Single-cell RNA-seq of heart reveals intercellular communication drivers of myocardial fibrosis in diabetic cardiomyopathy

  1. Wei Li
  2. Xinqi Lou
  3. Yingjie Zha
  4. Yinyin Qin
  5. Jun Zha
  6. Lei Hong
  7. Zhanli Xie
  8. Shudi Yang
  9. Chen Wang
  10. Jianzhong An
  11. Zhenhao Zhang
  12. Shigang Qiao

  • Curated by eLife

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    The precise cellular and molecular mechanisms and signaling mediators underpinning the development of cardiomyopathy and heart failure in diabetes still remains unclear. In-depth investigations of the cardiac heterogeneity and cell-to-cell interactions could be of use to reveal the pathogenesis of diabetic myocardial fibrosis and thereby identify potential targets for the treatment of cardiac myopathy and heart failure. Utilizing a mouse model as well as in-vitro studies, this manuscript demonstrates cardiac cell mapping that provides novel insights into novel drivers of intercellular communication contributing to pathological extracellular matrix remodeling during diabetic myocardial fibrosis. The work provides compelling and convincing evidence to improve the understanding the cellular and molecular mechanisms of diabetes-induced cardiac pathology.

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Abstract

Myocardial fibrosis is the characteristic pathology of diabetes-induced cardiomyopathy. Therefore, an in-depth study of cardiac heterogeneity and cell-to-cell interactions can help elucidate the pathogenesis of diabetic myocardial fibrosis and identify treatment targets for the treatment of this disease. In this study, we investigated intercellular communication drivers of myocardial fibrosis in mouse heart with high-fat-diet/streptozotocin-induced diabetes at single-cell resolution. Intercellular and protein–protein interaction networks of fibroblasts and macrophages, endothelial cells, as well as fibroblasts and epicardial cells revealed critical changes in ligand–receptor interactions such as Pdgf(s)–Pdgfra and Efemp1–Egfr, which promote the development of a profibrotic microenvironment during the progression of and confirmed that the specific inhibition of the Pdgfra axis could significantly improve diabetic myocardial fibrosis. We also identified phenotypically distinct Hrc hi and Postn hi fibroblast subpopulations associated with pathological extracellular matrix remodeling, of which the Hrc hi fibroblasts were found to be the most profibrogenic under diabetic conditions. Finally, we validated the role of the Itgb1 hub gene-mediated intercellular communication drivers of diabetic myocardial fibrosis in Hrc hi fibroblasts, and confirmed the results through AAV9-mediated Itgb1 knockdown in the heart of diabetic mice. In summary, cardiac cell mapping provides novel insights into intercellular communication drivers involved in pathological extracellular matrix remodeling during diabetic myocardial fibrosis.

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  1. Version published to 10.7554/elife.80479 on eLife
  2. eLife

    Author Response

    Reviewer #2 (Public Review):

    1. A major point of the manuscript is the description of Hrc+ fibroblasts (Fibroblast 3) as profibrogenic in diabetes. However, fibroblast 3 expresses several cardiomyocyte markers Nppa, Ryr2, Ttn alongside Hrc which is described to play a role in Ca2+ handling at the sarcoplasmic reticulum in cardiomyocytes (Fig. 4C) and shows a low correlation with other fibroblast clusters (Fig. 4B). A possible explanation is technical, e.g. if two nuclei (one fibroblast, one cardiomyocyte) were captured together in one droplet (barcode collisions or doublets). Unfortunately, this uncertainty makes interpretation of all following snRNA-seq analyses based on this fibroblast subpopulation impossible.

    Thank you very much for the precious comments of the reviewer. We went over scRNA-seq results carefully. Firstly, for quality of cells, we used a relatively high threshold to ensure that we have filtered out the most of barcodes associated with empty partitions or doublet cells. We quantified the number of genes and UMIs, and kept high quality cells with the detection threshold of 500-2,500 genes and 600-8,000 UMIs. Then cells with unusually high detection rate of mitochondrial gene expression (≥10%) were excluded in this study. Taking into account the multicellular effects as you mentioned, we tried to identify doublets cells by applying the DoubletFinder (v2.0.3) by the generation of artificial doublets, using the PC distance to find each cell’s proportion of artificial k nearest neighbors (pANN) and ranking them according to the expected number of doublets. We finded that 3.20% cells (19 cells) were labeled as doublets in fibroblast-3 (594 cells). Then 19 doublet cells were removed, the trends of cell proportion and the Hrc gene expression trend in fibroblast-3 was the same as before. Therefore, our data analysis results do not affect the conclusions in this study, and it was also validated by Hrc and vimentin double immunostaining experiments (Figure 4E). Thanks again to the reviewer for these professional comments.

    1. To follow the study and be able to appreciate the data quality, individual sample metadata and UMAPs colored based on a sample and/or condition (diabetes or control) would be helpful. The paper would benefit from an analysis to show if the differences in the number of detected genes are due to the number of nuclei per cluster or if the bigger clusters are really also the ones with the most dramatic changes. Instead of showing expression levels of differentially regulated genes in distinct clusters (Fig1 S2), the differential expression could be displayed with violin plots or heatmaps that illustrate values for both conditions. Clusters that did not reveal any differential expressed genes, e.g. Adipo can be removed. Fig 1F these KEGG enrichments are hard to interpret since they can be confounded by highly expressed cardiomyocyte genes that are detected in all clusters (1B) and thus drive the GO enrichment of e.g. "cardiac muscle contraction" in T cells.

    Thanks to the reviewer for these comments. Fig1 S2 shows top 10 upregulated genes in different cell populations and the expression characteristics of these genes in a concise way. More detailed expressions levels of differentially regulated genes in distinct clusters can be seen in supplemental file 2-5. At the same time, if we use violin plot or heat maps to show the differential expression information of top 10 upregulated genes, we need too many supplement figures in the main text and therefore take up too much space. On the other hand, cell populations without differentially expressed genes in Figure 1E have been removed as you suggested.

    1. The study looks into the pathogenesis of cardiac fibrosis in diabetic mice. The authors show that downregulation of Itgb1 with siRNA (Fig 6I) leads to less fibrosis in diabetic mice. This effect might be expected since Itgb1 is an extracellular matrix-linked gene and might indicate that downregulation could be beneficial. Given this, it is confusing to see the following analysis which links several genetic variants associated with Type 2 Diabetes to Itgb1 (one leading to premature stop) and its ligand. This analysis seems out of place in relation to the remainder of the study which focuses to identify the downstream effects of diabetes on cardiac fibrosis.

    Thank you very much for the precious comments of the reviewer. We have deleted the results of the association of Itgb1 variants with diabetic cardiac fibrosis in the revised manuscript.

  3. eLife

    eLife assessment

    The precise cellular and molecular mechanisms and signaling mediators underpinning the development of cardiomyopathy and heart failure in diabetes still remains unclear. In-depth investigations of the cardiac heterogeneity and cell-to-cell interactions could be of use to reveal the pathogenesis of diabetic myocardial fibrosis and thereby identify potential targets for the treatment of cardiac myopathy and heart failure. Utilizing a mouse model as well as in-vitro studies, this manuscript demonstrates cardiac cell mapping that provides novel insights into novel drivers of intercellular communication contributing to pathological extracellular matrix remodeling during diabetic myocardial fibrosis. The work provides compelling and convincing evidence to improve the understanding the cellular and molecular mechanisms of diabetes-induced cardiac pathology.

  4. eLife

    Reviewer #1 (Public Review):

    Utilizing mouse models as well as in-vitro studies, the authors demonstrate that cardiac cell mapping provides novel insights into intercellular communication drivers underlying pathological extracellular matrix remodeling during diabetic myocardial fibrosis.The work provides new perspectives to help understanding the cellular and molecular mechanisms of diabetes-induced cardiac pathology.

  5. eLife

    Reviewer #2 (Public Review):

    In their manuscript, "Single-Cell RNA-seq of Heart Reveals Intercellular Communication Drivers of Myocardial Fibrosis in Diabetic Mice", Wei Li et al. study the pathogenesis of cardiac fibrosis in mouse hearts in response to high-fat-diet/streptozotocin-induced diabetes. They infer cellular interactions from single nucleus RNA-seq data and highlight some ligand-receptor pairs including PDGFs and PDGFRa. They further aim to identify fibroblast subtypes associated with fibrosis and to identify factors driving diabetic myocardial fibrosis.

    This study addresses an important problem (cardiac fibrosis as a consequence of diabetes), using single nucleus RNA-seq and several follow-up experiments in a diabetic mouse model. While many of the described findings, including PDGFRa involvement in fibrosis and a Postn positive fibroblast population (reflecting activated fibroblasts), are expected, the most exciting novel insight would come from the Hrc+ fibroblast population and its characterization. However, based on the currently presented data and analysis it is not clear if this is indeed a fibroblast subtype or due to technical factors.

    1. A major point of the manuscript is the description of Hrc+ fibroblasts (Fibroblast 3) as profibrogenic in diabetes. However, fibroblast 3 expresses several cardiomyocyte markers Nppa, Ryr2, Ttn alongside Hrc which is described to play a role in Ca2+ handling at the sarcoplasmic reticulum in cardiomyocytes (Fig. 4C) and shows a low correlation with other fibroblast clusters (Fig. 4B). A possible explanation is technical, e.g. if two nuclei (one fibroblast, one cardiomyocyte) were captured together in one droplet (barcode collisions or doublets). Unfortunately, this uncertainty makes interpretation of all following snRNA-seq analyses based on this fibroblast subpopulation impossible.

    2. To follow the study and be able to appreciate the data quality, individual sample metadata and UMAPs colored based on a sample and/or condition (diabetes or control) would be helpful. The paper would benefit from an analysis to show if the differences in the number of detected genes are due to the number of nuclei per cluster or if the bigger clusters are really also the ones with the most dramatic changes. Instead of showing expression levels of differentially regulated genes in distinct clusters (Fig1 S2), the differential expression could be displayed with violin plots or heatmaps that illustrate values for both conditions. Clusters that did not reveal any differential expressed genes, e.g. Adipo can be removed. Fig 1F these KEGG enrichments are hard to interpret since they can be confounded by highly expressed cardiomyocyte genes that are detected in all clusters (1B) and thus drive the GO enrichment of e.g. "cardiac muscle contraction" in T cells.

    3. The study looks into the pathogenesis of cardiac fibrosis in diabetic mice. The authors show that downregulation of Itgb1 with siRNA (Fig 6I) leads to less fibrosis in diabetic mice. This effect might be expected since Itgb1 is an extracellular matrix-linked gene and might indicate that downregulation could be beneficial. Given this, it is confusing to see the following analysis which links several genetic variants associated with Type 2 Diabetes to Itgb1 (one leading to premature stop) and its ligand. This analysis seems out of place in relation to the remainder of the study which focuses to identify the downstream effects of diabetes on cardiac fibrosis.

  6. eLife

    Reviewer #3 (Public Review):

    The authors attempted to dissect the intercellular mechanisms implicated in the development of diabetic cardiomyopathy. They used one time point to determine the expressional changes in the STZ-high caloric diet model vs non-diabetic. They also attempted to interfere with fibrosis using a PFGFRa antagonist and silencing of Itgb1. Finally, they looked at some variants of the Itgb1 in patients with diabetes to determine a possible association.

    Strengths: This is one of the first transcriptomics study a single cell level of the mouse diabetic heart. The study is technically sound.

    Weakness: The study is mainly associative. A cause relationship effect is difficult to be extracted. A major problem is that they studied only a single time point at an advanced stage of the disease, therefore it is difficult to determine if the observed changes are epiphenomena. They also use only one diabetic model where STZ was superimposed on the high caloric diet. STZ can cause unspecific effects and more models are generally requested. They also used male mice only while diabetic cardiomyopathy is more prevalent in females. No functional data are provided to study the capacity of treatment to rescue cardiac contractility and diastolic function, which is certainly affected by fibrosis.
    The methodological part can help further studies provided the limits indicated above are considered.

  7. Version published to 10.1101/2022.06.29.498129v1 on bioRxiv