big Jewish community is the focus of his travel, he doesnt notice other things, rest is fictious

One could argue that expanding renewable energy production helps achieve these goals, both of energy access and energy security.

money economy needed for modern bureaucracy

sometimes those who's office can be taken from them favored as the are more forced to abide by convention

for life positions in this instance still don't assign duty as officials "property"

use of popular vote - worse candidates

Political powers care about things other than technical competence.

laymen don't know what's needed as much as superior

starts off with appointments and moves to elections

sometimes acquired status is official (run for office for position) and can reflect old patriarchies that trying to come back

social position of official is stronger where there's a demand for trained experts- a training that only the wealthy can afford

loyalty to office = loyalty to an impersonal and functional purpose as opposed to a person

regulation not derived from individual judgement but from accordance with rules

training in field of specialization- increasingly important

items of official different from org.

monocratically organized hierarchy- lower offices supervised by higher ones and can be appealed to them

King was a religious man, as many were back then. He used his religion for hope that things would become better from these bad times, in which they did.

Boston King was promised "great rewards" and received very little for his long walk to pass on the letter. He was promised things but in the end he didn't get his deserved reward.

American's were enslaving blacks, so they felt their only way of escape from slavery was joining the British. And with joining the British and not being slaves, it opened them up to better opportunities and not being stuck in an enslaved, poor quality life.

Boston King was part of the British Military and was enslaved by the Americans but escaped back to the British, This is his memoir which was written while he was still alive.

A "loyalist" is someone who loyally fought against the British, against the Americans. Boston King was a good soldier for the British.

?

eLife Assessment

This valuable study proposes that protein secreted by colon cancer cells induces cells with Paneth-like properties that favor colon cancer metastasis. The evidence supporting the conclusions is solid but the study would benefit from more direct experiments to test the functional role of Paneth-like cells and to monitor metastasis from colon tumors. The work will be of interest to researchers studying colon cancer metastasis.

Reviewer #1 (Public review):

Summary:

The authors addressed the influence of DKK2 on colorectal cancer (CRC) metastasis to the liver using an orthotopic model transferring AKP-mutant organoids into the spleens of wild-type animals. They found that DKK2 expression in tumor cells led to enhanced liver metastasis and poor survival in mice. Mechanistically, they associate Dkk2-deficiency in donor AKP tumor organoids with reduced Paneth-like cell properties, particularly Lz1 and Lyz2, and defects in glycolysis. Quantitative gene expression analysis showed no significant changes in Hnf4a1 expression upon Dkk2 deletion. Ingenuity Pathway Analysis of RNA-Seq data and ATAC-seq data point to a Hnf4a1 motif as a potential target. They also show that HNF4a binds to the promoter region of Sox9, which leads to LYZ expression and upregulation of Paneth-like properties. By analyzing available scRNA data from human CRC data, the authors found higher expression of LYZ in metastatic and primary tumor samples compared to normal colonic tissue; reinforcing their proposed link, HNF4a was highly expressed in LYZ+ cancer cells compared to LYZ- cancer cells.

Strengths:

Overall, this study contributes a novel mechanistic pathway that may be related to metastatic progression in CRC.

Weaknesses:

The main concerns are related to incremental gains, missing in vivo support for several of their conclusions in murine models, and missing human data analyses.

Main comments

Novelty:<br /> The authors previously described the role of DKK2 in primary CRC, correlating increased DKK2 levels to higher Src phosphorylation and HNF4a1 degradation, which in turn enhances LGR5 expression and "stemness" of cancer cells, resulting in tumor progression (PMID: 33997693). A role for DKK2 in metastasis has also been previously described (sarcoma, PMID: 23204234)

Mouse data:<br /> (a) The authors analyzed liver mets, but the main differences between AKT and AKP/Dkk2 KO organoids could arise during the initial tumor cell egress from the intestinal tissue (which cannot be addressed in their splenic injection model), or during pre-liver stages, such as endothelial attachment. While the analysis of liver mets is interesting, given that Paneth cells play a role in the intestinal stem cell niche, it is questionable whether a study that does not involve the intestine can appropriately address this pathway in CRC metastasis.<br /> (b) The overall number of Paneth cells found in the scRNA-seq analysis of liver mets was low (17 cells, Fig.3), and assuming that these cells are driving the differences seems somewhat far-fetched.<br /> (c) Fig. 6 suggests a signaling cascade in which the absence of DKK2 leads to enhanced HNF4A expression, which in turn results in reduced Sox9 expression and hence reduced expression of Paneth cell properties. It is therefore crucial that the authors perform in vivo (splenic organoid injection) loss-of-function experiments, knockdown of Sox9 expression in AKP organoids, and Sox9 overexpression experiments in AKP/Dkk2 KO organoids to demonstrate Sox9 as the central downstream transcription factor regulating liver CRC metastasis.<br /> (d) Given the previous description of the role of DKK2 in primary CRC, it is important to define the step of liver metastasis affected by Dkk2 deficiency in the metastasis model. Does it affect extravasation, liver survival, etc.?

Human data:<br /> Can the authors address whether the expression of Dkk2 changes in human CRC and whether mutations in Dkk2 as correlated with metastatic disease or CRC stage?

Bioinformatic analysis<br /> GEO repositories remain not open (at the time of the re-review) and SRA links for raw data are still unavailable. Without access to raw data, it is not possible to verify the analyses or fully assess the results. A part of the article was made by re-analyzing public data so the authors should make even the raw available and not just the count tables

Reviewer #2 (Public review):

Summary:

The authors propose that DKK2 is necessary for the metastasis of colon cancer organoids. They then claim that DKK2 mediates this effect by permitting the generation of lysozyme-positive Paneth-like cells within the tumor microenvironmental niche. They argue that these lysozyme-positive cells have Paneth-like properties in both mouse and human contexts. They then implicate HNF4A as the causal factor responsive to DKK2 to generate lysozyme-positive cells through Sox9.

Strengths:

The use of a genetically defined organoid line is state-of-the-art. The data in Figure 1 and the dependence of DKK2 for splenic injection and liver engraftment, as well as the long-term effect on animal survival, are interesting and convincing. The rescue using DKK2 administration for some of their phenotype in vitro is good. The inclusion and analysis of human data sets help explore the role of DKK2 in human cancer and help ground the overall work in a clinical context.

Remaining Weaknesses after revision:

(1) The authors have effectively explained the regulation of HNF4A at both mRNA and protein levels. To further strengthen their findings, I recommend using CRISPR technology to generate DKK2 and HNF4A double knockout organoids. This approach would allow the authors to investigate whether the AKP liver metastasis is restored in the double knockout condition. Such an experiment would provide more direct evidence that HNF4A protein stabilization is the crucial mechanism for liver metastasis suppression following DKK2 knockout.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors addressed the influence of DKK2 on colorectal cancer (CRC) metastasis to the liver using an orthotopic model transferring AKP-mutant organoids into the spleens of wild-type animals. They found that DKK2 expression in tumor cells led to enhanced liver metastasis and poor survival in mice. Mechanistically, they associate Dkk2-deficiency in donor AKP tumor organoids with reduced Paneth-like cell properties, particularly Lz1 and Lyz2, and defects in glycolysis. Quantitative gene expression analysis showed no significant changes in Hnf4a1 expression upon Dkk2 deletion. Ingenuity Pathway Analysis of RNA-Seq data and ATAC-seq data point to a Hnf4a1 motif as a potential target. They also show that HNF4a binds to the promoter region of Sox9, which leads to LYZ expression and upregulation of Paneth-like properties. By analyzing available scRNA data from human CRC data, the authors found higher expression of LYZ in metastatic and primary tumor samples compared to normal colonic tissue; reinforcing their proposed link, HNF4a was highly expressed in LYZ+ cancer cells compared to LYZ- cancer cells. 

Strengths: 

Overall, this study contributes a novel mechanistic pathway that may be related to metastatic progression in CRC. 

Weaknesses: 

The main concerns are related to incremental gains, missing in vivo support for several of their conclusions in murine models, and missing human data analyses. Additionally, methods and statistical analyses require further clarification. 

Main comments: 

(1) Novelty 

The authors previously described the role of DKK2 in primary CRC, correlating increased DKK2 levels to higher Src phosphorylation and HNF4a1 degradation, which in turn enhances LGR5 expression and "stemness" of cancer cells, resulting in tumor progression (PMID: 33997693). A role for DKK2 in metastasis has also been previously described (sarcoma, PMID: 23204234). 

(2) Mouse data 

a) The authors analyzed liver mets, but the main differences between AKT and AKP/Dkk2 KO organoids could arise during the initial tumor cell egress from the intestinal tissue (which cannot be addressed in their splenic injection model), or during pre-liver stages, such as endothelial attachment. While the analysis of liver mets is interesting, given that Paneths cells play a role in the intestinal stem cell niche, it is questionable whether a study that does not involve the intestine can appropriately address this pathway in CRC metastasis. 

We value the reviewer’s comment that the splenic injection model cannot represent metastasis from the primary tumors, intravasation and extravasation. Therefore, we performed the orthotopic transplantation of AKP and KO organoids into the colon directly then, tested metastasis of cancer.

Author response image 1.

Primary tumor formation and liver metastasis by orthotopic transplantation of AKP or KO colon cancer organoids. 6-8 week-old male C57BL/6J mice were treated with 2.5% DSS dissolved in drinking water for 5 days, followed by regular water for 2 days to remove gut epithelium. After recovery with the regular water, the colon was flushed with 1000 μl of 0.1% BSA in PBS. Then, 200,000 dissociated organoid cells in 200 μl of 5% Matrigel and 0.1% BSA in PBS were instilled into the colonic luminal space. After infusion, the anal verge was sealed with Vaseline. 8 weeks after transplantation, the mice were sacrificed to measure primary tumor formation and liver metastasis.

As a result, 4 out 6 mice in the control group successfully formed colorectal primary tumors whereas only 2 out 6 mice showed primary tumor formation in the KO group (Author response image 1A). The size of tumors was reduced by about half (10-12 mm to 5-7 mm). Only one AKP mouse developed metastasized nodules in the liver (Author response image 1B). Next, to measure the circulating tumor cells, we harvested at least 500 ul of bloods from the portal vein and then analyzed tdTomato-positive tumor cells (Author response image 2). Flow cytometry analysis of PBMCs showed the presence of tdTomatohiCD45- cells as well as tdTomatomidCD45+ cells in 2 out of 6 AKP mice, while no tdTomato-positive cells were observed in the PBMCs of KO organoid-transplanted mice.

Due to the limited numbers of mice showed primary and metastatic tumor formation, we cannot provide a statistic analysis of DKK2-mediated metastasis. However, our revised data indicate a trend that DKK2 KO reduced primary tumor formation, the number of circulating tumor cells and liver metastasis. This trend is consistent with our previous report in the iScience paper, which showed that DKK2 KO reduced AOM/DSS-induced polyp formation about 60 % and decreased metastasis in the splenic injection model system in this manuscript. Further studies are necessary to confirm this trend and to provide the underlying mechanisms of intravasation and extravasation of circulating tumor cells.

Author response image 2.

Flow cytometry analysis of tdTomato+ circulating colon tumor cells in PBMCs. PBMCs were harvested via the portal vein after euthanasia. CD45 and tdTomato were analyzed by flow cytometry.

b) The overall number of Paneth cells found in the scRNA-seq analysis of liver mets was strikingly low (17 cells, Figure 3), and assuming that these cells are driving the differences seems somewhat far-fetched. Adding to this concern is inappropriate gating in the flow plot shown in Figure 6. This should be addressed experimentally and in the interpretation of data. 

We appreciate for reviewer’s comments to clarify this point. Since the number of LYZ+ cells is low in our scRNA-seq analysis, we performed flow cytometry in Figure 6H showing the clear population expressing LYZ in the same splenic injection model of metastasis. Figure 6H is a representative image of triplicates for each group and we performed this experiment three times, independently. As suggested, we changed the graph format and updated the gating and statistical analysis in Fig 6H and 6I. This in vivo result confirmed our in vitro data showing that DKK2 KO reduced LYZ+ cells while increase the HNF4α1 proteins.

c) Figures 3, 5, and 6 show the individual gene analyses with unclear statistical data. It seems that the p-values were not adjusted, and it is unclear how they reached significance in several graphs. Additionally, it was not stated how many animals per group and cells per animal/group were included in the analyses. 

In Fig. 3, mouse scRNA-seq data were generated from pooled cancer samples from 5 animals per group. The Wilcoxon signed-rank test was performed for each gene and/or regulon activity. Since multiple testing adjustments were not performed, a p-value adjustment is neither needed nor applicable..

In Fig. 5, human data were analyzed. Cells from the same sample are dependent, but differential gene expression (DEG) analysis typically calculates statistics under the assumption that they are independent. This assumption may explain the low p-values observed in our data. To address this issue, we applied pseudobulk DEG analysis to our human single-cell data. Even after correcting for statistical error, we confirmed that the genes of interest still exhibited significantly different expression patterns (Author response image 3).

Author response image 3.

Pseudobulk DEG analysis confirmed the differential expression genes of interest.

In Fig.6H-6I, the number of animals per group is provided in the figure legend.

d) Figure 6 suggests a signaling cascade in which the absence of DKK2 leads to enhanced HNF4A expression, which in turn results in reduced Sox9 expression and hence reduced expression of Paneth cell properties. It is therefore crucial that the authors perform in vivo (splenic organoid injection) loss-of-function experiments, knockdown of Sox9 expression in AKP organoids, and Sox9 overexpression experiments in AKP/Dkk2 KO organoids to demonstrate Sox9 as the central downstream transcription factor regulating liver CRC metastasis. 

Sox9 is a well-established marker gene for Paneth cell formation in the gut. Therefore, overexpression or knockout of the Sox9 gene would result in either an increase or decrease in Paneth cells in the organoids. We believe that the suggested experiments fall outside the scope of this manuscript. Instead, we demonstrated the change in the Paneth cell differentiation marker, Sox9, in the presence or absence of DKK2.

e) Given the previous description of the role of DKK2 in primary CRC, it is important to define the step of liver metastasis affected by Dkk2 deficiency in the metastasis model. Does it affect extravasation, liver survival, etc.? 

We appreciate the reviewer’s insights and perspectives. Regarding liver survival, it is well known that stem cell niche formation is a critical step for the outgrowth of metastasized cancer cells (Fumagalli et al. 2019, Cell Stem Cell). LYZ+ Paneth cells are recognized as stem cell niche cells in the intestine, and human scRNA-seq data have shown that LYZ+ cancer cells express stem cell niche factors such as Wnt and Notch ligands. To determine whether LYZ+ cancer cells act as stem cell niche cells, we performed confocal microscopy to assess whether LYZ+ cancer cells express WNT3A and DLL4 in AKP organoids (Author response image 4). The results show that LYZ labeling co-localizes with DLL4 and WNT3A expression, while the organoid reporter tdTomato is evenly distributed. Additionally, our in vitro and in vivo data indicate that DKK2 deficiency leads to a reduction of LYZ+ cancer cells, which may contribute to stem cell niche formation. Based on these findings, we propose that DKK2 is an essential factor for stem cell niche formation, which is required for cancer cell survival in the liver during the early stages of metastasis. Although our revised data confirmed the trend that DKK2 deficiency decreases liver metastasis, we have not yet determined whether DKK2 is involved in extravasation. This research topic should be addressed in future studies.

Author response image 4.

Confocal microscopy analysis for lysozyme (LYZ) and Paneth cell-derived stem cell niche factors, WNT3A and DLL4 in AKP colon cancer organoids.

The method is described in the supplemental information. The list of antibodies used: DLL4 (delta-like 4) Polyclonal Antibody (Invitrogen, PA5-85931), WNT3A Polyclonal Antibody (Invitrogen, PA5-102317), Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor™ 488 (Invitrogen, A-11008), Anti-Lysozyme C antibody (H-10, Santacurz, sc-518083), Goat anti-Mouse IgM (Heavy chain) Secondary Antibody, Alexa Fluor™ 647 (Invitrogen, A-21238).

(3) Human data 

Can the authors address whether the expression of Dkk2 changes in human CRC and whether mutations in Dkk2 as correlated with metastatic disease or CRC stage? 

The human data were useful in identifying the presence of LYZ+ cancer cells with Paneth cell properties. However, due to the limited number of late-stage patient samples with high DKK2 expression, the results were not statistically significant. Nevertheless, the trend suggests a positive correlation between DKK2 expression and the malignant stage of CRC.

(4) Bioinformatic analysis 

The authors did not provide sufficient information on bioinformatic analyses. The authors did not include information about the software, cutoffs, or scripts used to make their analyses or output those figures in the manuscript, which challenges the interpretation and assessment of the results. Terms like "Quantitative gene expression analyses" (line 136) "visualized in a Uniform Approximation and Projection" (line 178) do not explain what was inputted and the analyses that were executed. There are multiple forms to align, preprocess, and visualize bulk, single cell, ATAC, and ChIP-seq data, and depending on which was used, the results vary greatly. For example, in the single-cell data, the authors did not inform how many cells were sequenced, nor how many cells had after alignment and quality filtering (RNA count, mt count, etc.), so the result on Paneth+ to Goblet+ percent in lines 184 and 185 cannot be reached because it depends on this information. The absence of a clustering cutoff for the single-cell data is concerning since this greatly affects the resulting cluster number (https://www.nature.com/articles/s41592-023-01933-9). The authors should provide a comprehensive explanation of all the data analyses and the steps used to obtain those results. 

We apologize for the insufficient information. Below, we provide detailed information on the data analyses, which are also available in the GEO database (Bulk RNA-seq: GSE157531, ATAC-seq: GSE157529, ChIP-seq: GSE277510). Methods are updated in the current version of supplemental information.

(5) Clarity of methods and experimental approaches 

The methods were incomplete and they require clarification. 

We’ve updated our methods as requested by the reviewer.

Reviewer #2 (Public Review): 

Summary: 

The authors propose that DKK2 is necessary for the metastasis of colon cancer organoids. They then claim that DKK2 mediates this effect by permitting the generation of lysozyme-positive Paneth-like cells within the tumor microenvironmental niche. They argue that these lysozyme-positive cells have Paneth-like properties in both mouse and human contexts. They then implicate HNF4A as the causal factor responsive to DKK2 to generate lysozyme-positive cells through Sox9. 

Strengths: 

The use of a genetically defined organoid line is state-of-the-art. The data in Figure 1 and the dependence of DKK2 for splenic injection and liver engraftment, as well as the long-term effect on animal survival, are interesting and convincing. The rescue using DKK2 administration for some of their phenotype in vitro is good. The inclusion and analysis of human data sets help explore the role of DKK2 in human cancer and help ground the overall work in a clinical context. 

Weaknesses: 

In this work by Shin et al., the authors expand upon prior work regarding the role of Dickkopf-2 in colorectal cancer (CRC) progression and the necessity of a Paneth-like population in driving CRC metastasis. The general topic of metastatic requirements for colon cancer is of general interest. However, much of the work focuses on characterizing cell populations in a mouse model of hepatic outgrowth via splenic transplantation. In particular, the concept of Paneth-like cells is primarily based on transcriptional programs seen in single-cell RNA sequencing data and needs more validation. Although including human samples is important for potential generality, the strength could be improved by doing immunohistochemistry in primary and metastatic lesions for Lyz+ cancer cells. Experiments that further bolster the causal role of Paneth-like CRC cells in metastasis are needed. 

Recommendations for the Authors:

Reviewing Editor (Recommendations for the Authors): 

Here we note several key concerns with regard to the main conclusions of the paper. Additional experiments to directly address these concerns would be required to substantially update the reviewer evaluation. 

(1) Demonstration of a causal role of Paneth-like cells in CRC metastasis, for example by sorting the Paneth-like cells - either by the markers they identified in the subsequent single cell or by scatter - to establish whether the frequency of the Paneth-like cells in a culture of organoids is directly correlated with tumorigenicity and engraftment. 

We sincerely appreciate the reviewing editor’s comment. First, as previously reported (Shin et al., iScience 2021), there is no difference in proliferation between WT and KO during in vitro organoid culture or in vivo colitis-induced tumors. However, DKK2 deficiency led to morphological changes, which we analyzed using bulk RNA-seq. As described in the manuscript, Paneth cell marker genes, such as Lysozymes and defensins, were significantly reduced in DKK2 KO AKP organoids.

Due to the nature of these markers, it is technically challenging to isolate live LYZ+ cancer cells. To address this issue in the future, we plan to develop organoids that express a reporter gene specific for Paneth cells. In this manuscript, we demonstrated a correlation between DKK2 and the formation of LYZ+ cancer cells. In both the splenic injection model (Fig. 1) and the orthotopic transplantation model (Fig. R1-R2), we observed that transplantation of cancer organoids with reduced numbers of LYZ+ cells (KO organoids) led to decreased metastatic tumor formation. The number of LYZ+ cells in KO-transplanted mice remained low in liver metastasized tumor nodules (Fig. 6H-I6). Immunohistochemistry further confirmed that LYZ+ cancer cells were barely detectable in KO samples (Author response image 5). These data suggest that DKK2 is essential for the formation of LYZ+ cancer cells, which are necessary for outgrowth following metastasis.

Author response image 5.

Histology of Lysozyme positive cells in metastasized tumor nodules in liver of colon cancer organoid transplanted mice. Immunohistochemistry of Lysozyme positive Paneth-like cells cells in liver metastasized colon cancer (Upper panels, DAB staining). Identification of tumor nodules by H&E staining (lower panels, Scale bar = 100 μm). Magnified tumor nodules are shown in the 2nd and 3rd columns (Scale bar = 25 μm). Arrows indicate Lysozyme positive Paneth like cells in tumor epithelial cells. Infiltration of Lysozyme positive myeloid cells is detected in both AKP and KO tumor nodules. AKP: Control colon cancer organoids carrying mutations in Apc, Kras and Tp53 genes. KO: Dkk2 knockout colon cancer organoids

(2) Further characterization of Lyz+/Paneth-like cells to further the authors' argument for the unique function that they have in their tumor model. Specifically, do the cells with Paneth-like cells secrete Wnt3, EGF, Notch ligand, and DII4 as normal Paneth cells do? 

We appreciate the reviewing editor’s comment. In response, we performed confocal microscopy analysis to examine the protein levels of LYZ, Wnt3A, and DLL4 in AKP colon cancer organoids (Author response image 4). The data presented above show that LYZ+ cancer cells express both Wnt3A and DLL4, suggesting that LYZ+ colon cancer cells may function similarly to Paneth cells, which are stem cell niche cells. Furthermore, using the Panglao database, we demonstrated that LYZ+/Paneth-like cells exhibit typical Paneth cell properties in human scRNA-seq data (Fig. 4 and Fig. 5). These findings suggest that LYZ+ colon cancer cells possess Paneth cell properties.

(3) Experiments to test metastasis, ideally from orthotopic colonic tumors, to ensure phenotypes aren't restricted to the splenic model of hepatic colonization and outgrowth used at present. 

We are in agreement with the reviewing editor and reviewers, which is why we conducted the orthotopic transplantation experiment. However, we encountered challenges in establishing this model effectively. After multiple trials, we observed that many mice did not form primary tumors, and the variability, particularly in metastasis, was difficult to control. Only a few AKP-transplanted mice developed liver metastasis. The representative revision data have been provided above. Nevertheless, we believe that this model needs further improvement and optimization to reliably study metastasis originating from primary tumors.

(4) To generalize claims to human cancer, the authors should test whether loss of DKK2 impacts LYZ+ cancer cells in human organoids and affects their engraftment in immunodeficient mice compared to control. Another more correlative way to validate the LYZ+ expression in human colon cancer would be to stain for LYZ in metastatic vs. primary colon cancer, expecting metastatic lesions to be enriched for LYZ+ cells. 

We agree with your point, and this will be addressed in future studies.

(5) Clarifying inconsistencies regarding effect of DKK2 loss on HNF4A (Figure 1E vs Figure 6I). 

In Figure 1 E, we measured the mRNA levels of HNF4A in metastasized foci by qPCR while in Figure 6I, we measured the protein level of HNF4A by flow cytometry. Recent studies, including our previous report, have shown that HNF4A protein levels are regulated by proteasomal degradation mediated by pSrc (Mori-Akiyama et al. 2007, Gastroenterology, Bastide et al. 2007, Journal of Cell Biology, Shin et al. 2021 iScience). Consequently, while the mRNA levels remained unchanged in Fig. 1E, we observed a reduction of HNF4A protein levels in Figure 6I.

(6) Addressing concerns about statistics and reporting as outlined by Reviewer 1. 

Thank you very much for your assistance in improving our manuscript. The updates have been incorporated as detailed above.

These are the central reviewer concerns that would require additional experimentation to update the editorial summary. Other concerns should be addressed in a revision response but do not require additional experimentation. 

Reviewer #1 (Recommendations For The Authors): 

Specific comments: 

• Do Dkk2-KO organoids grow normally?

Yes, in vitro.

Since the authors reported on the effects of Dkk2 in the induction/maintenance of the Paneth cell niche, changes in AKP organoid numbers of growth rate between Dkk2-WT and KO would be an expected outcome. 

Disruption of Paneth cell formation in normal organoids is expected to alter growth. However, DKK2 KO in colon cancer organoids with mutations in the Apc, Kras, and Tp53 genes exhibits growth rates and organoid sizes similar to those of WT AKP controls. In contrast to in vitro observations, we observed a significant reduction in metastasized tumor growth in vivo. Further analyses of factors derived from LYZ+ cancer cells will help address the discrepancy in DKK2's absence between in vitro and in vivo conditions.

• Figure 1: 

- Panel C: The legend indicates what c.p. stands for.

c.p.m. stands for count per minutes for in vivo imaging analysis. This has been updated in the Figure legend.

- Panel E: Please comment on the possible underlying reasons for the lack of change in HNF4a1 levels. 

This has been updated in response to the reviewing editor’s comment (5) above.

- Panel E: Number of mice from which isolated cancer nodules were harvested. 

Total mice per group were 5. This has been updated in the legend.

• Figure 2: 

- Suggestion: Panel A should be presented in Figure 1 since Dkk2 KO organoids are already used in Figure 1. 

We added this to present the recovery of DKK2 by adding recombinant DKK2 proteins in Fig.2.

- Panel B: Please explain why these genes are marked in blue. 

It has been described in the legend. “Paneth cell marker genes are highlighted as blue circles (AKP=3 and KO=5 biological replicates were analyzed).”

• Figure 3: 

- Indicate the number of cells recovered from AKP vs. KO mice (since liver metastasis was already reduced in KO mice). This should be shown in a UMAP. 

- Panel A: 4th line in the pathways, correct "Singel" typo. 

We appreciate your correction. It has been fixed.

- Panel A: There are multiple versions of PanglaoDB with different markers; a list of all that was used to determine cell type should be provided. 

- Panel C: Bar value for the WNT pathway is not displayed, and there is no legend to indicate the direction of the analysis (that is, AKPvsKO or KOvsAKP). 

It is KOvsAKP, described in the figure legend.

- Panel C: Ingenuity pathway analysis is not a good tool to look at this type of result because it does not include the gene fold changes in the analysis, so it only provides a Z-score of the presence of that pathway and not the degree it is increased or fold changes - recommend substituting any type of GSEA analysis, such as fgsea. -o Panel D: the term "Patient" to refer to mice is confusing. Use "Mice" or "Treatment" or "Condition" instead. 

Corrected

- Panel D: Information about the number of mice per group, cells per animal (or liver let) used, and additional clarification about the statistical analysis used is required, as differences shown in this panel appear subtle given the standard variation in each group. Box plots need to show individual/raw values. 

• Figure 4: 

- Panel E: It would be helpful to show the cutoff lines for the Paneth cell score and Lyz expression in the graphs. 

It has been updated in response to the reviewer’s request.

• Figure 5: 

- Panel B: again, information about the number of "patients" or cells used and clarification about the statistical analysis used is required as the display of data generates concerns about the distribution within groups. Box plots need to show individual/raw values

It has been updated in response to the reviewer’s request.

• Figure 6: 

- Panel A: Add a legend to inform the direction of the process (e.g., red, activation, blue, repression). We noticed the Yap1 bar data had no color. Is there a reason for that? Please explain this point in the revised manuscript. 

Red color added for the Yap1.

- Panel A: Ingenuity pathway analysis is not a good tool to look at this type of results because it does not include the gene Foldchanges in the analysis, so it only provides a Z-score of the presence of that pathway and not the degree it is increased or not. I recommend substituting any type of GSEA analysis, such as fgsea. 

- Panels A&B: Again, only p-value scores were provided, while fold changes are necessary to define the ratio of presence increase of normal vs. AKP. 

- Panel D: No raw or pre-processed ChIP-seq data was provided. Additionally, please indicate exactly the genome location (it seems the image was edited from a raw made on UCSC genome browser-it should be remade by adding coordinates and other important information (genes around, epigenetic, etc.). 

- Panel H/I: Flow cytometry gating is inappropriate, as its catching cells are negative for LYZ in both AKP and KO cells, resulting in an overestimation of the number of Lyz cells. Gating should specifically select very few LYZ-positive cells in the top/left quadrant. 

The updates have been made, and the statistical data have been re-analyzed.

- Panel J: Information about the number of animals/organoids or cells used and clarification about the statistical analysis used is required, as the display of data generates concerns about the distribution within groups. Box plots need to show individual/raw values. 

• Overall: 

- A supplementary table with all the sequenced libraries and their depth, read length/cell count should be provided.

All of the information is now available in the GEO database. We used previously published human epithelial datasets for human single cell analysis (Joanito*, Wirapati*, Zhao*, Nawaz* et al, Nat Genetics, 2022, PMID: 35773407).

- The Hallmark Geneset used is very broad, and the authors should confirm the results on GO bp. 

Using Gene Ontology biological processes (GO bp), we observed that glycolysis-related genes were enriched in our newly described cell population, although the adjusted p-value did not exceed 0.05.

Author response image 6

GSEA with GOBP pathway highlighted glycoprotein and protein localization to extracellular region, both of which are related Paneth cell functions. Paneth cells secrete α-defensins, angiogenin-4, lysozyme and secretory phospholipase A2. The enriched glycoprotein process and protein localization not extracellular region reflect the characteristics of Paneth cells. 

- qPCR is not a good way to confirm sequencing results; while PCR data is pre-normalized, sequencing is normalized only after quantification, so results on 6 E and F should be shown on the sequencing data. 

The expression level of Sox9 is relatively low. In our bulk RNA-seq data, the averages for Sox9 in AKP versus DKK2 KO are 28.2 and 25.1, respectively. While there is a similar trend, the difference is not statistically significant in this dataset, and we did not include an experimental group for reconstitution. Therefore, we conducted qPCR experiments for the reconstitution study by adding recombinant DKK2 (rmDKK2) protein to the culture. Furthermore, it is well established that Sox9 is an essential transcription factor for the formation of LYZ+ Paneth cells. Based on this, we assessed the levels of LYZ and Sox9 using qPCR and confocal microscopy in the presence or absence of DKK2.

• Edits in the text: 

- There are several typographical errors. Specific suggestions are provided below. 

- Line 43: "Chromatin immunoprecipitation followed by sequencing analysis," state analysis of what cells before continuing with "revealed..." revealed... 

- Line 77: Recent findings have identified 

- Line 138: were reduced in KO tumor samples à rephrase to clarify "KO-derived liver tumors" 

- Line 167: Recombinant mouse DKK2 protein treatment in KO organoids partially rescued this effect. Add "partially" since adding rmDkk2 didn't fully restore Lyz1 and Lyz2 levels. 

- Line 185-187: the authors should not reference Figure 6 because it has not been introduced yet. 

- Line 198-199: The authors claimed a correlation between Dkk2 expression and Lgr5 expression; however, the graph presented in Figure 3B does not indicate this. The R-value was 0.11, which does not indicate a correlative expression between these genes. 

- Line 232-233: the authors need to show any connection to Dkk2 gene expression in human samples in order to draw that conclusion. 

- Line 294: expression, leading to the formation 

- Line 347: Wnt ligand (correct Wng typo) 

We have modified our manuscript in accordance with the reviewer’s suggestions.

Reviewer #2 (Recommendations For The Authors): 

Specific criticisms/suggestions: 

Author claim 1: Dkk2 is necessary for liver metastasis of colon cancer organoids. <br /> This model is one of hepatic colonization and eventual outgrowth and not metastasis. Metastasis is optimally assessed using autochthonous models of cancer generation, with the concomitant intravasation, extravasation, and growth of cancer cells at the distant site. The authors should inject their various organoids in an orthotopic colonic transplantation assay, which permits the growth of tumors in the colon, and they can then identify metastasis in the liver that results from that primary cancer lesion (i.e., to better model physiologic metastasis from the colon to liver). 

The data of orthotopic colonic transplantation data has been provided above (Author response images 1 and 2).

Author claim 2: DKK2 is required for the formation of lysozyme-positive cells in colon cancer. 

It would greatly strengthen the authors' claim if supraphysiologic or very high amounts of DKK2 enhance CRC organoid line engraftment ( i.e., the specific experiment being pre-treatment with high levels of DKK2 and immediate transplantation to see a number of outgrowing clones). If DKK2 is causal for the engraftment of the tumors, increased DKK2 should enhance their capacity for engraftment. 

Paneth cells have physical properties permitting sorting and are readily identifiable on flow cytometry. The authors should demonstrate increased tumorigenicity and engraftment by sorting the Paneth-like cells-either by the markers they identified in the subsequent single cell or by scatter to establish whether the frequency of the Paneth-like cells in a culture of organoids is directly correlated with engraftment potential. 

Further characterization of the Paneth-like cells would help further the authors' argument for the unique function that they have in their tumor model. Specifically, do the cells with Paneth-like cells secrete Wnt3, EGF, Notch ligand, and DII4 as normal Paneth cells do? Immunofluorescence, sorting, or western blots would all be reasonable methods to assess protein levels in the sorted population. 

This has been performed and provided above (Author response images 1 and 3)

Author claim 3: Lyzosome (LYZ)+ cancer cells exhibit Paneth cell properties in both mouse and human systems. 

For the claim to be general to human cancer, the author should demonstrate that loss of DKK2 impacts LYZ+ cancer cells in human organoids and affects their engraftment in immunodeficient mice compared to control. Another more correlative way to validate the LYZ+ expression in human colon cancer would be to stain for LYZ in metastatic vs. primary colon cancer, expecting metastatic lesions to be enriched for LYZ+ cells. 

The claims on the metabolic function of Paneth-like cells need more clarification. Do the cancer cells with Paneth features have a distinct metabolic profile compared to the other cell populations? The authors should address this through metabolic characterization of isolated LYZ+ cells with Seahorse or comparison of Dkk2 KO to WT organoids (i.e., +/-LYZ+ cancer cell population). 

To address this question, we need to develop organoids with a Paneth cell reporter gene. We appreciate the reviewer’s comment, and this should be pursued in future studies.

Author claim 4: HNF4A mediates the formation of Lysozyme (Lyz)-positive colon cancer cells by DKK2. 

The authors implicate HNF4A and Sox9 as causal effectors of the Paneth-like cell phenotype and subsequent metastatic potential. There appears to be some discordance regarding the effect of DKK2 loss on HNF4A. In Figure 1E, the authors show that gene expression in metastatic colon cancer cells for HNF4A in DKK2 knockout vs AKP control is insignificant. However, in Figure 6I, there is a highly significant difference in the number of HNF4A positive cells, more than a 3-fold percentage difference, with a p-value of <0.0001. If there is the emergence of a rare but highly expressing HNF4A cell type that on aggregate bulk expression leads to no difference, but sorts differentially, why is it not identified in the single-cell data set? These data together are highly inconsistent with regards to the effect of DKK2 on HNF4A and require clarification. 

Previous studies have demonstrated that HNF4A is regulated by proteasomal degradation mediated by pSrc. As a result, the mRNA level of HNF4A remains unchanged, while the protein level is significantly reduced in colon cancer cells. DKK2 KO leads to decreased Src phosphorylation, resulting in the recovery of HNF4A protein levels. This explains why HNF4A cannot be detected in scRNA-seq datasets, which measure mRNA. We have shown this in our previous report. In this manuscript, based on ChIP-seq data using an anti-HNF4A monoclonal antibody, as well as confocal microscopy and qPCR data for the Sox9 gene, we propose that HNF4A acts as a regulator of cancer cells exhibiting Paneth cell properties.

由于不同“一带一路”国家的气候、地貌、文化、生活习惯、经济条件和农村生活污水质量不同，其农村生活污水处理应遵循以下原则：1 .选择最简单、抗冲击负荷能力强、顺应性稳定的工艺。农村地区日污水量较小，水量主要分布在早上、中午和晚上，日变化系数为5-10 （Wang, 2018）。根据水量、水质变化大的特点，在处理农村污水时应优先采用抗冲击负荷能力强的工艺。2。选择无电或能耗少、运行成本低的节能工艺。农村污水处理厂的运行成本高，规模小，单位能耗高，对于经济条件薄弱的农村地区来说是难以承受的（Zheng et al., 2020），这使得这些设施在后期由于缺乏资金而容易出现运行失败。iii选择操作简单、维护少的工艺。农村地区广泛分布着各种农村污水站，缺乏污水处理专业人员进行操作和维护。因此，在农村污水处理中，操作维护简单、无需频繁进行工艺测试或调整的设施是必不可少的（Wang, 2021a, 2021b）。iv.选择有利于资源利用的进程。污水资源化利用是指污水经过无害化处理后达到一定的水质标准，可以作为再生水用于居民生活、生态供水、农业灌溉等，替代常规水资源，或者从污水中提取其他资源和能源。对增加水资源供给，缓解灌溉水短缺，保障水生态安全具有重要意义。五、因地制宜进行调整。农村污水处理工艺选择前应进行实地调查。根据接收水的功能要求，综合考虑农村经济条件、基础设施完备状况、自然环境、农村用水习惯、用水量、常住人口等因素，确定适合本地区的处理工艺。周边工厂、养殖场的气候条件、污水状况及最终排水目的地

自然生物处理是利用自然生态或人工生态功能对污水进行净化的工艺技术。污水是通过土壤或人工载体的理化作用、自然生物的净化功能、水生植物的截留吸收等方式净化的。它具有净化效率高、运行维护成本低、管理水平低等特点，得到了广泛的应用。人工湿地和稳定塘是农村污水处理中常用的两种处理方法。

与城市相比，村镇人口分布分散，地形复杂，缺乏完整的管网，使得污水收集和处理更加困难（Deng & Wheatley, 2016）。由于世界各地农村地区在地理、气候、生态条件的多样性下，经济社会条件、人口因素、风俗习惯等方面存在很大差异，生活污水的质量和数量也不尽相同。此外，由于选择不同的处理工艺作为污水处理终端，可能导致基础设施成本、处理效果、运维管理的复杂性等方面存在较大差异。显然，应充分考虑当地情况，采用处理效果好、能耗低、运维管理简单、排放达标的可靠、安全的污水处理工艺，而不应采用单一的污水处理工艺。

与美国和日本相比，中国农村污水处理在21世纪初起步较晚，经历了婴儿期、发展状态和快速发展阶段。第一阶段，婴儿期（2005-2008）。中国开始高度重视农村环境保护，并尝试通过制定政策来引导产业发展。第二阶段，发展阶段（2009-2015）。政府以政策研讨、资金支持、示范基地建设为重点.第三阶段，快速发展阶段（2016 -）。这是一个政策机制完善、区域综合服务蓬勃发展的阶段。

管理政策是解决生态环境问题的动力。但我国农村污水的管理还没有城市污水的管理发达。美国和日本是最早研究农村污水管理的国家之一。中国作为发展中国家和世界第二大经济体，在农村污水治理方面也积累了丰富的经验

农村生活污水未经有效处理/回收而排放后，会造成一系列环境风险，包括(A)污染饮用水源。将未经处理的生活污水或低于标准的污水排入地表水，可能污染饮用水源，造成疾病传播。(b)土壤和地下水污染。在地下水位较低的地区，未经有效处理的农村生活污水可能引发地下水污染的高风险，如大肠杆菌超标。(c)水生生态系统平衡受到破坏，水生生物的稳定性和多样性减少，影响鱼类的生存和渔业生产。(d)又黑又臭的水。农村生活污水中的有机物持续发酵，会产生硫化氢、硫醇、氨等恶臭物质，以及硫化铁、硫化锰等黑色物质，产生黑臭水，丧失功能，影响景观和人体健康。(e)水体富营养化。水生生物的过度繁殖会导致水体透明度和溶解氧迅速下降，污染物指标急剧增加，水质恶化。(f)滋生蚊子和苍蝇。生活污水的积累为蚊子和苍蝇等害虫的繁殖提供了温床

由于经济和技术限制，“一带一路”发展中国家农村地区的生活污水处理率较低。经济增长良好的华东、东南沿海发达地区率先开展农村污水处理，投入巨资进行污水处理，实现了农村环境处理基础设施完备，农村污水处理全覆盖。相反，中西部地区由于农村环境治理基础设施投入不足以及生活习惯和自然条件的影响，农村污水处理率较低

农村污水排放与气候条件、经济发展水平密切相关，具有显著的地域性特征。与东南地区的居民不同，西北地区的农村居民受干旱气候的影响，生活用水量较少，特别是洗碗频率较低，污水排放量较少。

农村生活污水受日常生活、周期性人口流动等因素的影响，其数量和质量波动较大。农村人口流动性大，节假日返乡人数多，生活污水排放量明显增加。农村生活污水排放量存在明显的季节变化。由于夏季居民洗浴需求大，导致洗漱污水排放量增加，因此污水排放主要表现为水量大、浓度低

村地区的生活污水通常采用以下处理方法。（一）杠杆化处理。城市污水集中收集系统附近的村庄，由市政和企业污水管网处理；（ii）独立处理。经济和管理发达的农村地区自行建立生活污水处理系统（如综合处理设备、人工湿地等），经处理后排放污水；（三）简单处理或直接排放。

农村居民居住区域分散，生活污水排放区域范围广，污水收集管网不完善，难以集中处理，是重要的水源污染源。

大部分“一带一路”国家都是发展中国家，农村人口多，生活污水排放总量大。中国有63.4万个行政村和2.2万个镇，年生活污水排放量超过200亿吨，占全国总量的一半以上。

人类粪便、食物残渣和含磷洗涤剂是生活污水中总磷的主要来源。与氮一样，磷是生物生长的必需元素，也是水体养分污染的指标(Powers et al., 2016；Withers et al., 2009)。水中过量的氮和磷可能导致藻类过度繁殖，引起富营养化。由于生物除磷效率不够稳定，化学除磷工艺存在操作维护困难、价格昂贵等缺陷，磷处理一直是农村生活污水处理的难题。

生活污水（洗澡间、洗衣房、厨房、厕所）的冲洗是农村地区生活污水的主要来源，其比例与气候条件、生活水平和生活习惯有关。根据污水的水质，生活污水（洗浴、洗衣、厨房、厕所）被归类为灰水，而由排泄物和粪便冲洗产生的冲厕所污水被称为黑水（Paulo et al., 2013）。与灰水相比，黑水的污染物浓度较高，不易处理，但作为一种潜在的资源，具有很高的资源利用价值。

例如，中国企业在“一带一路”沿线国家开展了多项环保配套项目，中国制造的水处理厂已大量出口到东南亚、中亚、中东、非洲等地区。集污水预处理、生物处理、沉淀、消毒于一体的一体化污水处理设备，具有结构紧凑、占地面积小、施工周期短、处理效率高、经济合理等特点，特别适用于没有管网的农村地区分散式生活污水处理。

this is how pure legal theory separates law as ought from the analogy to law as a norm (like morality as a norm)

this is how law still has an element of its ideological past, in that it law is understood as a norm (something we 'ought' to follow)

무한한 flow 에 대해서 시간 역학을 적용해 계산한다는 것으로 이해 했다는데 제시 된 식이 어떤 방식으로 유한하게 계산이 되는지 설명해 주실 수 있나요?

eLife Assessment

This useful study presents a novel microscopy technique called "Expansion Tiling Light Sheet Microscopy" and an accompanying computational pipeline for the faster collection and analysis of 3D volumetric images in animals like planarians. This approach produces beautiful 3D microscropy images and is solid on a technical level. However, due to the use of antibody reagents that visualize many – but not all – neurons and muscle subtypes, the evidence for the biological conclusions in this study remains incomplete. With the claims appropriately contextualized, this paper will be of interest to cell biologists working on imaging and analyzing whole animals.

Reviewer #1 (Public review):

Summary:

The planarian flatworm Schmidtea mediterranea is widely used as a model system for regeneration because of its remarkable ability to regenerate its entire body plan from very small fragments of tissue, including the complete and rapid regeneration of the CNS. Prior to this study, analysis of CNS regeneration in planaria has mostly been performed on a gross anatomical level. Despite its simplicity compared to vertebrates, the CNS of many invertebrates, including planaria, is nonetheless complex, intricate, and densely packed. Some invertebrate models allow the visualization of individual cellular components of the CNS using transgenic techniques. Until transgenesis becomes commonplace in planaria, the visualization and analysis of detailed CNS anatomy must rely on alternate approaches in order to capitalize on the immense promise of this system as a model for CNS regeneration. Another challenge for the study of the CNS more broadly is how to perform imaging of a complete CNS on a reasonable timescale such that multiple individuals per experimental condition can be imaged.

Strengths:

In this report, Lu et al. describe a careful and detailed analysis of the planarian neuroanatomy and musculature in both the homeostatic and regenerating contexts. To improve the effective resolution of their imaging, the authors optimized a tissue expansion protocol for planaria. Imaging was performed by light sheet microscopy, and the resulting optical sections were tiled to reconstruct whole worms. Labelled tissues and cells were then segmented to allow quantification of neurons and muscle fibers, as well as all cells in individual worms using a DNA dye. The resulting workflow can produce highly detailed and quantifiable 3D reconstructions at a rate that is fast enough to allow the analysis of large numbers of animals.

Weaknesses:

Lu et al. use their workflow to visualize RNA expression of five enzymes that are each involved in the biosynthetic pathway of different neurotransmitters/modulators, namely chat (cholinergeric), gad (GABAergic), tbh (octopaminergic), th (dopaminergic), and tph (serotonergic). In this way, they generate an anatomical atlas of neurons that produce these molecules. Collectively these markers are referred to as the "neuronpool." They overstate when they write, "The combination of these five types of neurons constitutes a neuron pool that enables the labeling of all neurons throughout the entire body." This statement does not accurately represent the state of our knowledge about the diversity of neurons in S. mediterranea. There are several lines of evidence that support the presence of glutamatergic and glycinergic neurons, including the following. The glutamate receptor agonists NMDA and AMPA both produce seizure-like behaviors in S. mediterranea that are blocked by the application of glutamate receptor antagonists MK-801 and DNQX (which antagonize NMDA and AMPA glutamate receptors, respectively; Rawls et al., 2009). scRNA-Seq data indicates that neurons in S. mediterranea express a vesicular glutamate transporter, a kainite-type glutamate receptor, a glycine receptor, and a glycine transporter (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022). Two AMPA glutamate receptors, GluR1 and GluR2, are known to be expressed in the CNS of another planarian species, D. japonica (Cebria et al., 2002). Likewise, there is abundant evidence for the presence of peptidergic neurons in S. mediterranea (Collins et al., 2010; Fraguas et al., 2012; Ong et al., 2016; Wyss et al., 2022; among others) and in D. japonica (Shimoyama et al., 2016). For these reasons, the authors should not assume that all neurons can be assayed using the five markers that they selected. The situation is made more complex by the fact that many neurons in S. mediterranea appear to produce more than one neurotransmitter/modulator/peptide (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022), which is common among animals (Vaaga et al., 2014; Brunet Avalos and Sprecher, 2021). However the published literature indicates that there are substantial populations of glutamatergic, glycinergic, and peptidergic neurons in S. mediterranea that do not produce other classes of neurotransmission molecule (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022). Thus it seems likely that the neuronpool will miss many neurons that only produce glutamate, glycine or a neuropeptide.

The authors use their technique to image the neural network of the CNS using antibodies raised vs. Arrestin, Synaptotagmin, and phospho-Ser/Thr. They document examples of both contralateral and ipsilateral projections from the eyes to the brain in the optic chiasma (Figure 1C-F). These data all seem to be drawn from a single animal in which there appears to be a greater than normal number of nerve fiber defasciculatations. It isn't clear how well their technique works for fibers that remain within a nerve tract or the brain. The markers used to image neural networks are broadly expressed, and it's possible that most nerve fibers are too densely packed (even after expansion) to allow for image segmentation. The authors also show a close association between estrella-positive glial cells and nerve fibers in the optic chiasma.

The authors count all cell types, neuron pool neurons, and neurons of each class assayed. They find that the cell number to body volume ratio remains stable during homeostasis (Figure S3C), and that the brain volume steadily increases with increasing body volume (Figure S3E). They also observe that the proportion of neurons to total body cells is higher in worms 2-6 mm in length than in worms 7-9 mm in length (Figure 2D, S3F). They find that the rate at which four classes of neurons (GABAergic, octopaminergic, dopaminergic, serotonergic) increase relative to the total body cell number is constant (Figure S3G-J). They write: "Since the pattern of cholinergic neurons is the major cell population in the brain, these results suggest that the above observation of the non-linear dynamics between neurons and cell numbers is likely from the cholinergic neurons." This conclusion should not be reached without first directly counting the number of cholinergic neurons and total body cells. Given that glutamatergic, glycinergic, and peptidergic neurons were not counted, it also remains possible that the non-linear dynamics are due (in part or in whole) to one or more of these populations.

The authors next assayed the production of different classes of neurons in regenerating post-pharyngeal tail fragments. At 14 dpa, they find significantly reduced proportions of octopaminergic, GABAergic, and dopaminergic neurons in these regenerated animals (Figure 3K). Given that these three neuron classes are primarily found in the brain region (Figure S2A), this suggests that the brains of these animals may not have finished regenerating by 14 dpa.

The authors next applied their imaging and segmentation technique to the musculature using the 6G10 antibody. They find that the body wall muscle fibers from the dorsal and ventral body walls integrate differently at the anterior end (to form a cobweb-like arrangement) compared to the posterior end (Figure 4I). They knock down β-catenin in regenerating head anterior fragments and find that the resulting double-headed worms produce a cobweb-like arrangement at both ends (Figure 4J).

RNAi knockdown of inr-1 is known to produce mobility defects and have elongated bodies relative to control animals (Lei et al., 2016; Figure S6A). To understand the nature of these defects, the authors image the muscle of inr-1 RNAi animals and find increased circular body wall muscle fibers on both dorsal and ventral sides, while β-catenin RNAi animals have increased longitudinal muscle fibers on the dorsal side (Figure 6C). The inr-1 RNAi animals also have reduced cholinergic neurons (Figure S6B), and ectopic expression of the GABAergic marker gad in the periphery (Figure S6B). Lastly the authors simultaneously image muscle and estrella-positive glia and find that these glia lack their typically elaborate stellate morphology in inr-1 RNAi animals (Figure 6E, S6E-K). The combination of this muscle, neuronal, and glial defects may account for the mobility defects observed in inr-1 RNAi worms.

Author response:

Reviewer #1 (Public review):

Lu et al. use their workflow to visualize RNA expression of five enzymes that are each involved in the biosynthetic pathway of different neurotransmitters/modulators, namely chat (cholinergeric), gad (GABAergic), tbh (octopaminergic), th (dopaminergic), and tph (serotonergic). In this way, they generate an anatomical atlas of neurons that produce these molecules. Collectively these markers are referred to as the "neuronpool." They overstate when they write, "The combination of these five types of neurons constitutes a neuron pool that enables the labeling of all neurons throughout the entire body." This statement does not accurately represent the state of our knowledge about the diversity of neurons in S. mediterranea. There are several lines of evidence that support the presence of glutamatergic and glycinergic neurons, including the following. The glutamate receptor agonists NMDA and AMPA both produce seizure-like behaviors in S. mediterranea that are blocked by the application of glutamate receptor antagonists MK-801 and DNQX (which antagonize NMDA and AMPA glutamate receptors, respectively; Rawls et al., 2009). scRNA-Seq data indicates that neurons in S. mediterranea express a vesicular glutamate transporter, a kainite-type glutamate receptor, a glycine receptor, and a glycine transporter (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022). Two AMPA glutamate receptors, GluR1 and GluR2, are known to be expressed in the CNS of another planarian species, D. japonica (Cebria et al., 2002). Likewise, there is abundant evidence for the presence of peptidergic neurons in S. mediterranea (Collins et al., 2010; Fraguas et al., 2012; Ong et al., 2016; Wyss et al., 2022; among others) and in D. japonica (Shimoyama et al., 2016). For these reasons, the authors should not assume that all neurons can be assayed using the five markers that they selected. The situation is made more complex by the fact that many neurons in S. mediterranea appear to produce more than one neurotransmitter/modulator/peptide (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022), which is common among animals (Vaaga et al., 2014; Brunet Avalos and Sprecher, 2021). However the published literature indicates that there are substantial populations of glutamatergic, glycinergic, and peptidergic neurons in S. mediterranea that do not produce other classes of neurotransmission molecule (Brunet Avalos and Sprecher, 2021; Wyss et al., 2022). Thus it seems likely that the neuronpool will miss many neurons that only produce glutamate, glycine or a neuropeptide.

In response to your comments, we agree that our initial statement regarding the "neuron pool" overstated the extent of neuronal coverage provided by the five selected markers. We have revised the sentence as “The combination of these five types of neurons constitutes a neuron pool that enables the labeling of most of the neurons throughout the entire body, including the eyes, brain, and pharynx”. 

Furthermore, we chose the five neurotransmitter systems (cholinergic, GABAergic, octopaminergic, dopaminergic, and serotonergic) based on their well-characterized roles in planarian neurobiology and the availability of reliable markers. However, we acknowledge the limitations of this approach and recognize that it does not encompass all neuron types, particularly those involved in glutamatergic, glycinergic, and peptidergic signaling, which have been documented in S. mediterranea. We will also add the content about other neuron types in our revised manuscript “Additionally, there is considerable diversity among glutamatergic, glycinergic, and peptidergic neurons in planarians. Many neurons in S. mediterranea express more than one neurotransmitter or neuropeptide, which adds further complexity to the system.”

The authors use their technique to image the neural network of the CNS using antibodies raised vs. Arrestin, Synaptotagmin, and phospho-Ser/Thr. They document examples of both contralateral and ipsilateral projections from the eyes to the brain in the optic chiasma (Figure 1C-F). These data all seem to be drawn from a single animal in which there appears to be a greater than normal number of nerve fiber defasciculatations. It isn't clear how well their technique works for fibers that remain within a nerve tract or the brain. The markers used to image neural networks are broadly expressed, and it's possible that most nerve fibers are too densely packed (even after expansion) to allow for image segmentation. The authors also show a close association between estrella-positive glial cells and nerve fibers in the optic chiasma. 

Thank you for your detailed feedback. While we did not perform segmentation of all neuron fibers, we were able to segment more isolated fibers that were not densely packed within the neural tracts. We use 120 nm resolution to segment neurons along the three axes. Our data show the presence of both contralateral and ipsilateral projections of visual neurons. Although Figure 1C-F shows data from one planarian, we imaged three independent specimens to confirm the consistency of these observations. In the revised manuscript, we will include a discussion on the limitations of TLSM in reconstructing neural networks, particularly when it comes to resolving fibers within densely packed regions of the nerve tracts.

The authors count all cell types, neuron pool neurons, and neurons of each class assayed. They find that the cell number to body volume ratio remains stable during homeostasis (Figure S3C), and that the brain volume steadily increases with increasing body volume (Figure S3E). They also observe that the proportion of neurons to total body cells is higher in worms 2-6 mm in length than in worms 7-9 mm in length (Figure 2D, S3F). They find that the rate at which four classes of neurons (GABAergic, octopaminergic, dopaminergic, serotonergic) increase relative to the total body cell number is constant (Figure S3G-J). They write: "Since the pattern of cholinergic neurons is the major cell population in the brain, these results suggest that the above observation of the non-linear dynamics between neurons and cell numbers is likely from the cholinergic neurons." This conclusion should not be reached without first directly counting the number of cholinergic neurons and total body cells. Given that glutamatergic, glycinergic, and peptidergic neurons were not counted, it also remains possible that the non-linear dynamics are due (in part or in whole) to one or more of these populations. 

We have removed the statement "Since the pattern of cholinergic neurons is the major cell population in the brain, these results suggest that the above observation of the non-linear dynamics between neurons and cell numbers is likely from the cholinergic neurons." We changed this statement into “These results suggest that the above observation of the non-linear dynamics between neurons and cell numbers is not likely from the octopaminergic, GABAergic, dopaminergic and serotonergic neurons. Since our neuron pool may not include glutamatergic, glycinergic, and peptidergic neurons, we would like to add the possibility that the non-linear dynamics may be from cholinergic neurons or other neurons not included in our staining.”

Reviewer #2 (Public review): 

Weaknesses: 

(1) The proprietary nature of the microscope, protected by a patent, limits the technical details provided, making the method hard to reproduce in other labs. 

Thank you for your comment. We understand the importance of reproducibility and transparency in scientific research. We would like to point out that the detailed design and technical specifications of the TLSM are publicly available in our published work: Chen et al., Cell Reports, 2020. Additionally, the protocol for C-MAP, including the specific experimental steps, is comprehensively described in the methods section of this paper. We believe that these resources should provide sufficient information for other labs to replicate the method.

(2) The resolution of the analyses is mostly limited to the cellular level, which does not fully leverage the advantages of expansion microscopy. Previous applications of expansion microscopy have revealed finer nanostructures in the planarian nervous system (see Fan et al. Methods in Cell Biology 2021; Wang et al. eLife 2021). It is unclear whether the current protocol can achieve a comparable resolution. 

Thank you for raising this important point. The strength of our C-MAP protocol lies in its fluorescence-protective nature and user convenience. Notably, the sample can be expanded up to 4.5-fold linearly without the need for heating or proteinase digestion, which helps preserve fluorescence signals. In addition, the entire expansion process can be completed within 48 hours. While our current analysis focused on cellular-level structures, our method can achieve comparable or better resolution and we will add this information in the revised manuscript.

(3) The data largely corroborate past observations, while the novel claims are insufficiently substantiated. 

A few major issues with the claims: 

(4) Line 303-304: While 6G10 is a widely used antibody to label muscle fibers in the planarian, it doesn't uniformly mark all muscle types (Scimone at al. Nature 2017). For a more complete view of muscle fibers, it is important to use a combination of antibodies targeting different fiber types or a generic marker such as phalloidin. This raises fundamental concerns about all the conclusions drawn from Figures 4 and 6 about differences between various muscle types. Additionally, the authors should cite the original paper that developed the 6G10 antibody (Ross et al. BMC Developmental Biology 2015). 

We appreciate the reviewer’s insightful comments and acknowledge that 6G10 does not uniformly label all muscle fiber types. We agree that this limitation should be recognized in the interpretation of our results. we will revise the manuscript to explicitly state the limitations of using 6G10 alone for muscle fiber labeling and highlight the need for additional markers. We would also clarify that the primary objective of our study was not to distinguish all muscle fiber types but rather to demonstrate the application of our 3D tissue reconstruction method in addressing traditional research questions. Nonetheless, we agree that expanding the labeling strategy in future studies would allow for a more thorough investigation of muscle fiber diversity. We will ensure all citations are properly revised and updated in our next version.

(5) Lines 371-379: The claim that DV muscles regenerate into longitudinal fibers lacks evidence. Furthermore, previous studies have shown that TFs specifying different muscle types (DV, circular, longitudinal, and intestinal) both during regeneration and homeostasis are completely different (Scimone et al., Nature 2017 and Scimone et al., Current Biology 2018). Single-cell RNAseq data further establishes the existence of divergent muscle progenitors giving rise to different muscle fibers. These observations directly contradict the authors' claim, which is only based on images of fixed samples at a coarse time resolution. 

Thank you for your valuable feedback. Our intent was not to suggest that DV muscles regenerate into longitudinal fibers. Our observations focused on the wound site, where DV muscle fibers appear to reconnect, and longitudinal fibers, along with other muscle types, gradually regenerate to restore the structure of the injured area. We will revise the relevant sections of the manuscript to clarify this dynamic process more accurately.

(6) Line 423: The manuscript lacks evidence to claim glia guide muscle fiber branching. 

We will remove this statement from the revised version. Instead, we will focus on describing our observations of the connections between glial cells and muscle fibers.

(7) Lines 432/478: The conclusion about neuronal and muscle guidance on glial projections is similarly speculative, lacking functional evidence. It is possible that the morphological defects of estrella+ cells after bcat1 RNAi are caused by Wnt signaling directly acting on estrella+ cells independent of muscles or neurons. 

We understand that this approach is insufficient and we will revise the manuscript to more clearly state the limitations of our data. We will describe our observations as preliminary and suggest that further experiments are required.

(8) Finally, several technical issues make the results difficult to interpret. For example, in line 125, cell boundaries appear to be determined using nucleus images; in line 136, the current resolution seems insufficient to reliably trace neural connections, at least based on the images presented. 

We use two setups for imaging cells and neuron projections. For cellular resolution imaging, we utilized a 1× air objective with a numerical aperture (NA) of 0.25 and a working distance of 60 mm (OLYMPUS MV PLAPO). The voxel size used was 0.8×0.8×2.5 µm3. This configuration resulted in a resolution of 2×2×5 µm3 and a spatial resolution of 0.5×0.5×1.25 µm3 with 4× isotropic expansion. Alternatively, for sub-cellular imaging, we employed a 10×0.6 SV MP water immersion objective with 0.8 NA and a working distance of 8 mm (OLYMPUS). The voxel size used in this configuration was 0.26×0.26×0.8 µm3. As a result of this configuration, we achieved a resolution of 0.5×0.5×1.6 µm3 and a spatial resolution of 0.12×0.12×0.4 µm3 with a 4.5× isotropic expansion. The higher resolution achieved with sub-cellular imaging allows us to observe finer structures and trace neural connections.

Regarding your question about cell boundaries, we will revise the manuscript to specify that the boundaries we identified are those of each nucleus, rather than entire cells. This distinction will be made clear in the revised version.

Reviewer #3 (Public review): 

Weaknesses: 

(1) The work would have been strengthened by a more careful consideration of previous literature. Many papers directly relevant to this work were not cited. Such omissions do the authors a disservice because in some cases, they fail to consider relevant information that impacts the choice of reagents they have used or the conclusions they are drawing. 

For example, when describing the antibody they use to label muscles (monoclonal 6G10), they do not cite the paper that generated this reagent (Ross et al PMCID: PMC4307677), and instead, one of the papers they do cite (Cebria 2016) that does not mention this antibody. Ross et al reported that 6G10 does not label all body wall muscles equivalently, but rather "predominantly labels circular and diagonal fibers" (which is apparent in Figure S5A-D of the manuscript being reviewed here). For this reason, the authors of the paper showing different body wall muscle populations play different roles in body patterning (Scimone et al 2017, PMCID: PMC6263039, also not cited in this paper) used this monoclonal in combination with a polyclonal antibody to label all body wall muscle types. Because their "pan-muscle" reagent does not label all muscle types equivalently, it calls into question their quantification of the different body wall muscle populations throughout the manuscript. It does not help matters that their initial description of the body wall muscle types fails to mention the layer of thin (inner) longitudinal muscles between the circular and diagonal muscles (Cebria 2016 and citations therein). 

Ipsilateral and contralateral projections of the visual axons were beautifully shown by dye-tracing experiments (Okamoto et al 2005, PMID: 15930826). This paper should be cited when the authors report that they are corroborating the existence of ipsilateral and contralateral projections. 

Thank you for your feedback. We will incorporate these citations and clarifications into the revised manuscript. We acknowledge the limitations of this approach and recognize that it does not encompass all neuron types, particularly those involved in glutamatergic, glycinergic, and peptidergic signaling. We will also add the content about other neuron types in our revised version.

(2) The proportional decrease of neurons with growth in S. mediterranea was shown by counting different cell types in macerated planarians (Baguna and Romero, 1981; https://link.springer.com/article/10.1007/BF00026179) and earlier histological observations cited there. These results have also been validated by single-cell sequencing (Emili et al, bioRxiv 2023, https://www.biorxiv.org/content/10.1101/2023.11.01.565140v). Allometric growth of the planaria tail (the tail is proportionately longer in large vs small planaria) can explain this decrease in animal size. The authors never really discuss allometric growth in a way that would help readers unfamiliar with the system understand this. 

Thank you for your feedback. We will incorporate these citations and clarifications into the revised manuscript.

(3) In some cases, the authors draw stronger conclusions than their results warrant. The authors claim that they are showing glial-muscle interactions, however, they do not provide any images of triple-stained samples labeling muscle, neurons, and glia, so it is impossible for the reader to judge whether the glial cells are interacting directly with body wall muscles or instead with the well-described submuscular nerve plexus. Their conclusion that neurons are unaffected by beta-cat or inr-1 RNAi based on anti-phospho-Ser/Thr staining (Fig. 6E) is unconvincing. They claim that during regeneration "DV muscles initially regenerate into longitudinal fibers at the anterior tip" (line 373). They provide no evidence for such switching of muscle cell types, so it is unclear why they say this. 

We acknowledge that some of our conclusions were overclaimed given the current data, and we appreciate the opportunity to clarify and refine these claims in the revised manuscript. Regarding the statement that "DV muscles initially regenerate into longitudinal fibers at the anterior tip" (line 373), as addressed in our previous response, this phrasing was unclear. Our intent was not to imply that DV muscles switch into longitudinal fibers. Instead, we observed that muscle fibers reconnect at the wound site, with longitudinal fibers and other muscle types gradually restoring the structure. We will revise this section to better describe the dynamic changes observed during regeneration.

(4) The authors show how their automated workflow compares to manual counts using PI-stained specimens (Figure S1T). I may have missed it, but I do not recall seeing a similar ground truth comparison for their muscle fiber counting workflow. I mention this because the segmented image of the posterior muscles in Figure 4I seems to be missing the vast majority of circular fibers visible to the naked eye in the original image. 

Thank you for raising this important point. We will include a ground truth comparison of our automated muscle fiber counting with manual counts in the supplementary figures. Regarding the observation of missing circular fibers in Figure 4I, we agree that the segmentation appears to have missed a significant number of circular fibers in this particular image. This may have been due to limitations in the current parameters of the segmentation algorithm, especially in distinguishing fibers in regions of varying intensity or overlap. We are revisiting the segmentation parameters to improve the accuracy of detecting circular fibers, and we will provide an updated version of Figure 4I in the revised manuscript.

(5) It is unclear why the abstract says, "We found the rate of neuron cell proliferation tends to lag..." (line 25). The authors did not measure proliferation in this work and neurons do not proliferate in planaria. 

Thank you for bringing this to our attention. What we intended to convey was the increase in neuron number during homeostasis. We will revise the abstract to avoid this mistake in this context and instead describe it as the increase in neuron numbers due to progenitor cell differentiation during homeostasis.

(6) It is unclear what readers are to make of the measurements of brain lobe angles. Why is this a useful measurement and what does it tell us? 

The measurement of brain lobe angles is intended to provide a quantitative assessment of the growth and morphological changes of the planarian brain during regeneration. Additionally, the relevance of brain lobe angles has been explored in previous studies, such as Arnold et al., Nature, 2016, further supporting its use as a meaningful parameter.

(7) The authors repeatedly say that this work lets them investigate planarians at the single-cell level, but they don't really make the case that they are seeing things that haven't already been described at the single-cell level using standard confocal microscopy. 

Thank you for your comment. We agree that single-cell level imaging has been previously achieved in planarians using conventional confocal microscopy. However, our goal was to extend the application of expansion microscopy by combining C-MAP with tiling light sheet microscopy (TLSM), which allows for faster and high-resolution 3D imaging of whole-mount planarians. This combination offers several key advantages over traditional confocal microscopy. For example, it enables high-throughput imaging across entire organisms with a level of detail and speed that is not easily achieved using confocal methods. This approach allows us to investigate the planarian nervous system at multiple developmental and regenerative stages in a more comprehensive manner, capturing large-scale structures while preserving fine cellular details. The ability to rapidly image whole planarians in 3D with this resolution provides a more efficient workflow for studying complex biological processes. We believe this distinction is significant and represents an advance over previous methods. We will clarify this point in the manuscript to better distinguish our approach from standard techniques.

Reviewer #2 (Public review):

Summary:

This manuscript builds on the authors' 2020 study by combining tissue expansion with light sheet microscopy to quantify the organism-wide spatial distribution of various cell types in the planarian.

Strengths:

(1) The quantification of cell types as a function of animal size and regeneration stages could be a useful resource for the planarian research community.

(2) The high-quality images can help clarify some anatomical structures within the planarian tissues.

Weaknesses:

(1) The proprietary nature of the microscope, protected by a patent, limits the technical details provided, making the method hard to reproduce in other labs.

(2) The resolution of the analyses is mostly limited to the cellular level, which does not fully leverage the advantages of expansion microscopy. Previous applications of expansion microscopy have revealed finer nanostructures in the planarian nervous system (see Fan et al. Methods in Cell Biology 2021; Wang et al. eLife 2021). It is unclear whether the current protocol can achieve a comparable resolution.

(3) The data largely corroborate past observations, while the novel claims are insufficiently substantiated.

A few major issues with the claims:

(4) Line 303-304: While 6G10 is a widely used antibody to label muscle fibers in the planarian, it doesn't uniformly mark all muscle types (Scimone at al. Nature 2017). For a more complete view of muscle fibers, it is important to use a combination of antibodies targeting different fiber types or a generic marker such as phalloidin. This raises fundamental concerns about all the conclusions drawn from Figures 4 and 6 about differences between various muscle types. Additionally, the authors should cite the original paper that developed the 6G10 antibody (Ross et al. BMC Developmental Biology 2015).

(5) Lines 371-379: The claim that DV muscles regenerate into longitudinal fibers lacks evidence. Furthermore, previous studies have shown that TFs specifying different muscle types (DV, circular, longitudinal, and intestinal) both during regeneration and homeostasis are completely different (Scimone et al., Nature 2017 and Scimone et al., Current Biology 2018). Single-cell RNAseq data further establishes the existence of divergent muscle progenitors giving rise to different muscle fibers. These observations directly contradict the authors' claim, which is only based on images of fixed samples at a coarse time resolution.

(6) Line 423: The manuscript lacks evidence to claim glia guide muscle fiber branching.

(7) Lines 432/478: The conclusion about neuronal and muscle guidance on glial projections is similarly speculative, lacking functional evidence. It is possible that the morphological defects of estrella+ cells after bcat1 RNAi are caused by Wnt signaling directly acting on estrella+ cells independent of muscles or neurons.

(8) Finally, several technical issues make the results difficult to interpret. For example, in line 125, cell boundaries appear to be determined using nucleus images; in line 136, the current resolution seems insufficient to reliably trace neural connections, at least based on the images presented.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors apply tissue expansion and tiling light sheet microscopy to study allometric growth and regeneration in planaria. They developed image analysis pipelines to help them quantify different neuronal subtypes and muscles in planaria of different sizes and during regeneration. Among the strengths of this work, the authors provide beautiful images that show the potential of the approaches they are taking and their ability to quantify specific cell types in relatively large numbers of whole animal samples. Many of their findings confirm previous results in the literature, which helps validate the techniques and pipelines they have applied here. Among their new observations, they find that the body wall muscles at the anterior and posterior poles of the worm are organized differently and show that the muscle pattern in the posterior head of beta-catenin RNAi worms resembles the anterior muscle pattern. They also show that glial cell processes appear to be altered in beta-catenin or insulin receptor-1 RNAi worms. Weaknesses include some over-interpretation of the data and lack of consideration or citation of relevant previous literature, as discussed below.

Strengths:

This method of tissue expansion will be useful for researchers interested in studying this experimental animal. The authors provide high-quality images that show the utility of this technique. Their analysis pipeline permits them to quantify cell types in relatively large numbers of whole animal samples.

The authors provide convincing data on changes in total neurons and neuronal sub-types in different-sized planaria. They report differences in body wall muscle pattern between the anterior and posterior poles of the planaria, and that these differences are lost when a posterior head forms in beta-catenin RNAi planaria. They also find that glial cell projections are reduced in insulin receptor-1 RNAi planaria.

Weaknesses:

The work would have been strengthened by a more careful consideration of previous literature. Many papers directly relevant to this work were not cited. Such omissions do the authors a disservice because in some cases, they fail to consider relevant information that impacts the choice of reagents they have used or the conclusions they are drawing.

For example, when describing the antibody they use to label muscles (monoclonal 6G10), they do not cite the paper that generated this reagent (Ross et al PMCID: PMC4307677), and instead, one of the papers they do cite (Cebria 2016) that does not mention this antibody. Ross et al reported that 6G10 does not label all body wall muscles equivalently, but rather "predominantly labels circular and diagonal fibers" (which is apparent in Figure S5A-D of the manuscript being reviewed here). For this reason, the authors of the paper showing different body wall muscle populations play different roles in body patterning (Scimone et al 2017, PMCID: PMC6263039, also not cited in this paper) used this monoclonal in combination with a polyclonal antibody to label all body wall muscle types. Because their "pan-muscle" reagent does not label all muscle types equivalently, it calls into question their quantification of the different body wall muscle populations throughout the manuscript. It does not help matters that their initial description of the body wall muscle types fails to mention the layer of thin (inner) longitudinal muscles between the circular and diagonal muscles (Cebria 2016 and citations therein).

Ipsilateral and contralateral projections of the visual axons were beautifully shown by dye-tracing experiments (Okamoto et al 2005, PMID: 15930826). This paper should be cited when the authors report that they are corroborating the existence of ipsilateral and contralateral projections.

The proportional decrease of neurons with growth in S. mediterranea was shown by counting different cell types in macerated planarians (Baguna and Romero, 1981; https://link.springer.com/article/10.1007/BF00026179) and earlier histological observations cited there. These results have also been validated by single-cell sequencing (Emili et al, bioRxiv 2023, https://www.biorxiv.org/content/10.1101/2023.11.01.565140v). Allometric growth of the planaria tail (the tail is proportionately longer in large vs small planaria) can explain this decrease in animal size. The authors never really discuss allometric growth in a way that would help readers unfamiliar with the system understand this.

In some cases, the authors draw stronger conclusions than their results warrant. The authors claim that they are showing glial-muscle interactions, however, they do not provide any images of triple-stained samples labeling muscle, neurons, and glia, so it is impossible for the reader to judge whether the glial cells are interacting directly with body wall muscles or instead with the well-described submuscular nerve plexus. Their conclusion that neurons are unaffected by beta-cat or inr-1 RNAi based on anti-phospho-Ser/Thr staining (Fig. 6E) is unconvincing. They claim that during regeneration "DV muscles initially regenerate into longitudinal fibers at the anterior tip" (line 373). They provide no evidence for such switching of muscle cell types, so it is unclear why they say this.

The authors show how their automated workflow compares to manual counts using PI-stained specimens (Figure S1T). I may have missed it, but I do not recall seeing a similar ground truth comparison for their muscle fiber counting workflow. I mention this because the segmented image of the posterior muscles in Figure 4I seems to be missing the vast majority of circular fibers visible to the naked eye in the original image.

It is unclear why the abstract says, "We found the rate of neuron cell proliferation tends to lag..." (line 25). The authors did not measure proliferation in this work and neurons do not proliferate in planaria.

It is unclear what readers are to make of the measurements of brain lobe angles. Why is this a useful measurement and what does it tell us?

The authors repeatedly say that this work lets them investigate planarians at the single-cell level, but they don't really make the case that they are seeing things that haven't already been described at the single-cell level using standard confocal microscopy.

Pulpa nekrozu ve kalan ürünler, iltihap apikal foramen, lateral kanallar, dentin kanalları veya iatrojenik yollar yoluyla periodonsiyuma iletilebilir.

Pulpa ile periodontal membran arasındaki yakın anatomik ilişkiler, iltihabın bu bölgeler arasında da geçebileceğini düşündürmektedir.

Endodontik tedavi sırasında anatomik apeksten 1,5 mm koronal olarak çalışmamızın sebebi (özellikle vital dişlerde) pulpa ile doğrudan ilişkide olan PDL'yi tahrip ederek bu bölgede patolojiye yol açmamaktır.

Apikal foramenin boyutu daha büyüktür, bu da patolojinin geçişi açısından daha risklidir

Boyut olarak en büyük bağlantı yolu

Servikal bölgede mine-sement birleşiminin olmadığı durumlar

eLife Assessment

This study provides a valuable perspective on visual cortex architecture by identifying two cortical gradients that change across the lifespan and have distinct functional and structural features. The first gradient captures well-mapped variations in cortical thickness and myelination markers from early sensory to higher-order cortex, while the second gradient shows divergence in these measures with a more localized structure, notably predicting a previously unknown cluster of visual field maps in the anterior temporal lobe. The large-scale lifespan data are compelling, but the evidence overall is incomplete with key questions around methodical checks and implementation, the standard of evidence for the new visual maps, and how the gradient model relates to sharp tissue boundaries parcellating the cortex.

Reviewer #1 (Public review):

Summary:

The manuscript uses large-scale existing datasets that span almost the full range of human life (5-100 years) to identify two distinct architectural cortical gradients within the visual cortex. These gradients are distinct: in one, cytoarchitecture and myeloarchitecture converge and in the other, they diverge. The authors tested whether these gradients mapped onto known functional properties of the visual cortex, as well as accounting for visual behaviours that are impacted throughout the lifespan. The manuscript also reports the identification of a hitherto unknown cluster of visual field maps in the anterior temporal lobe.

Strengths:

A major strength of the current manuscript is the use of large-scale measurements of human brain structure throughout the lifespan, courtesy of the Human Connectome Project Initiative. The scope of this cross-sectional analysis would be rare, if not impossible to achieve through an individual project.

The approach employed holds promise for assessing the link between large-scale anatomical gradients in the brain and functional/behavioural properties. The current manuscript focuses on the visual cortex but the approach could easily be implemented across the brain in general.

Weaknesses:

While the evidence in favour of the two gradients largely supports the claims, the evidence for a new visual field map cluster in the anterior temporal lobe falls short of the level used historically when identifying visual field maps in the visual cortex and is, at present, not convincing.

More specifically, the progressions of polar angle within the putative anterior lobe cluster are highly variable across subjects. Few subjects have convincing polar angle reversals at either the horizontal or vertical meridians. In other cases, a putative border is shown that spans different polar angles, which does not align with the accepted definitions for visual field maps in the cortex.

Reviewer #2 (Public review):

Summary:

The authors used large MRI data sets of the Human Connectome Project (HCP) and also conducted additional pRF analyses to describe the structural architecture of the human visual cortex in reference to its functional features. By conducting a PCA, they identify 2 components that explain around 50% of the variance, the driven by a positive co-variance between cortical thickness and T1/T2 ratio, the second by their negative co-variance. The first PC spans most early visual cortex and hence shows a relation to pRF size when taking both early and late visual areas into account. The second is more variable in location and does not relate to pRF size or visual hierarchy. The relationship between these two gradients to cell body density using the BigBrain is explored.

Strengths:

The authors make an attempt to describe the overall architectural features of the cortex and link it to features of functional representations, and the underlying histology, using different sets of datasets and methods, including histology. They highlight that investigating the structural architecture of the cortex provides important information on their intrinsic organization and common features.

Weaknesses:

The neurobiological model does not take into consideration present knowledge about the microstructural organization of the visual system. This limits the way the results are interpreted correctly. Critical information on the layer-specific myeloarchitecture and cytoarchitecture (and their relation to cortical thickness), as explored for example by Sereno et al. 2013 Cereb Cortex, is missing. There is no information given with respect to how different visual areas differ in their microstructural profile. It is also not mentioned that cortical parcellation is indeed characterized by sharp boundaries between areas, rather than structural gradients, so it remains unclear why focusing on a gradient is of interest. The authors cite the parcellation atlas by Glasser et al. 2016, but do not discuss the rationale of this publication, which was not the definition of gradients, but the definition of sharp boundaries for cortex parcellation. Indeed (as explained below), the results of the authors seem to a large extent to be driven by cortex parcellation, but instead of acknowledging this fact, the authors write (line 179) that "we hypothesize that these local deviations from the canonical thickness and density of cortex underlie the finer-scale division of visual cortex into categorically distinct regions. That is, does the realization of the cortex into distinct regions involve these regions becoming more distinct from a prototypical cortical sheet (i.e., gradient 1)?" - While the first sentence is reasonable, the second sentence is pure speculation ignoring present knowledge on cortical parcellation of this area according to which there is no "prototypical cortical sheet", but each area has its distinct microstructural profile.

Instead of building on present, detailed knowledge of brain anatomy and in-vivo cortex parcellation of the visual system and its known relation to visual maps, the authors focus on two metrics of cortex architecture (mean T1/T1 over depth and cortical thickness), and conduct a PCA to explore their shared variance. It needs to be clarified if the PCA was conducted correctly. There is no mention of standardizing the variables, which could bias the results. In addition, in a PCA, all possible features are categorized as vector components, and those are scanned through the samples, hence, one such analysis per vertex. But the authors write "in which participants are features and cortical vertices are samples" and "the thickness and tissue density maps were concatenated". This needs clarification. The architecture of the PCA should be visualized better.

Because the PCA only contains two features, PC1 is driven by the positive relationship between cortical thickness and mean T1/T2, whereas PC2 is driven by their negative relationship. Because in the early visual cortex, cortical thickness and mean T1/T2 correlate positively, it naturally follows that PC1 relates to pRF size (but mediated by the actual cortex parcellation). However, it is unclear why this insight is interesting. I also do not share the view that "these findings demonstrate that gradient 1 acts as a global gradient enveloping the entire visual cortex (...) while gradient 2 acts as a local gradient specific to individual visual streams". I think this relationship between cortical thickness and T1/T2 ratio does not have much to do with local and global gradients. But if so, stronger arguments as to why this should be the case should be presented.

What the authors make of this result (particularly the discussion starting line 366) is not clear to me. I cannot follow the line of argumentation, which in my view is too far away from the data.

I'm a firm believer in the fact that every single person is entitled to be the sexual creature they want to be, as long as it doesn't cross the line and hurt another, break a crime, or lie to gain access to. Of course, the culture of sex had to change when the nature of sexual creativity and wants themselves changed. As we evolve, culture evolves with and within us. Wanting to feel good or become sexually close or aware is expected, especially since we, as humans, have little knowledge of what it is that truly makes us humans. We need to be taught Anatomy far past eyes, ears, nose, and so on. How can we understand how our culture truly changes if we do not understand why we need to change.

I find it absolute magic that we can gather in groups of anything we want with the digital dawn of AI's, "friends", "likes","pages", and such we are able to.

Has an ABABCC rhyme scheme that is consistent throughout the entire poem.

scrap value = the estimated worth of an asset at the end of its useful life.

Very Informative

Lombard and Vera have a disagreement during a seemingly peaceful conversation, and based on the subsequent text, Vera appears to be quite convinced of her suspicions.

Why does General MacArthur say that everyone is waiting for the end while also stating that Vera won't understand this as a form of release?

In the current situation with multiple deaths, people have different perspectives on the issues at hand. However, some have also identified the killer's tricks, revealing the killer's talent for confusion and malicious enjoyment.

Mikroorganizmaların ve toksik ürünlerin periodontal membrandan geçmesine neden olur

Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology.<br /> See the peer reviews and the recommendation.

Reviewer #2 (Public review):

Dipasree Hajra et al demonstrated that Salmonella was able to modulate the expression of Sirtuins (Sirt1 and Sirt3) and regulate the metabolic switch in both host and Salmonella, promoting its pathogenesis. The authors found Salmonella infection induced high levels of Sirt1 and Sirt3 in macrophages, which were skewed toward the M2 phenotype allowing Salmonella to hyper-proliferate. Mechanistically, Sirt1 and Sirt3 regulated the acetylation of HIF-1alpha and PDHA1, therefore mediating Salmonella-induced host metabolic shift in the infected macrophages. Interestingly, Sirt1 and Sirt3-driven host metabolic switch also had an effect on the metabolic profile of Salmonella. Counterintuitively, inhibition of Sirt1/3 led to increased pathogen burdens in an in vivo mouse model. Overall, this is a well-designed study.

The revised manuscript has addressed all of the previous comments. The re-analysis of flow cytometry and WB data by authors makes the results and conclusion more complete and convincing.

Reviewer #3 (Public review):

Summary:

In this paper Hajra et al have attempted to identify the role of Sirt1 and Sirt3 in regulating metabolic reprogramming and macrophage host defense. They have performed gene knock down experiments in RAW macrophage cell line to show that depletion of Sirt1 or Sirt3 enhances the ability of macrophages to eliminate Salmonella Typhimurium. However, in mice inhibition of Sirt1 resulted in dissemination of the bacteria but the bacterial burden was still reduced in macrophages. They suggest that the effect they have observed is due to increased inflammation and ROS production by macrophages. They also try to establish a weak link with metabolism. They present data to show that the switch in metabolism from glycolysis to fatty acid oxidation is regulated by acetylation of Hif1a, and PDHA1.

Strengths:

The strength of the manuscript is that the role of Sirtuins in host-pathogen interactions have not been previously explored in-depth making the study interesting. It is also interesting to see that depletion of either Sirt1 or Sirt3 result in a similar outcome.

Weaknesses:

The major weakness of the paper is the low quality of data, making it harder to substantiate the claims. Also, there are too many pathways and mechanisms being investigated. It would have been better if the authors had focussed on either Sirt1 or Sirt3 and elucidated how it reprograms metabolism to eventually modulate host response against Salmonella Typhimurium. Experimental evidences are also lacking to prove the proposed mechanisms. For instance they show correlative data that knock down of Sirt1 mediated shift in metabolism is due to HIF1a acetylation but this needs to be proven with further experiments.

Author response:

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

Reviewer #2 (Public Review):

Dipasree Hajra et al demonstrated that Salmonella was able to modulate the expression of Sirtuins (Sirt1 and Sirt3) and regulate the metabolic switch in both host and Salmonella, promoting its pathogenesis. The authors found Salmonella infection induced high levels of Sirt1 and Sirt3 in macrophages, which were skewed toward the M2 phenotype allowing Salmonella to hyper-proliferate. Mechanistically, Sirt1 and Sirt3 regulated the acetylation of HIF-1alpha and PDHA1, therefore mediating Salmonella-induced host metabolic shift in the infected macrophages. Interestingly, Sirt1 and Sirt3-driven host metabolic switch also had an effect on the metabolic profile of Salmonella. Counterintuitively, inhibition of Sirt1/3 led to increased pathogen burdens in an in vivo mouse model. Overall, this is a well-designed study.

Comments on revised version:

The authors have performed additional experiments to address the discrepancy between in vitro and in vivo data. While this offers some potential insights into the in vivo role of Sirt1/3 in different cell types and how this affects bacterial growth/dissemination, I still believe that Sirt1/3 inhibitors could have some effect on the gut microbiota contributing to increased pathogen counts. This possibility can be discussed briefly to give a better scenario of how Sirt1/3 inhibitors work in vivo. Additionally, the manuscript would improve significantly if some of the flow cytometry analysis and WB data could be better analyzed.

We are highly grateful for your valuable and insightful comments. Thank you for appreciating the merit of our manuscript. As rightly pointed out by the eminent reviewer, we acknowledge the probable link of Sirtuin on gut microbiota and its effect on increased bacterial loads as indicated by previous literature studies (PMID: 22115311, PMID: 19228061). These reports suggested that a low dose of Sirt1 activator, resveratrol treatment in rats for 25 days treatment under 5% DSS induced colitis condition led to alterations in gut microbiota profile with increased lactobacilli and bifidobacteria alongside reduced abundance of enterobacteria. This study correlates with our study wherein we have detected enhanced Salmonella (belonging to Enterobacteriaceae family) loads under both Sirt1/3 in vivo knockdown condition or inhibitor-treated condition in C57BL/6 mice and reduced burden under Sirt-1 activator treatment SRT1720.

As per your valid suggestion, we have discussed this possibility in our discussion section. (Line- 541-548).

We have incorporated the suggestions for the improvement in the analysis of WB data and flow cytometry.

Reviewer #3 (Public Review):

Summary:

In this paper Hajra et al have attempted to identify the role of Sirt1 and Sirt3 in regulating metabolic reprogramming and macrophage host defense. They have performed gene knock down experiments in RAW macrophage cell line to show that depletion of Sirt1 or Sirt3 enhances the ability of macrophages to eliminate Salmonella Typhimurium. However, in mice inhibition of Sirt1 resulted in dissemination of the bacteria but the bacterial burden was still reduced in macrophages. They suggest that the effect they have observed is due to increased inflammation and ROS production by macrophages. They also try to establish a weak link with metabolism. They present data to show that the switch in metabolism from glycolysis to fatty acid oxidation is regulated by acetylation of Hif1a, and PDHA1.

Strengths:

The strength of the manuscript is that the role of Sirtuins in host-pathogen interactions has not been previously explored in-depth making the study interesting. It is also interesting to see that depletion of either Sirt1 or Sirt3 results in a similar outcome.

Weaknesses:

The major weakness of the paper is the low quality of data, making it harder to substantiate the claims. Also, there are too many pathways and mechanisms being investigated. It would have been better if the authors had focussed on either Sirt1 or Sirt3 and elucidated how it reprograms metabolism to eventually modulate host response against Salmonella Typhimurium. Experimental evidence is also lacking to prove the proposed mechanisms. For instance they show correlative data that knock down of Sirt1 mediated shift in metabolism is due to HIF1a acetylation but this needs to be proven with further experiments.

We appreciate the reviewer’s critical analysis of our work. In the revised manuscript, we aimed to eliminate the low-quality data sets and have tried to substantiate them with better and conclusive ones, as directed in the recommendations for the author section. We agree with the reviewer that the inclusion of both Sirtuins 1 and 3 has resulted in too many pathways and mechanisms and focusing on one SIRT and its mechanism of metabolic reprogramming and immune modulation would have been a less complicated alternative approach. However, as rightly pointed out, our work demonstrated the shared and few overlapping roles of the two sirtuins, SIRT1 and SIRT3, together mediating the immune-metabolic switch upon Salmonella infection. As per the reviewer’s suggestion, we have performed additional experiments with HIF-1α inhibitor treatment in our revised manuscript to substantiate our correlative findings on SIRT1-mediated regulation of host glycolysis (Fig.7G). We wanted to clarify our claim in this regard. Our results suggested that loss of SIRT1 function triggered increased host glycolysis alongside hyperacetylation of HIF-1α. HIF-1α is reported to be one of the important players in glycolysis regulation (Kierans SJ, Taylor CT. Regulation of glycolysis by the hypoxia-inducible factor (HIF): implications for cellular physiology. J Physiol. 2021;599(1):23-37. doi:10.1113/JP280572.) and additionally, SIRT1 has been shown to regulate HIF-1α acetylation status (Lim JH, Lee YM, Chun YS, Chen J, Kim JE, Park JW. Sirtuin 1 modulates cellular responses to hypoxia by deacetylating hypoxia-inducible factor 1 alpha. Mol Cell. 2010;38(6):864-878. doi:10.1016/j.molcel.2010.05.023.) Further, ectopic expression of SIRT1 has been demonstrated to reduce glycolysis by negatively regulating HIF-1α. (Wang Y, Bi Y, Chen X, et al. Histone Deacetylase SIRT1 Negatively Regulates the Differentiation of Interleukin-9-Producing CD4(+) T Cells. Immunity. 2016;44(6):1337-1349. doi:10.1016/j.immuni.2016.05.009). We have subsequently shown in Fig. 7G, that the increase in host glycolysis upon SIRT knockdown in the infected macrophages gets lowered upon HIF-1α inhibitor treatment, suggesting that one of the mechanisms of SIRT-mediated regulation of host glycolysis is via regulation of HIF-1α. However, this warrants further future mechanistic research.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

(1) Figures 8I-S: are only viable cells used for analysis? Please provide gating strategy used for these analyses.

(2) Many changes seen in WB seem to be marginal. Since the authors used densitometric plot to quantify the band intensities, I expect these experiments were repeated at least three times. Please indicate the number of repeats. For instance, Figures 7C, 7I (UI SCR vs UI shSIRT3), 7J, show marginal changes or no changes. What do other WB images look like? Are they more convincing than the ones currently shown? Please provide them in the response letter.

(3) Figure 7C: label is a bit misleading. Please relabel the figure title to Acetylated HIF vs total levels

(4) Figure 7J: which band is AcPDHA1?

(1) We are highly apologetic for not clarifying our gating strategy for the analysis.

We initially gated the viable splenocyte population based on Forward scatter (FSC) and Side Scatter (SSC). This gated population was further subjected to gating based on cell FSC-H (height) versus FSC-A (area). Subsequently, the population was gated as per SSC-A and GFP (expressed by intracellular bacteria) based on the autofluorescence exhibited by the uninfected control (Fig. 8I-J).

Author response image 1.

UNINFECTED

Author response image 2.

VEHICLE CONTROL INFECTED

Author response image 3.

EX-527 INFECTED

Author response image 4.

3TYP INFECTED

Author response image 5.

SRT 1720 INFECTED

For gating different cell types such as F4/80 (PE) positive population in Fig. 8K-L, the viable cell population was gated based on SSC-A versus PE-A to gate the macrophage population. These macrophage populations were gated further based on GFP (Salmonella) + population to obtain the percentage of macrophage population harboring GFP+ bacteria. Similar strategies were followed for other cell types as depicted in Fig. 8M-S, Fig. S8.

(2) We agree with the reviewer’s concern with the marginal changes in the western blots (Figures 7C, 7I (UI SCR vs UI shSIRT3), 7J). As per the suggestions, we have provided the alternate blot images and have indicated the number of repeats in the manuscript. The alternate blot images are provided herewith:

Author response image 6.

Alternate blot images for Fig. 7B-C

Author response image 7.

Alternate blot images for Fig. 7I, J

(1) We are highly thankful to the reviewer for recommending this suggestion. We have made the necessary modifications of relabelling Fig. C to Acetylated HIF-1α over total HIF-1α as per the suggestion.

(2) 7J Acetylated PDHA1 has been duly pointed as per the suggestion. We are extremely apologetic for the inconvenience caused.

Author response image 8

Reviewer #3 (Recommendations For The Authors):

The authors have done some work to improve the manuscript. However, the data presented lacks clarity.

Fig 4B: I still do not see a change in Ac p65 in the less saturated blot. It looks reduced as the band is distorted. I am not sure how this could be quantified.

Fig S2 b-actin bands are hyper saturated, and it is not possible to decipher the knockdown efficiency. It is probably better to provide a ponceau staining similar to S2C. The band intensity values are out of place.

Fig 5F HADHA blot: Lane 1 expression appears to be significantly higher than lane 3, but the values mentioned do not match the intensity of the bands.

It is hard to interpret the authors' claim that the shift in metabolism is HIF1a-dependent.

Fig 7B: I would expect HIF1a acetylation to be increased in UI ShSIRT1 compared to UI SCR. The blot shows reduced HIF1a acetylation.

Fig 7D: SIRT1 immunoprecipitates with HIF1a equally under all conditions. Is this what the authors expect? Labelling of the blots are not clear. It looks like the bottom SIRT1 blot is from Beads IgG control.

Fig 7H: How does PDHA1 interact with SIRT3 so strongly in shSIRT3 cells (lane 2)?

Authors have mentioned in their response that a knockdown of 40% has been achieved in the uninfected but the blot does not reflect that. SIRT3 expression seems to be more in the knockdown.

Blots are also not labelled properly especially Input. The lanes are not marked.

We thank the reviewer for acknowledging the improvements in the revised version and for suggesting further clarifications and improvements.

We have tried to incorporate the specified modifications to the best of our abilities in the revised manuscript.

We are highly apologetic for the inconclusive blot image in the figure 4B. We have provided an alternative blot image with better clarity for Fig.4B used for quantification analysis.

Author response image 9.

As per the reviewer’s valuable suggestions, we have provided the ponceau image in the Fig. S2B.

We thank the reviewers for rightly pointing out the discrepancy in the band intensity quantification in the Fig. 5F. We have re-evaluated the intensities on imageJ and have provided with the correct band intensities. We are highly apologetic for the inaccuracies.

As per the reviewer’s previous suggestion, we have performed additional experiments with HIF-1α inhibitor treatment in our revised manuscript to substantiate our correlative findings on SIRT1-mediated regulation of host glycolysis (Fig.7G). We wanted to clarify our claim in this regard. Our results suggested that loss of SIRT1 function triggered increased host glycolysis alongside hyperacetylation of HIF-1α. HIF-1α is reported to be one of the important players of glycolysis regulation (Kierans SJ, Taylor CT. Regulation of glycolysis by the hypoxia-inducible factor (HIF): implications for cellular physiology. J Physiol. 2021;599(1):23-37. doi:10.1113/JP280572.) and additionally, SIRT1 has been shown to regulate HIF-1α acetylation status (Lim JH, Lee YM, Chun YS, Chen J, Kim JE, Park JW. Sirtuin 1 modulates cellular responses to hypoxia by deacetylating hypoxia-inducible factor 1alpha. Mol Cell. 2010;38(6):864-878. doi:10.1016/j.molcel.2010.05.023.) Further, ectopic expression of SIRT1 has been demonstrated to reduce glycolysis by negatively regulating HIF-1α. (Wang Y, Bi Y, Chen X, et al. Histone Deacetylase SIRT1 Negatively Regulates the Differentiation of Interleukin-9-Producing CD4(+) T Cells. Immunity. 2016;44(6):1337-1349. doi:10.1016/j.immuni.2016.05.009). We have subsequently shown in Fig. 7G, that the increase in host glycolysis upon SIRT knockdown in the infected macrophages gets lowered upon HIF-1α inhibitor treatment, suggesting that one of the mechanisms of SIRT-mediated regulation of host glycolysis is via regulation of HIF-1α. However, this warrants further future mechanistic research.

We agree with the reviewer’s claim of increased HIF-1α acetylation in the UI sh1 versus UI SCR. The apparent reduced acetylation depicted in UI sh1 in Fig. 7B could be attributed to lower HIF-1α levels in the UI sh1 compared to UI SCR. Therefore, we have provided an alternate blot image that been used for quantification in Fig. 7C (Author response image 6).

To answer the reviewer’s question in Fig. 7D, we have noticed more or less equal degree of immunoprecipitation of HIF-1α under pull down of HIF-1α in all the sample cohorts under conditions of SIRT1 inhibitor treatment. However, we have observed reduced interaction of HIF-1α with SIRT1 in the infected sample upon SIRT1 inhibitor treatment.

We thank the reviewers for suggesting improvements in the blot labelling and for raising this concern. We have corrected the blot labelling to avoid the previous confusion.

We appreciate the reviewer’s concern and therefore we have provided an alternate blot image for Fig. 7H which might address the previous stated concern wherein we have achieved an enhanced SIRT3 knockdown percentage.

We are extremely apologetic for the improper labelling of the Input blot with unmarked lanes. We have addressed this issue by labelling the lanes in the input section of the blots.

Die CO2-Emissionen durch Waldbrände haben seit 2001 um 60% zugenommen, wie sich aus einer neuen Studie ergibt. Den größten Anteil daran haben die borealen Wälder Kanadas und Sibiriens. Sie gehören zu einem Typ von Wäldern, der besonders schlecht an die globale Erhitzung angepasst ist. Die beobachteten Emissionen durch Waldbrände machen unwahscheinlicher, dass Wälder in Zukunft von Menschen emittiertes CO2 reduzieren können. https://www.nytimes.com/2024/10/17/climate/carbon-fires-forests-global-warming.html

Studie: https://www.science.org/doi/10.1126/science.adl5889

https://theconversation.com/extreme-wildfires-have-doubled-in-just-20-years-heres-the-science-233375

Hier ist die Beschriftung des Codes verrückt. Punkt 4 und 5 weisen auf die falschen Zeilen im Code.

Shall we add some screenshots here?

Remove this line and add this line: In some very special cases, the Percentage fulfilled (%) may not display data as you expect. For example, you use Card to display the overall Percentage fulfilled (%) for a period, in this case the Percentage fulfilled (%) will be a sum instead of an average.

When the effort type is FTE, the data of Demand, Fulfilled Demand or Unmet Demand may vary while the selected time period varies. Take an example, if a position (as each position has a role, role represents position in this new data source) has 2 FTEs of Demand in August and September, but no demand in both July and October, you will see the position has 2 FTEs of Demand if the time period is from August to September, but you will see approximately 1 FTEs of Demand for the position if you change the period from July to October. If you think FTE is not so straightforward, try another two effort types Person Days or Hours. The Demand will not change in above case if the effort type is Person Days or Hours.

If a team has multiple levels sub-teams, only the current team and its direct sub team are displayed, allocations for level three and higher sub-teams are rolled up to their level two ancestral team.

eLife Assessment

The authors present three valuable transgenic models carrying three representative exon deletions of the dystrophin gene. The findings are supported by rigorous biochemical assays and state-of-the-art microscopy methods, although the evidence, while overall solid, is only partially developed, and some points could be improved.

Reviewer #1 (Public review):

Summary:

In this article the authors described mouse models presenting with backer muscular dystrophy, they created three transgenic models carrying three representative exon deletions: ex45-48 del., ex45-47 19 del., and ex45-49 del.. This article is well written but needs improvement in some points.

Strengths:

This article is well written. The evidence supporting the authors' claims is robust, though further implementation is necessary. The experiments conducted align with the current state-of-the-art methodologies.

Weaknesses:

This article does not analyze atrophy in the various mouse models. Implementing this point would improve the impact of the work

Reviewer #2 (Public review):

Miyazaki et al. established three distinct BMD mouse models by deleting different exon regions of the dystrophin gene, observed in human BMD. The authors demonstrated that these models exhibit pathophysiological changes, including variations in body weight, muscle force, muscle degeneration, and levels of fibrosis, alongside underlying molecular alterations such as changes in dystrophin and nNOS levels. Notably, these molecular and pathological changes progress at different rates depending on the specific exon deletions in the dystrophin gene. Additionally, the authors conducted extensive fiber typing, revealing a site-specific decline in type IIa fibers in BMD mice, which they suggest may be due to muscle degeneration and reduced capillary formation around these fibers.

Strengths:

The manuscript introduces three novel BMD mouse models with different dystrophin exon deletions, each demonstrating varying rates of disease progression similar to the human BMD phenotype. The authors also conducted extensive fiber typing across different muscles and regions within the muscles, effectively highlighting a site-specific decline in type IIa muscle fibers in BMD mice.

Weaknesses:

The authors have inadequate experiments to support their hypothesis that the decay of type IIa muscle fibers is likely due to muscle degeneration and reduced capillary formation. Further investigation into capillary density and histopathological changes across different muscle fibers is needed, which could clarify the mechanisms behind these observations.

Author response:

Reviewer #1 (Public review):

Summary:

In this article the authors described mouse models presenting with backer muscular dystrophy, they created three transgenic models carrying three representative exon deletions: ex45-48 del., ex45-47 19 del., and ex45-49 del. This article is well written but needs improvement in some points.

Strengths:

This article is well written. The evidence supporting the authors' claims is robust, though further implementation is necessary. The experiments conducted align with the current state-of-the-art methodologies.

Weaknesses:

This article does not analyze atrophy in the various mouse models. Implementing this point would improve the impact of the work

We thank the reviewer for their constructive suggestions and comments on this work. Muscle hypertrophy is shown with growth in dystrophin-deficient skeletal muscle in mdx mice; thus, we did not pay attention to the factors associated with muscle atrophy in BMD mice. As the reviewer suggested, the examination of the association between type IIa fiber reduction and muscle atrophy is important, and the result is considered to be helpful in resolving the cause of type IIa fiber reduction in BMD mice.

Thus, we are planning to:

(1) Evaluate the cross-sectional areas (CSA) of muscles and compare them with the changes in the proportion of type IIa fibers.

(2) Evaluate the expression levels of Murf1 and Atrogin1 as markers of muscle atrophy using RT-PCR.

Reviewer #2 (Public review):

Summary

Miyazaki et al. established three distinct BMD mouse models by deleting different exon regions of the dystrophin gene, observed in human BMD. The authors demonstrated that these models exhibit pathophysiological changes, including variations in body weight, muscle force, muscle degeneration, and levels of fibrosis, alongside underlying molecular alterations such as changes in dystrophin and nNOS levels. Notably, these molecular and pathological changes progress at different rates depending on the specific exon deletions in the dystrophin gene. Additionally, the authors conducted extensive fiber typing, revealing a site-specific decline in type IIa fibers in BMD mice, which they suggest may be due to muscle degeneration and reduced capillary formation around these fibers.

Strengths:

The manuscript introduces three novel BMD mouse models with different dystrophin exon deletions, each demonstrating varying rates of disease progression similar to the human BMD phenotype. The authors also conducted extensive fiber typing across different muscles and regions within the muscles, effectively highlighting a site-specific decline in type IIa muscle fibers in BMD mice.

Weaknesses:

The authors have inadequate experiments to support their hypothesis that the decay of type IIa muscle fibers is likely due to muscle degeneration and reduced capillary formation. Further investigation into capillary density and histopathological changes across different muscle fibers is needed, which could clarify the mechanisms behind these observations.

We thank the reviewer for these positive comments and the very important suggestion about type IIa fiber reduction and capillary change around muscle fibers in BMD mice. From the results of the cardiotoxin-induced muscle degeneration and regeneration model, type IIa and IIx fibers showed delayed recovery compared with that of type-IIb fibers. However, this delayed recovery of type IIa and IIx could not explain the cause of the selective muscle fiber reduction limited to type IIa fibers in BMD mice. Therefore, we considered vascular dysfunction as the reason for the selective type IIa fiber reduction, and we found morphological capillary changes from a “ring pattern” to a “dot pattern” around type IIa fibers in BMD mice. However, the association between selective type IIa fiber reduction and the capillary change around muscle fibers in BMD mice remains unclear due to the lack of information about capillaries around type IIx and IIb fibers. The reviewer pointed out this insufficient evaluation of capillaries around other muscle fibers (except for type IIa fibers), and this suggestion is very helpful for explaining the association between selective type IIa fiber reduction and vascular dysfunction in BMD mice.

Thus, we are planning to:

(1) Evaluate the changes in capillary formation around other muscle fibers, except for type IIa fibers (e.g., type IIx and IIb fibers).

(2) Evaluate the endothelial area around other muscle fibers, except for type IIa fibers.

eLife Assessment

This study analyzes a cohort of small intestine neuroendocrine tumors, and the description of tumor-intrinsic programs that govern such rare cancers is felt to be valuable. The methods are for the most part felt to be solid, however, some broad concerns were raised that the possible separation of samples by a program may be impacted by fresh versus frozen sequencing. Similarly, the heterogeneity of the SiNET tumor microenvironment is unclear given a mixing of subtypes and the proliferation of NE and immune cells in SiNET could be influenced by technical factors. Recommendations were made to extend these data with other published datasets of SiNET tumors, expanding technical clarity and details, and validating findings using cell lines/PDX if available.

Reviewer #1 (Public review):

Summary:

The authors have assembled a cohort of 10 SiNET, 1 SiAdeno, and 1 lung MiNEN samples to explore the biology of neuroendocrine neoplasms. They employ single-cell RNA sequencing to profile 5 samples (siAdeno, SiNETs 1-3, MiNEN) and single-nuclei RNA sequencing to profile seven frozen samples (SiNET 4-10).

They identify two subtypes of siNETs, characterized by either epithelial or neuronal NE cells, through a series of DE analyses. They also report findings of higher proliferation in non-malignant cell types across both subtypes. Additionally, they identify a potential progenitor cell population in a single-lung MiNEN sample.

Strengths:

Overall, this study adds interesting insights into this set of rare cancers that could be very informative for the cancer research community. The team probes an understudied cancer type and provides thoughtful investigations and observations that may have translational relevance.

Weaknesses:

The study could be improved by clarifying some of the technical approaches and aspects as currently presented, toward enhancing the support of the conclusions:

(1) Methods: As currently presented, it is possible that the separation of samples by program may be impacted by tissue source (fresh vs. frozen) and/or the associated sequencing modality (single cell vs. single nuclei). For instance, two (SiNET1 and SiNET2) of the three fresh tissues are categorized into the same subtype, while the third (SiNET9) has very few neuroendocrine cells. Additionally, samples from patient 1 (SiNET1 and SiNET6) are separated into different subtypes based on fresh and frozen tissue. The current text alludes to investigations (i.e.: "Technical effects (e.g., fresh vs. frozen samples) could also impact the capture of distinct cell types, although we did not observe a clear pattern of such bias."), but the study would be strengthened with more detail.

(2) Results:<br /> Heterogeneity in the SiNET tumor microenvironment: It is unclear if the current analysis of intratumor heterogeneity distinguishes the subtypes. It may be informative if patterns of tumor microenvironment (TME) heterogeneity were identified between samples of the same subtype. The team could also evaluate this in an extension cohort of published SiNET tumors (i.e. revisiting additional analyses using the SiNET bulk RNAseq from Alvarez et al 2018, a subset of single-cell data from Hoffman et al 2023, or additional bulk RNAseq validation cohorts for this cancer type if they exist [if they do not, then this could be mentioned as a need in Discussion])

(3) Proliferation of NE and immune cells in SiNETs: The observed proliferation of NE and immune cells in SiNETs may also be influenced by technical factors (including those noted above). For instance, prior studies have shown that scRNA-seq tends to capture a higher proportion of immune cells compared to snRNA-seq, which should be considered in the interpretation of these results. Could the team clarify this element?

(4) Putative progenitors in mixed tumors: As written, the identification of putative progenitors in a single lung MiNEN sample feels somewhat disconnected from the rest of the study. These findings are interesting - are similar progenitor cell populations identified in SiNET samples? Recognizing that ideally additional validation is needed to confidently label and characterize these cells beyond gene expression data in this rare tumor, this limitation could be addressed in a revised Discussion.

Reviewer #2 (Public review):

Summary:

The research identifies two main SiNET subtypes (epithelial-like and neuronal-like) and reveals heterogeneity in non-neuroendocrine cells within the tumor microenvironment. The study validates findings using external datasets and explores unexpected proliferation patterns. While it contributes to understanding SiNET oncogenic processes, the limited sample size and depth of analysis present challenges to the robustness of the conclusions.

Strengths:

The studies effectively identified two subtypes of SiNET based on epithelial and neuronal markers. Key findings include the low proliferation rates of neuroendocrine (NE) cells and the role of the tumor microenvironment (TME), such as the impact of Macrophage Migration Inhibitory Factor (MIF).

Weaknesses:

However, the analysis faces challenges such as a small sample size, lack of clear biological interpretation in some analyses, and concerns about batch effects and statistical significance.

Reviewer #3 (Public review):

Summary:

In this study, the authors set out to profile small intestine neuroendocrine tumors (siNETs) using single-cell/nucleus RNA sequencing, an established method to characterize the diversity of cell types and states in a tumor. Leveraging this dataset, they identified distinct malignant subtypes (epithelial-like versus neuronal-like) and characterized the proliferative index of malignant neuroendocrine cells versus non-malignant microenvironment cells. They found that malignant neuroendocrine cells were far less proliferative than some of their non-malignant counterparts (e.g., B cells, plasma cells, epithelial cells) and there was a strong subtype association such that epithelial-like siNETs were linked to high B/plasma cell proliferation, potentially mediated by MIF signaling, whereas neuronal-like siNETs were correlated with low B/plasma cell proliferation. The authors also examined a single case of a mixed lung tumor (neuroendocrine and squamous) and found evidence of intermediate/mixed and stem-like progenitor states that suggest the two differentiated tumor types may arise from the same progenitor.

Strengths:

The strengths of the paper include the unique dataset, which is the largest to date for siNETs, and the potentially clinically relevant hypotheses generated by their analysis of the data.

Weaknesses:

The weaknesses of the paper include the relatively small number of independent patients (n = 8 for siNETs), lack of direct comparison to other published single-cell NET datasets, mixing of two distinct methods (single-cell and single-nucleus RNA-seq), lack of direct cell-cell interaction analyses and spatially-resolved data, and lack of in vitro or in vivo functional validation of their findings.

The analytical methods applied in this study appear to be appropriate, but the methods used are fairly standard to the field of single-cell omics without significant methodological innovation. As the authors bring forth in the Discussion, the results of the study do raise several compelling questions related to the possibility of distinct biology underlying the epithelial-like and neuronal-like subtypes, the origin of mixed tumors, drivers of proliferation, and microenvironmental heterogeneity. However, this study was not able to further explore these questions through spatially-resolved data or functional experiments.

Author response:

Reviewer #1 (Public review):

Summary:

The authors have assembled a cohort of 10 SiNET, 1 SiAdeno, and 1 lung MiNEN samples to explore the biology of neuroendocrine neoplasms. They employ single-cell RNA sequencing to profile 5 samples (siAdeno, SiNETs 1-3, MiNEN) and single-nuclei RNA sequencing to profile seven frozen samples (SiNET 4-10).

They identify two subtypes of siNETs, characterized by either epithelial or neuronal NE cells, through a series of DE analyses. They also report findings of higher proliferation in non-malignant cell types across both subtypes. Additionally, they identify a potential progenitor cell population in a single-lung MiNEN sample.

Strengths:

Overall, this study adds interesting insights into this set of rare cancers that could be very informative for the cancer research community. The team probes an understudied cancer type and provides thoughtful investigations and observations that may have translational relevance.

Weaknesses:

The study could be improved by clarifying some of the technical approaches and aspects as currently presented, toward enhancing the support of the conclusions:

(1) Methods: As currently presented, it is possible that the separation of samples by program may be impacted by tissue source (fresh vs. frozen) and/or the associated sequencing modality (single cell vs. single nuclei). For instance, two (SiNET1 and SiNET2) of the three fresh tissues are categorized into the same subtype, while the third (SiNET9) has very few neuroendocrine cells. Additionally, samples from patient 1 (SiNET1 and SiNET6) are separated into different subtypes based on fresh and frozen tissue. The current text alludes to investigations (i.e.: "Technical effects (e.g., fresh vs. frozen samples) could also impact the capture of distinct cell types, although we did not observe a clear pattern of such bias."), but the study would be strengthened with more detail.

We thank the reviewer for the thoughtful and constructive review. Due to the difficulty in obtaining enough SiNET samples, we used two platforms to generate data - single cell analysis of fresh samples, and single nuclei analysis of frozen samples. We opted to combine both sample types in our analysis while being fully aware of the potential for batch effects. We therefore agree that this is a limitation of our work, and that differences between samples should be interpreted with caution.

Nevertheless, we argue that the two SiNET subtypes that we have identified are very unlikely to be due to such batch effect. First, the epithelial SiNET subtype was not only detected in two fresh samples but also in one frozen sample (albeit with relatively few cells, as the reviewer correctly noted). Second, and more importantly, the epithelial SiNET subtype was also identified in analysis of an external and much larger cohort of bulk RNA-seq SiNET samples that does not share the issue of two platforms (as seen in Fig. 2f). Moreover, the proportion of samples assigned to the two subtypes is similar between our data and the external data. We therefore argue that the identification of two SiNET subtypes cannot be explained by the use of two data platforms. However, we agree that the results should be further investigated and validated by future studies, as is often done in research on rare tumors.

The reviewer also commented that two samples from the same patient which were profiled by different platforms (SiNET1 and SiNET6) were separated into different subtypes. We would like to clarify that this is not the case, since SiNET6 was not included in the subtype analysis due to too few detected Neuroendocrine cells, and was not assigned to any subtype, as noted in the text and as can be seen by its exclusion from Figure 2 where subtypes are defined. We apologize that our manuscript may have gave the wrong impression about SiNET6 classification (it is labeled in Fig. 4a in a misleading manner). In the revised manuscript, we will correct the labeling in Fig. 4a and clarify that SiNET is not assigned to any subtype. We will further acknowledge the limitation of the two platforms and the arguments in favor of the existence of two SiNET subtypes.

(2) Results:<br /> Heterogeneity in the SiNET tumor microenvironment: It is unclear if the current analysis of intratumor heterogeneity distinguishes the subtypes. It may be informative if patterns of tumor microenvironment (TME) heterogeneity were identified between samples of the same subtype. The team could also evaluate this in an extension cohort of published SiNET tumors (i.e. revisiting additional analyses using the SiNET bulk RNAseq from Alvarez et al 2018, a subset of single-cell data from Hoffman et al 2023, or additional bulk RNAseq validation cohorts for this cancer type if they exist [if they do not, then this could be mentioned as a need in Discussion])

We agree that analysis of an independent cohort will assist in defining the association between TME and the SiNET subtype. However, the sample size required for that is significantly larger than the data available. In the revised manuscript we will note that as a direction for future studies.

(3) Proliferation of NE and immune cells in SiNETs: The observed proliferation of NE and immune cells in SiNETs may also be influenced by technical factors (including those noted above). For instance, prior studies have shown that scRNA-seq tends to capture a higher proportion of immune cells compared to snRNA-seq, which should be considered in the interpretation of these results. Could the team clarify this element?

We agree that different platforms could affect the observed proportions of immune cells, and more generally the proportions of specific cell types. However, the low proliferation of Neuroendocrine cells and the higher proliferation of immune cells (especially B cells, but also T cells and macrophages) is consistently observed in both platforms, as shown in Fig. 4a, and therefore appears to be reliable despite the limitations of our work. We will clarify this consistency in the revised manuscript. 

(4) Putative progenitors in mixed tumors: As written, the identification of putative progenitors in a single lung MiNEN sample feels somewhat disconnected from the rest of the study. These findings are interesting - are similar progenitor cell populations identified in SiNET samples? Recognizing that ideally additional validation is needed to confidently label and characterize these cells beyond gene expression data in this rare tumor, this limitation could be addressed in a revised Discussion.

We agree with this comment and will add the need for additional validation for this finding in the revised Discussion.

Reviewer #2 (Public review):

Summary:

The research identifies two main SiNET subtypes (epithelial-like and neuronal-like) and reveals heterogeneity in non-neuroendocrine cells within the tumor microenvironment. The study validates findings using external datasets and explores unexpected proliferation patterns. While it contributes to understanding SiNET oncogenic processes, the limited sample size and depth of analysis present challenges to the robustness of the conclusions.

Strengths:

The studies effectively identified two subtypes of SiNET based on epithelial and neuronal markers. Key findings include the low proliferation rates of neuroendocrine (NE) cells and the role of the tumor microenvironment (TME), such as the impact of Macrophage Migration Inhibitory Factor (MIF).

Weaknesses:

However, the analysis faces challenges such as a small sample size, lack of clear biological interpretation in some analyses, and concerns about batch effects and statistical significance.

Reviewer #3 (Public review):

Summary:

In this study, the authors set out to profile small intestine neuroendocrine tumors (siNETs) using single-cell/nucleus RNA sequencing, an established method to characterize the diversity of cell types and states in a tumor. Leveraging this dataset, they identified distinct malignant subtypes (epithelial-like versus neuronal-like) and characterized the proliferative index of malignant neuroendocrine cells versus non-malignant microenvironment cells. They found that malignant neuroendocrine cells were far less proliferative than some of their non-malignant counterparts (e.g., B cells, plasma cells, epithelial cells) and there was a strong subtype association such that epithelial-like siNETs were linked to high B/plasma cell proliferation, potentially mediated by MIF signaling, whereas neuronal-like siNETs were correlated with low B/plasma cell proliferation. The authors also examined a single case of a mixed lung tumor (neuroendocrine and squamous) and found evidence of intermediate/mixed and stem-like progenitor states that suggest the two differentiated tumor types may arise from the same progenitor.

Strengths:

The strengths of the paper include the unique dataset, which is the largest to date for siNETs, and the potentially clinically relevant hypotheses generated by their analysis of the data.

Weaknesses:

The weaknesses of the paper include the relatively small number of independent patients (n = 8 for siNETs), lack of direct comparison to other published single-cell NET datasets, mixing of two distinct methods (single-cell and single-nucleus RNA-seq), lack of direct cell-cell interaction analyses and spatially-resolved data, and lack of in vitro or in vivo functional validation of their findings.

The analytical methods applied in this study appear to be appropriate, but the methods used are fairly standard to the field of single-cell omics without significant methodological innovation. As the authors bring forth in the Discussion, the results of the study do raise several compelling questions related to the possibility of distinct biology underlying the epithelial-like and neuronal-like subtypes, the origin of mixed tumors, drivers of proliferation, and microenvironmental heterogeneity. However, this study was not able to further explore these questions through spatially-resolved data or functional experiments.

RIGHTTT

TRUEEE

the reader and author connection (found in memoirs, essays) vs the reader and the characters connection (found in historical accounts, reports, but most especially novels)

difficult questions concerning ethics of writing CNF

Het known-groups comparison design is lastig intern te controleren om de volgende redenen:

Geen willekeurige toewijzing: In tegenstelling tot simulatiestudies worden de groepen in een known-groups comparison niet willekeurig toegewezen aan experimentele condities. In plaats daarvan worden deelnemers geselecteerd op basis van reeds bestaande groepen, zoals mensen die als malingerers of als mensen met een echte stoornis zijn geclassificeerd. Omdat onderzoekers geen controle hebben over welke mensen in welke groep terechtkomen, hebben ze minder invloed op de omstandigheden waarin die groepen worden bestudeerd.

Afhankelijk van bestaande klinische of forensische data: Dit ontwerp vertrouwt sterk op de classificaties die eerder door deskundigen zijn gemaakt, zoals het identificeren van malingerers of mensen met authentieke stoornissen. Als deze initiële classificaties onjuist of onnauwkeurig zijn, kan dat de interne validiteit van de studie aantasten. De onderzoekers hebben weinig controle over hoe deze groepen in het verleden zijn vastgesteld, wat kan leiden tot fouten in de groepstoewijzing.

Beperkte controle over variabelen: Omdat het onderzoek plaatsvindt in een real-world setting (zoals een kliniek of rechtbank), is er minder controle over andere factoren die invloed kunnen hebben op het gedrag van de deelnemers. Dit kunnen variabelen zijn zoals de ernst van de stoornis, persoonlijke omstandigheden of de specifieke juridische situatie. Deze variabelen kunnen moeilijk worden gestandaardiseerd in een known-groups comparison, wat de controle en nauwkeurigheid van het onderzoek beïnvloedt.

Geen manipulatiecontroles: In simulatiestudies kunnen onderzoekers manipulatiecontroles gebruiken om te controleren of deelnemers zich op de juiste manier aan de condities houden (bijvoorbeeld of ze daadwerkelijk proberen te simuleren). In een known-groups comparison is dit niet mogelijk, omdat de groepen op basis van bestaande kenmerken worden vergeleken en er geen directe experimentele manipulatie plaatsvindt.

Zwakke externe validiteit. Mensen worden aangewezen en geïnstrueerd om te gaan malingeren, maar je weet niet of ze minder hun best doen dan dat ze doen in real life als ze bijvoorbeeld zouden malingeren. De omstandigheden waarin deelnemers zich bevinden zijn kunstmatig en missen vaak de emotionele, juridische of financiële druk die mensen in de werkelijkheid kunnen ervaren. In echte situaties, zoals in een rechtszaak of bij een klinisch consult, zijn de gevolgen van malingering vaak veel serieuzer, wat het gedrag van mensen beïnvloedt.

Dit betekent dat bij malingering (opzettelijk simuleren of overdrijven van symptomen), slechts een klein deel van het gedrag zichtbaar is, terwijl het grootste deel verborgen blijft.

Feigning is het opzettelijk fabriceren of overdrijven van symptomen, zonder aannames te doen over de motieven van de persoon. Psychologische tests kunnen worden gebruikt om feigning te identificeren.

Veelgemaakte fout: Te specifiek beschrijven van hoe iemand reageert, zelfs als de gegevens niet duidelijk of tegenstrijdig zijn. Aanbeveling: Werk in twee stappen: eerst algemeen, dan specifiek. Begin met een algemene beschrijving (bijv. "onbetrouwbare bron"). Als dat klopt, kijk dan of er genoeg gegevens zijn om meer gedetailleerd te worden.

Reviewer #1 (Public review):

Summary:

This study evaluates whether species can shift geographically, temporally, or both ways in response to climate change. It also teases out the relative importance of geographic context, temperature variability, and functional traits in predicting the shifts. The study system is large occurrence datasets for dragonflies and damselflies split between two time periods and two continents. Results indicate that more species exhibited both shifts than one or the other or neither, and that geographic context and temp variability were more influential than traits. The results have implications for future analyses (e.g. incorporating habitat availability) and for choosing winner and loser species under climate change. The methodology would be useful for other taxa and study regions with strong community/citizen science and extensive occurrence data.

Strengths:

This is an organized and well-written paper that builds on a popular topic and moves it forward. It has the right idea and approach, and the results are useful answers to the predictions and for conservation planning (i.e. identifying climate winners and losers). There is technical proficiency and analytical rigor driven by an understanding of the data and its limitations.

Weaknesses:

(1) The habitat classifications (Table S3) are often wrong. "Both" is overused. In North America, for example, Anax junius, Cordulia shurtleffii, Epitheca cynosura, Erythemis simplicicollis, Libellula pulchella, Pachydiplax longipennis, Pantala flavescens, Perithemis tenera, Ischnura posita, the Lestes species, and several Enallagma species are not lotic breeding. These species rarely occur let alone successfully reproduce at lotic sites. Other species are arguably "both", like Rhionaeschna multicolor which is mostly lentic. Not saying this would have altered the conclusions, but it may have exacerbated the weak trait effects.

(2) The conservative spatial resolution (100 x 100 km) limits the analysis to wide-ranging and generalist species. There's no rationale given, so not sure if this was by design or necessity, but it limits the number of analyzable species and potentially changes the inference.

(3) The objective includes a prediction about generalists vs specialists (L99-103) yet there is no further mention of this dichotomy in the abstract, methods, results, or discussion.

(4) Key references were overlooked or dismissed, like in the new edition of Dragonflies & Damselflies model organisms book, especially chapters 24 and 27.

Reviewer #2 (Public review):

Summary:

This paper explores a highly interesting question regarding how species migration success relates to phenology shifts, and it finds a positive relationship. The findings are significant, and the strength of the evidence is solid. However, there are substantial issues with the writing, presentation, and analyses that need to be addressed. First, I disagree with the conclusion that species that don't migrate are "losers" - some species might not migrate simply because they have broad climatic niches and are less sensitive to climate change. Second, the results concerning species' southern range limits could provide valuable insights. These could be used to assess whether sampling bias has influenced the results. If species are truly migrating, we should observe northward shifts in their southern range limits. However, if this is an artifact of increased sampling over time, we would expect broader distributions both north and south. Finally, Figure 1 is missed panel B, which needs to be addressed.

Reviewer #3 (Public review):

Summary:

In their article "Range geographies, not functional traits, explain convergent range and phenology shifts under climate change," the authors rigorously investigate the temporal shifts in odonate species and their potential predictors. Specifically, they examine whether species shift their geographic ranges poleward or alter their phenology to avoid extreme conditions. Leveraging opportunistic observations of European and North American odonates, they find that species showing significant range shifts also exhibited earlier phenological shifts. Considering a broad range of potential predictors, their results reveal that geographical factors, but not functional traits, are associated with these shifts.

Strengths:

The article addresses an important topic in ecology and conservation that is particularly timely in the face of reports of substantial insect declines in North America and Europe over the past decades. Through data integration the authors leverage the rich natural history record for odonates, broadening the taxonomic scope of analyses of temporal trends in phenology and distribution to this taxon. The combination of phenological and range shifts in one framework presents an elegant way to reconcile previous findings improving our understanding of the drivers of biodiversity loss.

Weaknesses:

The introduction and discussion of the article would benefit from a stronger contextualization of recent studies on biological responses to climate change and the underpinning mechanism.

The presentation of the results (particularly in figures) should be improved to address the integrative character of the work and help readers extract the main results. While the writing of the article is generally good, particularly the captions and results contain many inconsistencies and lack important detail. With the multitude of the relationships that were tested (the influence of traits) the article needs more coherence.

Primordialism is the idea that nations or ethnic identities are fixed, natural, and ancient.

eLife Assessment

The study by Chen and Phillips provides evidence for a dynamic switch in the small RNA repertoire of the Argonaute protein NRDE-3 during embryogenesis in C. elegans. The work is supported by solid experimental data, although some conclusions regarding the functional role of specific RNA granules remain uncertain. Nevertheless, this study offers valuable insights into RNA regulation and developmental biology, with broader implications for understanding small RNA pathways in other systems.

Reviewer #1 (Public review):

Summary:

Chen and Phillips describe the dynamic appearance of cytoplasmic granules during embryogenesis analogous to SIMR germ granules, and distinct from CSR-1-containing granules, in the C. elegans germline. They show that the nuclear Argonaute NRDE-3, when mutated to abrogate small RNA binding, or in specific genetic mutants, partially colocalizes to these granules along with other RNAi factors, such as SIMR-1, ENRI-2, RDE-3, and RRF-1. Furthermore, NRDE-3 RIP-seq analysis in early vs. late embryos is used to conclude that NRDE-3 binds CSR-1-dependent 22G RNAs in early embryos and ERGO-1-dependent 22G RNAs in late embryos. These data lead to their model that NRDE-3 undergoes small RNA substrate "switching" that occurs in these embryonic SIMR granules and functions to silence two distinct sets of target transcripts - maternal, CSR-1 targeted mRNAs in early embryos and duplicated genes and repeat elements in late embryos.

Strengths:

The identification and function of small RNA-related granules during embryogenesis is a poorly understood area and this study will provide the impetus for future studies on the identification and potential functional compartmentalization of small RNA pathways and machinery during embryogenesis.

Weaknesses:

(1) While the authors acknowledge the following issue, their finding that loss of SIMR granules has no apparent impact on NRDE-3 small RNA loading puts the functional relevance of these structures into question. As they note in their Discussion, it is entirely possible that these embryonic granules may be "incidental condensates." It would be very welcomed if the authors could include some evidence that these SIMR granules have some function; for example, does the loss of these SIMR granules have an effect on CSR-1 targets in early embryos and ERGO-1-dependent targets in late embryos?

(2) The analysis of small RNA class "switching" requires some clarification. The authors re-define ERGO-1-dependent targets in this study to arrive at a very limited set of genes and their justification for doing this is not convincing. What happens if the published set of ERGO-1 targets is used? Further, the NRDE-3 RIP-seq data is used to conclude that NRDE-3 predominantly binds CSR-1 class 22G RNAs in early embryos, while ERGO-1-dependent 22G RNAs are enriched in late embryos. a) The relative ratios of each class of small RNAs are given in terms of unique targets. What is the total abundance of sequenced reads of each class in the NRDE-3 IPs? b) The "switching" model is problematic given that even in late embryos, the majority of 22G RNAs bound by NRDE-3 is in the CSR-1 class (Figure 5D). c) A major difference between NRDE-3 small RNA binding in eri-1 and simr-1 mutants appears to be that NRDE-3 robustly binds CSR-122G RNAs in eri-1 but not in simr-1 in late embryos. This result should be better discussed.

(3) Ultimately, if the switching is functionally important, then its impact should be observed in the expression of their targets. RNA-seq or RT-qPCR of select CSR-1 and ERGO-1 targets should be assessed in nrde-3 mutants during early vs late embryogenesis.

Reviewer #2 (Public review):

Summary:

NRDE-3 is a nuclear WAGO-clade Argonaute that, in somatic cells, binds small RNAs amplified in response to the ERGO-class 26G RNAs that target repetitive sequences. This manuscript reports that, in the germline and early embryos, NRDE-3 interacts with a different set of small RNAs that target mRNAs. This class of small RNAs was previously shown to bind to a different WAGO-clade Argonaute called CSR-1, which is cytoplasmic, unlike nuclear NRDE-3. The switch in NRDE-3 specificity parallels recent findings in Ascaris where the Ascaris NRDE homolog was shown to switch from sRNAs that target repetitive sequences to CSR-class sRNAs that target mRNAs.

The manuscript also correlates the change in NRDE-3 specificity with the appearance in embryos of cytoplasmic condensates that accumulate SIMR-1, a scaffolding protein that the authors previously implicated in sRNA loading for a different nuclear Argonaute HRDE-1. By analogy, and through a set of corelative evidence, the authors argue that SIMR foci arise in embryogenesis to facilitate the change in NRDE-3 small RNA repertoire. The paper presents lots of data that beautifully documents the appearance and composition of the embryonic SIMR-1 foci, including evidence that a mutated NRDE-3 that cannot bind sRNAs accumulates in SIMR-1 foci in a SIMR-1-dependent fashion.

Weaknesses:

The genetic evidence, however, does not support a requirement for SIMR-1 foci: the authors detected no defect in NRDE-3 sRNA loading in simr-1 mutants. Although the authors acknowledge this negative result in the discussion, they still argue for a model (Figure 7) that is not supported by genetic data. My main suggestion is that the authors give equal consideration to other models - see below for specifics.

Reviewer #3 (Public review):

Summary:

Chen and Phillips present intriguing work that extends our view on the C. elegans small RNA network significantly. While the precise findings are rather C. elegans specific there are also messages for the broader field, most notably the switching of small RNA populations bound to an argonaute, and RNA granules behavior depending on developmental stage. The work also starts to shed more light on the still poorly understood role of the CSR-1 argonaute protein and supports its role in the decay of maternal transcripts. Overall, the work is of excellent quality, and the messages have a significant impact.

Strengths:

Compelling evidence for major shift in activities of an argonaute protein during development, and implications for how small RNAs affect early development. Very balanced and thoughtful discussion.

Weaknesses:

Claims on col-localization of specific 'granules' are not well supported by quantitative data.

eLife Assessment

This study presents valuable findings on changes in neuronal alpha activity elicited by prolonged pain in healthy human participants. The evidence supporting the claims of the authors, however, is incomplete and would benefit from clarifications of analytical strategies, additional statistical analyses, and shaping of the interpretations. With the methodological and interpretative parts strengthened, the work will be of interest to neuroscientists investigating the brain mechanisms of pain to identify new approaches to pain treatment

Reviewer #1 (Public review):

Summary:

Furman et al. reanalyze data from a previous study and investigate alterations of peak alpha frequency (PAF) and alpha power (AP) in the context of prolonged pain with electroencephalography (EEG). Using two experimental pain models (phasic and capsaicin heat pain), they set out to clarify if previously reported changes in alpha activity in chronic pain can already be observed during prolonged pain in healthy human participants. They conclude that PAF is reliably slowed, and AP reliably decreased in response to prolonged pain. From the patterns of their findings, they furthermore deduce that AP changes indicate the presence of ongoing pain while PAF changes reflect pain-associated states like sensitization which can outlast ongoing pain percepts and indicate a potential for experiencing pain. Lastly, they conclude that the reported changes in alpha activity are likely due to specific power decreases in the faster alpha range between 10 and 12 Hz and discuss potential clinical implications of their findings in terms of risk biomarkers and early pain interventions.

Strengths:

The study focuses on a timely topic with potential implications for chronic pain diagnosis and treatment, an area that urgently needs new approaches. The addressed questions nicely build upon and extend the previous work of the authors. The analyzed data set is comprehensive including two different prolonged pain paradigms, two visits following the same experimental procedures, and a total sample size of n = 61 participants. Thereby, it enabled internal replications of findings across both paradigms and visits, which is important to confirm the consistency of findings.

Weaknesses:

One overarching difficulty is the high number of analyses presented by the authors. They were in part developed "on the go", are not always easy to follow, and sidetrack the reader from the main findings. Only a minor part of the analyses is described in the methods section, while many analyses are outlined within the results, the supplementary material, and/or figure legends. In addition, a range of purely descriptive findings are displayed. Overall, the manuscript would clearly benefit from a more streamlined and consistent presentation of the applied methods and results.

Concerning the main findings, the presented evidence for a slowing of PAF and a reduction of AP in the context of both phasic and capsaicin heat pain and across both visits is convincing. The location of the peak of the effect at left frontocentral areas, however, remains puzzling. The authors convincingly show that the effect cannot be explained by activity related to the pain rating procedure and provide evidence that an effect of the same direction can also observed at corresponding electrodes contralateral to pain stimulation. However, further reasons are not discussed.

The conclusion that PAF slowing might be more related to pain-associated states like sensitization rather than the presence of ongoing pain is deduced from a continued slowing of PAF after capsaicin-induced pain has subsided, while AP goes back to baseline values. Although this speculation is interesting, the readers should be aware that this dissociation was unexpected and resulted in changes in the main a-priori-defined statistical contrasts presented in the methods section. Further replications in future studies are needed to strengthen this finding.

The last conclusion made by the authors is that the observed changes in alpha activity are caused by specific changes in the faster alpha range and are the least convincing. If I understand correctly, the only presented statistical evidence corroborating this conclusion is based on the single selected electrode C3 shown in Figure 5 A, D, and E. With the remaining parts of Figure 5 and Figure 6, differences are discussed but Figures do not include statistical results. Unless the discussed findings are backed up more clearly, the degree of mechanistic conclusions concerning the 10-12 Hz power changes throughout the title, abstract, and main manuscript and in relation to the multiple oscillators model seems not justified.

Lastly, it is important to note that the current manuscript was published as a preprint in 2021. Thus, the cited literature still needs to be updated, and the present findings need to be integrated with the work published since. For example, a recent systematic review on potential M/EEG-based biomarkers of chronic pain (Zebhauser et al., 2023, Pain) revealed that previous evidence concerning changes of alpha activity in chronic pain is much less consistent than currently outlined in the manuscript.

Overall:

All in all, the presented findings extend previous knowledge concerning the role of alpha activity in pain and thus represent a valuable contribution towards a better understanding of the mechanisms of pain and potential new treatment targets.

Reviewer #2 (Public review):

Summary:

This study investigated the modulation of alpha oscillations, specifically peak alpha frequency (PAF) and alpha power, during prolonged pain. The findings suggest that the alpha rhythm consists of multiple, independent oscillators, and suggest that the modulation of a "fast" oscillator may represent a promising therapeutic target for ongoing pain management.

Strengths:

EEG data were collected from a relatively large sample of participants, and the experiment was conducted using two prolonged pain models - phasic heat pain and capsaicin heat pain - at two separate testing visits approximately 8 weeks apart. The study produced reliable results across different pain models and at different testing intervals.

Weaknesses:

There are discrepancies between the results and their interpretation, indicating a need for more appropriate data analyses. Additionally, the experimental design does not adequately control for the potential time effects, which cannot be ruled out as a confounding factor.

Reviewer #3 (Public review):

Summary:

Furman et al. investigated how exposure to prolonged pain impacts human alpha oscillations recorded by electroencephalography (EEG). Two experimental models of prolonged pain were employed in healthy participants, phasic heat pain (PHP) and capsaicin heat pain (CHP). 61 participants completed two identical study visits separated by at least 8 weeks. Peak alpha frequency was reliably slowed by exposure to prolonged pain, whereas overall alpha power was reliably reduced. Both effects appeared to reflect a specific decrease in higher frequency (10-12Hz) alpha activity. The authors suggest that slowing of alpha oscillations is a reliable neural correlate of pain exposure and that manipulation of alpha activity may hold promise for treating chronic pain.

Strengths:

The study uses a within-participants design to show that exposure to pain is associated with acute changes in both the power and frequency of alpha oscillations.

By employing two experimental models of pain exposure and two separate testing visits, the authors were able to show that the effects of pain exposure on alpha activity are replicable across models and time.

Rigorous analysis approaches are used throughout.

Weaknesses:

No a priori power analysis is presented and (due to exclusions) most of the analyses conducted included (sometimes considerably) fewer participants than the overall sample size.

It is not clear whether the power and frequency changes represent two sides of the same coin or whether they reflect distinct mechanisms. The authors suggest in the manuscript that both effects may be explained by decreased power in 'fast' (8-12 Hz) alpha activity, but at other times interpret the effects to potentially represent distinct mechanisms. It would be useful for the authors to further clarify their thoughts on this point.

The statistical significance of some of the effects was dependent on analysis choices such as the exact frequency range chosen to identify alpha peaks.

No control condition was used, and I was left wondering if the effects would be specific to painful stimuli, or would also see the same effects for pleasant or neutral somatosensory stimuli?

eLife Assessment

The study is a valuable addition to the field, showing how particulate matter may be acting via nociceptor neurons towards neutrophilic asthma exacerbations. The solid evidence for the role of a nociceptive pathway in model systems is relevant to human asthma in its current form but would be further strengthened by mechanistic insights. This would be particularly relevant to further translational research towards blocking the exacerbating effect of air pollution on asthma.

Reviewer #1 (Public review):

Summary:

In the presented study, the authors aim to explore the role of nociceptors in the fine particulate matter (FPM) mediated Asthma phenotype, using rodent models of allergic airway inflammation. This manuscript builds on previous studies, and identify transciptomic reprogramming and an increased sensitivity of the jugular nodose complex (JNC) neurons, one of the major sensory ganglion for the airways, on exposure to FPM along with Ova during the challenge phase. The authors then use OX-314 a selectively permeable form of lidocaine, and TRPV1 knockouts to demonstrate that nociceptor blocking can reduce airway inflammation in their experimental setup.

The authors further identify the presence of Gfra3 on the JNC neurons, a receptor for the protein Artemin, and demonstrate their sensitivity to Artmein as a ligand. They further show that alveolar macrophages release Artemin on exposure to FPM.

Strengths:

The study builds on results available from multiple previous work, and presents important results which allow insights into the mixed phenotypes of Asthma seen clinically. In addition, by identifying the role of nociceptors, they identify potential therapeutic targets which bear high translational potential.

Weaknesses:

While the results presented in the study are highly relevant, there is a need for further mechanistic dissection to allow better inferences. Currently certain results seem assocaitive. Also, certain visualisations and experimental protocols presented in the manuscript need careful assessment and interpretation.

While Asthma is a chronic disease, the presented results are particularly important to explore Asthma exacerbations in response to acute expsoure to air pollutants. This is relevant in today's age of increasing air pollution and increasing global travel.

Reviewer #2 (Public review):

Summary:

The authors sought to investigate the role of nociceptor neurons in the pathogenesis of pollution-mediated neutrophilic asthma.

Strengths:

The authors utilize TRPV1 ablated mice to confirm effects of intranasally administered QX-314 utilized to block sodium currents.

The authors demonstrate that via artemin, which is upregulated in alveolar macrophages in response to pollution, sensitizes JNC neurons thereby increasing their responsiveness to pollution. Ablation or inactivity of nociceptor neurons prevented the pollution induced increase in inflammation.

Weaknesses:

While neutrophilic, the model used doesn't appear to truly recapitulate a Th2/Th17 phenotype. No IL-17A is visible/evident in the BALF fluid within the model. (Figure 3F).

Unclear of the relevance of the RNAseq dataset, none of the identified DEGs were evaluated in the context of mechanism.

The authors overall achieved the aim of demonstrating that nociceptor neurons are important to the pathogenesis of pollution-exacerbated asthma. Their results support their conclusions overall, although there are ways the study findings can be strengthened. This work further evaluates how nociceptor neurons contribute to asthma pathogenesis important for consideration while proposing treatment strategies for undertreated asthma endotypes.

Reviewer #3 (Public review):

Asthma is a complex disease that includes endogenous epithelial, immune, and neural components that respond awkwardly to environmental stimuli. Small airborne particles with diameters in the range of 2.5 micrometers or less, so-called PM2.5, are generally thought to contribute to some forms of asthma. These forms of asthma may have increased numbers of neutrophils and/or eosinophils present in bronchoalveolar lavage fluid and are difficult to treat effectively as they tend to be poorly responsive to steroids. Here, Wang and colleagues build on a recent model that incorporated PM2.5 which was found to have a neutrophilic component. Wang altered the model to provide an extra kick via the incorporation of ovalbumin. Building on their prior expertise linking nociceptors and inflammation, they find that silencing TRPV1-expressing neurons either pharmacologically or genetically, abrogated inflammation and the accumulation of neutrophils. By examining bronchoalveolar lavage fluid, they found not only that levels of the number of cytokines were increased, but also that artemin, a protein that supports neuronal development and function, was elevated, which did not occur in nociceptor-ablated mice. They also found that alveolar macrophages exposed to PM2.5 particles had increased artemin transcription, suggesting a further link between pollutants, and immune and neural interactions.

There are substantial caveats that must be attached to the suggestions by the authors that targeting nociceptors might provide an approach to the treatment of neutrophilic airway inflammation in pollution-driven asthma in general and wildfire-associated respiratory problems in particular. These caveats include the uncertainty of the relevance of the conventional source of PM2.5, to pollution and asthma. According to the National Institute of Standards and Technology (NIST), the standard reference material (SRM) 2786 is a mix obtained from an air intake system in the Czech Republic. It is not clear exactly what is in the mix, and a recent bioRxiv preprint, https://www.biorxiv.org/content/10.1101/2023.08.18.553903v3.full.pdf reveals the presence of endotoxin. Care should thus be taken in interpreting data using particulate matter. Regarding wildfires, there is data that indicates that such exposure is toxic to macrophages. What impact might that then have on the production of cytokines, and artemin, in humans?

eLife Assessment

This important study presents the first identification of the odorant receptor for the trail pheromone in termites. The evidence supporting the conclusions is compelling, with state-of-the-art neurophysiological and genetic methods. The work will be of broad interest in multiple disciplines, such as entomology, chemical ecology, and sensory physiology.

Reviewer #1 (Public review):

Summary:

In their comprehensive analysis Diallo et al. deorphanise the first olfactory receptor of a non-hymenopteran eusocial insect - a termite and identified the well-established trail pheromone neocembrene as the receptor's best ligand. By using a large set of odorants the authors convincingly show that, as expected for a pheromone receptor, PsimOR14 is very narrowly tuned. While the authors first make use of an ectopic expression system, the empty neuron of Drosophila melanogaster, to characterise the receptor's responses, they next perform single sensillum recordings with different sensilla types on the termite antenna. By that, they are able to identify a sensillum that houses three neurons, of which the B neuron exhibits the narrow responses described for PsimOR14. Hence the authors do not only identify the first pheromone receptor in a termite but can even localize its expression on the antenna. The authors in addition perform a structural analysis to explain the binding properties of the receptor and its major and minor ligands (as this is beyond my expertise, I cannot judge this part of the manuscript). Finally, they compare expression patterns of ORs in different castes and find that PsimOR14 is more strongly expressed in workers than in soldier termites, which corresponds well with stronger antennal responses in the worker caste.

Strengths:

The manuscript is well-written and a pleasure to read. The figures are beautiful and clear. I actually had a hard time coming up with suggestions.

Weaknesses:

Whenever it comes to the deorphanization of a receptor and its potential role in behaviour (in the case of the manuscript it would be trail-following of the termite) one thinks immediately of knocking out the receptor to check whether it is necessary for the behaviour. However, I definitely do not want to ask for this (especially as the establishment of CRISPR Cas-9 in eusocial insects usually turns out to be a nightmare). I also do not know either, whether knockdowns via RNAi have been established in termites, but maybe the authors could consider some speculation on this in the discussion.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors performed the functional analysis of odorant receptors (ORs) of the termite Prorhinotermes simplex to identify the receptor of trail-following pheromone. The authors performed single-sensillum recording (SSR) using the transgenic Drosophila flies expressing a candidate of the pheromone receptor and revealed that PsimOR14 strongly responds to neocembrene, the major component of the pheromone. Also, the authors found that one sensillum type (S I) detects neocembrene and also performed SSR for S I in wild termite workers. Furthermore, the authors revealed the gene, transcript, and protein structures of PsimOR14, predicted the 3D model and ligand docking of PsimOR14, and demonstrated that PsimOR14 is higher expressed in workers than soldiers using RNA-seq for heads of workers and soldiers of P. simplex and that EAG response to neocembrene is higher in workers than soldiers. I consider that this study will contribute to further understanding of the molecular and evolutionary mechanisms of the chemoreception system in termites.

Strength:

The manuscript is well written. As far as I know, this study is the first study that identified a pheromone receptor in termites. The authors not only present a methodology for analyzing the function of termite pheromone receptors but also provide important insights in terms of the evolution of ligand selectivity of termite pheromone receptors.

Weakness:

As you can see in the "Recommendations to the Authors" section below, there are several things in this paper that are not fully explained about experimental methods. Except for this point, this paper appears to me to have no major weaknesses.

Reviewer #3 (Public review):

Summary:

Chemical communication is essential for the organization of eusocial insect societies. It is used in various important contexts, such as foraging and recruiting colony members to food sources. While such pheromones have been chemically identified and their function demonstrated in bioassays, little is known about their perception. Excellent candidates are the odorant receptors that have been shown to be involved in pheromone perception in other insects including ants and bees but not termites. The authors investigated the function of the odorant receptor PsimOR14, which was one of four target odorant receptors based on gene sequences and phylogenetic analyses. They used the Drosophila empty neuron system to demonstrate that the receptor was narrowly tuned to the trail pheromone neocembrene. Similar responses to the odor panel and neocembrene in antennal recordings suggested that one specific antennal sensillum expresses PsimOR14. Additional protein modeling approaches characterized the properties of the ligand binding pocket in the receptor. Finally, PsimOR14 transcripts were found to be significantly higher in worker antennae compared to soldier antennae, which corresponds to the worker's higher sensitivity to neocembrene.

Strengths:

The study presents an excellent characterization of a trail pheromone receptor in a termite species. The integration of receptor phylogeny, receptor functional characterization, antennal sensilla responses, receptor structure modeling, and transcriptomic analysis is especially powerful. All parts build on each other and are well supported with a good sample size.

Weaknesses:

The manuscript would benefit from a more detailed explanation of the research advances this work provides. Stating that this is the first deorphanization of an odorant receptor in a clade is insufficient. The introduction primarily reviews termite chemical communication and deorphanization of olfactory receptors previously performed. Although this is essential background, it lacks a good integration into explaining what problem the current study solves.

Selecting target ORs for deorphanization is an essential step in the approach. Unfortunately, the process of choosing these ORs has not been described. Were the authors just lucky that they found the correct OR out of the 50, or was there a specific selection process that increased the probability of success?

The authors assigned antennal sensilla into five categories. Unfortunately, they did not support their categories well. It is not clear how they were able to differentiate SI and SII in their antennal recordings.

The authors used a large odorant panel to determine receptor tuning. The panel included volatile polar compounds and non-volatile non-polar hydrocarbons. Usually, some heat is applied to such non-volatile odorants to increase volatility for receptor testing. It is unclear how it is possible that these non-volatile compounds can reach the tested sensilla without heat application.

eLife Assessment

This valuable study builds on previous work by the authors by presenting a potentially key method for correcting optical aberrations in GRIN lens-based micro endoscopes used for imaging deep brain regions. By combining simulations and experiments, the authors show that the obtained field of view is significantly increased with corrected, versus uncorrected microendoscopes. The evidence supporting the claims of the authors is solid, although some aspects of the manuscript should be clarified and missing information provided. Because the approach described in this paper does not require any microscope or software modifications, it can be readily adopted by neuroscientists who wish to image neuronal activity deep in the brain.

Reviewer #1 (Public review):

Summary:

Sattin, Nardin, and colleagues designed and evaluated corrective microlenses that increase the useable field of view of two long (>6mm) thin (500 um diameter) GRIN lenses used in deep-tissue two-photon imaging. This paper closely follows the thread of earlier work from the same group (e.g. Antonini et al, 2020; eLife), filling out the quiver of available extended-field-of-view 2P endoscopes with these longer lenses. The lenses are made by a molding process that appears practical and easy to adopt with conventional two-photon microscopes.

Simulations are used to motivate the benefits of extended field of view, demonstrating that more cells can be recorded, with less mixing of signals in extracted traces, when recorded with higher optical resolution. In vivo tests were performed in the piriform cortex, which is difficult to access, especially in chronic preparations.

The design, characterization, and simulations are clear and thorough, but not exhaustive (see below), and do not break new ground in optical design or biological application. However, the approach shows much promise, including for applications not mentioned in the present text such as miniaturized GRIN-based microscopes. Readers will largely be interested in this work for practical reasons: to apply the authors' corrected endoscopes.

Strengths:

The text is clearly written, the ex vivo analysis is thorough and well-supported, and the figures are clear. The authors achieved their aims, as evidenced by the images presented, and were able to make measurements from large numbers of cells simultaneously in vivo in a difficult preparation.

Weaknesses:

(1) The novelty of the present work over previous efforts from the same group is not well explained. What needed to be done differently to correct these longer GRIN lenses?

(2) Some strong motivations for the method are not presented. For example, the introduction (page 3) focuses on identifying neurons with different coding properties, but this can be done with electrophysiology (albeit with different strengths and weaknesses). Compared to electrophysiology, optical methods more clearly excel at genetic targeting, subcellular measurements, and molecular specificity; these could be mentioned. Another example, in comparing microfabricated lenses to other approaches, an unmentioned advantage is miniaturization and potential application to mini-2P microscopes, which use GRIN lenses.

(3) Some potentially useful information is lacking, leaving critical questions for potential adopters:

How sensitive is the assembly to decenter between the corrective optic and the GRIN lens? What is the yield of fabrication and of assembly?

Supplementary Figure 1: Is this really a good agreement between the design and measured profile? Does the figure error (~10 um in some cases on average) noticeably degrade the image? How do individual radial profiles compare to the presented means?<br /> What is the practical effect of the strong field curvature? Are the edges of the field, which come very close to the lens surface, a practical limitation?

The lenses appear to be corrected for monochromatic light; high-performance microscopes are generally achromatic. Is the bandwidth of two-photon excitation sufficient to warrant optimization over multiple wavelengths?

GRIN lenses are often used to access a 3D volume by scanning in z (including in this study). How does the corrective lens affect imaging performance over the 3D field of view?

(4) The in vivo images (Figure 7D) have a less impressive resolution and field than the ex vivo images (Figure 4B), and the reason for this is not clear. Given the difference in performance, how does this compare to an uncorrected endoscope in the same preparation? Is the reduced performance related to uncorrected motion, field curvature, working distance, etc? Regarding Figure 7, there is no analysis of the biological significance of the calcium signals or even a description of where olfactory stimuli were presented. The timescale of jGCaMP8f signals in Figure 7E is uncharacteristically slow for this indicator (compared to Zhang et al 2023 (Nature)), though perhaps this is related to the physiology of these cells or the stimuli.

(5) The claim of unprecedented spatial resolution across the FOV (page 18) is hard to evaluate and is not supported by references to quantitative comparisons. The promises of the method for future studies (pages 18-19) could also be better supported by analysis or experiment, but these are minor and to me, do not detract from the appeal of the work.

(6) The text is lengthy and the material is repeated, especially between the introduction and conclusion. Consolidating introductory material to the introduction would avoid diluting interesting points in the discussion.

Reviewer #2 (Public review):

In this manuscript, the authors present an approach to correct GRIN lens aberrations, which primarily cause a decrease in signal-to-noise ratio (SNR), particularly in the lateral regions of the field-of-view (FOV), thereby limiting the usable FOV. The authors propose to mitigate these aberrations by designing and fabricating aspherical corrective lenses using ray trace simulations and two-photon lithography, respectively; the corrective lenses are then mounted on the back aperture of the GRIN lens.

This approach was previously demonstrated by the same lab for GRIN lenses shorter than 4.1 mm (Antonini et al., eLife, 2020). In the current work, the authors extend their method to a new class of GRIN lenses with lengths exceeding 6 mm, enabling access to deeper brain regions as most ventral regions of the mouse brain. Specifically, they designed and characterized corrective lenses for GRIN lenses measuring 6.4 mm and 8.8 mm in length. Finally, they applied these corrected long micro-endoscopes to perform high-precision calcium signal recordings in the olfactory cortex.

Compared with alternative approaches using adaptive optics, the main strength of this method is that it does not require hardware or software modifications, nor does it limit the system's temporal resolution. The manuscript is well-written, the data are clearly presented, and the experiments convincingly demonstrate the advantages of the corrective lenses.

The implementation of these long corrected micro-endoscopes, demonstrated here for deep imaging in the mouse olfactory bulb, will also enable deep imaging in larger mammals such as rats or marmosets.

Reviewer #3 (Public review):

Summary:

This work presents the development, characterization, and use of new thin microendoscopes (500µm diameter) whose accessible field of view has been extended by the addition of a corrective optical element glued to the entrance face. Two micro endoscopes of different lengths (6.4mm and 8.8mm) have been developed, allowing imaging of neuronal activity in brain regions >4mm deep. An alternative solution to increase the field of view could be to add an adaptive optics loop to the microscope to correct the aberrations of the GRIN lens. The solution presented in this paper does not require any modification of the optical microscope and can therefore be easily accessible to any neuroscience laboratory performing optical imaging of neuronal activity.

Strengths:

(1) The paper is generally clear and well-written. The scientific approach is well structured and numerous experiments and simulations are presented to evaluate the performance of corrected microendoscopes. In particular, we can highlight several consistent and convincing pieces of evidence for the improved performance of corrected micro endoscopes:<br /> a) PSFs measured with corrected micro endoscopes 75µm from the centre of the FOV show a significant reduction in optical aberrations compared to PSFs measured with uncorrected micro endoscopes.<br /> b) Morphological imaging of fixed brain slices shows that optical resolution is maintained over a larger field of view with corrected micro endoscopes compared to uncorrected ones, allowing neuronal processes to be revealed even close to the edge of the FOV.<br /> c) Using synthetic calcium data, the authors showed that the signals obtained with the corrected microendoscopes have a significantly stronger correlation with the ground truth signals than those obtained with uncorrected microendoscopes.

(2) There is a strong need for high-quality micro endoscopes to image deep brain regions in vivo. The solution proposed by the authors is simple, efficient, and potentially easy to disseminate within the neuroscience community.

Weaknesses:

(1) Many points need to be clarified/discussed. Here are a few examples:

a) It is written in the methods: « The uncorrected microendoscopes were assembled either using different optical elements compared to the corrected ones or were obtained from the corrected probes after the mechanical removal of the corrective lens. »<br /> This is not very clear: the uncorrected microendoscopes are not simply the unmodified GRIN lenses?

b) In the results of the simulation of neuronal activity (Figure 5A, for example), the neurons in the center of the FOV have a very large diameter (of about 30µm). This should be discussed. Also, why is the optical resolution so low on these images?

c) It seems that we can't see the same neurons on the left and right panels of Figure 5D. This should be discussed.

d) It is not very clear to me why in Figure 6A, F the fraction of adjacent cell pairs that are more correlated than expected increases as a function of the threshold on peak SNR. The authors showed in Supplementary Figure 3B that the mean purity index increases as a function of the threshold on peak SNR for all micro endoscopes. Therefore, I would have expected the correlation between adjacent cells to decrease as a function of the threshold on peak SNR. Similarly, the mean purity index for the corrected short microendoscope is close to 1 for high thresholds on peak SNR: therefore, I would have expected the fraction of adjacent cell pairs that are more correlated than expected to be close to 0 under these conditions. It would be interesting to clarify these points.

e) Figures 6C, H: I think it would be fairer to compare the uncorrected and corrected endomicroscopes using the same effective FOV.

f) Figure 7E: Many calcium transients have a strange shape, with a very fast decay following a plateau or a slower decay. Is this the result of motion artefacts or analysis artefacts? Also, the duration of many calcium transients seems to be long (several seconds) for GCaMP8f. These points should be discussed.

g) The authors do not mention the influence of the neuropil on their data. Did they subtract the neuropil's contribution to the signals from the somata? It is known from the literature that the presence of the neuropil creates artificial correlations between neurons, which decrease with the distance between the neurons (Grødem, S., Nymoen, I., Vatne, G.H. et al. An updated suite of viral vectors for in vivo calcium imaging using intracerebral and retro-orbital injections in male mice. Nat Commun 14, 608 (2023). https://doi.org/10.1038/s41467-023-36324-3; Keemink SW, Lowe SC, Pakan JMP, Dylda E, van Rossum MCW, Rochefort NL. FISSA: A neuropil decontamination toolbox for calcium imaging signals. Sci Rep. 2018 Feb 22;8(1):3493. doi: 10.1038/s41598-018-21640-2. PMID: 29472547; PMCID: PMC5823956)<br /> This point should be addressed.

h) Also, what are the expected correlations between neurons in the pyriform cortex? Are there measurements in the literature with which the authors could compare their data?

(2) The way the data is presented doesn't always make it easy to compare the performance of corrected and uncorrected lenses. Here are two examples:

a) In Figures 4 to 6, it would be easier to compare the FOVs of corrected and uncorrected lenses if the scale bars (at the centre of the FOV) were identical. In this way, the neurons at the centre of the FOV would appear the same size in the two images, and the distances between the neurons at the centre of the FOV would appear similar. Here, the scale bar is significantly larger for the corrected lenses, which may give the illusion of a larger effective FOV.

b) In Figures 3A-D it would be more informative to plot the distances in microns rather than pixels. This would also allow a better comparison of the micro endoscopes (as the pixel sizes seem to be different for the corrected and uncorrected micro endoscopes).

(3) There seems to be a discrepancy between the performance of the long lenses (8.8mm) in the different experiments, which should be discussed in the article. For example, the results in Figure 4 show a considerable enlargement of the FOV, whereas the results in Figure 6 show a very moderate enlargement of the distance at which the person's correlation with the first ground truth emitter starts to drop.

a) There is also a significant discrepancy between measured and simulated optical performance, which is not discussed. Optical simulations (Figure 1) show that the useful FOV (defined as the radius for which the size of the PSF along the optical axis remains below 10µm) should be at least 90µm for the corrected microendoscopes of both lengths. However, for the long microendoscopes, Figure 3J shows that the axial resolution at 90µm is 17µm. It would be interesting to discuss the origin of this discrepancy: does it depend on the microendoscope used? Are there inaccuracies in the construction of the aspheric corrective lens or in the assembly with the GRIN lens? If there is variability between different lenses, how are the lenses selected for imaging experiments?

eLife Assessment

The study provides valuable insight into the biological significance of SARS-CoV-2 by using a series of computational analyses of viral proteins. While evidence is solid, it is obscured by a lack of clarity about the objectives of the analyses and in the overall writing of the article. The study will be impactful to the researchers in the field but will benefit from improved presentation.

Reviewer #1 (Public review):

Summary:

Park et al. conducted various analyses attempting to elucidate the biological significance of SARS-CoV-2 mutations. However, the study lacks a clear objective. The specific goals of the analyses in each subsection are unclear, as is how the results from these subsections are interconnected. Compiling results from unrelated analyses into a single paper can be confusing for readers. Clarifying the objective and narrowing down the topics would make the paper's purpose clearer.

The logic of the study is also unclear. For instance, the authors developed an evaluation score, APESS, for analyzing viral sequences. Although they state that the APESS score correlates with viral infectivity, there is no explanation in the results section about why this is the case.

The structure of the paper should be reconsidered.

Reviewer #2 (Public review):

Summary:

The authors have developed a machine learning tool AIVE to predict the infectivity of SARS-CoV-2 variants and also a scoring metric to measure infectivity. A large number of virus sequences were used with a very detailed analysis that incorporates hydrophobic, hydrophilic, acid, and alkaline characteristics. The protein structures were also considered to measure infectivity and search for core mutations. The study especially focused on the S protein of SARS-CoV-2. The contents of this study would be of interest to many researchers related to this area and the web service would be helpful to easily analyze such data without in-depth bioinformatics expertise.

Strengths:

- Analysis of large-scale data.

- Experimental validation on a partial set of searched mutations.

- A user-friendly web-based analysis platform that is made public.

Weaknesses:

- Complexity of the research.

eLife Assessment

This important paper employs multiple experimental approaches and presents evidence that changes in membrane voltage directly affect ERK signaling to regulate cell division. This result is relevant because it supports an ion channel-independent pathway by which changes in membrane voltage can affect cell growth. The reviewers point out that some experimental results and interpretations are compelling, but the strength of evidence is incomplete and additional experiments are needed to rule out other possible interpretations of the data.

Reviewer #1 (Public review):

Summary:

This is a contribution to the field of developmental bioelectricity. How do changes of resting potential at the cell membrane affect downstream processes? Zhou et al. reported in 2015 that phosphatidylserine and K-Ras cluster upon plasma membrane depolarization and that voltage-dependent ERK activation occurs when constitutive active K-RasG12V mutants are overexpressed. In this paper, the authors advance the knowledge of this phenomenon by showing that membrane depolarization up-regulates mitosis and that this process is dependent on voltage-dependent activation of ERK. ERK activity's voltage-dependence is derived from changes in the dynamics of phosphatidylserine in the plasma membrane and not by extracellular calcium dynamics.

Strengths:

Bioelectricity is an important field for areas of cell, developmental, and evolutionary biology, as well as for biomedicine. Confirmation of ERK as a transduction mechanism, and a characterization of the molecular details involved in control of cell proliferation, is interesting and impactful.

Weaknesses:

The functional cell division data need to be stronger. They show that increasing K+ increases proliferation and argue that since a MEK inhibitor (U0126) reduces proliferation in K+ treated cells, K+ induces cell division via ERK. But I don't see statistics to show that the rescue is significant, and I don't see a key U0126-only control. If the U0126 alone reduces proliferation, the combined effect wouldn't prove much.

Also, unless I'm missing something, it looks like every sample in their control has exactly the same number of mitotic cells. I understand that they are normalizing to this column, but shouldn't they be normalizing to the mean, with the independent values scattering around 1? It doesn't seem like it can be paired replicates since there are 6 replicates in the control and 4 replicates in one of the conditions?

Reviewer #2 (Public review):

Sasaki et al. use a combination of live-cell biosensors and patch-clamp electrophysiology to investigate the effect of membrane potential on the ERK MAPK signaling pathway, and probe associated effects on proliferation. This is an effect that has long been proposed, but convincing demonstration has remained elusive, because it is difficult to perturb membrane potential without disturbing other aspects of cell physiology in complex ways. The time-resolved measurements here are a nice contribution to this question, and the perforated patch clamp experiments with an ERK biosensor are fantastic - they come closer to addressing the above difficulty of perturbing voltage than any prior work. It would have been difficult to obtain these observations with any other combination of tools.

However, there are still some concerns as detailed in specific comments below:

Specific comments:<br /> (1) All the observations of ERK activation, by both high extracellular K+ and voltage clamp, could be explained by cell volume increase (more discussion in subsequent comments). There is a substantial literature on ERK activation by hypotonic cell swelling (e.g. https://doi.org/10.1042/bj3090013, https://doi.org/10.1002/j.1460-2075.1996.tb00938.x, among others). Here are some possible observations that could demonstrate that ERK activation by volume change is distinct from the effects reported here:<br /> (i) Does hypotonic shock activate ERK in U2OS cells?<br /> (ii) Can hypotonic shock activate ERK even after PS depletion, whereas extracellular K+ cannot?<br /> (iii) Does high extracellular K+ change cell volume in U2OS cells, measured via an accurate method such as fluorescence exclusion microscopy?<br /> (iv) It would be helpful to check the osmolality of all the extracellular solutions, even though they were nominally targeted to be iso-osmotic.

(2) Some more details about the experimental design and the results are needed from Figure 1:<br /> (i) For how long are the cells serum-starved? From the Methods section, it seems like the G1 release in different K+ concentration is done without serum, is this correct? Is the prior thymidine treatment also performed in the absence of serum?<br /> (ii) There is a question of whether depolarization constitutes a physiologically relevant mechanism to regulate proliferation, and how depolarization interacts with other extracellular signals that might be present in an in vivo context. Does depolarization only promote proliferation after extended serum starvation (in what is presumably a stressed cell state)? What fraction of total cells are observed to be mitotic (without normalization), and how does this compare to the proliferation of these cells growing in serum-supplemented media? Can K+ concentration tune proliferation rate even in serum-supplemented media?

(3) In Figure 2, there are some possible concerns with the perfusion experiment:<br /> (i) Is the buffer static in the period before perfusion with high K+, or is it perfused? This is not clear from the Methods. If it is static, how does the ERK activity change when perfused with 5 mM K+? In other words, how much of the response is due to flow/media exchange versus change in K+ concentration?<br /> (ii) Why do there appear to be population-average decreases in ERK activity in the period before perfusion with high K+ (especially in contrast to Fig. 3)? The imaging period does not seem frequent enough for photobleaching to be significant.

(4) Figure 3 contains important results on couplings between membrane potential and MAPK signaling. However, there are a few concerns:<br /> (i) Does cell volume change upon voltage clamping? Previous authors have shown that depolarizing voltage clamp can cause cells to swell, at least in the whole-cell configuration: https://www.cell.com/biophysj/fulltext/S0006-3495(18)30441-7 . Could it be possible that the clamping protocol induces changes in ERK signaling due to changes in cell volume, and not by an independent mechanism?<br /> (ii) Does the -80 mV clamp begin at time 0 minutes? If so, one might expect a transient decrease in sensor FRET ratio, depending on the original resting potential of the cells. Typical estimates for resting potential in HEK293 cells range from -40 mV to -15 mV, which would reach the range that induces an ERK response by depolarizing clamp in Fig. 3B. What are the resting potentials of the cells before they are clamped to -80 mV, and why do we not see this downward transient?

(5) The activation of ERK by perforated voltage clamp and by high extracellular K+ are each convincing, but it is unclear whether they need to act purely through the same mechanism - while additional extracellular K+ does depolarize the cell, it could also be affecting function of voltage-independent transporters and cell volume regulatory mechanisms on the timescales studied. To more strongly show this, the following should be done with the HEK cells where there is already voltage clamp data:<br /> (i) Measure resting potential using the perforated patch in zero-current configuration in the high K+ medium. Ideally this should be done in the time window after high K+ addition where ERK activation is observed (10-20 minutes) to minimize the possibility of drift due to changes in transporter and channel activity due to post-translational regulation.<br /> (ii) Measure YFP/CFP ratio of the HEK cells in the high K+ medium (in contrast to the U2OS cells from Fig. 2 where there is no patch data).<br /> (iii) The assertion that high K+ is equivalent to changes in Vmem for ERK signaling would be supported if the YFP/CFP change from K+ addition is comparable to that induced by voltage clamp to the same potential. This would be particularly convincing if the experiment could be done with each of the 15 mM, 30 mM, and 145 mM conditions.

(6) Line 170: "ERK activity was reduced with a fast time course (within 1 minute) after repolarization to -80 mV." I don't see this in the data: in Fig. 3C, it looks like ERK remains elevated for > 10 min after the electrical stimulus has returned to -80 mV

Reviewer #3 (Public review):

Summary:

This paper demonstrates that membrane depolarization induces a small increase in cell entry into mitosis. Based on previous work from another lab, the authors propose that ERK activation might be involved. They show convincingly using a combination of assays that ERK is activated by membrane depolarization. They show this is Ca2+ independent and is a result of activation of the whole K-Ras/ERK cascade which results from changed dynamics of phosphatidylserine in the plasma membrane that activates K-Ras. Although the activation of the Ras/ERK pathway by membrane depolarization is not new, linking it to an increase in cell proliferation is novel.

Strengths

A major strength of the study is the use of different techniques - live imaging with ERK reporters, as well as Western blotting to demonstrate ERK activation as well as different methods for inducing membrane depolarization. They also use a number of different cell lines. Via Western blotting the authors are also able to show that the whole MAPK cascade is activated.

Weaknesses

A weakness of the study is the data in Figure 1 showing that membrane depolarization results in an increase of cells entering mitosis. There are very few cells entering mitosis in their sample in any condition. This should be done with many more cells to increase confidence in the results. The study also lacks a mechanistic link between ERK activation by membrane depolarization and increased cell proliferation.

The authors did achieve their aims with the caveat that the cell proliferation results could be strengthened. The results for the most part support the conclusions.

This work suggests that alterations in membrane potential may have more physiological functions than action potential in the neural system as it has an effect on intracellular signalling and potentially cell proliferation.

Don't oil the segment.

Interesting that Kasten recommends against oiling the carriage rails of portables and standard machines which run on ball bearings.

dry cleaning solvents in 1941 were likely Varsol or Stoddard's Formula.

compare to trichloroethane<br /> https://hypothes.is/a/EyBIAFXAEe-AcP-Atlj_aQ

Note discontinuation of carbon tetrachloride<br /> https://hypothes.is/a/bfdi_I90Ee-OQLN0HpsE7Q

toothpick as a typewriter tool

测试

https://en.wikipedia.org/wiki/Carbon_tetrachloride

Carbon tetrachloride or carbon tet is a non-flammable, dense, colorless liquid which was often used as a cleaning agent in the mid 1900s, but was phased out due to safety and environmental concerns. High exposure can affect the central nervous system and cause damage to the liver and kidneys. Prolonged exposure can be fatal.

fdsf

Hints for a Happy Typewriter<br /> Bryan Kravitz, Nancy Gorrell, 1983<br /> https://typewriterdatabase.com/1983-Hints4HappyTypewriter.index.manual

Some good, basic home care and use from 1983. Home mechanics in 2024 are probably capable of a bit more without the backstop of a typewriter mechanic.

This guide suggest the use of solvents like alcohol or trichloroethane for cleaning type slugs and internals. Note that trichloroethane manufacture and use has diminished significantly since 1996 when it was identified by the Montreal Protocol as a contributor to ozone depletion.

This is the very conflation that zionist make in their representation of israel as the expression of all "jewish collectivity." Are zionists therefore in violation of IHRA?

The author of this "working definition," Kenneth Stern, has made plain that the legal formalization of IHRA is an "attack on academic freedom and free speech," and that IHRA should never have been adopted as "campus hate speech code."

What is the actual purpose of this qualifier? Who decides what kind or class of criticism is similar to another? Leveled by whom?

The organization or the definition?

Why "any other country"? Not all countries are genocidal, settler-colonial, apartheid states + the biggest recipient of U.S. military aid. Israel and the U.S. are acting in ways not replicated by any other states currently, so deserve singling out.

This is dog-whistle Islamophobia - "religious extremism" as a cause of Jew-hatred is an animating tenet of anti-Muslim racism, particularly since the War on Terror.

While a legitimate example of antisemitism, this example has been used to shield Israel and Zionists from criticism of their influence of these institutions. E.g. On p.3 of the Academic Engagement Network's 2022 Guide and Resource Book (https://academicengagement.org/2022-guide/), the BDS movement is described in these terms: "...the BDS movement's vehement anti-Zionist and anti-Israel rhetoric, campaigns, and programming often end up trafficking in centuries-old conspiracies, tropes, and canards about Jewish power, greed, and undue influence."

The framing offers another way to put forward the case for the “collective Jew.”

This framing continues the harmful Zionist logic that exceptionalizes the Nazi holocaust of Jews as a single most horrible atrocity while denying the magnitude and significance of other genocides and atrocities. It also resists a structural analysis of colonialism, imperialism, and racism that would illuminate connections between the atrocities committed by Nazis and those perpetrated by Zionists and other colonial powers/regimes.

Up until recently, the legislative Jewish caucus in California had the Israeli flag as its banner image. how is such an image supposed to be read?

Makes self-determination synonymous with statehood which is 1) an absurd proposition and 2) ignores the fact that Zionism denies Palestinian self-determination in any form. It also presumes that describing a state as racist threatens its existence. if that were the case, no modern nation-state would exist.

While a legitimate example of antisemitism, this accusation has been hurled to discredit investigative accounts of actual war crimes committed by Israel. * E.g. “Israel accused the ICJ of blood libel” (https://www.theguardian.com/world/2023/dec/29/south-africa-accuses-israel-of-committing-genocide-in-gaza); “Tales of infanticide have stoked hatred of Jews for centuries. They echo still today” (https://www.theguardian.com/commentisfree/2024/oct/06/tales-of-infanticide-have-stoked-hatred-of-jews-for-centuries-they-echo-still-today).

Ignores the fact that Israel is not a democracy.

This sentence highlights the importance of focusing your research by narrowing your topic, which helps you find more specific and relevant information for a deeper, more effective project.

This sentence stresses the importance of choosing the right keywords when conducting online searches, as they help you find the most relevant information for your research.

I like the ideas of "to do" tables. I'd put them at the front instead though. Also maybe include fewer detail to de-densify the whole operation.

Lab report requirements will be interlaced throughout the assignment, with a comprehensive check off table at the end.

CUT

Maybe we write the graphing for them or otherwise simplify this sign off, but this "do X task and plot it" style of sign off doesn't always serve a learning objective and generally takes a lot of time.

These would all be separated into their own code blocks with function requirements and demo instructions in text blocks above

This sign off was a pain. It would have been easier to check if students made their robot do a little show.

Sign off 3: * Demonstrate servo_jp() by moving between two joint positions * Demonstrate interpolate_jp() by moving between the same two positions over 10 seconds * Interpolate between XXXX and YYYY over 20 seconds while printing out measured_js * Interpolate between YYYY and XXXX over 20 seconds while printing out setpoint_js * Interpolate between XXXX and YYYY over 20 seconds while printing out goal_js

Completely out of place! This should come BEFORE the instructions on what functions to write. Students should be following these directions WHILE they write those functions. I'm moving this to sign-off 2 territory.

This sign off is perfect as is--maybe it's so simple it's not even necessary. Run the provided code, show me that your robot moves in an arc, that's all I need to see.

Honestly I don't even know if there are any comprehension questions I can add that align with the LOs of the course. This is just a logistical step.

I might upgrade this "important" to a "warning: do NOT modify these files unless told". We're reworking the files to "accident-proof" the robots a bit, and we'd rather students not disable our protections.

Pre-labs are going to be a big part of at least one or two of these rewritten labs. Might be worth having a section dedicated to reminding students what they did (or should have done).

Ok, this is where the procedure actually begins. Little bit deceiving that it's signified with some bold + underline 12pt text and not a proper heading.

Important information. I'd be more likely to read it if it were a figure caption

This paragraph is redundant and tautological

No, this is not important. If students are messing with the file these IDs are used in (even in the current version of the lab), they've seriously screwed up.

This lab talks a lot of talk about teamwork, but is absolutely toothless when it comes to measuring student achievement in it! Major alignment issue.

I like this. The rewrite will have different objectives, but likely a similar objectives section.

Good! This is how I'd introduce the lab.

In re: "Team programming": This LO needs to be HAMMERED wayyyyy harder than it's being treated by this lab doc. We need to introduce measurable objectives (e.g. commit quotas, branch requirements, reflections on who did what that reference commit IDs).

This is essentially a list of all course objectives. I agree that this should be presented in the first lab to give a roadmap, but this list doesn't provide me with much information about how these topics relate to one another and why they are in this course together.

Overall comments: This section is all over the place. We bounce from "what we will be doing in this lab" to "specific robot details" to "litany of course objectives; most of which are not addressed in this lab"

This paragraph gives high level lab goals. Unfortunately, it fails to highlight any learning objective of the course! The rewrite will have a paragraph that serves the same purpose, but it will have a higher focus on LOs.

These two paragraphs introduce the robotic arm used in the class. A little more detail than is truly necessary.

The sentence expresses a student's frustration about having to include other people's ideas before sharing their own opinion, asking why they can't just skip that and state their view directly.

The original case?

Explain how Snow learned about the agent, the host, and the environment. The Agent: The white "Rice" particles that he identified in the water but also humans stool The Host: Humans who harbored the disease Environment: Using the same contaminated water source

He found the source, the particles that were also found in the stools of the victims

How he came to the conclusion about how it spread

He assumed this was the cause of the disease

The agent: the cause of the disease

The host: the organisms that harbors the disease

The environment: the factors that cause or allow the transmission of disease [not just the physical environment, but also the social environment (actions/behaviors of people)]

Outbreak of chloera caused the investigation

reply to u/ArousedByApostasy at https://old.reddit.com/r/Zettelkasten/comments/1g8diq4/any_books_about_how_someone_used_zettelkasten_to/

If you're suffering from the delusion (and many do) that Zettelkasten is only about Luhmann and his own writing and 4-5 recent books on the topic, you're only lacking creativity and some research skills. Seemingly Luhmann has lots of good PR, particularly since 2013, but this doesn't mitigate the fact that huge swaths of the late 1800s to the late 1900s are chock-a-block full of books produced by these methods. Loads of examples exist under other names prior to that including florilegia, commonplace books, the card system, card indexes, etc.

Your proximal issue is that the scaffolding used to write all these books is generally invisible because authors rarely, if ever, talk about their methods and as a result, they're hard to "see". This doesn't mean that they don't exist.

I've got a list of about 50+ books about the topic of zettelkasten or incredibly closely related methods dating back to 1548 if you want to peruse some: https://www.zotero.org/groups/4676190/tools_for_thought/collections/V9RPUCXJ/tags/note%20taking%20manuals/items/F8WSEABT/item-list

There are a variety of examples of people's note collections that you can see in various media and compare to their published output. I've collected several dozens of examples, many of which you can find here: https://boffosocko.com/research/zettelkasten-commonplace-books-and-note-taking-collection/

Interesting examples to get you started:

• Vladimir Nabokov's estate published copies of his index cards for the novel The Original of Laura which you can purchase and read in its index card format. You can find a copy of his index card diary as Insomniac Dreams from Princeton University Press: https://press.princeton.edu/books/paperback/9780691196909/insomniac-dreams
• S.D. Goitein - researchers on the Cairo Geniza still use his note collection to produce new scholarship; though he had 1/3 the number of note cards compared to Luhmann, his academic writing output was 3 times larger. If you dig around you can find a .pdf copy of his collection of almost 30,000 notes and compare it to his written work.
• There's a digitized collection of W. Ross Ashby's notes (in notebook and index card format) which you can use to cross reference his written books and articles. https://ashby.info/
• Wittgenstein had a well-known note collection which underpinned his works (as well as posthumous works). See: Wittgenstein, Ludwig. Zettel. Edited by Gertrude Elizabeth Margaret Anscombe and Georg Henrik von Wright. Translated by Gertrude Elizabeth Margaret Anscombe. Second California Paperback Printing. 1967. Reprint, Berkeley and Los Angeles, California: University of California Press, 2007.
• Roland Barthes had a significant collection from which he both taught and wrote; His notes following his mother's death can be read in the book Morning Diary which were published as index card-based notes.
• The Marbach exhibition in 2013 explored six well-known zettelkasten (including Luhmann's): Gfrereis, Heike, and Ellen Strittmatter. Zettelkästen: Maschinen der Phantasie. 1st edition. Marbach am Neckar: Deutsche Schillerges, 2013. https://www.amazon.de/-/en/Heike-Gfrereis/dp/3937384855/.
• Philosopher John Locke wrote a famous treatise on indexing commonplace books which underlay his own commonplacing and writing work: Locke, John, 1632-1704. A New Method of Making Common-Place-Books. 1685. Reprint, London, 1706. https://archive.org/details/gu_newmethodmaki00lock/mode/2up.
• Historian Jacques Barzun, a professor, dean and later provost at Columbia, not only wrote dozens of scholarly books, articles, and essays out of his own note collection, but also wrote a book about some of the process in a book which has over half a dozen editions: Barzun, Jacques, and Henry F. Graff. The Modern Researcher. New York, Harcourt, Brace, 1957. http://archive.org/details/modernreseracher0000unse. In his private life, he also kept a separate shared zettelkasten documenting the detective fiction which he read and was a fan. From this he produced A Catalogue of Crime: Being a Reader's Guide to the Literature of Mystery, Detection, and Related Genres (with Wendell Hertig Taylor). 1971. Revised edition, Harper & Row, 1989: ISBN 0-06-015796-8.
• Erasmus, Agricola, and Melanchthon all wrote treatises which included a variation of the note taking methods which were widely taught in the late 1500s at universities and other schools.
• The Jonathan Edwards Center at Yale has a digitized version of his note collection called the Miscellanies that you can use to cross reference his written works.
• A recent example I've come across but haven't mentioned to others until now is that of Barrett Wendell, a professor at Harvard in the late 1800s, taught composition using a zettelkasten or card system method.
• Director David Lynch used a card index method for writing and directing his movies based on the method taught to him by Frank Daniel, a dean at the American Film Institute.
• Mortimer J. Adler et al. created a massive group zettelkasten of western literature from which they wrote volumes 2 and 3 (aka The Syntopicon) of the Great Books of the Western World. See: https://forum.zettelkasten.de/discussion/2623/mortimer-j-adlers-syntopicon-a-topically-arranged-collaborative-slipbox
• Before he died, historian Victor Margolin made a YouTube video of how he wrote the massive two volume World History of Design which included a zettelkasten workflow: https://www.youtube.com/watch?v=Kxyy0THLfuI
• Martin Luther King, Jr. kept a zettelkasten which is still extant and might allow you to reference his notes to his written words.
• The Brothers Grimm used a zettelkasten method (though theirs was slips nailed to a wall) to create The Deutsches Wörterbuch (The German Dictionary that preceeded the Oxford Dictionary). The DWB was begun in 1838 by Jacob Grimm and Wilhelm Grimm who worked on it through the letter F prior to their deaths. The dictionary project was ended in 1961 after 123 years of work which resulted in 16 volumes. A further 17th source volume was released in 1971.
• Here's an interesting video of Ryan Holliday's method condensed over time: https://www.youtube.com/watch?v=dU7efgGEOgk
• Because Halloween is around the corner, I'll even give you a published example of death by zettelkasten described by Nobel Prize winner Anatole France in one of his books: https://boffosocko.com/2022/10/24/death-by-zettelkasten/

If you dig in a bit you can find and see the processes of others like Anne Lamott, Gottfried Wilhelm Leibniz, Bob Hope, Michael Ende, Twyla Tharp, Kate Grenville, Marcel Mauss, Claude Lévi-Strauss, Phyllis Diller, Carl Linnaeus, Beatrice Webb, Isaac Newton, Harold Innis, Joan Rivers, Umberto Eco, Georg Christoph Lichtenberg, Raymond, Llull, George Carlin, and Eminem who all did variations of this for themselves for a variety of output types.

These barely scratch the surface of even Western intellectual history much less other cultures which have broadly similar methods (including oral cultures). If you do a bit of research into any major intellectual, you're likely to uncover a similar underlying method of work.

While there are some who lionize Luhmann, he didn't invent or even perfect these methods, but is just a drop of water in a vast sea of intellectual history.

And how did I write this short essay response? How do I have all these examples to hand? I had your same question years ago and read and researched my way into an answer. I have both paper and digital zettelkasten from which to query and write. I don't count my individual paper slips of which there are over 15,000 now, but my digital repository is easily over 20,000 (though only 19K+ are public).

I hope you manage to figure out some version of the system for yourself and manage to create something interesting and unique out of it. It's not a fluke and it's not "just a method for writing material about zettelkasten itself".

interesting -- not the history of the fact of homosexuality, but the identity category

Is there a way to address this issue in the robots without trashing them? Is this a training Ai issue? Either way, this must be addressed.

I have always found it interesting that the government has a say/controls specifically how elders are treated in a family. In our western culture there is no specific requirement for how elders are cared for.

How is it looking now? What impact did COVID have on this timeline?

If the cost of robot care is similar to an elders home, what would people choose?