Single-cell RNA sequencing of mouse islets exposed to proinflammatory cytokines

Single-cell RNA-seq was used to show that islet endocrine cells respond to acute cytokine exposure with an increase in the expression of protective genes and the absence of apoptotic gene expression.

--An editable version of the final text (.DOC or .DOCX) is needed for copyediting (no PDFs).
--High-resolution figure, supplementary figure and video files uploaded as individual files: See our detailed guidelines for preparing your production-ready images, https://www.life-sciencealliance.org/authors --Summary blurb (enter in submission system): A short text summarizing in a single sentence the study (max. 200 characters including spaces). This text is used in conjunction with the titles of papers, hence should be informative and complementary to the title and running title. It should describe the context and significance of the findings for a general readership; it should be written in the present tense and refer to the work in the third person. Author names should not be mentioned.

B. MANUSCRIPT ORGANIZATION AND FORMATTING:
Full guidelines are available on our Instructions for Authors page, https://www.life-sciencealliance.org/authors We encourage our authors to provide original source data, particularly uncropped/-processed electrophoretic blots and spreadsheets for the main figures of the manuscript. If you would like to add source data, we would welcome one PDF/Excel-file per figure for this information. These files will be linked online as supplementary "Source Data" files. ***IMPORTANT: It is Life Science Alliance policy that if requested, original data images must be made available. Failure to provide original images upon request will result in unavoidable delays in publication. Please ensure that you have access to all original microscopy and blot data images before submitting your revision.*** Stancill et al present single cell RNAseq data and analysis on mouse islet treated with a short (6 hr) time with cytokines interleukin 1beta, interferon gamma or both. These cytokines are thought to be involved in the destruction of pancreatic beta cells in Type 1 diabetes, and their effects on islets have been studied extensively. Two aspects of this study add value: first, the analysis of single islet cells and second, using only a 6 hr treatment with cytokine so the direct effect was seen rather than one later down a cascade of events. Mouse islets were treated , dispersed and analyzed., with over 7700 single islet cells passing quality control. The most important findings were supported and included :1) there was heterogeneity of response to the cytokines with similar genes were induced in the responsive cells whether beta cells ( 70% of total beta cells), glucagon producing alpha cells (39%) or somatostatin expressing delta cells ( 69%); 2) IL1beta had the most effect on the beta cell identity; 3) IL1beta had antiviral effects which had previously been attributed only to IFNg; 4) the combination of both cytokines had greater effects that the sum of both alone.; 5) there was no immediate induction of apoptotic genes; ) the beta cells were more sensitive to either single cytokine than the other islet cells.
The authors provide an excellent introduction and explanation for the rationale for doing scRNAseq on islets treated with cytokines, providing references to original resources rather than falling into the unfortunate habit of referring only to recent reviews. While there are always issues whether the heterogeneity found in sc RNA seq is due to the inherent differences due to cells out of synchrony or the less extensive array of expressed genes detected, the differences here were robust and often confirmed with PCR.
One suggestion that would improve this paper is to include supplemental tables of the differentially expressed genes. It would be particularly of interest to the islet biologists to know what were the 13 genes (6 identified in text) repressed in beta cells by Il1b and the 7 by IFNg. Were there additional ones from the combination?
Reviewer #2 (Comments to the Authors (Required)): Stancill et al describe gene expression changes in mouse islets upon exposure to proinflammatory cytokines followed single-cell RNA-seq. Authors identify Nos2 expressing cells and other gene expression changes, some of these changes were further confirmed using qPCR. Although this study attempts to provide understanding of role of cytokines in gene expression changes in islets, however, there are several caveats that should be addressed. 1) How many samples per group were used? What is the batch of treatments, 10x genomics emulsion formation and sequencing? What are sequencing depth for each of the samples? What are the median genes and UMIs in each sample and in each of the clusters shown in Figure 1B. How were the cytokine doses and treatment duration determined? 2) 10x genomics emulsion formation is prone to doublet cell capture (~5%). Authors should elaborate on how the doublets were identified and removed. 3) What are the gene expression differences in samples within the treatment group? The Cluster 7 in Figure 1B has been highlighted over and over again. This cluster is formed of few cells and doesn't separate well from the other groups of cells (cluster 0, 1, 4 etc.) and therefore it could simply be a result of not applying optimal clustering parameters. If the resolution is reduced, it will merge with neighboring clusters. Single-cell datasets are prone to sample processing (tissue dissociation) effects that sometimes induce stress related genes. Cluster 5 shows clear stress gene signature based on Figure S1 (authors made no effort to indicate what color indicates which cell type in the heatmap). Any cell type of interest (in this case Cluster 7, Nos2 positive cells) should be represented in multiple samples and should not be accounted by one sample. Authors have not made any efforts to clarify this issue. 4) Although authors indicate that Ins1 and Ins2 are markers of beta cells, Figure 1C. shows these genes expressed in almost all cell types. This indicates ambient RNA contamination that arises during the tissue dissociation processes. Processing multiple samples instead of one will help determine the background gene expression is indeed due to ambient RNA (technical) or biological observation. In addition, it will help deduce the extent of such ambient RNA contamination to other cell types. 5) Cluster 6 and cluster 11 are indicated as proliferating cells. However, these cell clusters lack classic and abundantly expressed markers such as Mki67 and Top2a ( Figure S1). More analysis on these cell cluster identification is required. 6) To identify differentially expressed genes among the different treatment groups, authors should use averaged gene expression (normalized to 10k, TP10k) profiles for a given cell type and then use DeSeq2 or similar statistical tools. Pathway analysis tools like DAVID can be used to aid the observations from DeSeq2 differential gene expression analysis. In figure 1 (E-I), authors do not make it clear what do the fold changes indicate. Are these cluster 7 verses all other clusters? 7) In figure 2, authors attempt to subcluster the beta cell types. The clusters are scattered, again indicating that optimal parameters were not used. Authors should provide a differential gene expression heatmap indicating top 10 differentially expressed genes in each of the 7 clusters. The Nos2 expressing b-cells are now shown to group into two clusters (cluster 4 and cluster 6). Are these two clusters split of cluster 7 in Figure 1B? 8) To determine differentially expressed genes between Nos2 positive cluster 4 and cluster 6, authors should use Seurat parameter (ex. "FindMarkers") and indicate top 10-20 genes. 9) Authors mention: "In addition, to the Nos2-expressing clusters, we also identified a cluster (b-cell Cluster 2) enriched for ribosomal proteins, chaperones, RNA-binding proteins, and mitochondrial proteins (Fig. 2F)." As mentioned earlier, b-cell cluster 2 expresses stress response genes which are often seen in single cell datasets and are result of tissue dissociation. (Brink et al, PMID: 28960196). 10) Authors consistently mention that "cluster 7" in Figure C are the "cytokine-responsive" cells since these cells express Nos2 in response to IL-1b and IFN-γ exposure. However many other clusters (cluster 0, 3, 8, 11, 14) show expression of Nos2. In fact, more than 25% of cells in cluster 0 and cluster 14 express Nos2. 11) To make it readable and easy to understand, authors should use cell type names in Figure 1C rather than using the cluster numbers.

1) One suggestion that would improve this paper is to include supplemental tables of the differentially expressed genes. It would be particularly of interest to the islet biologists to know what were the 13 genes (6 identified in text) repressed in beta cells by Il1b and the 7 by IFNg. Were there additional ones from the combination?
Response: We appreciate this suggestion and agree. We have included the suggested supplementary tables in the revised manuscript.

Reviewer #2 (Comments to the Authors):
1) How many samples per group were used? What is the batch of treatments, 10x genomics emulsion formation and sequencing? What are sequencing depth for each of the samples? What are the median genes and UMIs in each sample and in each of the clusters shown in Figure 1B. How were the cytokine doses and treatment duration determined?
Response: While single cell RNAseq was performed on one sample per treatment group, key findings were confirmed by qPCR for classes of genes increased and decreased in expression levels. Single cell capture was performed using the 10X Genomics Chromium Single Cell 3′ v3 Reagent Kit. Sequencing was performed using the Illumina NextSeq 500/550 High Output Kit v2.5 flow cell (150 cycles). We have updated the text to include these requested details in the Methods. The sequencing depth of each Sample was added to the revised text, and we have added Supplemental Tables listing the mean genes and UMIs per cluster per sample. The results are also shown below for your convenience.
2) 10x genomics emulsion formation is prone to doublet cell capture (~5%). Authors should elaborate on how the doublets were identified and removed.
Response: Outlier cells were removed (number of genes less than 200 or greater than 4000). This has been clarified in the Methods.
3) What are the gene expression differences in samples within the treatment group? The Cluster 7 in Figure 1B has been highlighted over and over again. This cluster is formed of few cells and doesn't separate well from the other groups of cells (cluster 0, 1, 4 etc.) and therefore it could simply be a result of not applying optimal clustering parameters. If the resolution is reduced, it will merge with neighboring clusters. Single-cell datasets are prone to sample processing (tissue dissociation) effects that sometimes induce stress related genes. Cluster 5 shows clear stress gene signature based on Figure S1  Response: Figures 3-6 focus on our differential expression analyses comparing gene expression differences across the treatments by cell type. We have now included new supplemental tables (Tables S4-S7) showing the genes that are differentially expressed based on those analyses. To address your comment about highlighting Cluster 7 -we have now included a supplemental figure (Fig S2) indicating the percentage of each cluster that originates from each of the 4 samples. We focused on Cluster 7 for much of the manuscript because this is the cluster of cells that is enriched for Nos2, which is characteristic of cytokine exposure. This new figure we included indicates that Cluster 7 is primarily made up of cells from the samples treated with IL-1β (Samples 2 and 4). This is expected, since Nos2 expression is IL-1β-dependent in β-cells. We would expect only minimal contribution to this cluster from untreated cells or cells treated only with IFN-γ. To address your other point, we have now re-labeled the heatmap in Fig S1 to indicate the cell types represented by each cluster. Figure 1C. shows these genes expressed in almost all cell types. This indicates ambient RNA contamination that arises during the tissue dissociation processes. Processing multiple samples instead of one will help determine the background gene expression is indeed due to ambient RNA (technical) or biological observation. In addition, it will help deduce the extent of such ambient RNA contamination to other cell types.

4) Although authors indicate that Ins1 and Ins2 are markers of beta cells,
Response: Because endocrine hormones are expressed at extremely high levels in islet cells, it is not surprising to observe ambient RNA contamination. Indeed, others have comparable RNA contamination using similar methodologies. In PMID 33432158 (Scientific Reports 2021) Figure S1, INS expression is found in human αcells and GCG expression in human β-cells). In PMID 32302527 (Cell Metabolism 2020) Figure S2, INS expression is observed in human α-, δ-, and PP-cells. Furthermore, while we list the islet hormones as the identifier genes of the endocrine cell clusters in Fig 1C (as is customary in the field), the clusters can be further identified based on expression of other characteristic genes. A new supplementary table (Table S1) has been included which lists all genes enriched in each cluster. Therefore, while there may be low levels of RNA contamination of Ins1 and Ins2 in non-β-cell clusters, we are confident that the cell types have been identified correctly. Figure S1). More analysis on these cell cluster identification is required.

5) Cluster 6 and cluster 11 are indicated as proliferating cells. However, these cell clusters lack classic and abundantly expressed markers such as Mki67 and Top2a (
Response: By our analysis, only 17 genes were enriched in Cluster 6. Other than Geminin (Gmnn), the remaining 16 genes are characteristic of -cells and were similarly enriched in other -cell clusters. Therefore, we have no other genes, besides Gmnn, to distinguish this cluster from the other -cell clusters. A greater number of genes (200) were enriched in Cluster 11. If we consider the top 20 enriched genes, all, with the exception of Gmnn, are similarly enriched in at least one of the other non--endocrine clusters (2, 3, 9, or 14). In fact, most of the top 20 genes in Cluster 11 were enriched in multiple non--endocrine clusters. New Supplemental Table S1 is included to allow readers to see all genes enriched in these clusters. We were surprised to identify these clusters in our analysis, because islet endocrine cells are terminally-differentiated and are not proliferative. Gmnn is known to be expressed in replicative cells (PMID 12107111). Therefore, we have identified these clusters to the best of our ability as "proliferative" cells and have indicated that they are enriched for Gmnn (Fig 1B). We assessed Mki67 and Top2a expression in all clusters and detected no expression (shown below). However, when we assessed cell cycle phase, Clusters 6 and 11 appear to consist entirely of cells in S Phase of the cell cycle (see below). Based on the expert reviewer's concern, we tempered the identification of these clusters in the revised text, stating that Gmnn expression "suggest[s] that they may represent proliferative cells."

6) To identify differentially expressed genes among the different treatment groups, authors should use averaged gene expression (normalized to 10k, TP10k) profiles for a given cell type and then use DeSeq2 or similar statistical tools. Pathway analysis tools like DAVID can be used to aid the observations from DeSeq2
Cluster 6 Cluster 11 differential gene expression analysis. In figure 1

(E-I), authors do not make it clear what do the fold changes indicate. Are these cluster 7 verses all other clusters?
Response: We have updated the Methods to include more details about the normalization method: "All samples were normalized using Seurat's default normalization settings. Briefly, reads in each cell for each gene were divided by the total number of reads within that cell, multiplied by a factor of 10000, and transformed using the natural logarithm." We have also updated the Legend of Figure 1 to clarify that the fold changes indicate changes in Cluster 7 compared to all other clusters. figure 2, authors attempt to subcluster the beta cell types. The clusters are scattered, again indicating that optimal parameters were not used. Authors should provide a differential gene expression heatmap indicating top 10 differentially expressed genes in each of the 7 clusters. The Nos2 expressing b-cells are now shown to group into two clusters (cluster 4 and cluster 6). Are these two clusters split of cluster 7 in Figure 1B?

7) In
Response: As requested by the reviewers, we have addressed each of the outlined issues in the new data presented in Fig S3, including a heat map of each of the Clusters of -cells.

8) To determine differentially expressed genes between Nos2 positive cluster 4 and cluster 6, authors should use Seurat parameter (ex. "FindMarkers") and indicate top 10-20 genes.
Response: We performed the analysis you suggested and have included the top 10 genes increased in Cluster 4 vs Cluster 6 and vice versa in the revised Supplemental Figure S3.

9) Authors mention: "In addition, to the Nos2-expressing clusters, we also identified a cluster (b-cell Cluster 2)
enriched for ribosomal proteins, chaperones, RNA-binding proteins, and mitochondrial proteins (Fig. 2F)." As mentioned earlier, b-cell cluster 2 expresses stress response genes which are often seen in single cell datasets and are result of tissue dissociation. (Brink et al, PMID: 28960196).
Response: Since the cells expressing stress response genes also do not respond to cytokines in the expected way, we think the stress occurred prior to the cytokine treatment, which, in our case, was prior to the islet dissociation for scRNA-seq. It is likely that the stress was induced by the islet isolation process, not by the dissociation to single cells. Regardless of the reason for the stress response genes, we now acknowledge in the Discussion that this is a result of the experimental process and does not likely represent an endogenous cell population. However, the inclusion of this population in our analysis allows us to speculate about why only ~75% of the β-cells treated with both cytokines responded by expressing Nos2likely because the remaining ~25% were stressed prior to the treatment. This conclusion aligns with previous studies (PMIDs: 9832444, 15315910, 7769124, 8706913). We think this is an important observation for the field because human islet preparations often express high levels of heat shock proteins, preventing them from responding properly to cytokine exposure (PMID: 10751413), and isolation stress is known to contribute to human islet cell death (PMID: 11141234). Moreover, this connection has been overlooked in the literature, and some studies have suggested that human islets do not respond to cytokines by expressing Nos2 (PMID: 7514190). Our data presented here supports the idea that human islets (as well as rodent islets) do not express Nos2 in response to cytokines when they are stressed. Figure C are the "cytokine-responsive" cells since these cells express Nos2 in response to IL-1b and IFN-γ exposure. However many other clusters (cluster 0, 3, 8, 11, 14) show expression of Nos2. In fact, more than 25% of cells in cluster 0 and cluster 14 express Nos2.

10) Authors consistently mention that "cluster 7" in
Response: We focused on Cluster 7 because the cells in this cluster are significantly enriched for Nos2 expression compared to expression in all other clusters. We were also interested in this cluster because it is primarily (if not entirely) made up of β-cells. The low percentage of β-cells in Cluster 0 that express Nos2, as you mention, is likely attributable to a small number of β-cells from Samples 2 and 4 (cells exposed to IL-1β) being assigned to that cluster rather than Cluster 7. This is supported by the data presented in Fig S2. While the cell clustering is not perfect, we hope you will agree that the majority of β-cells expressing Nos2 reside in Cluster 7, which is why we chose to focus on those cells for much of the manuscript. Most of the clusters you mention as having cells expressing Nos2 are other cell types (non-β-cells), including Clusters 3, 8, 11, and 14. We would not expect those cells to be sufficiently different from others of the same cell type to cluster with a primarily β-cell cluster. As we discuss later in the manuscript (Fig 4), other islet cell types express Nos2 in response to cytokines, not just β-cells (including α-cells and δ-cells). Figure 1C rather than using the cluster numbers.

11) To make it readable and easy to understand, authors should use cell type names in
Response: We agree and have made this change. Thank you for submitting your revised manuscript entitled "Single-cell RNA-sequencing of mouse islets exposed to proinflammatory cytokines". We would be happy to publish your paper in Life Science Alliance pending final revisions necessary to meet our formatting guidelines.
While the reviewers were unable to comment on the revised manuscript, the revisions were assessed by our internal team of editors and an advisory member with the subject matter expertise and were deemed to have been appropriately performed.
Along with the points listed below, please also attend to the following, -please add callouts for Figures S6A, B, C; S8A to your main manuscript text -please use the [10 author names, et al.] format in your references (i.e. limit the author names to the first 10) -Panel 8A and Panel S8A; Panel 8D first column Panel S8E first column are the same. Looks like that is by design, but we would appreciate it if you can clarify this in the figure legends, so the readers are aware that the figure panels have been repeated.
If you are planning a press release on your work, please inform us immediately to allow informing our production team and scheduling a release date.
To upload the final version of your manuscript, please log in to your account: https://lsa.msubmit.net/cgi-bin/main.plex You will be guided to complete the submission of your revised manuscript and to fill in all necessary information. Please get in touch in case you do not know or remember your login name.
To avoid unnecessary delays in the acceptance and publication of your paper, please read the following information carefully.
A. FINAL FILES: These items are required for acceptance.
--An editable version of the final text (.DOC or .DOCX) is needed for copyediting (no PDFs).
--High-resolution figure, supplementary figure and video files uploaded as individual files: See our detailed guidelines for preparing your production-ready images, https://www.life-sciencealliance.org/authors --Summary blurb (enter in submission system): A short text summarizing in a single sentence the study (max. 200 characters including spaces). This text is used in conjunction with the titles of papers, hence should be informative and complementary to the title. It should describe the context and significance of the findings for a general readership; it should be written in the present tense and refer to the work in the third person. Author names should not be mentioned.

B. MANUSCRIPT ORGANIZATION AND FORMATTING:
Full guidelines are available on our Instructions for Authors page, https://www.life-sciencealliance.org/authors We encourage our authors to provide original source data, particularly uncropped/-processed electrophoretic blots and spreadsheets for the main figures of the manuscript. If you would like to add source data, we would welcome one PDF/Excel-file per figure for this information. These files will be linked online as supplementary "Source Data" files. **Submission of a paper that does not conform to Life Science Alliance guidelines will delay the acceptance of your manuscript.** **It is Life Science Alliance policy that if requested, original data images must be made available to the editors. Failure to provide original images upon request will result in unavoidable delays in publication. Please ensure that you have access to all original data images prior to final submission.** **The license to publish form must be signed before your manuscript can be sent to production. A link to the electronic license to publish form will be sent to the corresponding author only. Please take a moment to check your funder requirements.** **Reviews, decision letters, and point-by-point responses associated with peer-review at Life Science Alliance will be published online, alongside the manuscript. If you do want to opt out of having the reviewer reports and your point-by-point responses displayed, please let us know immediately.** Thank you for your attention to these final processing requirements. Please revise and format the manuscript and upload materials within 5 days.
Thank you for this interesting contribution, we look forward to publishing your paper in Life Science Alliance. Thank you for submitting your Research Article entitled "Single-cell RNA-sequencing of mouse islets exposed to proinflammatory cytokines". It is a pleasure to let you know that your manuscript is now accepted for publication in Life Science Alliance. Congratulations on this interesting work.
The final published version of your manuscript will be deposited by us to PubMed Central upon online publication.
Your manuscript will now progress through copyediting and proofing. It is journal policy that authors provide original data upon request.
Reviews, decision letters, and point-by-point responses associated with peer-review at Life Science Alliance will be published online, alongside the manuscript. If you do want to opt out of having the reviewer reports and your point-by-point responses displayed, please let us know immediately. ***IMPORTANT: If you will be unreachable at any time, please provide us with the email address of an alternate author. Failure to respond to routine queries may lead to unavoidable delays in publication.*** Scheduling details will be available from our production department. You will receive proofs shortly before the publication date. Only essential corrections can be made at the proof stage so if there are any minor final changes you wish to make to the manuscript, please let the journal office know now.

DISTRIBUTION OF MATERIALS:
Authors are required to distribute freely any materials used in experiments published in Life Science Alliance. Authors are encouraged to deposit materials used in their studies to the appropriate repositories for distribution to researchers.
You can contact the journal office with any questions, contact@life-science-alliance.org Again, congratulations on a very nice paper. I hope you found the review process to be constructive and are pleased with how the manuscript was handled editorially. We look forward to future exciting submissions from your lab.