see diversity as part of the core, not an add-on.

This fear of shifting paradigms isn’t just about losing control; it’s about the lack of support for teachers to learn new methods. Multicultural education can’t work if educators are left to navigate the change alone, they need structured training, not just pressure to be more inclusive.

In my home country, the college application process is centralized: the government provides free workshops, and schools have mandatory counseling sessions for all seniors. But here, the burden is on the studen, and if you don’t have someone to teach you the rules, you’re already at a disadvantage. This isn’t a lack of effort, it’s a lack of access to the knowledge that wealthy families pass down as a given.

This early tracking isn’t just about skill level but about bias.The labels stick early, becoming a self-fulfilling prophecy, if you’re called a worm, teachers expect less, peers mock you, and eventually you believe you’re not smart enough. It’s a cruel way schools structure inequality before kids even understand what "class" means.

Same as China. The primary school and middle school is decide by the home place. But if you want to go to good high school, you have to get good grade in the test. I think it is important to give a chace for every student rechoosing their education enviroment by fair.