Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors conducted an important study that explored an innovative regenerative treatment for pediatric craniofacial bone loss, with a particular focus on investigating the impacts of JAGGED1 (JAG1) signaling.

Strengths:

Building on their prior research involving the effect of JAG1 on murine cranial neural crest cells, the authors demonstrated successful bone regeneration in an in vivo murine bone loss model with a critically-sized cranial defect, where they delivered JAG1 with pediatric human bone-derived osteoblast-like cells in the hydrogel. Additionally, their findings unveiled a crucial mechanism wherein JAG1 induces pediatric osteoblast commitment and bone regeneration through the phosphorylation of p70 S6K. This discovery offers a promising avenue for potential treatment, involving targeted delivery of JAG1 and activation of downstream p70 s6K, for pediatric craniofacial bone loss. Overall, the experimental design is appropriate, and the results are clearly presented.

Reviewer #2 (Public Review):

The current manuscript undoubtedly demonstrates that JAG1 can induced osteogenesis via non-canonical signaling. In fact, using the mouse-calvarial critical defect model, the authors have clearly shown the anabolic regenerative effect of JAG1 in via non-canonical pathways. Exploring the molecular mechanisms, the authors have shown that non-canonically JAG1 is regulating multiple pathways including STAT5, AKT, P38, JNK, NF-ĸB, and p70 S6K, which together possibly culminate to the activation of p70 S6K. In summary these findings have significant implications in designing new approaches for bone regenerative research.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1)  Regarding the cell studies of human pediatric bone-derived osteoblast-like cells (HBO), the authors should provide a rationale for their selection of specific cell lines (15,16, 17, 19, 20, 23, 24) in this study. As for animal studies, could the authors clarify which cell lines were utilized in the murine in vivo experiments?

We appreciate the opportunity to address this. To reduce confusion, we have numbered the patient primary cell lines used in these studies sequentially from 1 – 7. Additionally, we have added “HBO cell lines used for experiments were selected based on the ability of the primary cell line to proliferate and mineralize in culture” to the Methods section. 

In vivo experiments: “HBO cell lines 2, 6 and 7 from separate individuals were selected for these experiments based on similar growth and passage characteristics.” This statement is included in the Methods section.

(2)  In this study, the authors performed the murine in vivo experiments using both male and female mice. Could the author clarify if any difference was observed between male and female mice in the findings? This information would contribute to a more comprehensive understanding of the study.

We agree and have added the following to the Results section: “There was no sex-based difference in regenerated bone volume.”

(3)  Although the histological results showed an elevated collagen expression in mice treated with BMP2, JAG1, and JAG1 + DAPT compared to those treated with the cells alone, the differences among groups were subtle. The authors should consider the immunohistochemical (IHC) staining for collagen 1 on the samples, allowing for a quantitative assessment of collagen 1 expression.

Thank you for this comment. The differences between BMP2, JAG1, and JAG1 + DAPT are indeed subtle. We have added Supplementary Figure 5, showing collagen staining of sections from the same FFPE blocks that were sectioned and stained with Masson Trichrome in Figure 2C. 

Minor Comments:

(4)  Please specify which cell lines are represented in the staining results shown in Fig.1A and Fig. 5A, respectively.

In Fig 1A the representative images are of HBO2. Fig 5A representative images are of HBO7. We have added this information to the figure legends for these figures. 

(5)  There appears to be a discrepancy in the specified size of the critical defect. The manuscript states that the size is 4mm, while Supplemental Figure 3 indicates 3.5mm.

Thank you for this catch! Yes, it should be 4mm. This has been corrected in Supplementary Figure 3.

(6)  The scale bar for Figure 2 C is missing.

Scale bars have been added which also gave us an opportunity to brighten the images equally, allowing for better distinction between the different colors of the Masson Trichrome staining.

(7)  In the methodological section 2.5 for JAG1 delivery, it would be helpful if the authors could review the initial dosage of JAG1 delivery to confirm if HBO cells were included or not, given that the MicroCT results indicate that all groups incorporated HBO cells. 

We appreciate this suggestion. In response to another question, we have added Supplementary Figure 4 which includes an “Empty Defect” condition with no HBO cells, making the original method statement accurate.

Reviewer #2 (Recommendations For The Authors):

In the current study, using in vitro and in vivo models the authors clearly show that JAG1 can enhance osteogenesis and thus can be helpful in designing new therapeutic approaches in the field of bone regenerative research. The in vivo mouse CF model is very convincing and shows that JAG1 promotes osteogenesis via non-canonical signaling. Mechanistically it seems that JAG1 activates STAT5, AKT, P38, JNK, NF-ĸB, and p70 S6K. However, additional evidence is needed to convincingly conclude that all the non-canonical pathways activated via JAG1 converge at p70 S6K activation. The following concerns need to be addressed.

(1) In Fig 1A: Even though the Jag1-Fc shows a very significant increase in HBO mineralization, there are no significant increases in cells in osteogenic media when compared to control growth media. Even though the different conditions were subjected to RNAseq analysis in the later figures, qPCR analysis of some osteogenic genes in Figure 1 might be helpful. 

We appreciate the opportunity to explore this question further. We conducted mineralization experiments in triplicate and performed qRT-PCR, assessing for gene expression of 5 osteogenic genes: ALPL, BGLAP (osteocalcin), COL1A1, RUNX2, and SP7. Results are shown in Figure 1C and this text was added to Results: “Additionally, PCR analysis of HBO1 cells from a repeat experiment collected at days 7, 14, and 21 showed significantly increased expression of osteogenic genes with JAG1-bds stimulation (Figure 1C). ALPL was significantly expressed at Day 7, with a 3.5-fold increase (p=0.0004) compared to HBO1 cells grown in growth media. In contrast, significant expression levels of COL1A1 and BGLAP were observed at 14 days, with a 5.1-fold increase (p=0.0021) of COL1A1 and a 12.3-fold increase (0.0002) of BGLAP when compared to growth media conditions. Interestingly, while some mineralization is observed in the osteogenic media and Fc-bds

(Figure 1A) conditions, there were no significant increases in osteogenic gene expression (Figure

1C). Expression of RUNX2 and SP7 was not significantly altered across all conditions and time points (not shown).”

(2) In Fig 2: even though not needed in respect to the hypothesis, was there any Control group without any cells or JAG1 beads? What were the changes in between that group and cells cells-only group?

We have not observed differences between the “Empty Defect” group and the “Cells alone” group.

We have addressed the reviewer’s comments by adding this comparison in Supplementary Figure 4.

(3) Transcriptional profiling and ELISA (Fig 3 and 4) show upregulation of NF-ĸB signaling in response to JAG1. In the discussion, the authors have referenced a previous study showing NF-ĸB as prosurvival in human OB cells. However, based on many published reports, NF-ĸB activation has been shown to inhibit OB function. Does JAG1 regulate HBO cell survival via NF-ĸB activation?

Experimenting using NF-ĸB inhibitor can be helpful to show that JAG1 mediates NF-ĸB activation is anabolic in this experimental setup.

We thank the reviewer for this excellent suggestion. We are eager to explore this new direction for our research in a subsequent study. We have added this to our future directions. 

(4) Fig 5: 

(A)  Condition showing JAG1+ DAPT is needed to compare between JAG1 canonical and noncanonical signaling. 

Thank you for pointing this out. We have added Supplementary Figure 6, which includes a dose response experiment for JAG1 + DAPT.

(B)  S6K18 alone seems to be increasing OB mineralization. Is that statistically significant?  

No, and we have added the statistical analysis for S6K-18 to Figure 5B.

(C)  Fc alone condition seems to have a very significant increase in OB mineralization. Does Fc alone upregulate OB function? 

We do see some upregulation of mineralization with Fc in vitro, which we also observed in our previous studies with mouse neural crest cells, but we have not found it to be osteogenic in vivo. We have added a statement to this effect, with references. Additionally, osteogenic gene expression was not upregulated in our in vitro mineralization experiments with Fc.  See Revised Figure 1.

(D)  Although overall quantification shows that S6K18 partially inhibits HBO mineralization, the representative images do not represent the quantification. Transcriptional analysis (qPCR) is required to validate these findings.

We performed qRT-PCR on cells from a repeat mineralization assay, collecting cells at 9, 14, and 21 days. We have added the following to the Results:” While inhibition of NOTCH and p70 S6K decreased mineralization in our mineralization assay, there are no statistically significant changes in gene expression for ALPL, COL1A1, or BGLAP (Supplementary Figure 7). These results suggest that the HBO cells phenotypes are maturing into osteocytes and that inhibiting p70 S6K hinders the cellular ability to mineralize but not the cell phenotype progression.”

(5) Finally, to convincingly conclude the data from Fig 5, the mouse CF model can be helpful to support the authors' claim that JAG1 acts via p70 S6K.

Thank you for this feedback. We have modified our conclusions to reflect that p70 S6K is one of the non-canonical pathways that JAG1 may be activating in bone regeneration.

Thank you very much for your consideration of our revised manuscript.

eLife Assessment

This study provides convincing evidence that the quality of research in female-dominated fields of research is systematically undervalued by the research community. The authors' findings are based on analyses of data from a research assessment exercise in New Zealand and data on funding success rates in Australia, Canada, the European Union and the United Kingdom. This work is an important contribution to the discourse on gender biases in academia, underlining the pervasive influence of gender on whole fields of research, as well as on individual researchers.

Reviewer #3 (Public Review):<br /> This study seeks to investigate one aspect of disparity in academia: how gender balance in a discipline is valued in terms of evaluated research quality score and funding success. This is important in understanding disparities within academia.<br /> This study uses publicly available data to investigate covariation between gender balance in an academic discipline and:<br /> individual research quality scores of New Zealand academics as evaluated by one of 14 broader subject panels.<br /> [ii] funding success in Australia, Canada, Europe, UK.

The authors have addressed the concerns I had about the original version

eLife assessment

This useful manuscript describes a proteomic analysis of plasma from subjects before and after an exercise regime consisting of endurance and resistance exercise. The work identifies a putative new exerkine, CD300LG, and finds associations of this protein with aspects of insulin sensitivity and angiogenesis. The characterization remains incomplete at present. Because CD300LG may have a transmembrane domain, one possibility is that exercise causes the release of extracellular vesicles containing this protein. As this study reports associations, additional studies will be needed to establish causality. The paper will hopefully prompt further studies to more fully elucidate the underlying biology.

Reviewer #1 (Public Review):

Summary:

In this paper, proteomics analysis of the plasma of human subjects that underwent an exercise training regime consisting of a combination of endurance and resistance exercise led to the identification of several proteins that were responsive to exercise training. Confirming previous studies, many exercise-responsive secreted proteins were found to be involved in the extra-cellular matrix. The protein CD300LG was singled out as a potential novel exercise biomarker and the subject of numerous follow-up analyses. The levels of CD300LG were correlated with insulin sensitivity. The analysis of various open-source datasets led to the tentative suggestion that CD300LG might be connected with angiogenesis, liver fat, and insulin sensitivity. CD300LG was found to be most highly expressed in subcutaneous adipose tissue and specifically in venular endothelial cells. In a subset of subjects from the UK Biobank, serum CD300LG levels were positively associated with several measures of physical activity - particularly vigorous activity. In addition, serum CD300LG levels were negatively associated with glucose levels and type 2 diabetes. Genetic studies hinted at these associations possibly being causal. Mice carrying alterations in the CD300LG gene displayed impaired glucose tolerance, but no change in fasting glucose and insulin. Whether the production of CD300LG is changed in the mutant mice is unclear.

Strengths:

The specific proteomics approach conducted to identify novel proteins impacted by exercise training is new. The authors are resourceful in the exploitation of existing datasets to gain additional information on CD300LG.

Weaknesses:

While the analyses of multiple open-source datasets are necessary and useful, they lead to relatively unspecific correlative data that collectively insufficiently advance our knowledge of CD300LG and merely represent the starting point for more detailed investigations. Additional more targeted experiments of CD300LG are necessary to gain a better understanding of the role of CD300LG and the mechanism by which exercise training may influence CD300LG levels. One should also be careful to rely on external data for such delicate experiments as mouse phenotyping. Can the authors vouch for the quality of the data collected?

Reviewer #2 (Public Review):

Summary:

This manuscript from Lee-Odegard et al reports proteomic profiling of exercise plasma in humans, leading to the discovery of CD300LG as a secreted exercise-inducible plasma protein. Correlational studies show associations of CD300LG with glycemic traits. Lastly, the authors query available public data from CD300LG-KO mice to establish a causal role for CD300LG as a potential link between exercise and glucose metabolism. However, the strengths of this manuscript were balanced by the moderate to major weaknesses. Therefore in my opinion, while this is an interesting study, the conclusions remain preliminary and are not fully supported by the experiments shown so far.

Strengths:

(1) Data from a well-phenotyped human cohort showing exercise-inducible increases in CD300LG.

(2) Associations between CD300LG and glucose and other cardiometabolic traits in humans, that have not previously been reported.

(3) Correlation to CD300LG mRNA levels in adipose provides additional evidence for exercise-inducible increases in CD300LG.

Weaknesses:

(1) CD300LG is by sequence a single-pass transmembrane protein that is exclusively localized to the plasma membrane. How CD300LG can be secreted remains a mystery. More evidence should be provided to understand the molecular nature of circulating CD300LG. Is it full-length? Is there a cleaved fragment? Where is the epitope where the o-link is binding to CD300LG? Does transfection of CD300LG to cells in vitro result in secreted CD300LG?

(2) There is a growing recognition of specificity issues with both the O-link and somalogic platforms. Therefore it is critical that the authors use antibodies, targeted mass spectrometry, or some other methods to validate that CD300LG really is increased instead of just relying on the O-link data.

(3) It is insufficient simply to query the IMPC phenotyping data for CD300LG; the authors should obtain the animals and reproduce or determine the glucose phenotypes in their own hands. In addition, this would allow the investigators to answer key questions like the phenotype of these animals after a GTT, whether glucose production or glucose uptake is affected, whether insulin secretion in response to glucose is normal, effects of high-fat diet, and other standard mouse metabolic phenotyping assays.

(4) I was unable to find the time point at which plasma was collected at the 12-week time point. Was it immediately after the last bout of exercise (an acute response) or after some time after the training protocol (trained state)?

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this paper, proteomics analysis of the plasma of human subjects that underwent an exercise training regime consisting of a combination of endurance and resistance exercise led to the identification of several proteins that were responsive to exercise training. Confirming previous studies, many exercise-responsive secreted proteins were found to be involved in the extra-cellular matrix. The protein CD300LG was singled out as a potential novel exercise biomarker and the subject of numerous follow-up analyses. The levels of CD300LG were correlated with insulin sensitivity. The analysis of various open-source datasets led to the tentative suggestion that CD300LG might be connected with angiogenesis, liver fat, and insulin sensitivity. CD300LG was found to be most highly expressed in subcutaneous adipose tissue and specifically in venular endothelial cells. In a subset of subjects from the UK Biobank, serum CD300LG levels were positively associated with several measures of physical activity - particularly vigorous activity. In addition, serum CD300LG levels were negatively associated with glucose levels and type 2 diabetes. Genetic studies hinted at these associations possibly being causal. Mice carrying alterations in the CD300LG gene displayed impaired glucose tolerance, but no change in fasting glucose and insulin. Whether the production of CD300LG is changed in the mutant mice is unclear.

Strengths:

The specific proteomics approach conducted to identify novel proteins impacted by exercise training is new. The authors are resourceful in the exploitation of existing datasets to gain additional information on CD300LG.

Weaknesses:

While the analyses of multiple open-source datasets are necessary and useful, they lead to relatively unspecific correlative data that collectively insufficiently advance our knowledge of CD300LG and merely represent the starting point for more detailed investigations. Additional more targeted experiments of CD300LG are necessary to gain a better understanding of the role of CD300LG and the mechanism by which exercise training may influence CD300LG levels. One should also be careful to rely on external data for such delicate experiments as mouse phenotyping. Can the authors vouch for the quality of the data collected. 

Thank you for the valuable feedback on our manuscript. We recognize concerns about the specificity of correlative data from open-source datasets and the limitations it presents for understanding CD300LG's role. To address this, we have expanded the manuscript with a paragraph in the discussion regarding the need of targeted experiments confirm CD300LG’s functions and relationship with glucose metabolism. We also emphazise caution regarding external data reliance and we acknowledge the need for generating primary data including direct phenotyping of mice with CD300LG gene alterations to better understand its regulatory mechanisms and effects on glucose tolerance. Please see lines 446-456.

Reviewer #2 (Public Review):

Summary:

This manuscript from Lee-Odegard et al reports proteomic profiling of exercise plasma in humans, leading to the discovery of CD300LG as a secreted exercise-inducible plasma protein. Correlational studies show associations of CD300LG with glycemic traits. Lastly, the authors query available public data from CD300LG-KO mice to establish a causal role for CD300LG as a potential link between exercise and glucose metabolism. However, the strengths of this manuscript were balanced by the moderate to major weaknesses. Therefore in my opinion, while this is an interesting study, the conclusions remain preliminary and are not fully supported by the experiments shown so far.

Strengths:

(1) Data from a well-phenotyped human cohort showing exercise-inducible increases in CD300LG.

(2) Associations between CD300LG and glucose and other cardiometabolic traits in humans, that have not previously been reported.

(3) Correlation to CD300LG mRNA levels in adipose provides additional evidence for exercise-inducible increases in CD300LG.

Weaknesses:

(1) CD300LG is by sequence a single-pass transmembrane protein that is exclusively localized to the plasma membrane. How CD300LG can be secreted remains a mystery. More evidence should be provided to understand the molecular nature of circulating CD300LG. Is it full-length? Is there a cleaved fragment? Where is the epitope where the o-link is binding to CD300LG? Does transfection of CD300LG to cells in vitro result in secreted CD300LG?

(2) There is a growing recognition of specificity issues with both the O-link and somalogic platforms. Therefore it is critical that the authors use antibodies, targeted mass spectrometry, or some other methods to validate that CD300LG really is increased instead of just relying on the O-link data.

(3) It is insufficient simply to query the IMPC phenotyping data for CD300LG; the authors should obtain the animals and reproduce or determine the glucose phenotypes in their own hands. In addition, this would allow the investigators to answer key questions like the phenotype of these animals after a GTT, whether glucose production or glucose uptake is affected, whether insulin secretion in response to glucose is normal, effects of high-fat diet, and other standard mouse metabolic phenotyping assays.

(4) I was unable to find the time point at which plasma was collected at the 12-week time point. Was it immediately after the last bout of exercise (an acute response) or after some time after the training protocol (trained state)?

We acknowledge the importance of understanding the molecular form of CD300LG in circulation. We have expanded the discussion with a paragraph regarding the need of follow-up experiments on whether circulating CD300LG is full-length or a cleaved fragment, to identify the epitope for O-link binding, and assess CD300LG secretion in vitro through transfection experiments. We also discuss the need of targeted mass spectrometry and antibody-based validation of O-link measurements of CD300LG, and the need for more validation experiments on CD300LG-deficient mice. Please see lines 446-456.

The plasma collected post-intervention is in a state that reflects the new baseline trained condition of the subjects, 3 days after the last exercise session during the intervention. We have clarified this in our manuscript. The information is updated in line 491-493.

Reviewer #1 (Recommendations For The Authors):

In the present form, the paper raises interest in the potential role of CD300LG in the response to exercise training but unfortunately does not provide clear answers. The authors should focus their efforts on firmly validating the status of CD300LG as an exercise biomarker in humans and carefully examine the function of CD300LG through mechanistic and animal-based studies.

The authors are encouraged to acquire CD300LG-deficient mice and perform specific experiments to validate hypotheses forthcoming from the analysis of the open-source datasets. In addition, it needs to be validated that the cd300lgtm1a(KOMP)Wtsi mice are actually deficient in CD300LG. It is not uncommon that Tm1a mice have (almost) normal expression of the targeted gene.

We have now revised the manuscript and added a new section to the discussion regarding the limitations with open-source data, cd300lgtm1a(KOMP)Wtsi mice and the need for more validation experiments on CD300LG-deficient mice. Please see lines 446-456.

The value of the correlative data presented in Figure 5 is rather limited. The same can be argued for the data presented in Supplementary Figure 2. If CD300LG is expressed in endothelial cells, it stands to reason that its expression is correlated with angiogenesis. Hence, this observation does not really carry any additional value.

We agree that correlations cannot imply causality. However, similar patterns were observed in several tissues and across different data sets, which at least suggest a role CD300LG related to angiogesis. We have included a section in the discussion were we clarify that our observations should only be regarded as indications and that follow-up studies are needed to confirm any causal role for CD300LG on angiogenesis/oxidativ capacity. Please see lines 446-456.

Figure 6 may be better accommodated in the supplement.

Figure 6 is now moved to the supplement.

Figure 3A and B are a bit awkward. The description "no overlap" is confusing. Isn't it more accurate to say "no enrichment" or "no over-representation"? There will always be some overlap with certain pathways. However, there may be no enrichment. Furthermore, the use of arrows to indicate No overlap is visually not very appealing. Maybe the numbers can be given a specific color?

We have now removed the arrows and text, and rather stated in the text that there were no enrichements other than for the proteins down-regulated in the overweight group.

The description of the figure legend of figure 5E-H is incomplete.

The description is now completed.

eLife assessment

This study provides useful insights into inter- and intra-site B cell receptor repertoire heterogeneity, noting that B cell clones from the tumour interact more with their draining lymph node than with the blood and that there is less mutation/expansion/activation of B cell clones in tumours. Unfortunately, the main claims are incomplete and only partially supported. The work could be of interest to an audience including medical biologists/immunologists and computational biologists across cancer specialities.

Reviewer #3 (Public Review):

In multiple cancers, the key roles of B cells are emerging in the tumor microenvironment (TME). The authors of this study appropriately introduce that B cells are relatively under-characterised in the TME and argue correctly that it is not known how the B cell receptor (BCR) repertoires across tumor, lymph node and peripheral blood relate. The authors therefore supply a potentially useful study evaluating the tumor, lymph node and peripheral blood BCR repertoires and site-to-site as well as intra-site relationships. The authors employ sophisticated analysis techniques, although the description of the methods is incomplete.

Major strengths:

(1) The authors provide a unique analysis of BCR repertoires across tumor, dLN, and peripheral blood. The work provides useful insights into inter- and intra-site BCR repertoire heterogeneity. While patient-to-patient variation is expected, the findings with regard to intra-tumor and intra-dLN heterogeneity with the use of fragments from the same tissue are of importance, contribute to the understanding of the TME, and will inform future study design.

(2) A particular strength of the study is the detailed CDR3 physicochemical properties analysis which leads the authors to observations that suggest a less-specific BCR repertoire of TIL-B compared to circulating B cells.

Concerns and comments on current version:

The revision has improved the manuscript but, in my opinion, remains inadequate. While most of my requested changes have been made, I do not see an expansion of Fig1A legend to incorporate more details about the analysis. Lacking details of methodology was a concern from all reviewers. Similarly, the 'fragmented' narrative was a concern of all reviewers. These matters have not been dealt with adequately enough - there are parts of the manuscript which remain fragmented and confusing. The narrative and analysis does not explain how the plasma cell bias has been dealt with adequately and in fact is simply just confusing. There is a paragraph at the beginning of the discussion re the plasma cell bias, which should be re-written to be clearer and moved to have a prominent place early in the results. Why are these results not properly presented? They are key for interpretation of the manuscript. Furthermore, the sorted plasma cell sequencing analysis also has only been performed on two patients. Another issue is that some disease cohorts are entirely composed of patients with metastasis, some without but metastasis is not mentioned. Metastasis has been shown to impact the immune landscape.

A reviewer brought up a concern about the overlap analysis and I also asked for an explanation on why this F2 metric chosen. Part of the rebuttal argues that another metric was explored showing similar results, thus conclusion reached is reasonable. Remarkably, these data are not only omitted from the manuscript, but is not even provided for the reviewers.

This manuscript certainly includes some interesting and useful work. Unfortunately, a comprehensive re-write was required to make the work much clearer and easier to understand and this has not been realised.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors attempt to fully characterize the immunoglobulin (Ig) heavy (H) chain repertoire of tumor-infiltrating B cells from three different cancer types by identifying the IgH repertoire overlap between these, their corresponding draining lymph nodes (DLNs), and peripheral B cells. The authors claim that B cells from tumors and DLNs have a closer IgH profile than those in peripheral blood and that DLNs are differentially involved with tumor B cells. The claim that tumor-resident B cells are more immature and less specific is made based on the characteristics of the CDR-H3 they express.

Strengths:

The authors show great expertise in developing in-house bioinformatics pipelines, as well as using tools developed by others, to explore the IgH repertoire expressed by B cells as a means of better characterizing tumor-associated B cells for the future generation of tumor-reactive antibodies as a therapy.

Weaknesses:

This paper needs major editing, both of the text and the figures, because as it stands it is convoluted and extremely difficult to follow. The conclusions reached are often not obvious from the figures themselves. Sufficient a priori details describing the framework for their analyses are not provided, making the outcome of their results questionable and leaving the reader wondering whether the findings are on solid ground.

The authors are encouraged to explain in more detail the premises used in their algorithms, as well as the criteria they follow to define clonotypes, clonal groups, and clonal lineages, which are currently poorly defined and are crucial elements that may influence their results and conclusions.

In response to this comment, we significantly expanded the paragraph dedicated to the tumor and non-tumor repertoire overlap and isotype composition. The following sections were added:

First, we characterized the relative similarity of IGH repertoires derived from tumors, DLN, and PBMC on the individual CDR-H3 clonotype level. We define clonotype as an instance with an identical CDR-H3 nucleotide sequence  and identical V- and J- segment attribution (isotype attribution may be different). Unlike other authors, here we do not pool together similar CDR-H3 sequences to account for hypermutation. (Hypermutation analysis is done separately and defined as clonal group analysis. )

As overlap metrics are dependent on overall repertoire richness, we normalized the comparison using the same number of top most frequent clonotypes of each isotype from each sample (N = 109). Repertoire data for each sample were split according to the immunoglobulin isotype, and the F2 metric was calculated for each isotype separately and plotted as an individual point.

We also analyzed D metric, which represents the relative overlap diversity uninfluenced by clonotype frequency (Dij\=dij/(di*dj), where dij is the number of clonotypes present in both samples, while di and dj are the diversities of samples i and j respectively). The results for D metric are not shown, as they indicate a similar trend to that of F2 metric. This observation allows us to conclude that tumor IGH repertoires are more similar to the repertoires of lymph nodes than to those of peripheral blood, both if clonotype frequency is taken into account, and when it is not.

Having excluded the IGHD gene segment from some of their analyses (at least those related to clonal lineage inference and phylogenetic trees), it is not well explained which region of CDR-H3 is responsible for the charge, interaction strength, and Kidera factors, since in some cases the authors mention that the central part of CDR-H3 consists of five amino acids and in others of seven amino acids.

We considered different ways of calculating amino acid properties of CDR3 and used different parameters for sample-average and individual-sequence CDR3s. Now plots for Fig S6 C are updated  for consistency and the parameters depicted there are now calculated using 5 central amino acids, as in other sections.

How can the authors justify that the threshold for CDR-H3 identity varies according to individual patient data? 

Ideal similarity threshold may depend on several factors, such as sampling, sequencing depth etc. For example, imagine a sample picking up 100% of the clonal lineage sequences which differ only 1 amino acid from each other, and a worse quality sample/sequencing picking up only every other sequence. Obviously, the minimal threshold required to accumulate these into a cluster/clonal group  would be different for these two cases (1aa for the former, and ~2 aa for the latter for single-linkage clustering). Or, in other words, the more the sequencing depth, the more dense the clusters will be. The method of individual threshold tailoring relies on the following: https://changeo.readthedocs.io/en/latest/examples/cloning.html

Although individual kidera factors that are significant in the context of our analysis are described in the text one by one on their first appearance, we now also added a sentence to describe Kidera factor analysis in general (page 8):

Kidera factors are a set of scores which quantify physicochemical properties of protein sequences (Nakai et al. 1988). 188 physical properties of the 20 amino acids are encoded using dimension reduction techniques.

Throughout the analyses, the reasons for choosing one type of cancer over another sometimes seem subjective and are not well justified in the text.

Whenever possible, we pooled all patients with all cancer types together, because the number of available samples did not allow us to draw any significant conclusions comparing between individual cancer types. When analyzing and showing individual patient data, we also did not attempt to depict any cancer-type-specific findings, but it is inevitable that we name a specific cancer type when labelling a sample coming from a specific tumor.

Overall, the narrative is fragmented. There is a lack of well-defined conclusions at the end of the results subheadings.

In addition to the described above, a conclusion was added to the paragraph describing hypermutation analysis:

IGHG clonotypes from lung cancer samples show higher number of hypermutations, possibly reflecting high mutational load found in lung cancer tissue. For melanoma, another cancer known for high mutational load, no statistically significant difference was found. This may be due to higher variance between melanoma samples, which hinders the analysis, or due to the small sample size.

The exact same paragraph is repeated twice in the results section.

Corrected.

The authors have also failed to synchronise the actual number of main figures with the text, and some panels are included in the main figures that are neither described nor mentioned in the text  (Venn diagram Fig. 2A and phylogenetic tree Fig. 5D). Overall, the manuscript appears to have been rushed and not thoroughly read before submission.

Corrected.

Reviewers are forced to wade through, unravel, and validate poorly explained algorithms in order to understand the authors' often bold conclusions.

We hope that the aforementioned additions to the text and also addition to the Figure 1 make the narrative more easily understandable.

Reviewer #2 (Public Review):

Summary:

The authors sampled the B cell receptor repertoires of Cancers, their draining lymph nodes, and blood. They characterized the clonal makeup of all B cells sampled and then analyzed these clones to identify clonal overlap between tissues and clonal activation as expressed by their mutation level and CDR3 amino acid characteristics and length. They conclude that B cell clones from the Tumor interact more with their draining lymph node than with the blood and that there is less mutation/expansion/activation of B cell clones in Tumors. These conclusions are interesting but hard to verify due to the under-sampling and short sequencing reads as well as confusion as to when analysis is across all individuals or of select individuals.

Strengths:

The main strength of their analysis is that they take into account multiple characteristics of clonal expansion and activation and their different modes of visualization, especially of clonal expansion and overlap. The triangle plots once one gets used to them are very nice.

Weaknesses:

The data used appears inadequate for the conclusions reached. The authors' sample size of B cells is small and they do not address how it could be sufficient. At such low sampling rates, compounded by the plasmablast bias they mention, it is unclear if the overlap trends they observe show real trends. Analyzing only top clones by size does not solve this issue. As it could be that the top 100 clones of one tissue are much bigger than those of another and that all overlap trends are simply because the clones are bigger in one tissue or the other. i.e there is equal overlap of clones with blood but blood is not sufficiently sampled given its greater diversity and smaller clones.

Regarding the number of clonotypes to be taken into account,  we were limited by the B cell infiltration of tumor samples and our ability to capture their repertoire. However, we use technical replicates on the level of cell suspension to ensure that at least top clonotypes are consistently sampled. So, this is how the data should be interpreted - as describing the most abundant clones in the repertoire (which also may be considered the most functionally relevant in case of tumor infiltrating lymphocytes).

To analyze the repertoire overlap, we generally use the F2 metric that takes clone size into account - because we think that clone size is an important functional factor. However, we have now added the description of using D metric (does not include clone frequency as a parameter) - which shows exactly the same trend as F2 metric. So, both F2 and D overlap metrics support our conclusion of higher overlap between tumor and LN.

The following text was added:

We also analyzed D metric, which represents the relative overlap diversity uninfluenced by clonotype frequency (Dij\=dij/(di*dj), where dij is the number of clonotypes present in both samples, while di and dj are the diversities of samples i and j respectively). The results for D metric are not shown, as they indicate a similar trend to that of F2 metric. This observation allows us to conclude that tumor IGH repertoires are more similar to the repertoires of lymph nodes than to those of peripheral blood, both if clonotype frequency is taken into account, and when it is not.

All in all, of course, the deeper the better, but given the data we were able to generate from the samples, this was the best approach to normalization that could be used.

Similarly, the read length (150bp X2) is too short, missing FWR1 and CDR1 and often parts of FWR2 if CDR3 is long. As the authors themselves note (and as was shown in (Zhang 2015 - PMC4811607) this makes mutation analysis difficult.

Indeed, we are aware of this problem, and therefore only a small part of the manuscript is dedicated to the hypermutation analysis. However, as the CDR-H3 region is the most mutated part, we still can capture significant diversity of mutations. To address the question of applicability of our data for the hypermutation phylogeny analysis, we compare the distribution of physico-chemical properties along the trees of hypermutation using the 150+150 and 300+300 data from the same donor and the same set of samples. The main conclusion is that neither for long, nor for short datasets could any correlation of physicochemical properties of the CDR-H3 region with the rank of the clonotype on the tree be found.  

It also makes the identification of V genes and thus clonal identification ambiguous. This issue becomes especially egregious when clones are mutated.

Again, this would be important for clonotype phylogeny analysis. However, for the simple questions that we address with our clonal group analysis, such as clonal group overlap between tissues etc, we consider this data acceptable, because if any mislabelling of V segment occurs, it is a) rare and b) is equally frequent in all types of samples. Therefore, any conclusions made are still valid despite this technical drawback.

To directly address the question of mislabelling of V-genes in our data, we looked at the average number of different  V-genes attributed to the same nucleotide sequence of CDR-H3 region in the short (150+150) and long (300+300) datasets from the same donor. Indeed, some ambiguity of V-gene labelling is observed (see below), but we think that it is unlikely to influence any of our cautious conclusions.

Author response image 1.

Finally, it is not completely clear when the analysis is of single individuals or across all individuals. If it is the former the authors did not explain how they chose the individuals analyzed and if the latter then it is not clear from the figures which measurements belong to which individual (i.e they are mixing measurements from different people).

We addressed this issue by adding a comment to each figure caption, describing whether a particular figure or panel describes individual or pooled data, and also whether the analysis is done on individual clonotype or clonal group level.

Also, in case pooled data were used, we added the number of patients that was pooled for a particular type of analysis. This number differs from one type of analysis to the other, because not all the patients had a complete set of tissues, and also not all samples passed a quality check for a particular analysis.

Here are the numbers listed:

Fig 2A: N=6 (we were only considering those who had all three tissues)

Fig 2C, N=14 (all)

2D: N=14 (all)

2E N=7 (have both tum and PBMC).

2F N=9 (have both tum and PBMC).

2G N=9 (have both tum and PBMC)

2H N=7 (have both tum and LN)

3A N=14 (all)

3B N=11 (only those with tumor)

3E - N=14

7F N=11 (all that have tumor)

Reviewer #3 (Public Review):

In multiple cancers, the key roles of B cells are emerging in the tumor microenvironment (TME). The authors of this study appropriately introduce that B cells are relatively under-characterised in the TME and argue correctly that it is not known how the B cell receptor (BCR) repertoires across tumors, lymph nodes, and peripheral blood relate. The authors therefore supply a potentially useful study evaluating the tumor, lymph node, and peripheral blood BCR repertoires and site-to-site as well as intra-site relationships. The authors employ sophisticated analysis techniques, although the description of the methods is incomplete. Among other interesting observations, the authors argue that the tumor BCR repertoire is more closely related to that of draining lymph node (dLN) than the peripheral blood in terms of clonal and isotype composition. Furthermore, the author's findings suggest that tumor-infiltrating B cells (TIL-B) exhibit a less mature and less specific BCR repertoire compared with circulating B cells. Overall, this is a potentially useful work that would be of interest to both medical and computational biologists working across cancer. However, there are aspects of the work that would have benefitted from further analysis and areas of the manuscript that could be written more clearly and proofread in further detail.

Major Strengths:

(1) The authors provide a unique analysis of BCR repertoires across tumor, dLN, and peripheral blood. The work provides useful insights into inter- and intra-site BCR repertoire heterogeneity. While patient-to-patient variation is expected, the findings with regard to intra-tumor and intra-dLN heterogeneity with the use of fragments from the same tissue are of importance, contribute to the understanding of the TME, and will inform future study design.

(2) A particular strength of the study is the detailed CDR3 physicochemical properties analysis which leads the authors to observations that suggest a less-specific BCR repertoire of TIL-B compared to circulating B cells.

Major Weaknesses:

The study would have benefitted from a deeper biological interpretation of the data. While given the low number of patients one can plausibly understand a reluctance to speculate about clinical details, there is limited discussion about what may contribute to observed heterogeneity.

We indeed do not want to overinterpret our data, especially where it comes to the difference between types of cancer. On the other hand, extracting similar patterns between different cancer types allows to pinpoint mechanisms that are more general and do not depend on cancer type. As for the potential source of intratumoral heterogeneity that we observe, we think that it may be coming from the selective sampling of tertiary lymphoid structures. We include IHC data for TLS detection in the supplementary Fig.5.  Also, tumor mutation clonality may correlate with differential antibody response (i.e. different IGH clonotypes developing to recognize different antigens) – as has been previously described for TCRs by the lab of B.Chain in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890490/.

For example, for the analysis of three lymph nodes taken per patient which were examined for inter-LN heterogeneity, there is a lack of information regarding these lymph nodes.

Unfortunately no clinical information about the lymph nodes was available.

'LN3' is deemed as exhibiting the most repertoire overlap with the tumor but there is no discussion as to why this may be the case.

The following phrases describes this in the “LN-to-LN heterogeneity in colorectal cancer” paragraph:

Similarly, an unequal interaction of tumors with DLNs was observed at the level of hypermutating clonal groups.

Functionally, this may again indicate that within a group of DLNs, nodes are unequal in terms of access to tumor antigens, and this inequality shapes the BCR repertoires within these lymph nodes.

(2) At times the manuscript is difficult to follow. In particular, the 'Intra-LN heterogeneity' section follows the 'LN-LN heterogeneity in colorectal cancer' section and compares the overlap of LN fragments (LN11, LN21, LN31) with the tumor in two separate patients (Fig 6A). In the previous section (LN-LN), LN11, LN21, LN31 are names given to separate lymph nodes from the same patient. The fragments are referred to as 'LN2' and the nodes in the previous section are referred to similarly. This conflation of naming for nodes and fragments is confusing.

We corrected this.

(3) There is a duplicated paragraph in 'Short vs long trees' and the following section 'Productive involvement in hypermutation lineages depends on CDR3 characteristics.

Corrected.

Reviewer #1 (Recommendations For The Authors):

- Figures:

Figure 1A lacks resolution

Corrected

Figure 2A, Venn diagram: What do the colors indicate?

Corrected

Figure 5D, why include this tree when there is no mention of it in the text?

Described

Figures 8, 9, and 10 are not to be found. One should not have to figure out that they became supplementary in the end.

Corrected

Regarding the physicochemical properties of CDR-H3, what do the authors mean by "the central part"? Do the authors refer to the CDR-H3 loop, and if so, how is that defined when the IGHD gene segment is excluded from the analyses? Is it 5 amino acids (Productive involvement in hypermutating lineages depends on CDR3 characteristics, Page 21/39 in merged document) and (CDR3 properties, Page 8/39 in merged document), or 7 amino acids (Short vs long trees phylogeny analysis, Page 19/39 in merged document)? Please clarify.  

We considered different ways of calculating amino acid properties of CDR3 and used different parameters for sample-average and individual-sequence CDR3s. Now plots for Fig S6 C are updated for consistency. IGHD segment was not excluded from the analysis. The reviewer might be confused by our description of phylogenetic inference, when an artificial outgroup with D segment deleted is added to the clonal group to facilitate the inference process. All other sequences were analyzed in their original form with the D segment. This way, we could avoid biases in phylogeny introduced by misassignment of D gene germline to the outgroup.

What was the threshold for CDR-H3 identity in their analyses? How can the authors justify that this value changes according to individual patient datasets? (Materials & methods, Clonal lineage inference Page 29/39 in merged document).

As described earlier, ideal similarity threshold may depend on several factors, such as sampling, sequencing depth etc. For example, imagine a sample picking up 100% of the clonal lineage sequences which differ only 1 amino acid from each other, and a worse quality sample/sequencing picking up only every other sequence. Obviously, the minimal threshold required to accumulate these into a clonotype would be different for these two cases (1aa for the former, and ~2 aa for the latter for single-linkage clustering). The method of individual threshold tailoring relies on this: https://changeo.readthedocs.io/en/latest/examples/cloning.html

What is the difference between tumor-induced and tumor-infiltrating B cells? How can the authors discriminate between the two? Page 6/39 in the merged document.

corrected to tumor-infiltrating

"Added nucleotides" meaning N additions? Page 3/39 in the merged document.

yes

How many cancer patients were enrolled? 17 or 14(Materials & methods page 27/39 in the merged document)? Please clarify.   

In the current project 14 patients were enrolled. The appropriate changes have been introduced in the final text. Supplementary table 2 has been added with the patient data.

Abbreviations are used without full descriptions.

According to reviewer’s recommendation, a list of abbreviations was added in the manuscript, and also full descriptions were added in the text upon first mentioning of the term.

Use either CDR3 or CDR-H3

We corrected the text to use CDR-H3 abbreviation throughout the text.

Reviewer #2 (Recommendations For The Authors):

I would like to start by apologizing for the time it took me to review.

As I mentioned above there are issues with the clonal sampling of the sequencing length and the statistics in this paper. From reading the paper I am not sure if they are fixable but there are some things that could be tried.

(1) The authors mention the diversity of their individual analysis - 17 individuals across 3 cancer types, but do not then systematically show us how the different things they measure track across the different individuals and cancer types. it is possible that some trends would be more convincing if we saw them happening again and again across all individuals. But, as I said above, the authors do not identify individuals clearly across all their types of analysis nor do they explain why sometimes they show analysis of specific individuals.

For overlap analysis (Fig. 2 except panel B), CDR3 properties analysis (Fig. 3, Fig. S7), clonal group analysis (Fig. 4) we used pooled data on all cancers, unless it is indicated otherwise on the panel. For overlap analysis, we used Cytoscape graph (Fig. 2B) for one patient, mp3, to illustrate the findings that were made on pooled data. For other types of analysis, such as overlap between individual lymph nodes, or tumor fragments (Fig. 5, 6, 7 except panel F) pooled analysis is not possible due to the individual nature of the processes in question.

(2) The authors do not address how lacking their sampling is nor the distribution of clone sizes in different tissues/ individuals/ subsets. Without such a discussion it is not clear how tenuous or convincing their conclusions are.

(3) The short sequencing lengths limit the ability to exactly identify V and thus the germline root of clones, whose positions are mutated and clonal association of sequences. The authors appear to be aware of this as they often use the most common ancestor as the start of their analysis... however, again there are inconsistencies that are not clearly described in the text. in creating trees with change they defined roots as the putative germline and at least in most cases also in clone association although in some analyses potentially similar clones were collapsed into clonotypes. Again it is not clear when one method was used or the other and how the choice was made what to choose.

Here we can only state that we consistently used the approach described in the Methods section, which was the following:

First, the repertoires were clustered into clonal lineages using the criteria described in “Methods: Clonal lineage inference” Assuming that each clonotype sequence in the clonal lineage originated from the same ancestor, we try to recover the phylogeny. Please note that we refer to the individual BCR sequences as “clonotypes”, and to a group of clonotypes that presumably share a common ancestor - as “clonal lineage” or “clonal group”.

The phylogeny of B-cell hypermutations was inferred for each clonal lineage of size five or more using the maximum likelihood method and the GTR GAMMA nucleotide substitution model. To find the most recent common ancestor (MRCA) or “root” of the tree, we used an artificial outgroup constructed as a conjugate of germline segments V and J defined by MIXCR and added it to the clonal lineage. The D segment was excluded from the outgroup formation, as there was insufficient confidence in the germline annotations due to its short length and high level of mutations. The rest of the clonotypes were still analyzed in their original form with D segment in place. Deleting D segment from the outgroup simply eliminates the risk of biasing the phylogeny by missasigning D segment germline sequence to the outgroup. The MUSCLE tool was used for multiple sequence alignment and RAxML software was used to build and root phylogenetic trees.

(4) Beyond the statistical issues mentioned above: the unclear selection of individual examples for comparison and significance testing, the mixing of individuals and cancer types without clear identification, etc. there is in general a lack of coherence in the statistical analysis performed. specifically:

(a) the authors should choose one cutoff for significance (0.01 for instance) and then just mention when things are significant and when not. There is no need and it is confusing to add the p-value for every comparison. P-values are not good measures of effect size.

We corrected the figures and left p-values only where they are below significance threshold.

(b) the Bonferroni correction used is not well characterized. For an alpha of 0.01 in Figures 3 C and D how many tests were performed?

The number of tests performed that was used for Bonferroni-Holm correction equals the number of comparisons on the heatmap which makes it 39 for each heatmap on Fig 3C and 13 for Fig 3D.

Finally some minor issues -

(1) Not all acronyms are described, for instance, TME and TIL. The first time any acronym is used it should be spelled out.  -> Katya B- список сокращений

(2) The figure captions are not all there...

(a) there is no caption for Figure 3E.

corrected

(b) there are Figure 7 F and G panels but no Figure 7E panel and Figure F is described after Figure G.

corrected

(3) A few problems with wording -

(a) bottom paragraph of page 3 - instead of :

"different lymph nodes from one draining lymph node pool may be more or less involved"

Corrected to "different lymph nodes from one draining lymph node pool may be differentially involved"

(b) figure caption for figure 3a: instead of:

"CDR3 are on average significantly higher in tumor"

Corrected to "CDR3 are on average significantly longer in tumor"

Reviewer #3 (Recommendations For The Authors):

- FIG1A - Suggest expanding the legend to include more information on the computational analyses.

added

- PAGE SIX: Suggest adding a table or some text on patient characteristics. Numbers of unique clonotypes per sample etc. Are there differences in age/sex that need to be considered? Some clonotype information is available in S1 but some summary and statistics would be appreciated.

Added patient information as Supplementary table 2.

- PAGE SIX: F2 Metric, suggestion to explain why this was used vs. other metrics.

We expanded the following paragraph to include information about F2 metric and D metric, and the reason why we are using F2.

Repertoire data for each sample were split according to the immunoglobulin isotype, and the F2 metric was calculated for each isotype separately and plotted as an individual point. We used the repertoire overlap metric F2 (Сlonotype-wise sum of geometric mean frequencies of overlapping clonotypes), which accounts for both the number and frequency of overlapping clonotypes (Fig. 2A). As expected, significantly lower overlaps were observed between the IGH repertoires of peripheral blood and tumors compared to LN/tumor overlaps. The LN/PBMC overlap also tended to be lower, but the difference was not statistically significant. We also analyzed D metric, which represents the relative overlap diversity uninfluenced by clonotype frequency (Dij\=dij/(di*dj), where dij is the number of clonotypes present in both samples, while di and dj are the diversities of samples i and j respectively). The results for D metric are not shown, as they indicate a similar trend to that of F2 metric. This observation allows us to conclude that tumor IGH repertoires are more similar to the repertoires of tumor-draining LNs than to those of peripheral blood, both if clonotype frequency is taken into account, and when it is not.

- PAGE SIX: Make clear in the text that mp3 is a patient.

Added “melanoma patient mp3”

- PAGE EIGHT: Suggest explaining kidera factors at first use - not all readers will know what they are.

We expanded the following paragraph to add more information about Kidera factors:

To explore CDR-H3 physicochemical properties, we calculated the mean charge, hydropathy, predicted interaction strength, and Kidera factors 1 - 9 (kf1-kf9) for five central amino acids of the CDR-H3 region for the 100 most frequent clonotypes of each sample using VDJtools. Kidera factors are a set of scores which quantify physicochemical properties of protein sequences 61. 188 physical properties of the 20 amino acids are encoded using dimension reduction techniques, to yield 9 factors which are used to quantitatively characterize physicochemical properties of amino acid sequences.

- Fig 5D is not referred to.

Corrected

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eLife assessment

This paper describes an important advance in a 2D in vitro neural culture system to generate mature, functional, diverse, and geometrically consistent cultures, in a 384-well format with defined dimensions and the absence of the necrotic core, which persists for up to 300 days. The well-based format and conserved geometry make it a promising tool for arrayed screening studies. Some of the evidence is incomplete and could benefit from a more direct head-to-head comparison with more standard culture methods and standardization of cell seeding density as well as further data on reproducibility in each well and for each cell line.

Reviewer #1 (Public Review):

Summary:

Kroeg et al. describe a novel method for 2D culture human induced pluripotent stem cells (hiPSCs) to form cortical tissue in a multiwell format. The method claims to offer a significant advancement over existing developmental models. Their approach allows them to generate cultures with precise, reproducible dimensions and structure with a single rosette; consistent geometry; incorporating multiple neuronal and glial cell types (cellular diversity); avoiding the necrotic core (often seen in free-floating models due to limited nutrient and oxygen diffusion). The researchers demonstrate the method's capacity for long-term culture, exceeding ten months, and show the formation of mature dendritic spines and considerable neuronal activity. The method aims to tackle multiple key problems of in vitro neural cultures: reproducibility, diversity, topological consistency, and electrophysiological activity. The authors suggest their potential in high-throughput screening and neurotoxicological studies.

Strengths:

The main advances in the paper seem to be: The culture developed by the authors appears to have optimal conditions for neural differentiation, lineage diversification, and long-term culture beyond 300 days. These seem to me as a major strength of the paper and an important contribution to the field. The authors present solid evidence about the high cell type diversity present in their cultures. It is a major point and therefore it could be better compared to the state of the art. I commend the authors for using three different IPS lines, this is a very important part of their proof. The staining and imaging quality of the manuscript is of excellent quality.

Weaknesses:

(1) The title is misleading: The presented cultures appear not to be organoids, but 2D neural cultures, with an insufficiently described intermediate EB stage. For nomenclature, see: doi: 10.1038/s41586-022-05219-6. Should the tissue develop considerable 3D depth, it would suffer from the same limited nutrient supply as 3D models - as the authors point out in their introduction.

(2) The method therefore should be compared to state-of-the-art (well-based or not) 2D cultures, which seems to be somewhat overlooked in the paper, therefore making it hard to assess what the advance is that is presented by this work.

(3) Reproducibility is prominently claimed throughout the manuscript. However, it is challenging to assess this claim based on the data presented, which mostly contain single frames of unquantified, high-resolution images. There are almost no systematic quantifications presented. The ones present (Figure S1D, Figure 4) show very large variability. However, the authors show sets of images across wells (Figure S1B, Figure S3) which hint that in some important aspects, the culture seems reproducible and robust.

(4) What is in the middle? All images show markers in cells present around the center. The center however seems to be a dense lump of cells based on DAPI staining. What is the identity of these cells? Do these cells persist throughout the protocol? Do they divide? Until when? Addressing this prominent cell population is currently lacking.

(5) This manuscript proposes a new method of 2D neural culture. However, the description and representation of the method are currently insufficient.<br /> (a) The results section would benefit from a clear and concise, but step-by-step overview of the protocol. The current description refers to an earlier paper and appears to skip over some key steps. This section would benefit from being completely rewritten. This is not a replacement for a clear methods section, but a section that allows readers to clearly interpret results presented later.<br /> (b) Along the same lines, the graphical abstract should be much more detailed. It should contain the time frames and the media used at the different stages of the protocol, seeding numbers, etc.

Reviewer #2 (Public Review):

Summary:

In this manuscript, van der Kroeg et al have developed a method for creating 3D cortical organoids using iPSC-derived neural progenitor cells in 384-well plates, thus scaling down the neural organoids to adherent culture and a smaller format that is amenable to high throughput cultivation. These adherent cortical organoids, measuring 3 x 3 x 0.2 mm, self-organize over eight weeks and include multiple neuronal subtypes, astrocytes, and oligodendrocyte lineage cells.

Strengths:

(1) The organoids can be cultured for up to 10 months, exhibiting mature dendritic spines, axonal myelination, and robust neuronal activity.

(2) Unlike free-floating organoids, these do not develop necrotic cores, making them ideal for high-throughput drug discovery, neurotoxicological screening, and brain disorder studies.

(3) The method addresses the technical challenge of achieving higher-order neural complexity with reduced heterogeneity and the issue of necrosis in larger organoids. The method presents a technical advance in organoid culture.

(4) The method has been demonstrated with multiple cell lines which is a strength.

(5) The manuscript provides high-quality immunostaining for multiple markers.

Weaknesses:

(1) Direct head-to-head comparison with standard organoid culture seems to be missing and may be valuable for benchmarking, ie what can be done with the new method that cannot be done with standard culture and vice versa, ie what are the aspects in which new method could be inferior to the standard.

(2) It would be important to further benchmark the throughput, ie what is the success rate in filling and successfully growing the organoids in the entire 384 well plate?

(3) For each NPC line an optimal seeding density was estimated based on the proliferation rate of that NPC line and via visual observation after 6 weeks of culture. It would be important to delineate this protocol in more robust terms, in order to enable reproducibility with different cell lines and amongst the labs.

Reviewer #3 (Public Review):

Summary:

Kroeg et al. have introduced a novel method to produce 3D cortical layer formation in hiPSC-derived models, revealing a remarkably consistent topography within compact dimensions. This technique involves seeding frontal cortex-patterned iPSC-derived neural progenitor cells in 384-well plates, triggering the spontaneous assembly of adherent cortical organoids consisting of various neuronal subtypes, astrocytes, and oligodendrocyte lineage cells.

Strengths:

Compared to existing brain organoid models, these adherent cortical organoids demonstrate enhanced reproducibility and cell viability during prolonged culture, thereby providing versatile opportunities for high-throughput drug discovery, neurotoxicological screening, and the investigation of brain disorder pathophysiology. This is an important and timely issue that needs to be addressed to improve the current brain organoid systems.

Weaknesses:

While the authors have provided significant data supporting this claim, several aspects necessitate further characterization and clarification. Mainly, highlighting the consistency of differentiation across different cell lines and standardizing functional outputs are crucial elements to emphasize the future broad potential of this new organoid system for large-scale pharmacological screening.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary: 

Kroeg et al. describe a novel method for 2D culture human induced pluripotent stem cells (hiPSCs) to form cortical tissue in a multiwell format. The method claims to offer a significant advancement over existing developmental models. Their approach allows them to generate cultures with precise, reproducible dimensions and structure with a single rosette; consistent geometry; incorporating multiple neuronal and glial cell types (cellular diversity); avoiding the necrotic core (often seen in free-floating models due to limited nutrient and oxygen diffusion). The researchers demonstrate the method's capacity for long-term culture, exceeding ten months, and show the formation of mature dendritic spines and considerable neuronal activity. The method aims to tackle multiple key problems of in vitro neural cultures: reproducibility, diversity, topological consistency, and electrophysiological activity. The authors suggest their potential in high-throughput screening and neurotoxicological studies.

Strengths: 

The main advances in the paper seem to be: The culture developed by the authors appears to have optimal conditions for neural differentiation, lineage diversification, and long-term culture beyond 300 days. These seem to me as a major strength of the paper and an important contribution to the field. The authors present solid evidence about the high cell type diversity present in their cultures. It is a major point and therefore it could be better compared to the state of the art. I commend the authors for using three different IPS lines, this is a very important part of their proof. The staining and imaging quality of the manuscript is of excellent quality.

We thank the reviewer for the positive comments on the potential of our novel platform to address key problems of in vitro neural culture, highlighting the longevity and reproducibility of the method across multiple cell lines.

Weaknesses: 

(1) The title is misleading: The presented cultures appear not to be organoids, but 2D neural cultures, with an insufficiently described intermediate EB stage. For nomenclature, see: doi: 10.1038/s41586-022-05219-6. Should the tissue develop considerable 3D depth, it would suffer from the same limited nutrient supply as 3D models - as the authors point out in their introduction. 

We appreciate the opportunity to clarify this point. We respectfully disagree that the cultures do not meet the consensus definition of an organoid. In fact, a direct quote from the seminal nomenclature paper referenced by the reviewer states: “We define organoids as in vitro-generated cellular systems that emerge by self-organization, include multiple cell types, and exhibit some cytoarchitectural and functional features reminiscent of an organ or organ region. Organoids can be generated as 3D cultures or by a combination of 3D and 2D approaches (also known as 2.5D) that can develop and mature over long periods of time (months to years).” (Pasca et al, 2022 doi10.1038/s41586-022-05219-6). Therefore, while many organoid types indeed have a more spherical or globular 3D shape, the term organoid also applies to semi-3D or non-globular adherent organoids, such as renal (Czerniecki et al 2018, doi.org/10.1016/j.stem.2018.04.022) and gastrointestinal organoids (Kakni et al 2022, doi.org/10.1016/j.tibtech.2022.01.006). Accordingly, the adherent cortical organoids described in the manuscript exhibit self-organization to single radial structures consisting of multiple cell layers in the z-axis, reaching ~200um thickness (therefore remaining within the limits for sufficient nutrient supply), with consistent cytoarchitectural topology and electrophysiological activity, and therefore meet the consensus definition of an organoid.

(2) The method therefore should be compared to state-of-the-art (well-based or not) 2D cultures, which seems to be somewhat overlooked in the paper, therefore making it hard to assess what the advance is that is presented by this work. 

It was not our intention to benchmark this model quantitatively against other culture systems. Rather, we have attempted to characterize the opportunities and limitations of this approach, with a qualitative contrast to other culture methods. Compared to state-of-the-art 2D neural network cultures, adherent cortical organoids provide distinct advantages in:

(1) Higher order self-organized structure formation, including segregation of deeper and upper cortical layers.

(2) Longevity: adherent cortical organoids can be successfully kept in culture up to 1 year where 2D cultures typically deteriorate after 8-12 weeks.

(3) Maturity, including the formation of dendritic mushroom spines and robust electrophysiological activity.

(4) Cell type diversity including a more physiological ratio of inhibitory and excitatory neurons (10% GAD67+/NeuN+ neurons in adherent cortical organoids, vs 1% in 2D neural networks) and the emergence of oligodendrocyte lineage cells.

On the other hand, limitations of adherent cortical organoids compared to 2D neural network cultures are:

(1) Culture times for organoids are much longer than for 2D cultures and the method can therefore be more laborious and more expensive.

(2) Whole cell patch clamping is not easily feasible in the organoids because of the restricting dimensions of the 384well plates.

(3) Reproducibility is prominently claimed throughout the manuscript. However, it is challenging to assess this claim based on the data presented, which mostly contain single frames of unquantified, high-resolution images. There are almost no systematic quantifications presented. The ones present (Figure S1D, Figure 4) show very large variability. However, the authors show sets of images across wells (Figure S1B, Figure S3) which hint that in some important aspects, the culture seems reproducible and robust. 

We made considerable efforts to establish quantitative metrics to assess reproducibility. We applied a quantitative scoring system of single radial structures at different time points for multiple batches of all three lines as indicated in Figure S1D. This figure represents a comprehensive dataset in which each dot represents the average of a different batch of organoids containing 10-40 organoids per batch. To emphasize this, we will adapt the graph to better reflect the breadth of the dataset. Additional quantifications are given in Figure S2 for progenitor and layer markers for Line 1 and in Figure S5 for interneurons across all three lines, showing relatively low variability. That being said, we acknowledge the reviewer’s concerns and will modify the text to reduce the emphasis of this point, pending more extensive data addressing reproducibility across a wide range of parameters.

(4) What is in the middle? All images show markers in cells present around the center. The center however seems to be a dense lump of cells based on DAPI staining. What is the identity of these cells? Do these cells persist throughout the protocol? Do they divide? Until when? Addressing this prominent cell population is currently lacking. 

A more comprehensive characterization of the cells in the center remains a significant challenge due to the high cell density hindering antibody penetration. However, dye-based staining methods such as DAPI and the LIVE/DEAD panel confirm a predominance of intact nuclei with very minimal cell death. The limited available data suggest that a substantial proportion of the cells in the center are proliferative neural progenitors, indicated by immunolabeling for SOX2 and Ki67. We will add additional figures to support these findings. Furthermore, we are currently optimizing the conditions to perform single cell / nuclear RNA sequencing to further characterize the cellular composition of the organoids.

(5) This manuscript proposes a new method of 2D neural culture. However, the description and representation of the method are currently insufficient. <br /> (a) The results section would benefit from a clear and concise, but step-by-step overview of the protocol. The current description refers to an earlier paper and appears to skip over some key steps. This section would benefit from being completely rewritten. This is not a replacement for a clear methods section, but a section that allows readers to clearly interpret results presented later.

We will revise the manuscript to include a more detailed step-by-step overview of the protocol.

(b) Along the same lines, the graphical abstract should be much more detailed. It should contain the time frames and the media used at the different stages of the protocol, seeding numbers, etc. 

As suggested, we will also adapt the graphical abstract to include more detail.

Reviewer #2 (Public Review): 

Summary: 

In this manuscript, van der Kroeg et al have developed a method for creating 3D cortical organoids using iPSC-derived neural progenitor cells in 384-well plates, thus scaling down the neural organoids to adherent culture and a smaller format that is amenable to high throughput cultivation. These adherent cortical organoids, measuring 3 x 3 x 0.2 mm, self-organize over eight weeks and include multiple neuronal subtypes, astrocytes, and oligodendrocyte lineage cells.

Strengths: 

(1) The organoids can be cultured for up to 10 months, exhibiting mature dendritic spines, axonal myelination, and robust neuronal activity. 

(2) Unlike free-floating organoids, these do not develop necrotic cores, making them ideal for high-throughput drug discovery, neurotoxicological screening, and brain disorder studies.

(3) The method addresses the technical challenge of achieving higher-order neural complexity with reduced heterogeneity and the issue of necrosis in larger organoids. The method presents a technical advance in organoid culture.

(4) The method has been demonstrated with multiple cell lines which is a strength. 

(5) The manuscript provides high-quality immunostaining for multiple markers. 

We appreciate the reviewer’s acknowledgement of the strengths of this novel platform as a technical advance in organoid culture that reduces heterogeneity and shows potential for higher throughput experiments.

Weaknesses: 

(1) Direct head-to-head comparison with standard organoid culture seems to be missing and may be valuable for benchmarking, ie what can be done with the new method that cannot be done with standard culture and vice versa, ie what are the aspects in which new method could be inferior to the standard.

In our opinion, it would be extremely difficult to directly compare methods because of substantial differences. Most notably, whole brain organoids grow to large and irregular globular shapes, while adherent cortical organoids have a highly standardized shape confined by the limits of a 384-well. Moreover, it was not our intention to benchmark this model quantitatively against other culture systems. Rather, we have attempted to characterize the opportunities and limitations of this approach, with a qualitative contrast to other culture methods.

(2) It would be important to further benchmark the throughput, ie what is the success rate in filling and successfully growing the organoids in the entire 384 well plate? 

Figure S1D shows the success rate of organoid formation and stability of the organoid structures over time. In addition, we will add the number of wells that were filled per plate.

(3) For each NPC line an optimal seeding density was estimated based on the proliferation rate of that NPC line and via visual observation after 6 weeks of culture. It would be important to delineate this protocol in more robust terms, in order to enable reproducibility with different cell lines and amongst the labs. 

Figure S1C provides the relationship between proliferation rate and seeding density, allowing estimation of seeding densities based on the proliferation rate of the NPCs. However, we appreciate the reviewers feedback and will modify the methods to provide more detail.

Reviewer #3 (Public Review): 

Summary: 

Kroeg et al. have introduced a novel method to produce 3D cortical layer formation in hiPSC-derived models, revealing a remarkably consistent topography within compact dimensions. This technique involves seeding frontal cortex-patterned iPSC-derived neural progenitor cells in 384-well plates, triggering the spontaneous assembly of adherent cortical organoids consisting of various neuronal subtypes, astrocytes, and oligodendrocyte lineage cells. 

Strengths: 

Compared to existing brain organoid models, these adherent cortical organoids demonstrate enhanced reproducibility and cell viability during prolonged culture, thereby providing versatile opportunities for high-throughput drug discovery, neurotoxicological screening, and the investigation of brain disorder pathophysiology. This is an important and timely issue that needs to be addressed to improve the current brain organoid systems. 

We thank the reviewer for highlighting the strengths of our novel platform. We appreciate that all three reviewers agree that the adherent cortical organoids presented in this manuscript reliably demonstrate increased reproducibility and longevity. They also commend its potential for higher throughput drug discovery and neurotoxicological/phenotype screening purposes.

Weaknesses: 

While the authors have provided significant data supporting this claim, several aspects necessitate further characterization and clarification. Mainly, highlighting the consistency of differentiation across different cell lines and standardizing functional outputs are crucial elements to emphasize the future broad potential of this new organoid system for large-scale pharmacological screening.

We appreciate the feedback and will add more detail on consistency and standardization of functional outputs.

Propositions de titres : - colloque hybride : un jeu multijoueurs ? - réflexions sur l'hybridité et parrallèle avec le game design - hydridation des interactions : concevoir un "potentiel hybride" - Colloque hybride : expérience vécue et investissement interactionnel des participants. - Hybridité et game design : réfexions à partir de l'exemple d'un colloque hybride.

j'ai rédigé cette partie, à améliorer par la suite.

Note

redefreiheit muss her, inklusive legalisierung von beleidigungen und "hassrede".<br /> warum soll ich meine revolutionspläne, gewaltfantasien, todeslisten, ... geheim halten?<br /> bei dem punkt sind die USA weiter, aber der globale trend geht eher richtung china : /

从大时间框看形态之后 去小时间框架去看形态形成的情况

斐波那契： 左边高点则是以高点开始 低点结束 右边高点则是以低点开始 高点结束 左高起，右高尾

sissies with platform shoes 穿着厚底鞋的娘娘腔

normal people with sneakers

interior

tài liệu

on the edge of change comparable to the rise of human life on Earth

contradictions between: - be equitable and shift power - building trusted relationships between philanthropists and grantees - enable system change - increase risk appetite of donors and catalyse innovation - to do everything based on data and evidence

for shift of power, there need to be focus on unrestricted funding with generous coverage of operational costs with minimal reporting requirements. But at the same time philanthropists want to enable systems change - which requires aligned strategic approach - i.e. trusted partnership and collaboration. Now this collaboration requires more, time and money and also so NGO's need to be up for it or it wouldn't be an equitable partnership. Minimal reporting can make it difficult to track down progress made by systems change, and counters evidence based approach to philanthropy. Focus can be made on learning. System change when translated as scale can itself exclude many org by prioritising scale, and without changing its understanding, it can be exclusionary for potential grantees and prevent their access to ‘systems change’ funding calls. Take more risks yet strategies formulated by those without lived experiences of the issues at hand. At the same time, philanthropy has a unique role to play in such partnerships to avoid this dynamic: it can leverage its influence and networks to ensure more equitable, power-sensitive approaches while building critical bridges between the profit-driven motivations of the private sector, the social goals of governments, and the justice goals of civil society actors. And so right questions need to be asked to give way to such transformation.

控制代码的调用顺序，这个方式值得学习，装波13

看代码先看这几个文件

符号 ∈ ∈ 是集合论中的一个基本符号，表示“属于”或“是……的元素”。

“离散”在这里的意思是指时间步是分开的、独立的、非连续的。也就是说，每个时间步都是一个单独的、不可再分的时间点，彼此之间没有连贯的连续时间段。

方法章节

具体路径

关于懂的定义

eLife assessment

This important study reports that a transcription factor that stimulates mRNA synthesis can stabilize its target transcripts, possibly through co-transcriptional assembly and action in the cytoplasm. While the primary observation is solid, whether an association of Sfp1 with specific transcripts in the cytoplasm is the critical step in transcript stabilization is not entirely clear. If confirmed by independent means, the authors would have found a novel mechanistic link between mRNA synthesis and cytoplasmic mRNA stability for specific transcripts. Such a finding would be of broad interest to the field of molecular biology.

Reviewer #2 (Public Review):

Summary:

The manuscript by Kelbert et al. presents results on the involvement of the yeast transcription factor Sfp1 in the stabilisation of transcripts whose synthesis it stimulates. Sfp1 is known to affect the synthesis of a number of important cellular transcripts, such as many of those that code for ribosomal proteins. The hypothesis that a transcription factor can remain bound to the nascent transcript and affect its cytoplasmic half-life is attractive. However, the association of Sfp1 with cytoplasmic transcripts remains to be validated, as explained in the following comments:

A two-hybrid based assay for protein-protein interactions identified Sfp1, a transcription factor known for its effects on ribosomal protein gene expression, as interacting with Rpb4, a subunit of RNA polymerase II. Classical two-hybrid experiments depend on the presence of the tested proteins in the nucleus of yeast cells, suggesting that the observed interaction occurs in the nucleus. Unfortunately, the two-hybrid method cannot determine whether the interaction is direct or mediated by nucleic acids. The revised version of the manuscript now states that the observed interaction could be indirect.

To understand to which RNA Sfp1 might bind, the authors used an N-terminally tagged fusion protein in a cross-linking and purification experiment. This method identified 264 transcripts for which the CRAC signal was considered positive and which mostly correspond to abundant mRNAs, including 74 ribosomal protein mRNAs or metabolic enzyme-abundant mRNAs such as PGK1. The authors did not provide evidence for the specificity of the observed CRAC signal, in particular what would be the background of a similar experiment performed without UV cross-linking. This is crucial, as Figure S2G shows very localized and sharp peaks for the CRAC signal, often associated with over-amplification of weak signal during sequencing library preparation.

In a validation experiment, the presence of several mRNAs in a purified SFP1 fraction was measured at levels that reflect the relative levels of RNA in a total RNA extract. Negative controls showing that abundant mRNAs not found in the CRAC experiment were clearly depleted from the purified fraction with Sfp1 would be crucial to assess the specificity of the observed protein-RNA interactions (to complement Fig. 2D). The CRAC-selected mRNAs were enriched for genes whose expression was previously shown to be upregulated upon Sfp1 overexpression (Albert et al., 2019). The presence of unspliced RPL30 pre-mRNA in the Sfp1 purification was interpreted as a sign of co-transcriptional assembly of Sfp1 into mRNA, but in the absence of valid negative controls, this hypothesis would require further experimental validation. Also, whether the fraction of mRNA bound by Sfp1 is nuclear or cytoplasmic is unclear.

To address the important question of whether co-transcriptional assembly of Spf1 with transcripts could alter their stability, the authors first used a reporter system in which the RPL30 transcription unit is transferred to vectors under different transcriptional contexts, as previously described by the Choder laboratory (Bregman et al. 2011). While RPL30 expressed under an ACT1 promoter was barely detectable, the highest levels of RNA were observed in the context of the native upstream RPL30 sequence when Rap1 binding sites were also present. Sfp1 showed better association with reporter mRNAs containing Rap1 binding sites in the promoter region. Removal of the Rap1 binding sites from the reporter vector also led to a drastic decrease in reporter mRNA levels. Co-purification of reporter RNA with Sfp1 was only observed when Rap1 binding sites were included in the reporter. Negative controls for all the purification experiments might be useful.

To complement the biochemical data presented in the first part of the manuscript, the authors turned to the deletion or rapid depletion of SFP1 and used labelling experiments to assess changes in the rate of synthesis, abundance and decay of mRNAs under these conditions. An important observation was that in the absence of Sfp1, mRNAs encoding ribosomal protein genes not only had a reduced synthesis rate, but also an increased degradation rate. This important observation needs careful validation, as genomic run-on experiments were used to measure half-lives, and this particular method was found to give results that correlated poorly with other measures of half-life in yeast (e.g. Chappelboim et al., 2022 for a comparison). As an additional validation, a temperature shift to 42{degree sign}C was used to show that , for specific ribosomal protein mRNA, the degradation was faster, assuming that transcription stops at that temperature. It would be important to cite and discuss the work from the Tollervey laboratory showing that a temperature shift to 42{degree sign}C leads to a strong and specific decrease in ribosomal protein mRNA levels, probably through an accelerated RNA degradation (Bresson et al., Mol Cell 2020, e.g. Fig 5E). Finally, the conclusion that mRNA deadenylation rate is altered in the absence of Sfp1, is difficult to assess from the presented results (Fig. 3D).

The effects of SFP1 on transcription were investigated by chromatin purification with Rpb3, a subunit of RNA polymerase, and the results were compared with synthesis rates determined by genomic run-on experiments. The decrease in polII presence on transcripts in the absence of SFP1 was not accompanied by a marked decrease in transcript output, suggesting an effect of Sfp1 in ensuring robust transcription and avoiding RNA polymerase backtracking. To further investigate the phenotypes associated with the depletion or absence of Sfp1, the authors examined the presence of Rpb4 along transcription units compared to Rpb3. An effect of spf1 deficiency was that this ratio, which decreased from the start of transcription towards the end of transcripts, increased slightly. To what extent this result is important for the main message of the manuscript is unclear.

Suggestions: a) please clearly indicate in the figures when they correspond to reanalyses of published results. b) In table S2, it would be important to mention what the results represent and what statistics were used for the selection of "positive" hits.

Strengths:

- Diversity of experimental approaches used.<br /> - Validation of large-scale results with appropriate reporters.

Weaknesses:

- Lack of controls for the CRAC results and lack of negative controls for the co-purification experiments that were used to validate specific mRNA targets potentially bound by Sfp1.<br /> - Several conclusions are derived from complex correlative analyses that fully depend on the validity of the aforementioned Sfp1-mRNA interactions.

https://www.youtube.com/watch?v=ZH1LWjzy6vY&ab_channel=South_Asian_Spirtuality%2BHugeInsomniaPlaylists

https://www.youtube.com/watch?v=Z41H-8DAspM&ab_channel=South_Asian_Spirtuality%2BHugeInsomniaPlaylists

geometric calibration

https://www.youtube.com/watch?v=2MmHWz6pV-s&ab_channel=South_Asian_Spirtuality%2BHugeInsomniaPlaylists

https://www.youtube.com/watch?v=BxqHWSXTJ1A&ab_channel=South_Asian_Spirtuality%2BHugeInsomniaPlaylists

如果你已经通过 CMake 生成了 Makefile，并且在之后修改了 CMakeLists.txt 文件，你可以直接使用 cmake --build . 来重新构建项目。

在使用 cmake --build . 命令时，CMake 会检测 CMakeLists.txt 文件的变更，并根据新的配置重新生成相应的构建系统文件（如 Makefile）。然后，它会调用生成的构建系统来执行编译、链接等构建操作，确保你的项目按照最新的配置进行构建，而不用先显式使用cmake.

这种方式可以简化构建过程，避免手动删除和重新生成 Makefile 的步骤，同时保证与 CMakeLists.txt 文件的同步性。

evidence

I am so familiar with this idea because I am a gamer who love playing action, survival and shooting games on my computer. I could say that I am addicted to games, but it does not make my life wrong as different cases we heard on news. I think it is similar to be aggressive when seeing violence on TV or social media. The more time we spend to observe any things, the deeper it is planted in our mind and behaviors. This is a real story, a few days ago before the course started, I spent a whole day like 14 hours to play a survival and shooting game with my friend. Then, when we about to offline, my friend, he told me that he saw someone standing outside in the balcony and his mind trick him to shoot that person just like what we were playing. (It is just his mind still in the game mode, he did not shoot anyone or owned a gun - just clarify). At that moment, we know that we are so tired and need to rest. There is a study about this effect. Here is the link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3003785/

To me, I think it is not only baby, but also everyone at every age will be affected by what that person observes or interacts with multiple times. However, there are some cases that show some people tends to hate what they saw so much. For example, some kids grow up in an unhappy family or with abusive parents, tends to be a better Mom or Dad in the future because they understand the pain.

Al final, el autor ejemplifica el proceso de investigación desde la epistemología cualitativa. Freud con un carácter disruptivo y desde una posición poco familiar para su época estudió la teoría del psicoanálisis, como exploración del inconsciente del ser humano y detalla que esto cobra importancia cuando un investigador a través de su tarea logra precisamente romper con lo establecido.

El investigador entonces debe librarse de normas y modelos, esto le permitirá realizar investigaciones de una forma inmersiva, planteando de esta forma nuevos recursos de lectura de una problemática o temática de estudio. El autor plantea que la estadística es un significado, pero que no es en su totalidad un criterio de legitimidad.

La objetividad enfatiza la idea de que los individuos y las creaciones de estos se ven atravesados por la producción simbólica predeterminada por un contexto normativo, el autor lo denomina "cultura".

Para mi, esta es la hipótesis del texto. Ya que es el punto de partida para que el autor se replanteé, precisamente a través de sus reflexiones una nueva forma de generar conocimiento o de hacer investigación, teniendo en cuenta la subjetividad del investigador, que se convierte en una variable a considerar en los resultados del estudio.

https://www.youtube.com/watch?v=utikhSseiqs

https://www.youtube.com/watch?v=utikhSseiqs

https://www.youtube.com/watch?v=utikhSseiqs

https://www.youtube.com/watch?v=utikhSseiqs

https://www.youtube.com/watch?v=utikhSseiqs

здесь мы просто сначала сортируем элементы по два. то есть, кладём в два массива по элементу. и далее обратно в остортированном порядке относительно этого элемента. выходим так, что в основном массиве заведома два элемента рядом будут остортированы, а потом уже берём по 4 элемента в два массива, и уже будет 4 по элемента отсортированных

и т.д

мы используем две вспомогательные ленты, где пытается составить отсортированные массивы. на одной такой ленте куча таких массивов. конец каждого помечен указателям.

как факт, мы точно уверен, что первые два массива из двух вспомогательных лент

дадут нам более отсортированный массив чем был до этого.

мы получаем два массива в который элементы идут по возрастанию: а мы как раз ходим по обеим массивом и смотри меньшей элемент.

мы заведома из двух массивов уже сделаем один отсортированный.

то есть с каждым разом на вспомогательных лентах будет все более и более большие отсортированные массивы

Practices to drive systems change: 1. Involve communities in decision-making 2. Use data, evidence, and research to develop solutions 3. Aim to influence policy 4. Build scalable solutions 5. Leverage technology for scale 6. Help strengthen institutional capacity in government 7. Partner with organisations to scale 8. Create viable markets for the under-privileged

есть так же алгоритм quickSelect придуманный тем, кто и этот придумал. он похож на этот, только главная суть поиска одного элемента в не остортированном массиве, так если бы он был был отсортирован.

там выбирается опорный элемент, но там ищется только расположение как раз опорного элемента, если бы от был отсортирован.

то есть, да мы просто ищем только один элемент

Habitus is everything that has made a person act, talk, move the way that they do and how they interpret things around them

This tells us that the way that we were raised and all the factors that go into that play major roles into who we become in the future and it takes time to change who we are and how we do things

Allama Iqbal Open University – AIOU Home https://aiouedupak.com/

ughrugh fuck you

something about cat hating jon and ned being distant from theon which leads both boys to leave and try to find their place in the world

oh ik his name

i would feel bad but theon lierally couldve hurt bran or even killed him instead

because your betraying everyone

youll learn eventually...i hope

can't even kill a man smh

ugh

oh yeah that IS strange

harrenhall...

nooo

free my girl

lmao

melisandre!!

not if you interpret it differently

WOAH

actually yeah

i think you get beheaded :(

also greyjoys??

:((

HUH I THOUGHT HE WAS RAMSEY??

NOOO

i feel bad for him

characters who wanna defy their faiths such a good trope

and thats what the starks are doing now

THE GREYJOYS UGH

huh

yupp

aegon?? yall are not targs

so thats what the dream meant

wtf thats so sad :(

hmm ben stark?? maybe someone else who went missing

children of the forest

uh oh

magic barriers?

this line was so common in the odyessy

you sweet summer child or whateevr they say

she's piecing it all together

包含地理信息方面的数据类型

包含集合这一数据类型

TikTok should be acknowledged here :)

layer.open是Layui框架中的一个函数，用于打开不同的层，默认层类型为信息框。以下是对layer.open函数中的type参数的详细解释：

问ai比较快 直接找到这些层的样式实现比较困难 0（信息框，默认）：表示默认的信息框方式。 1（页面层）：表示打开一个新的页面层。 2（iframe层）：表示通过iframe层以HTML内容的形式打开。 3（加载层）：表示展示加载动画。 4（tips层）：表示显示简单的全局提示信息。

I don't see my preview test ;o

Test également

DOI: 10.1016/j.celrep.2021.108862

Resource: (Roche Cat# 12013819001, RRID:AB_390917)

Curator: @Naa003

SciCrunch record: RRID:AB_390917

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What is this?

DOI: 10.1016/j.celrep.2021.108862

Resource: (Roche Cat# 12013819001, RRID:AB_390917)

Curator: @Naa003

SciCrunch record: RRID:AB_390917

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What is this?

DOI: 10.3390/biomedicines12071550

Resource: (CCLV Cat# CCLV-RIE 1018, RRID:CVCL_0063)

Curator: @evieth

SciCrunch record: RRID:CVCL_0063

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What is this?

DOI: 10.1016/j.xpro.2024.103186

Resource: (ATCC Cat# TIB-71, RRID:CVCL_0493)

Curator: @evieth

SciCrunch record: RRID:CVCL_0493

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What is this?

DOI: 10.1016/j.isci.2024.110156

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

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:000664

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What is this?

DOI: 10.1016/j.isci.2024.109912

Resource: (IMSR Cat# JAX_007676,RRID:IMSR_JAX:007676)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:007676

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What is this?

DOI: 10.1016/j.isci.2024.109912

Resource: (IMSR Cat# JAX_008463,RRID:IMSR_JAX:008463)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:008463

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What is this?

DOI: 10.1016/j.isci.2024.109912

Resource: (IMSR Cat# JAX_003474,RRID:IMSR_JAX:003474)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:003474

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What is this?

DOI: 10.1016/j.isci.2024.109912

Resource: (IMSR Cat# JAX_008875,RRID:IMSR_JAX:008875)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:008875

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What is this?

DOI: 10.1016/j.isci.2024.109912

Resource: (ATCC Cat# CRL-2647, RRID:CVCL_0635)

Curator: @evieth

SciCrunch record: RRID:CVCL_0635

---

What is this?

DOI: 10.1016/j.isci.2024.110138

Resource: (CCLV Cat# CCLV-RIE 1018, RRID:CVCL_0063)

Curator: @evieth

SciCrunch record: RRID:CVCL_0063

---

What is this?

DOI: 10.1016/j.xpro.2024.103148

Resource: RRID:Addgene_12259

Curator: @evieth

SciCrunch record: RRID:Addgene_12259

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What is this?

DOI: 10.1016/j.isci.2024.110114

Resource: IMSR_JAX:032406

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:032406

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What is this?

DOI: 10.1016/j.isci.2024.110114

Resource: (IMSR Cat# JAX_008449,RRID:IMSR_JAX:008449)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:008449

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What is this?

DOI: 10.1016/j.isci.2024.110114

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

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:000664

---

What is this?

DOI: 10.15252/embr.201948892

Resource: (BDSC Cat# 1988,RRID:BDSC_1988)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_1988

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What is this?

DOI: 10.15252/embr.201948892

Resource: (BDSC Cat# 1620,RRID:BDSC_1620)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_1620

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What is this?

DOI: 10.15252/embr.201948892

Resource: BDSC_8209

Curator: @bpowell22

SciCrunch record: RRID:BDSC_8209

---

What is this?

DOI: 10.15252/embr.201948892

Resource: (BDSC Cat# 30007,RRID:BDSC_30007)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_30007

---

What is this?

DOI: 10.1016/j.isci.2024.110208

Resource: (RRID:CVCL_5J31)

Curator: @evieth

SciCrunch record: RRID:CVCL_5J31

---

What is this?

DOI: 10.1016/j.isci.2024.110208

Resource: (RRID:CVCL_D615)

Curator: @evieth

SciCrunch record: RRID:CVCL_D615

---

What is this?

DOI: 10.1016/j.isci.2024.110208

Resource: (IZSLER Cat# BS CL 86, RRID:CVCL_0059)

Curator: @evieth

SciCrunch record: RRID:CVCL_0059

---

What is this?

DOI: 10.1534/g3.120.401800

Resource: (BDSC Cat# 4779,RRID:BDSC_4779)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_4779

---

What is this?

DOI: 10.3791/61911

Resource: BDSC_8749

Curator: @bpowell22

SciCrunch record: RRID:BDSC_8749

---

What is this?

DOI: 10.3791/61911

Resource: (BDSC Cat# 3605,RRID:BDSC_3605)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_3605

---

What is this?

DOI: 10.1016/j.isci.2024.110241

Resource: (DSHB Cat# F5D, RRID:AB_2146602)

Curator: @evieth

SciCrunch record: RRID:AB_2146602

---

What is this?

DOI: 10.1016/j.isci.2024.110241

Resource: (IMSR Cat# JAX_010530,RRID:IMSR_JAX:010530)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:010530

---

What is this?

DOI: 10.1016/j.isci.2024.110241

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

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:000664

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: (BDSC Cat# 27656,RRID:BDSC_27656)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_27656

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: (BDSC Cat# 51941,RRID:BDSC_51941)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_51941

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: (BDSC Cat# 25684,RRID:BDSC_25684)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_25684

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: (BDSC Cat# 27343,RRID:BDSC_27343)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_27343

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: (BDSC Cat# 5137,RRID:BDSC_5137)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_5137

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: RRID:BDSC_41286

Curator: @bpowell22

SciCrunch record: RRID:BDSC_41286

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: (BDSC Cat# 33068,RRID:BDSC_33068)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_33068

---

What is this?

DOI: 10.1371/journal.pgen.1009181

Resource: (BDSC Cat# 26654,RRID:BDSC_26654)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_26654

---

What is this?

DOI: 10.1186/s12863-020-00866-y

Resource: (BDSC Cat# 64349,RRID:BDSC_64349)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_64349

---

What is this?

DOI: 10.1242/bio.049692

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bpowell22

SciCrunch record: RRID:SCR_006457

---

What is this?

DOI: 10.1080/19336934.2020.1832416

Resource: (BDSC Cat# 55821,RRID:BDSC_55821)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_55821

---

What is this?

DOI: 10.1080/19336934.2020.1832416

Resource: RRID:BDSC_90850

Curator: @bpowell22

SciCrunch record: RRID:BDSC_90850

---

What is this?

DOI: 10.1080/19336934.2020.1832416

Resource: BDSC_90851

Curator: @bpowell22

SciCrunch record: RRID:BDSC_90851

---

What is this?

DOI: 10.1080/19336934.2020.1832416

Resource: RRID:BDSC_90852

Curator: @bpowell22

SciCrunch record: RRID:BDSC_90852

---

What is this?

DOI: 10.1080/19336934.2020.1832416

Resource: (BDSC Cat# 51324,RRID:BDSC_51324)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_51324

---

What is this?

DOI: 10.1038/s41598-020-75009-5

Resource: (BDSC Cat# 51324,RRID:BDSC_51324)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_51324

---

What is this?

DOI: 10.1038/s41598-020-75009-5

Resource: BDSC_28057

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28057

---

What is this?

DOI: 10.1038/s41598-020-75009-5

Resource: BDSC_44427

Curator: @bpowell22

SciCrunch record: RRID:BDSC_44427

---

What is this?

DOI: 10.1038/s41598-020-75009-5

Resource: (BDSC Cat# 9332,RRID:BDSC_9332)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_9332

---

What is this?

DOI: 10.1038/s41598-020-75009-5

Resource: BDSC_38358

Curator: @bpowell22

SciCrunch record: RRID:BDSC_38358

---

What is this?

DOI: 10.1038/s41598-020-75009-5

Resource: BDSC_42631

Curator: @bpowell22

SciCrunch record: RRID:BDSC_42631

---

What is this?

DOI: 10.1038/s41598-020-75009-5

Resource: (BDSC Cat# 6452,RRID:BDSC_6452)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_6452

---

What is this?

DOI: 10.1007/s12192-020-01171-4

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bpowell22

SciCrunch record: RRID:SCR_006457

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: RRID:BDSC_19933

Curator: @bpowell22

SciCrunch record: RRID:BDSC_19933

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: BDSC_67857

Curator: @bpowell22

SciCrunch record: RRID:BDSC_67857

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: (BDSC Cat# 8442,RRID:BDSC_8442)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_8442

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: (BDSC Cat# 35787,RRID:BDSC_35787)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_35787

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: (BDSC Cat# 25750,RRID:BDSC_25750)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_25750

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: (BDSC Cat# 27390,RRID:BDSC_27390)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_27390

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: (BDSC Cat# 7415,RRID:BDSC_7415)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_7415

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: (BDSC Cat# 3954,RRID:BDSC_3954)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_3954

---

What is this?

DOI: 10.1016/j.redox.2020.101762

Resource: (BDSC Cat# 3605,RRID:BDSC_3605)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_3605

---

What is this?

DOI: 10.1111/jnc.16142

Resource: (Genox Corpooration Cat# MHN-100P, RRID:AB_1106813)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_1106813

---

What is this?

DOI: 10.3389/fnins.2020.547746

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @bpowell22

SciCrunch record: RRID:SCR_006457

---

What is this?

DOI: 10.3389/fnins.2020.547746

Resource: RRID:BDSC_2537

Curator: @bpowell22

SciCrunch record: RRID:BDSC_2537

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_28265

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28265

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_28262

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28262

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_28227

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28227

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_25199

Curator: @bpowell22

SciCrunch record: RRID:BDSC_25199

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_28204

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28204

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_28278

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28278

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 28189,RRID:BDSC_28189)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28189

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 25186,RRID:BDSC_25186)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_25186

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: RRID:BDSC_28142

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28142

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: RRID:BDSC_28128

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28128

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 55026,RRID:BDSC_55026)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_55026

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 6899,RRID:BDSC_6899)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_6899

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 68365,RRID:BDSC_68365)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_68365

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 68336,RRID:BDSC_68336)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_68336

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: RRID:BDSC_64296

Curator: @bpowell22

SciCrunch record: RRID:BDSC_64296

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 33062,RRID:BDSC_33062)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_33062

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 32184,RRID:BDSC_32184)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_32184

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 28280,RRID:BDSC_28280)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_28280

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 5193,RRID:BDSC_5193)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_5193

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: (BDSC Cat# 32219,RRID:BDSC_32219)

Curator: @bpowell22

SciCrunch record: RRID:BDSC_32219

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: RRID:BDSC_57109

Curator: @bpowell22

SciCrunch record: RRID:BDSC_57109

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_57108

Curator: @bpowell22

SciCrunch record: RRID:BDSC_57108

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_30274

Curator: @bpowell22

SciCrunch record: RRID:BDSC_30274

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: BDSC_23296

Curator: @bpowell22

SciCrunch record: RRID:BDSC_23296

---

What is this?

DOI: 10.1523/JNEUROSCI.0142-20.2020

Resource: RRID:BDSC_51087

Curator: @bpowell22

SciCrunch record: RRID:BDSC_51087

---

What is this?