Identification of two cancer stem cell-like populations in triple-negative breast cancer xenografts

  1. Jun Nakayama
  2. Hiroko Matsunaga
  3. Koji Arikawa
  4. Takuya Yoda
  5. Masahito Hosokawa
  6. Haruko Takeyama
  7. Yusuke Yamamoto
  8. Kentaro Semba

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Abstract

Gene expression analysis at the single-cell level by next-generation sequencing has revealed the existence of clonal dissemination and microheterogeneity in cancer metastasis. The current spatial analysis technologies can elucidate the heterogeneity of cell–cell interactions in situ. To reveal the regional and expressional heterogeneity in primary tumors and metastases, we performed transcriptomic analysis of microtissues dissected from a triple-negative breast cancer (TNBC) cell line MDA-MB-231 xenograft model with our automated tissue microdissection punching technology. This multiple-microtissue transcriptome analysis revealed three cancer cell-type clusters in the primary tumor and axillary lymph node metastasis, two of which were cancer stem cell (CSC)-like clusters (CD44/MYC-high, HMGA1-high). Reanalysis of public single-cell RNA-sequencing datasets confirmed that the two CSC-like populations existed in TNBC xenograft models and in TNBC patients. The diversity of these multiple CSC-like populations could cause differential anticancer drug resistance, increasing the difficulty of curing this cancer.

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  1. Version published to 10.1242/dmm.049538
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    Reply to the reviewers

    Manuscript number: RC-2021-01118

    Corresponding author(s): Jun, Nakayama and Kentaro, Semba

    1. General Statements

    We are grateful to all of the reviewers for their critical comments and insightful suggestions that have helped us considerably improve our paper. As indicated in the responses that follow, we have taken all of these comments and suggestions into account in the revised version of our paper, including the supplementary information.

    In the revised manuscript, we focus on the existence of two cancer stem cell-like populations in TNBC xenograft model and patients. The response to each reviewer is described below.

    Sincerely,

    Jun Nakayama

    Kentaro Semba

    Department of Life Science and Medical Bioscience

    School of Advanced Science and Engineering

    Waseda University

    E-mail: junakaya@ncc.go.jp or jnakayama.re@gmail.com to JN

    ksemba@waseda.jp to KS

    2. Point-by-point description of the revisions

    *Reviewer #1 (Evidence, reproducibility and clarity (Required)): ** **Summary:** Nakayama and colleagues use their previously developed automated tissue microdissection punching platform to perform spatial transcriptomics on a breast cancer xenograft model. Using transcriptomics on multiple clumps of 10-30 cells from different regions in a tumor and a lymph node metastasis they identified different cell-type clusters. Two of these clusters expressed different cancer stem cell markers. This led the authors to suggest that two distinct cancer stem cell(-like) populations may exist within one (breast) tumor, which could potentially make tumors more drug-resilient.

    **Major comments:** While the quality of the presented sequencing data is good and the manuscript is mostly written in a clear and accessible style, there are some concerns that limit the impact of this story. Most importantly, the manuscript in its present form does not convince me that the MDA-MB-231 xenografts indeed contain two distinct populations of cancer stem(-like) cells.

    1.The data obtained are not single cell data, which makes it difficult -if not impossible- to draw conclusions about presence of cancer stem cells. Each data point is the average of 10-30 cells, and the interpretation of the data is severely limited by this. How can the quantification of expression of CD44/MYC/HMGA1 in clumps of 10-30 cells teach us something about the stemness of tumor cells? *

    Answer: We would thank the comment. The reviewer’s suggestion is an important point; however, this is technical limitation of spatial transcriptomics technology. Most advanced spatial transcriptomics technologies, e.g. Visium (10x Genomics), also have the same problem. It means that our technology and the advanced technologies are technics to analyze gene expression and characteristics of tissues from 10-30 cells in each spot. Although high resolution spatial transcriptomics has been developed in 2021 [1], it is not generally used yet as described in the comment (Significance) from reviewer1.

    From our spatial analysis, we identified that CD44, MYC, and HMGA1 were expressed from human cancer cell. Their expression profiles were distinct among specific parts of the tumor section. To validate the existence of two types of cancer stem-like cells in TNBC tumors, we performed the additional analysis with the public scRNA-seq datasets of high-metastatic MDA-MB-23-LM2 xenograft model (GSE163210) [2]. This study performed scRNA-seq analysis of primary tumor and circulating tumor cells in MDA-MB-231-LM2 xenograft model. We analyzed it with Seurat/R (Figure A-1). As a result of reanalysis, HMGA1 and CD44 expression were confirmed at single-cell resolution (Figure A-2,3). These results verified the existence of two cancer stem cell-like populations (HMGA1-high, CD44-high) in MDA-MB-231 xenograft. Hence, the study of MDA-MB-231 xenograft supported our findings from spatial transcriptomics.

    Additionally, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As a result, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B). We believe that our findings are solid results because the findings were also validated by other methods.

    In the revised manuscript, Figure A are incorporated as Figure 3B-E. Figure B is incorporated as Figure 3A. Hope our new results will be now accepted by the learned Reviewer and Editor.

    Figure A-1. Reanalysis of scRNA-seq of metastatic MDA-MB-231 xenograft

    Flowchart of the public single-cell RNA-seq (scRNA-seq) reanalysis using GSE163210 datasets.

    __Figure A-2. UMAP plots of xenograft and CD44/HMGA1 expression __

    UMAP plot of MDA-MB-231-LM2 xenograft tumors and circulating tumor cells (Left). Expression of CD44 and HMGA1 in the UMAP plot (Right).

    __Figure A-3. Pie chart of CD44/HMGA1 positive cancer cells in MDA-MB-231 xenograft __

    Pie chart of cancer stem cell-like population ratio in MDA-MB-231-LM2 xenografts.

    Figure B. Fluorescent immuno-staining of MDA-MB-231 primary tumor

    Representative images immunostained with CD44 and HMGA1 in primary tumor sections of the MDA-MB-231 xenograft model. Red: HMGA1, Green: CD44, and Blue: Nucleus. Scale bars, 20 μm (left), 10 μm (right). White arrows represent cancer cells that independently expressed or co-expressed.

    2.Furthermore, the authors should better explain their data analysis strategy with identification of gene expression profiles. It is unclear how they found CD44, MYC, and HMGA1 other than by cherry-picking from the list of cluster markers. *Answer: In this research, to identify the characteristics of clusters, we analyzed differentially expressed genes (DEGs) by ‘FindAllMarkers’ function of Seurat. As a result, ‘Cluster 0’ significantly expressed HMGA1 gene, and ‘cluster 1’ significantly expressed CD44. HMGA1 and CD44 are popular cancer stem cell markers in triple-negative breast cancer [3, 4]. In this study, we focus on metastasis-related genes and cancer stem cell markers (described in introduction section). Therefore, we focus on cancer-stem cell markers in the presented study. Cancer stemness is an important concept in cancer metastasis [5-7]. These results suggested that the existence of two cancer stem cell-like populations could potentially make tumors more drug-resilient in xenograft models and clinical patients.

    To improve the manuscript, we revised the description in the revised manuscript (Pages 5-6, Lines 97-105).

    3.Following up on the above point: I looked in the supplementary tables, but couldn't find MYC. How did the authors conclude that MYC is involved in cluster 1? In fact, when I ran a quick analysis in EnrichR, I saw that putative MYC target genes were strongly enriched among the markers in the HMGA1 cluster, but not the CD44/MYC. That's opposite to what I would expect. *__Answer: __We apologize for our confusing data and description. First, we found the expression of CD44 and HMGA1 in each cluster. Therefore, we performed the up-stream enrichment analysis using gene signatures of FindAllMakers by Metascape. From the result of enrichment analysis, we found the MYC activation in CD44 high-cluster; therefore, we named the cluster “CD44/MYC-high” cluster.

    To improve the manuscript, we revised the Figure2, Supplementary Table S3, and manuscript (Pages 5-6, Lines 103-106).

    4.All data were produced from 1 primary tumor and 1 metastasis. Thus, reproducibility and robustness of the methodology cannot be evaluated. The interpretation of the data could be strengthened when xenografts from at least 3 different mice are shown. *__Answer: __We would thank the suggestion. As the reviewer’s comment, we performed 1 primary tumor and 1 metastasis lesion from a transplanted mouse. Since this experiment take a long time, we tried to validate the findings by other methods (Figure A: scRNA-seq analysis of MDA-MB-231 xenografts, Figure B: Immuno-staining of MDA-MB-231 primary tumor, Figure C: scRNA-seq analysis of TNBC patients).

    First, we reanalyzed the public dataset which performed single-cell RNA-seq analysis of MDA-MB-231 xenografted tumor and circulating tumor cells in immunodeficient mice as shown in the answer to comment 1 (Figure A). Next, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As results, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B). Next, we performed the reanalysis of 19 scRNA-seq samples from integrated 3 TNBC cohorts (Figure C-1). In a UMAP plot, differences between CD44-positive cancer cell and HMGA1-positive cancer cell were observed; however, these cells did not visually form the specific clusters (Figure C-2). CD44 and HMGA1 expressed globally in the UMAP plot, but CD44 makes some specific clusters (cluster at right side). Additionally, following the comment, we performed the population analysis in each patient (Figure C-3 and C-4). Detection of double-positive population in TNBC patients suggested that the population may be more undifferentiated cancer stem cells diving into both CD44-positive cells and HMGA1-positive cells.

    In addition, we reanalyzed primary tumors and metastasis lesions from other mice as a test trial sample (Figure D-1). The microspots including test trial samples showed 3 human clusters which were classified into CD44/MYC, HMGA1, and Marker-low clusters. We believe that our findings are solid results because the findings were also validated by other methods.

    In the revised manuscript, Figure A are incorporated as Figure 3B-E. Figure B is incorporated as Figure 3A. Figure C is incorporated as Figure 5. We only showed Figure D in the response to the reviewer’s comment. Hope our new results will be now accepted by the learned Reviewer and Editor.

    Figure C-1. Reanalysis of integrated TNBC patients scRNA-seq

    A flowchart of the reanalysis of a public scRNA-seq dataset. We downloaded GSE161529, GSE176078, and GSE180286 (scRNA-seq data of 19 TNBC patients). Integrated datasets were analyzed with Seurat. Log normalization, scaling, PCA and UMAP visualization were performed following the basic protocol in Seurat. To extract the cancer cells, cells expressing EPCAM/KRT8 (epithelial marker) were filtered. A UMAP plot of cancer cell from 19 TNBC patients (right).

    Figure C-2. CD44/HMGA1 expression in TNBC patients

    Expression analysis of CD44 (Expression level > 2) and HMGA1 (Expression level > 2) with UMAP plots.

    Figure C-3. CD44/HMGA1-positive cancer cell with UMAP plot

    UMAP plots of CD44-high, HMGA1-high, HMGA1/CD44-high, and Negative cancer cells.

    Figure C-4. Ratio of CD44/HMGA1-positive cancer cell in each patient

    The bar plot showed the ratio of cancer cells that expressed CD44 and HMGA1.

    Figure D-1. Analysis of microspots of MDA-MB-231 xenografts including test trial samples

    UMAP plots of CD44-high, HMGA1-high, and Marker-low clusters with test trial samples (2 primary tumors and 1 lung metastasis). ‘Primary tumor 1’ has 20 microspots, ‘Primary tumor 2’ has 24 microspots, and ‘lung metastasis’ has 7 microspots. Most microspots of lung metastasis failed extraction of RNA; therefore, these spots classified into Marker-low cluster.

    __Figure D-2. Expression analysis of CD44, HMGA1, and MYC __

    Feature plot of CD44-high, HMGA1-high, and Marker-low clusters with test trial samples.

    5.The only methodology is single cell RNA-sequencing. Immuno-staining on relevant markers such as CD44, MYC, HMGA1 plus human epithelium and cell cycle markers would provide strong additional support for the claims made by the authors, because it's a complementary technique and it allows quantification at single cell resolution. *__Answer: __We would thank the comment. As described in the responses to the reviewer’s comment 1 and 4, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As a result, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B).

    In the revised manuscript, Figure B is incorporated as Figure 3A.

    6.Line 173-175. The marker-low cluster look to me simply like spots containing a relatively high amount of dead/dying (tumor) cells. The identity/state of cells in the marker-low cluster should be characterized and discussed more extensively. *__Answer: __We would thank the comment. This suggestion is important. In fact, total count of RNA in the Marker-low cluster decreased as compared to HMGA1-high and CD44/MYC-high (Supplementary Figure S1B). Additionally, Ttr-high mouse cluster also has low total count of RNA (Supplementary Figure S1C).

    Following the comment, we described that the Marker-low cluster and Ttr-high cluster have the possibility to include dead/dying cells (Page 13, Lines 268-279).

    7.Figure 5 and accompanying text in line 182-194; the authors try to infer cell-to-cell interactions using a previously published tool. However, any biological interpretation is lacking. What can be concluded from this analysis? *__Answer: __Initially, algorithms of cell-to-cell interaction were reported with previously published tool [8, 9]; however, in this manuscript, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data) as previously described [10, 11]. We aimed to estimate the cell-to-cell interaction in each spot (including 10-30 cells). We think that this analysis will be helpful for discovering the cancer stem cell niche and metastatic niche [6].

    However, in the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Therefore, CCI analysis in previous Figure 5 moved to Supplementary Figure S7. Previous Figure 6 is removed from revised manuscript.

    8.Figure 6. Can the authors please explain more clearly what they mean by "PT" and "Mix" groups? I had a very hard time to understand what the data in figure mean. Again, an overall interpretation at the end (line 211) is lacking. *__Answer: __We apologize for the confusing result. We examined the combinations of human cancer cell cluster and mouse stromal cell cluster. To summarize, there are 10 combinations in the MDA-MB-231 xenograft. The combination groups in only primary tumor were named “PT”; on the other hand, the combination groups in both primary tumor and lymph-node metastasis were named “Mix”. These CCI analysis focused on cluster types of cancer cell and stromal cell. However, according to this revision, our presented study mainly focuses on the existence of two types of cancer stem cell-like population in TNBC xenograft and patients. Therefore, CCI analysis with cluster types was deleted from revised manuscript.

    In the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Previous Figure 6 was removed from the revised manuscript.

    9.Figure 7. I like the effort to align the results with public scRNA-seq data. But although the expression of the cluster-signatures is heterogeneous, there is no evidence for distinct (CSC-like) cell populations. Why don't these HMGA1 vs CD44 signature cells cluster away from each other in the UMAPs? Perhaps the patient-to-patient heterogeneity overwhelms differences within tumors, but in that case the authors could re-run their analysis for each patient separately, to make 6 patient-specific UMAPs. In its present form, this analysis does not convince me that two distinct CSC(-like) populations within one TNBC exist. *Answer: We would thank the comment. To improve the quality of reanalysis of clinical cohorts, we performed the reanalysis of 19 scRNA-seq samples from integrated 3 TNBC cohorts (Figure C-1). In a UMAP plot, there are differences between CD44-positive cancer cells and HMGA1-positive cancer cells; however, these cells did not visually form the specific clusters (Figure C-2). CD44 and HMGA1 were expressed globally in the UMAP plot, but CD44 made some specific clusters (cluster at right side). Additionally, following the comment, we performed the population analysis in each patient (Figure C-3 and C-4). There is double-positive population in TNBC patients suggesting that this population may be more undifferentiated cancer stem cells, dividing into both CD44-positive cells and HMGA1-positive cells.

    In the revised manuscript, Figure C is incorporated as Figure 5.

    __**Minor comments:** __ 10.In the Supplemental table 2 noticed that many of the marker genes have adjusted P values well above 0.05 (and even above 0.1). That makes the statistical analysis rather weak. This could especially be problematic since the authors entirely base their main claims on this marker analysis, and I recommend that the authors use more stringent P-value cut-offs in the cluster analysis. *Answer: We would thank the comment. We reshaped the list of differentially expressed genes (DEGs). Significantly expressed genes (adjusted p-value In mouse clusters, the enrichment analysis using significantly DEGs showed that only Tcell-like clusters had a lot of enriched terms. Citric acid (TCA) cycle, chemical stress response, and fatty acid oxidation were enriched in Tcell-like populations (Page 7, Lines 141-144).

    In the revised manuscript, enrichment analyses are showed as Supplementary Figure S2 and S3B. We revised the sentence of enrichment analyses (Page 6, Lines 114-121), (Page 7, Lines 141-144). The network visualization of enrichment analysis was removed from the revised manuscript because this result did not support conclusions of the presented study.

    11.Line 129/130. If I look at figure 3A, I don't see this tendency that the authors describe. Can the authors provide statistical support or visual aid to make their claim more apparent to the reader? *__Answer: __We would thank the suggestion. Following the comment, we performed the statistical analysis of spot position. The spots were categorized outer side (tumor edge) and Inner site (Center of tumor) in the primary tumor section (Figure E-1 upside). We counted the spot numbers of the clusters (Figure E-1 table) and performed statistical test by chi-test. As a result, CD44/MYC clusters significantly resided at outer side of primary tumor (Figure E-1 barplot). On the other hand, the spots in lymph-node metastasis are not readily defined the outer or inner. In addition, cell cycle analysis in the primary tumor and lymph node metastasis was performed with statistical test. As a result, HMGA1-high cluster and CD44/MYC-high cluster significantly proliferated in the lymph node metastasis section (Figure E-2).

    Therefore, in the revised manuscript, we revised the sentence of spot position in lymph-node metastasis (Pages 8-9, Lines 159-172). Figure E-1 is incorporated as Figure 4D. Figure E-2 is incorporated as Figure 4F. Hope our new results will be now accepted by the Reviewer and Editor.

    Figure E-1. Statistical analysis of spot position

    Chi-test was performed by R. *p Figure E-2. Statistical analysis of cell cycle index

    Fisher’s exact test was performed by R. *p * 12.Line 217; shouldn't this be 6 patients? I see six clusters and in the original paper six patients are mentioned. *Answer: We would thank the comment. ‘6 patients’ is correct, we revised it. However, in the revised manuscript, we added integrated analysis of TNBC as shown in the answer to comment 9.

    Previous reanalysis of clinical scRNA-seq (previous Figure 7) was removed from the revised manuscript. The reanalysis using 3 integrated TNBC cohorts (Figure C) is incorporated as Figure 5.

    *Reviewer #1 (Significance (Required)): ** Conceptual/biological impact: Showing the existence of distinct populations of CSCs within one (breast-)tumor potentially has a high impact on the field of fundamental and translational cancer research. As the authors state, it could be one key reason underlying drug resistance. However, the technology used by the authors does in my view not allow to make such a claim. First and foremost because the technology does not allow analysis at single cell resolution.

    Technical impact: The platform used by the authors can be of interest for some applications, but they already published this in Scientic Reports a few years ago. I'm afraid that with the rapid recent developments in the field of spatial single cell transcriptomics (See for example Srivatsan et al Science 2021; 373: 111-117), the technical impact on the field is relatively low.

    Audience: Researchers in the field of cancer biology with an interest to perform low-cost molecular analysis at low-resolution spatial-resolved tissue specimens (transcriptomics, but perhaps expanded with bisulfite sequencing, or ATAC sequencing) could be interested in the technology presented in this manuscript.

    My expertise: single cell transcriptomics, (cancer) cell cycle, cancer drug resistance, cell plasticity, mouse models. *

    * *

    ***Referee Cross-commenting** ** I have read the comments and align mostly with reviewer #2. The authors need to improve this manuscript a lot before it's suitable for publication in any of the Review Commons journals. *Answer: We are grateful to the reviewers. As indicated in the responses that follow, we have taken all of these comments and suggestions into account in the revised version of our paper, including the supplementary information.

    * *

    *Reviewer #2 (Evidence, reproducibility and clarity (Required)): ** This manuscript uses spatial transcriptomics to perform single cell-like expression analysis between a breast cancer cell line and tumor microenvironment in mice xenografted with these cells. Unfortunately, from the title, abstract, and introduction, it is difficult to understand exactly what the authors are focusing and discussing. It is also unclear the advantage of their technique for evaluating the populations observed within this manuscript. Furthermore, there is very little explanation of the results, and it does not appear to be a scientific logical structure. Hence, this manuscript is not suitable for acceptance in the journal. In order to improve the scientific quality of this study, the following concerns are presented.

    __**Major concerns:** __ 1.Is cell-cell interaction (CCI) analysis novel method? If so, please specify detail in the manuscript. If the basic concept and the principle of CCI analysis have not been published, please mention in the discussion section as a limitation that a manuscript on CCI analysis is under submission to the preprint. In addition, please revise the abstract and related text. *__Answer: __Initially, algorithms of cell-to-cell interaction were reported with previously published tool [8, 9]; however, in this manuscript, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data) as previously described [10, 11]. We aimed to estimate the cell-to-cell interaction in each spot (including 10-30 cells). We think that this analysis will be helpful for discovering the cancer stem cell niche and metastatic niche [6].

    However, in the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Therefore, CCI analysis in previous Figure 5 is moved to Supplementary Figure S7. Previous Figure 6 are removed from the revised manuscript. We revised the description in the manuscript (Page 18, Lines 385-387).

    2.The reviewer thinks that spatial transcriptomics plays an important role in your manuscript. Please describe the technique in the introduction. *__Answer: __We would thank the comments. Following the comments, we described the spatial technics in Introduction section. We revised the manuscript (Page 4, Lines 63-65) (Page 12, Lines 250-253).

    3.The classification by expression profile (HMGA1, CD44/MYC and marker-low) lacks an explanation. Authors should mention in detail how these populations were extracted from breast cancer cell lines. *Answer: In this research, to identify the characteristics of clusters, we analyzed differentially expressed genes (DEGs) by FindAllmarkers function of Seurat. As a result, ‘Cluster 0’ significantly expressed HMGA1 gene, and ‘cluster 1’ significantly expressed CD44. Next, we performed the up-stream enrichment analysis using gene signatures of FindAllMakers by Metascape. From result of enrichment analysis, we found the MYC activation in CD44 high-cluster; therefore, we named the cluster “CD44/MYC-high” cluster.

    HMGA1 and CD44 are popular cancer stem cell markers in triple-negative breast cancer [3, 4]; therefore, we focus on cancer-stem cell marker in presented study. Cancer stemness is an important concept in cancer metastasis [5-7].These results suggested that the existence of two cancer stem cell-like populations could potentially make tumors more drug-resilient in xenograft model and clinical patient.

    To improve the manuscript, we revised the Figure2, Supplementary Table S2 and S4, and manuscript (Pages 5-6, Lines 97-106).

    4.The description of the results is back and forth and confusing. Please reconsider the flow of the analysis. *__Answer: __We would thank the comment. We reconsidered the description and structure of manuscript. In revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients.

    To improve the manuscript, we revised the Figure2 for examination of cluster characteristics by clustering and gene expression profiling. Figure 3 was revised for the validation of two cancer stem cell-like populations in TNBC xenograft model. Figure 4 was revised for the elucidation of spatial characteristics of each cluster. Figure 5 was revised for the validation of two cancer stem cell-like populations in TNBC patients.

    5.How did you evaluate the outsides of the samples with very different spot positions in Figure 3A? Please mention your evaluation method in a scientific manner. In particular, authors should clearly indicate the outer evaluation for the metastatic case. *

    Answer: We would thank the suggestion. Following the comment, we performed the statistical analysis of spot position. The spots were categorized outer side (tumor edge) and Inner site (Center of tumor) in primary tumor section (Figure E-1 upside). We counted the spot numbers of the clusters (Figure E-1 table) and performed statistical test by chi-test. As a result, CD44/MYC clusters significantly resided at outer side of primary tumor (Figure E-1 bar plot). On the other hand, the spots in lymph-node metastasis are not readily defined the outer or inner. In addition, cell cycle analysis in the primary tumor and lymph node metastasis was performed with statistical test. As a result, HMGA1-high cluster and CD44/MYC-high cluster significantly proliferated in the lymph node metastasis section (Figure E-2).

    Therefore, in the revised manuscript, we revised the sentence of spot position in lymph-node metastasis (Pages 8-9, Lines 153-172). Figure E-1 are incorporated as Figure 4D. Figure E-2 are incorporated as Figure 4F. Hope our new results will be now accepted by the Reviewer and Editor.

    Figure E-1. Statistical analysis of spot position

    Chi-test was performed by R. *p Figure E-2. Statistical analysis of cell cycle index

    Fisher’s exact test was performed by R. *p * 6.The spots in primary tumor have few counts derived from mouse stromal/immune cells, as shown in Figure S1A. Nevertheless, Figure 3C shows that mouse stromal/immune cells are evaluated in the same way in primary and metastatic sites. The reviewer thinks that the regions identified as Tcell-like in the metastatic site, where there are many mouse-derived counts, and in the primary, where there are few mouse-derived counts, do not have the same characteristics. If many mouse-derived counts were detected in a spot using the spatial transcriptomics, then there must be many mouse-derived cells in the spot. Please discuss how this expression is evaluated on this technique, which is not a single cell analysis. *__Answer: __We would thank the comment. The reviewer’s suggestion is an important point; however, this suggestion is technical limitation of spatial transcriptomics technology. Most advanced spatial transcriptomics technologies, e.g. Visium (10x Genomics), also have the same problem. It means that our technology and the advanced technologies are technics to analyze gene expression and characteristics of tissues from 10-30 cells in each spot.

    In this spatial transcriptome analysis of mouse genes, we first performed the log normalization and scaling. Since Seurat used variable features among the samples for single-cell or spot clustering, we extracted the variable features for detection of clusters using the ‘FindVariableFeatures’ function. PCA and clustering using only mouse genes was performed for detecting the neighboring samples. After the clustering of mouse spots, we identified the character of clusters by finding the gene signatures. As the indication by the reviewer, the detected RNA counts and features are different, so it is difficult to define the exact character and cell type of stromal cells. Theoretically, spatial transcriptomics could only detect some kinds of stromal cells expressing the T-cell marker gene in the spot. Therefore, we named the cluster as “Tcell-like”. Not all of the Tcell-like cluster have the same characteristics or cell types, but they certainly express T-cell marker genes. This is also a technical limitation of spatial transcriptomics. Spatial transcriptomics with higher resolution probably is able to detect the stromal cells as a single-cell resolution, such as the one developed in previous research [1].

    In the revised manuscript, we focused on the two types of cancer stem cell-like populations that were validated by other methods (scRNA-seq and Immuno-staining). As the method is not able to define the exact cluster characters, we moved CCI analyses to supplementary figures or removed partly.

    We also revised the discussion in the revised manuscript (Pages 13-14, Lines 279-283).

    7.Please explain how the gene symbols listed in Figure 4A were selected. Also, please indicate the characteristics of the gene groups that are not listed. *__Answer: __We selected the gene signature list from results of ‘FindAllMarker’ function in Seurat. ‘FindAllMarker’ function enables to extract the significantly expressed genes in each cluster. Heatmap in previous Figure 4A was drawn using these marker genes (Adjusted p-value 0.1). Highlighted genes in the heatmap have been reported as cancer-related genes or cell cycle-related genes.

    The genes used for drawing heatmap are shown in Supplementary Table S2 and S4.

    8.Please describe the details of the division and cycle index in lines 141-142. *__Answer: __Cell cycle index is a basic function of Seurat [12] (https://satijalab.org/seurat/archive/v3.1/cell_cycle_vignette.html). A list of cell cycle markers is loaded with Seurat. We can segregate this list into markers of G2/M phase and markers of S phase. We subjected this function into our spatial transcriptomics to estimate the cell cycle in each spot.

    We revised the description manuscript (Page 16, Lines 331-332).

    9.In Line 148-151, the expression and prognosis of TMSB10, CTSD, and LGALS1 is mentioned based on the previous reports. Aren't these findings the result of bulk? Is the HMGA1 cluster that the authors found involved in the prognosis of mice? Please clarify, as it is unclear what you want to discuss. *

    Answer: We apologize for our confusing data and description. These highlighted genes (TMSB10, CTSD, LGALS1, CENPK, and CENPN) were extracted as DEGs of human cancer clusters (Supplementary Table S2). Previously, these genes have been reported as cancer-related genes or cell cycle-related genes, described in the manuscript (Page 6, Lines 107-110). To show the other expressed genes in each human cluster, we focused on these genes in the manuscript.

    We extracted the gene signatures from DEGs and showed the gene signatures from HMGA1-high cluster correlated to poor prognosis in TNBC patients. Our data suggested that the HMGA1 signatures from the microspot resolution has the potential to be a novel biomarker for diagnosis, and HMGA1-high cancer stem cells may contribute to poor prognosis.

    In this revision, since we reperformed DEGs analysis with significant threshold; therefore, survival analysis was reperformed with novel gene signatures with METABRIC TNBC cohorts (Figure F).

    To improve the manuscript, we revised the description of DEGs extraction and heatmap (Page 6, Lines 106-112). Hope our Reviewer will approve this revised sentence.

    Figure F. Survival analysis with gene signatures of HMGA1-high and CD44/MYC-high

    Survival analysis of TNBC patients (claudin-low subtype and basal-like subtype) in METABRIC cohorts by the Kaplan-Meier method. (Left) Survival analysis with the expression of the HMGA1 signatures (High = 151, Low = 247). Shading along the curve indicates 95% confidential interval. Log-rank test, p = 0.012. (Right) Survival analysis with the expression of the CD44/MYC signatures (High = 333, Low = 65). Log-rank test, p = 0.079.

    10.Please provide details of all statistical tests used in this manuscript and describe significance levels used in the p-values and FDR. *__Answer: __We performed the extraction of differentially expressed genes (DEGs) by ‘FindAllMarkers’ function with MAST method. MAST method identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data [13]. Adjusted p-value is calculated based on Bonferroni correction using all features in the dataset. In spatial spot analysis, statistical analyses were performed by Chi-test and Fisher’s exact test.

    We revised materials and methods section in the manuscript (Page 19, Lines 391-394).

    11.Please mention CCI score (line 198). *Answer: As described in answer to comment 1, the algorithms of CCI score calculation were performed using previously published tool [8, 9]; however, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data). We extracted the genes whose expression value was greater than 2. We selected the combinations representing ligand__-__receptor interactions, in which both ligand genes and receptor genes were expressed in the same spot.

    We revised materials and methods section in the manuscript and Supplementary Legends (Page 18, Lines 385-387).

    12.Lines 204-206 and Figure 6G show specific interaction of ITGB1 and CST3, but it is unclear why only these molecules were extracted. What about the other molecules? At least ITGB1 is not scored in mix5. *Answer: We selected genes that have been reported as cancer-related ones in breast cancer to discuss the interactions in primary tumor and lymph-node metastasis. However, according to this revision, our presented study mainly focused on the existence of two types of cancer stem cell-like population in TNBC xenografts and patients. Therefore, CCI analysis with cluster types moved to supplementary Figure or some were not shown now.

    In the revised manuscript, previous Figure 6 is removed.

    13.HMGA1 signature appears in Line 214, please explain in detail. *__Answer: __As described in answer to comment 7, we selected the gene signature list from results of ‘FindAllMarker’ function. ‘FindAllMarker’ function enables to extract the significantly expressed genes in each cluster. HMGA1 signature genes were selected from significantly differentially expressed genes of HMGA1-high clusters.

    We revised the description in the revised manuscript (Pages 9-10, Lines 190-193).

    14.Authors should discuss how the previously reported bulk expression data used in Figure 7E can be linked to the single-cell-like analysis in this study. *__Answer: __Previous research reported that gene signatures extracted from specific clusters in scRNA-seq study have the potential to be a prognosis marker [14]. We showed the gene signatures from HMGA1-high cluster correlated to poor prognosis in TNBC patients. Our results suggested that the gene signatures from the resolution of microspot (10-30 cells) could have the potential to be prognosis markers. This punching microdissection system enables to extract only the parts of a section that are necessary for diagnosis of cancer and to analyze at low-cost. It could be applied to diagnostics instead of the laser-capture microdissection methods.

    We performed additional survival analysis with METABRIC cohorts. As described in this revision, since we reperformed DEGs analysis with significant threshold, survival analysis was reperformed with novel gene signatures with METABRIC TNBC cohorts (Figure F).

    In revised manuscript, Figure F were incorporated as Figure 6. The usefulness of gene signatures from microspot resolution was additionally discussed (Page 12, Lines 242-245, 250-253).

    __**Minor concerns:** __ 15.Please describe how the normalized centrality was calculated in UMAP algorithm and explain what this means in the results. *__Answer: __The data showed that the expressional diversity in each cluster based on the network centrality of a correlational network with graph theory. The differences in the centrality among the clusters suggested expressional diversity in each (Supplementary Figure 4). Higher centrality represented lower expressional diversity and vice versa. The detailed method for the calculation of centrality was previously shown to reveal the difference between smokers and never-smokers [10, 11].

    We added the description in the Legend (Pages 7-8, Lines 145-150).

    16.Please mention an explanation for the red X in Figure 1B to the legend. *__Answer: __The red X means failure spot for RNA extraction. We added the description in Figure 1B.

    17.Please spell out the abbreviations in all figure legends. *__Answer: __We added the abbreviations in the legends of all figures.

    18.Please explain what is meant by the color of the lines and the size of the circles in Figure 4D. *__Answer: __The network analysis was performed by Metascape (https://metascape.org/gp/index.html#/main/step1) [15]. The node size is proportional to the number of genes belonging to the term, and the node color represents the identity of the cluster. However, as described in the answer to reviewer’s comment 9, we reperformed enrichment analysis with significant DEGs. As a result, only CD44/MYC cluster had a lot of enrichment terms.

    Therefore, network visualizations were removed from the revised manuscript.

    19.Please mention an explanation for the color of the spots in Figure 5D and 5F to the legend. *__Answer: __The color showed the spots categorized into the selected group.

    In the revised manuscript, previous Figure 5 was incorporated as Supplementary Figure S7. We added the description in Supplementary Figure S7 and S8 with the legends.

    20.Is "S51" in Line 148 a typo for "S5A"? *Answer: Thank you. We revised “S5A”.

    21.Please mention an explanation for the bars in Figure 6D and 6F to the legend. *__Answer: __The bars showed relative CCI scores. As described below, we removed the results of CCI analysis with cluster group (previous Figure 6) in the revised manuscript.

    22.Please mention an explanation for the colors in Figure 7E to the legend. *__Answer: __The color showed patients’ group based on expression levels of gene signatures. We added the description in the Legend of Figure 6.

    * *

    *Reviewer #2 (Significance (Required)): ** The approach in Figure 5 is interesting, but the rest of the results do not take full advantage of the technology developed by the authors. The structure of the manuscript should be re-examined and new perspectives added. I look forward to the future of the authors' research.

    * *

    Reviewer #3 (Evidence, reproducibility and clarity (Required)):* Microtissue transcriptome analysis of triple-negative breast cancer cell line MDA-MB-231 xenograft model using automated tissue microdissection punching techonology revealed that the existence of three cell-type clusters in the primary tumor and axillary lymph node metastasis. The CD44/MYC-high cluster showed aggressive proliferation with MYC expression, the HMGA1-high cluster exhibited HIF1A activation and upregulation of ribosomal processes. The cell-cell-interaction analysis revealed the interaction dynamics generated by the combination of cancer cells and stromal cells in primary tumors and metastases. The gene signature of the HMGA1-high cancer stem cell-like cluster has the potential to serve as a novel biomarker for diagnosis. The key conclusions are convincing. The data and methods are presented in a reproducible way. The experiments are adequately replicated and statistical analysis is adequate. Prior studies are appropriately referenced. The text and figures are clear and accurate. *__Answer: __We would thank the valuable comments. As the reviewer mentioned, our findings showed that the existence of two cancer stem cell-like populations has the potential to make tumors more drug-resilient. Our results suggested that the gene signatures from the resolution of microspot (10-30 cells) could have the potential to be prognosis markers. This punching microdissection system enables to extract only the parts of a section that are necessary for diagnosis of cancer and to analyze at low-cost. It could be applied to diagnostics instead of the laser-capture microdissection methods.

    In this revision, we focused on the existence of two cancer stem cell-like populations in TNBC xenografts and patients. Following the other reviewer’s comments, we performed the extraction of DEGs with significant threshold; therefore, we revised the results of enrichment analysis but it did not influence our main findings.

    To validate the existence of two types of cancer stem-like cells in TNBC tumors, we performed the additional analyses (reanalysis of public scRNA-seq datasets and immuno-staining of MDA-MB-231 primary tumor). These results verified two cancer stem cell-like populations (HMGA1-high, CD44-high) in MDA-MB-231 xenograft and TNBC patients. We believe that our findings are solid results because the findings were also validated by other methods.

    Again, we would thank kind reviewing our manuscript.

    * *

    *Reviewer #3 (Significance (Required)): ** In the past several studies showed the heterogeneity of cell-cell interactions between cancer cells and stromal cells in situ (Andersson et al, 2021; Wu et al, 2021) and tumor microheterogeneity (Jiang et al, 2016; Liu et al, 2016; Zhang et al, 2020). Spatial transcriptomics methods are important to reveal microheterogeneity of cancer. As a physician working in gynecology and obstetrics in my opinion the results of the study and spatial transcriptomic methods could be relevant to detect new biomarkers for diagnosis and prognosis of breast cancer in future and to find novel therapeutic targets to overcome drug resistance and facilitate curative treatment of breast cancer.

    References in response letter

    1. Srivatsan SR, Regier MC, Barkan E, Franks JM, Packer JS, Grosjean P, et al. Embryo-scale, single-cell spatial transcriptomics. Science. 2021;373(6550):111-7. Epub 2021/07/03. doi: 10.1126/science.abb9536. PubMed PMID: 34210887.
    2. Moravec JC, Lanfear R, Spector DL, Diermeier SD, Gavryushkin A. Cancer phylogenetics using single-cell RNA-seq data. bioRxiv. 2021:2021.01.07.425804. doi: 10.1101/2021.01.07.425804.
    3. Liu H, Patel MR, Prescher JA, Patsialou A, Qian D, Lin J, et al. Cancer stem cells from human breast tumors are involved in spontaneous metastases in orthotopic mouse models. Proc Natl Acad Sci U S A. 2010;107(42):18115-20. Epub 2010/10/06. doi: 10.1073/pnas.1006732107. PubMed PMID: 20921380; PubMed Central PMCID: PMC2964232.
    4. Pegoraro S, Ros G, Piazza S, Sommaggio R, Ciani Y, Rosato A, et al. HMGA1 promotes metastatic processes in basal-like breast cancer regulating EMT and stemness. Oncotarget. 2013;4(8):1293-308. Epub 2013/08/16. doi: 10.18632/oncotarget.1136. PubMed PMID: 23945276; PubMed Central PMCID: PMC3787158.
    5. Weiss F, Lauffenburger D, Friedl P. Towards targeting of shared mechanisms of cancer metastasis and therapy resistance. Nat Rev Cancer. 2022. Epub 2022/01/12. doi: 10.1038/s41568-021-00427-0. PubMed PMID: 35013601.
    6. Oskarsson T, Batlle E, Massagué J. Metastatic Stem Cells: Sources, Niches, and Vital Pathways. Cell Stem Cell. 2014;14(3):306-21. doi: https://doi.org/10.1016/j.stem.2014.02.002.
    7. Turdo A, Veschi V, Gaggianesi M, Chinnici A, Bianca P, Todaro M, et al. Meeting the Challenge of Targeting Cancer Stem Cells. Front Cell Dev Biol. 2019;7:16. Epub 2019/03/06. doi: 10.3389/fcell.2019.00016. PubMed PMID: 30834247; PubMed Central PMCID: PMC6387961.
    8. Armingol E, Officer A, Harismendy O, Lewis NE. Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet. 2021;22(2):71-88. Epub 2020/11/11. doi: 10.1038/s41576-020-00292-x. PubMed PMID: 33168968; PubMed Central PMCID: PMC7649713.
    9. Kumar MP, Du J, Lagoudas G, Jiao Y, Sawyer A, Drummond DC, et al. Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics. Cell Rep. 2018;25(6):1458-68.e4. Epub 2018/11/08. doi: 10.1016/j.celrep.2018.10.047. PubMed PMID: 30404002; PubMed Central PMCID: PMCPMC7009724.
    10. Watanabe N, Nakayama J, Fujita Y, Mori Y, Kadota T, Shimomura I, et al. Single-cell Transcriptome Analysis Reveals an Anomalous Epithelial Variation and Ectopic Inflammatory Response in Chronic Obstructive Pulmonary Disease. medRxiv. 2020:2020.12.03.20242412. doi: 10.1101/2020.12.03.20242412.
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    12. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, 3rd, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888-902.e21. Epub 2019/06/11. doi: 10.1016/j.cell.2019.05.031. PubMed PMID: 31178118; PubMed Central PMCID: PMC6687398.
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  3. Review Commons

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

    Learn more at Review Commons

    Referee #3

    Evidence, reproducibility and clarity

    Microtissue transcriptome analysis of triple-negative breast cancer cell line MDA-MB-231 xenograft model using automated tissue microdissection punching techonology revealed that the existence of three cell-type clusters in the primary tumor and axillary lymph node metastasis. The CD44/MYC-high cluster showed aggressive proliferation with MYC expression, the HMGA1-high cluster exhibited HIF1A activation and upregulation of ribosomal processes. The cell-cell-interaction analysis revealed the interaction dynamics generated by the combination of cancer cells and stromal cells in primary tumors and metastases. The gene signature of the HMGA1-high cancer stem cell-like cluster has the potential to serve as a novel biomarker for diagnosis.

    The key conclusions are convincing. The data and methods are presented in a reproducible way. The experiments are adequately replicated and statistical analysis is adequate.

    Prior studies are appropriately referenced. The text and figures are clear and accurate.

    Significance

    In the past several studies showed the heterogeneity of cell-cell interactions between cancer cells and stromal cells in situ (Andersson et al, 2021; Wu et al, 2021) and tumor microheterogeneity (Jiang et al, 2016; Liu et al, 2016; Zhang et al, 2020). Spatial transcriptomics methods are important to reveal microheterogeneity of cancer. As a physician working in gynecology and obstetrics in my opinion the results of the study and spatial transcriptomic methods could be relevant to detect new biomarkers for diagnosis and prognosis of breast cancer in future and to find novel therapeutic targets to overcome drug resistance and facilitate curative treatment of breast cancer.

  4. Review Commons

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

    Learn more at Review Commons

    Referee #2

    Evidence, reproducibility and clarity

    This manuscript uses spatial transcriptomics to perform single cell-like expression analysis between a breast cancer cell line and tumor microenvironment in mice xenografted with these cells. Unfortunately, from the title, abstract, and introduction, it is difficult to understand exactly what the authors are focusing and discussing. It is also unclear the advantage of their technique for evaluating the populations observed within this manuscript. Furthermore, there is very little explanation of the results, and it does not appear to be a scientific logical structure. Hence, this manuscript is not suitable for acceptance in the journal. In order to improve the scientific quality of this study, the following concerns are presented.

    Major concerns:

    1.Is cell-cell interaction (CCI) analysis novel method? If so, please specify detail in the manuscript. If the basic concept and the principle of CCI analysis have not been published, please mention in the discussion section as a limitation that a manuscript on CCI analysis is under submission to the preprint. In addition, please revise the abstract and related text.

    2.The reviewer thinks that spatial transcriptomics plays an important role in your manuscript. Please describe the technique in the introduction.

    3.The classification by expression profile (HMGA1, CD44/MYC and marker-low) lacks an explanation. Authors should mention in detail how these populations were extracted from breast cancer cell lines.

    4.The description of the results is back and forth and confusing. Please reconsider the flow of the analysis.

    5.How did you evaluate the outsides of the samples with very different spot positions in Figure 3A? Please mention your evaluation method in a scientific manner. In particular, authors should clearly indicate the outer evaluation for the metastatic case.

    6.The spots in primary tumor have few counts derived from mouse stromal/immune cells, as shown in Figure S1A. Nevertheless, Figure 3C shows that mouse stromal/immune cells are evaluated in the same way in primary and metastatic sites. The reviewer thinks that the regions identified as Tcell-like in the metastatic site, where there are many mouse-derived counts, and in the primary, where there are few mouse-derived counts, do not have the same characteristics. If many mouse-derived counts were detected in a spot using the spatial transcriptomics, then there must be many mouse-derived cells in the spot. Please discuss how this expression is evaluated on this technique, which is not a single cell analysis.

    7.Please explain how the gene symbols listed in Figure 4A were selected. Also, please indicate the characteristics of the gene groups that are not listed.

    8.Please describe the details of the division and cycle index in lines 141-142.

    9.In Line 148-151, the expression and prognosis of TMSB10, CTSD, and LGALS1 is mentioned based on the previous reports. Aren't these findings the result of bulk? Is the HMGA1 cluster that the authors found involved in the prognosis of mice? Please clarify, as it is unclear what you want to discuss.

    10.Please provide details of all statistical tests used in this manuscript and describe significance levels used in the p-values and FDR.

    11.Please mention CCI score (line 198).

    12.Lines 204-206 and Figure 6G show specific interaction of ITGB1 and CST3, but it is unclear why only these molecules were extracted. What about the other molecules? At least ITGB1 is not scored in mix5.

    13.HMGA1 signature appears in Line 214, please explain in detail.

    14.Authors should discuss how the previously reported bulk expression data used in Figure 7E can be linked to the single-cell-like analysis in this study.

    Minor concerns:

    15.Please describe how the normalized centrality was calculated in UMAP algorithm and explain what this means in the results.

    16.Please mention an explanation for the red X in Figure 1B to the legend.

    17.Please spell out the abbreviations in all figure legends.

    18.Please explain what is meant by the color of the lines and the size of the circles in Figure 4D.

    19.Please mention an explanation for the color of the spots in Figure 5D and 5F to the legend.

    20.Is "S51" in Line 148 a typo for "S5A"?

    21.Please mention an explanation for the bars in Figure 6D and 6F to the legend.

    22.Please mention an explanation for the colors in Figure 7E to the legend.

    Significance

    The approach in Figure 5 is interesting, but the rest of the results do not take full advantage of the technology developed by the authors. The structure of the manuscript should be re-examined and new perspectives added. I look forward to the future of the authors' research.

  5. Review Commons

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

    Learn more at Review Commons

    Referee #1

    Evidence, reproducibility and clarity

    Summary:

    Nakayama and colleagues use their previously developed automated tissue microdissection punching platform to perform spatial transcriptomics on a breast cancer xenograft model. Using transcriptomics on multiple clumps of 10-30 cells from different regions in a tumor and a lymph node metastasis they identified different cell-type clusters. Two of these clusters expressed different cancer stem cell markers. This led the authors to suggest that two distinct cancer stem cell(-like) populations may exist within one (breast) tumor, which could potentially make tumors more drug-resilient.

    Major comments:

    While the quality of the presented sequencing data is good and the manuscript is mostly written in a clear and accessible style, there are some concerns that limit the impact of this story. Most importantly, the manuscript in its present form does not convince me that the MDA-MB-231 xenografts indeed contain two distinct populations of cancer stem(-like) cells.

    1.The data obtained are not single cell data, which makes it difficult -if not impossible- to draw conclusions about presence of cancer stem cells. Each data point is the average of 10-30 cells, and the interpretation of the data is severely limited by this. How can the quantification of expression of CD44/MYC/HMGA1 in clumps of 10-30 cells teach us something about the stemness of tumor cells?

    2.Furthermore, the authors should better explain their data analysis strategy with identification of gene expression profiles. It is unclear how they found CD44, MYC, and HMGA1 other than by cherry-picking from the list of cluster markers.

    3.Following up on the above point: I looked in the supplementary tables, but couldn't find MYC. How did the authors conclude that MYC is involved in cluster 1? In fact, when I ran a quick analysis in EnrichR, I saw that putative MYC target genes were strongly enriched among the markers in the HMGA1 cluster, but not the CD44/MYC. That's opposite to what I would expect.

    4.All data were produced from 1 primary tumor and 1 metastasis. Thus, reproducibility and robustness of the methodology cannot be evaluated. The interpretation of the data could be strengthened when xenografts from at least 3 different mice are shown.

    5.The only methodology is single cell RNA-sequencing. Immuno-staining on relevant markers such as CD44, MYC, HMGA1 plus human epithelium and cell cycle markers would provide strong additional support for the claims made by the authors, because it's a complementary technique and it allows quantification at single cell resolution.

    6.Line 173-175. The marker-low cluster look to me simply like spots containing a relatively high amount of dead/dying (tumor) cells. The identity/state of cells in the marker-low cluster should be characterized and discussed more extensively.

    7.Figure 5 and accompanying text in line 182-194; the authors try to infer cell-to-cell interactions using a previously published tool. However, any biological interpretation is lacking. What can be concluded from this analysis?

    8.Figure 6. Can the authors please explain more clearly what they mean by "PT" and "Mix" groups? I had a very hard time to understand what the data in figure mean. Again, an overall interpretation at the end (line 211) is lacking.

    9.Figure 7. I like the effort to align the results with public scRNA-seq data. But although the expression of the cluster-signatures is heterogeneous, there is no evidence for distinct (CSC-like) cell populations. Why don't these HMGA1 vs CD44 signature cells cluster away from each other in the UMAPs? Perhaps the patient-to-patient heterogeneity overwhelms differences within tumors, but in that case the authors could re-run their analysis for each patient separately, to make 6 patient-specific UMAPs. In its present form, this analysis does not convince me that two distinct CSC(-like) populations within one TNBC exist.

    Minor comments:

    10.In the Supplemental table 2 noticed that many of the marker genes have adjusted P values well above 0.05 (and even above 0.1). That makes the statistical analysis rather weak. This could especially be problematic since the authors entirely base their main claims on this marker analysis, and I recommend that the authors use more stringent P-value cut-offs in the cluster analysis.

    11.Line 129/130. If I look at figure 3A, I don't see this tendency that the authors describe. Can the authors provide statistical support or visual aid to make their claim more apparent to the reader?

    12.Line 217; shouldn't this be 6 patients? I see six clusters and in the original paper six patients are mentioned.

    Significance

    Conceptual/biological impact: Showing the existence of distinct populations of CSCs within one (breast-)tumor potentially has a high impact on the field of fundamental and translational cancer research. As the authors state, it could be one key reason underlying drug resistance. However, the technology used by the authors does in my view not allow to make such a claim. First and foremost because the technology does not allow analysis at single cell resolution.

    Technical impact: The platform used by the authors can be of interest for some applications, but they already published this in Scientic Reports a few years ago. I'm afraid that with the rapid recent developments in the field of spatial single cell transcriptomics (See for example Srivatsan et al Science 2021; 373: 111-117), the technical impact on the field is relatively low.

    Audience: Researchers in the field of cancer biology with an interest to perform low-cost molecular analysis at low-resolution spatial-resolved tissue specimens (transcriptomics, but perhaps expanded with bisulfite sequencing, or ATAC sequencing) could be interested in the technology presented in this manuscript.

    My expertise: single cell transcriptomics, (cancer) cell cycle, cancer drug resistance, cell plasticity, mouse models.

    Referee Cross-commenting

    I have read the comments and align mostly with reviewer #2. The authors need to improve this manuscript a lot before it's suitable for publication in any of the Review Commons journals.

  6. Version published to 10.1101/2021.10.21.465257v2 on bioRxiv
  7. Version published to 10.1101/2021.10.21.465257v1 on bioRxiv