A single-cell atlas depicting the cellular and molecular features in human anterior cruciate ligamental degeneration: A single cell combined spatial transcriptomics study

  1. Runze Yang
  2. Tianhao Xu
  3. Lei Zhang
  4. Minghao Ge
  5. Liwei Yan
  6. Jian Li
  7. Weili Fu

  • Curated by eLife

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    The creation of a single-cell atlas of normal and degenerative human anterior cruciate ligament (ACL) tissues using a single-cell RNA sequencing method is an important approach to understanding the pathological mechanisms of ACL degeneration. The data of this study showed the existence of fibroblasts, endothelial cells, pericytes, and immune cells in healthy ACL, and their ratios altered in the degenerative ACL, mainly exhibited as an increase in fibroblasts and immune cells. The data analysis suggests that alterations of spatial transcriptome and changes in gene expression and signaling pathways may contribute to ACL degeneration.

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Abstract

To systematically identify cell types in the human ligament, investigate how ligamental cell identities, functions, and interactions participated in the process of ligamental degeneration, and explore the changes of ligamental microenvironment homeostasis in the disease progression.

Methods:

Using single-cell RNA sequencing and spatial RNA sequencing of approximately 49,356 cells, we created a comprehensive cell atlas of healthy and degenerated human anterior cruciate ligaments. We explored the variations of the cell subtypes’ spatial distributions and the different processes involved in the disease progression, linked them with the ligamental degeneration process using computational analysis, and verified findings with immunohistochemical and immunofluorescent staining.

Results:

We identified new fibroblast subgroups that contributed to the disease, mapped out their spatial distribution in the tissue and revealed two dynamic trajectories in the process of the degenerative process. We compared the cellular interactions between different tissue states and identified important signaling pathways that may contribute to the disease.

Conclusions:

This cell atlas provides the molecular foundation for investigating how ligamental cell identities, biochemical functions, and interactions contributed to the ligamental degeneration process. The discoveries revealed the pathogenesis of ligamental degeneration at the single-cell and spatial level, which is characterized by extracellular matrix remodeling. Our results provide new insights into the control of ligamental degeneration and potential clues to developing novel diagnostic and therapeutic strategies.

Funding:

This study was funded by the National Natural Science Foundation of China (81972123, 82172508, 82372490) and 1.3.5 Project for Disciplines of Excellence of West China Hospital Sichuan University (ZYJC21030, ZY2017301).

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  1. Version published to 10.7554/elife.85700 on eLife
  2. eLife

    Author Response

    Reviewer #2 (Public Review):

    In this study, Yang et al. used single-cell technology to construct the cell profiles of normal and pathological ligaments and identified the critical cell subpopulations and signaling pathways involved in ligament degeneration. The authors identified four major cell types: fibroblasts, endothelial cells, pericytes, and immune cells from four normal and four pathological human ligament samples. They further revealed the increased number of fibroblast subpopulations associated with ECM remodelling and inflammation in pathological ligaments. In addition, the authors further resolved the heterogeneity of endothelial and immune cells and identified an increase in pericyte subpopulations with muscle cell characteristics and macrophages in pathological ACL. Ligand-receptor interaction analysis revealed the involvement of FGF7 and TGFB signaling in interactions between pathological tendon subpopulations. Spatial transcriptome data analysis also validated the spatial proximity of disease-specific fibroblast subpopulations to endothelial and macrophages, suggesting their interactions in pathological ligaments. This study offers a comprehensive atlas of normal and pathological cells in human ligaments, providing valuable data for understanding the cellular composition of ligaments and screening for critical pathological targets. However, more in-depth analyses and experimental validation are needed to enhance the study.

    1. In this study, the authors performed deconvolution analysis between bulk RNA sequencing results and scRNA-seq results (L204-L208). However, the analysis of this section is not sufficiently in-depth and the authors failed to present the proportion of different cell subpopulations of the bulk sequencing samples to further increase the reliability of the results of the single cell data analysis.

    Thank you for the suggestion. We selected the top 50 Degs in each subpopulation of scRNA-seq, and scored the gene sets at the bulk RNA sequencing data level by GSVA method, so as to present the proportion of different cell subpopulations of the bulk sequencing samples to some extent. The results illustrated that, in the bulk RNA-seq data, fibroblast subpopulations (fibroblast 1,2,8,9) scored higher in the diseased group than in the normal group and fibroblast subpopulations (fibroblast 3,4) scored higher in the normal group than in the diseased group, which are consistent with the results of scRNA-seq.

    1. In results 5, the authors should clearly describe whether the analysis is based only on pathological subpopulations of ligament cells or includes a mixture of normal and pathological subpopulations; the corresponding description should also be indicated in Figure 5. Besides, although the authors claimed that "the TGF-β pathway was involved in many cell-cell interactions among fibroblasts subpopulations and macrophages", Figure 5C displayed that the CD8+NKT-like cells displayed the most TGFB signaling interactions with fibroblasts subpopulations.

    Thank you for your great questions. In results 5, our analysis is based on the mixture of normal and diseased subpopulations. We have also added a description of the data sample in the corresponding position in our manuscript.

    As for the question of the TGF-β pathway in cell-cell interaction analysis, we claimed that “the TGF-β pathway was involved in many cell-cell interactions among fibroblasts subpopulations and macrophages”, because we took into account the proportion of each subpopulation of immune cells. Macrophages are the largest subpopulation of immune cells, and the number of macrophages is significantly increased in the degenerative group, suggesting that they are closely related to disease progression. However, the proportion of CD8+NKT-like cells in immune cells was very small, and the number of them was basically unchanged between the normal and diseased groups. So, macrophages are the focus of our attention, and after comprehensive analysis, we did not mention the strength TGFB signaling interactions of CD8+NKT-like cells.

    1. In result 6, the authors performed spatial transcriptome sequencing, however, the sample numbers were relatively limited, with only one sample from each group; in addition, the results of this part failed to correlate and correspond well with the single-cell results. The subgroups labelled in L382 and L384 should be carefully checked. Besides, expression data of FGF7 and TGFB ligand and receptor molecules based on the spatial transcriptomes should be added to further confirm the critical signalling pathway in regulating the cellular interactions in pathological ACL.

    Thanks for your reminding. The purpose of our spatial transcriptome sequencing (spRNA-seq) was to verify the scRNA-seq results, so only one representative sample from each group was selected for spRNA-seq. We believe that the results of our spRNA-seq were correlated and corresponded well with the scRNA-seq results. The scRNA-seq results were validated on the spRNA-seq data using marker transfer and spotlight methods, respectively. The results showed that more fibroblast4 in the normal group and more fibroblast9 in the diseased group of the scRNA-seq data were also consistent in the distribution of spRNA-seq samples. As shown in the spotlight plots, the more fibroblast subsets (fibroblast1,2,8,9) identified in the scRNA-seq data of the disease group were more widely distributed in the spRNA-seq sample of the disease group, and were closer to endothelial cells and immune cells in spatial location. We have revised the subgroups labelled in L382 and L384.

    According to your suggestions, FGF7 and TGFB related ligand and receptor genes were mapped on spRNA-seq data, and the results were consistent with the results of cellchat analysis in scRNA-seq.

  3. eLife

    eLife assessment

    The creation of a single-cell atlas of normal and degenerative human anterior cruciate ligament (ACL) tissues using a single-cell RNA sequencing method is an important approach to understanding the pathological mechanisms of ACL degeneration. The data of this study showed the existence of fibroblasts, endothelial cells, pericytes, and immune cells in healthy ACL, and their ratios altered in the degenerative ACL, mainly exhibited as an increase in fibroblasts and immune cells. The data analysis suggests that alterations of spatial transcriptome and changes in gene expression and signaling pathways may contribute to ACL degeneration.

  4. eLife

    Reviewer #1 (Public Review):

    In this project, the authors used a single-cell RNA sequencing technique, created a cell atlas of normal and diseased human anterior cruciate ligaments of 49,356 cells from 8 patients, explored the variations of the cell subtypes' spatial distributions, and found their associations with ligamental degeneration. Using the single-cell RNA sequencing data, the authors identified fibroblast subsets unique to normal and diseased tissues, revealed two processes of acute and chronic disease outcome in ligamental degeneration and found immune cell and stromal cell subclusters changed the extracellular matrix in ligament and contributed to the disease. Combined with spatial transcriptome sequencing, they found the spatial distribution of immune and stromal cells associated with the disease and demonstrated cell-cell communications among endothelial cells, macrophages, and fibroblasts.

  5. eLife

    Reviewer #2 (Public Review):

    In this study, Yang et al. used single-cell technology to construct the cell profiles of normal and pathological ligaments and identified the critical cell subpopulations and signaling pathways involved in ligament degeneration. The authors identified four major cell types: fibroblasts, endothelial cells, pericytes, and immune cells from four normal and four pathological human ligament samples. They further revealed the increased number of fibroblast subpopulations associated with ECM remodelling and inflammation in pathological ligaments. In addition, the authors further resolved the heterogeneity of endothelial and immune cells and identified an increase in pericyte subpopulations with muscle cell characteristics and macrophages in pathological ACL. Ligand-receptor interaction analysis revealed the involvement of FGF7 and TGFB signaling in interactions between pathological tendon subpopulations. Spatial transcriptome data analysis also validated the spatial proximity of disease-specific fibroblast subpopulations to endothelial and macrophages, suggesting their interactions in pathological ligaments. This study offers a comprehensive atlas of normal and pathological cells in human ligaments, providing valuable data for understanding the cellular composition of ligaments and screening for critical pathological targets. However, more in-depth analyses and experimental validation are needed to enhance the study.

    1. In this study, the authors performed deconvolution analysis between bulk RNA sequencing results and scRNA-seq results (L204-L208). However, the analysis of this section is not sufficiently in-depth and the authors failed to present the proportion of different cell subpopulations of the bulk sequencing samples to further increase the reliability of the results of the single cell data analysis.
    2. In results 5, the authors should clearly describe whether the analysis is based only on pathological subpopulations of ligament cells or includes a mixture of normal and pathological subpopulations; the corresponding description should also be indicated in Figure 5. Besides, Although the authors claimed that "the TGF-β pathway was involved in many cell-cell interactions among fibroblasts subpopulations and macrophages", Figure 5C displayed that the CD8+NKT-like cells displayed the most TGFB signaling interactions with fibroblasts subpopulations.
    3. In result 6, the authors performed spatial transcriptome sequencing, however, the sample numbers were relatively limited, with only one sample from each group; in addition, the results of this part failed to correlate and correspond well with the single-cell results. The subgroups labelled in L382 and L384 should be carefully checked. Besides, expression data of FGF7 and TGFB ligand and receptor molecules based on the spatial transcriptomes should be added to further confirm the critical signalling pathway in regulating the cellular interactions in pathological ACL.
  6. eLife

    Reviewer #3 (Public Review):

    In this manuscript, Yang et al. claimed the creation of a single-cell atlas of the human anterior cruciate ligament (ACL) using scRNA-seq, spRNA-seq, and transcriptomic profiling. Upon analysis of about 25K cells from healthy and degenerated human ACL, the authors reported the existence of fibroblasts, endothelial cells, pericytes, and immune cells in healthy ACL. Their ratios altered in the degenerative ACL, featuring an increase in fibroblasts and immune cells, as demonstrated by the UMAP. Further characterization revealed the presence of subclusters in each of the four major types of cells. The evolution trajectory, spatial transcriptome, and signaling pathways that may contribute to biphasic ACL degeneration were also explored. These data are valuable, to some extent, in improving the current knowledge regarding ACL cellular heterogeneity, homeostasis, and ligamental degeneration. However, the abovementioned findings are purely derived from computational modeling; the authors haven't validated any of them experimentally in vitro and in vivo, particularly regarding whether there are multiple fibroblast subclusters in the ACL with distinct biology. The spatial transcriptomic analysis is also superficial, and few novel insights were generated. The reported work seems like a window show of fancy technologies rather than a hypothesis-driven investigation. Some figures were not clearly labeled, and figure legends were too brief to follow up the studies. Therefore, the significance of this work and its value as a cell atlas of ACL are compromised.

  7. Version published to 10.1101/2023.01.05.23284219v1 on medRxiv