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Strategies for arterial graft optimization at the single-cell level

Nature Cardiovascular Research (2024)Cite this article

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Abstract

Common arterial grafts used in coronary artery bypass grafting include internal thoracic artery (ITA), radial artery (RA) and right gastroepiploic artery (RGA) grafts; of these, the ITA has the best clinical outcome. Here, by analyzing the single-cell transcriptome of different arterial grafts, we suggest optimization strategies for the RA and RGA based on the ITA as a reference. Compared with the ITA, the RA had more lipid-handling-related CD36+ endothelial cells. Vascular smooth muscle cells from the RGA were more susceptible to spasm, followed by those from the RA; comparison with the ITA suggested that potassium channel openers may counteract vasospasm. Fibroblasts from the RA and RGA highly expressed GDF10 and CREB5, respectively; both GDF10 and CREB5 are associated with extracellular matrix deposition. Cell–cell communication analysis revealed high levels of macrophage migration inhibitory factor signaling in the RA. Administration of macrophage migration inhibitory factor inhibitor to mice with partial carotid artery ligation blocked neointimal hyperplasia induced by disturbed flow. Modulation of identified targets may have protective effects on arterial grafts.

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Fig. 1: Profiling of the 38,814 single cells isolated from three groups of arterial grafts.
Fig. 2: Subtype analysis of ECs.
Fig. 3: Intervention targets of ECs.
Fig. 4: Subtype analysis of VSMCs.
Fig. 5: Subtype analysis of FBs.
Fig. 6: Subtype analysis of immune cells.
Fig. 7: MIF as an intervention target for anti-neointimal hyperplasia.
Fig. 8: MIF-induced VSMC proliferation.

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Data availability

The scRNA-seq data generated from this study have been deposited with Figshare at https://doi.org/10.6084/m9.figshare.24922941 (ref. 53). CellRanger v.5.0.1 was used with default parameters to map all the data from the samples to the human reference genome (GRCh38) provided by 10X Genomics. The bulk RNA-seq data (the cultured human VSMCs) generated from this study have been deposited with Zenodo at https://doi.org/10.5281/zenodo.10797867 (ref. 54). Source data are provided with this paper.

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Acknowledgements

We thank the National Natural Science Fund for Distinguished Young Scholars of China (grant no. 82125004), the Chinese Academy of Medical Sciences (CAMS) for the CAMS Innovation Fund for Medical Sciences (grant no. 2021-I2M-1-064), the National High Level Hospital Clinical Research Funding (2022-GSP-QZ-03, 2023-GSP-RC-03), the National Natural Science Foundation of China (grant no. 31570932), the Chinese Academy of Sciences for Young Scientists in Basic Research Project (grant no. YSBR-073) and the Beijing Municipal Science & Technology Commission (grant no. Z231100007223010).

Author information

Author notes

  1. These authors contributed equally: Zhan Hu, Min Dai, Yuan Chang.

Authors and Affiliations

  1. Beijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials, Animal Experimental Centre, Fuwai Hospital, National Centre for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Zhan Hu, Yuan Chang, Xiumeng Hua, Ningning Zhang, Xiao Chen, Yixuan Sheng, Zhenyu Xu, Hang Zhang, Yu Zhang, Hao Cui, Hao Jia & Jiangping Song

  2. State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Zhan Hu, Yuan Chang, Xiumeng Hua, Ningning Zhang, Xiao Chen, Yixuan Sheng, Zhenyu Xu, Hang Zhang, Yu Zhang, Hao Cui, Hao Jia & Jiangping Song

  3. Institute of Genetics and Developmental Biology, Innovation Academy of Seed Design, Chinese Academy of Sciences, Beijing, China

    Min Dai & Xiu-Jie Wang

  4. University of the Chinese Academy of Sciences, Beijing, China

    Min Dai & Xiu-Jie Wang

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Contributions

J.S. and X.-J.W. conceived and directed the study. Z.H., X.C., X.H. and H.Z. collected the arterial tissues and isolated the single cells for subsequent scRNA-seq. Z.H. and Y.C. performed the animal work. Y.S., N.Z., Z.X. and Y.C. performed the cell culture and immunostaining experiments. Y.Z., H.C. and H.J. performed the qPCR experiments. Y.C., M.D. and Z.H. analyzed the scRNA-seq data and reviewed the statistical methods. Z.H., M.D. and Y.C. wrote the manuscript.

Corresponding authors

Correspondence to Xiu-Jie Wang or Jiangping Song.

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The authors declare no competing interests.

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Nature Cardiovascular Research thanks Domenico Bruno, Joanna Kalucka and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 HE staining for ITA, RA, and RGA.

a. HE staining for ITA. b. HE staining for RA. c. HE staining for RGA. ITA: internal thoracic artery, RA: radial artery, RGA: right gastroepiploic artery. 2 independent experiments obtained the similar results (a-c).

Extended Data Fig. 2 Quality control (QC) analysis.

a. The data quality metrics before and after QC. b. The summary table of cell numbers after QC. c. Doublet detected by scrublet analysis. d. The evaluation of the proportion of stress gene expression caused by cell dissociation. e. The UMAP visualization of batch effect correction effects by scVI. f. The UMAP visualization of batch effect correction effects by Harmony.

Extended Data Fig. 3 Evaluation of individual batch effect.

UMAP distribution of Individual sample

Extended Data Fig. 4 Quantification of VSMC and FB in three types of arterial grafts.

a. α-SMA and VIM stain of ITA, RA, and RGA. b. The proportion of FB and VSMC in tunica media among three arterial grafts. c. EVG stain in ITA, RA, and RGA. d. Comparison of elastic fibers area among ITA, RA, and RGA. EVG: Verhoeff’s Van Gieson. Data are presented as mean ± s.e.m.; P values were calculated using one-way ANOVA unpaired multiple comparisons; n = 7, n = 7, and n = 3 biologically independent samples for ITA, RA, and RGA, respectively (b, d).

Source data

Extended Data Fig. 5 The distribution of VWF and EC0’s top marker before QC.

a. VWF indicated the location of EC on UMAP before QC (dashed circle). b. Enriched gene activities of EC0’s top marker genes on UMAP before QC (Black arrow). QC: Quality control.

Extended Data Fig. 6 Function analysis of EC8.

a. The expression of SEMA3G in EC0-EC9. b. GO function of EC8.

Extended Data Fig. 7 Immunofluorescence staining verification for ECs.

a. Validate high expression of ENDRB in RGA. Immunofluorescence staining for CD31 (Red), CLDN11 (Green), EDNRB (Yellow), and DAPI (Blue) among three arterial grafts. b. Mean fluorescence intensity of EDNRB in ECs among three arterial grafts. c. Validate high expression of PTGS2 and NOS3 in ITA. Immunofluorescence staining for CD31 (Red), PTGS2 (Green), NOS3 (Yellow), and DAPI (Blue) among three arterial grafts. d. Mean fluorescence intensity of PTGS2 and NOS3 in ECs among three arterial grafts. Data are presented as mean ± s.e.m.; P values were calculated using one-way ANOVA unpaired multiple comparisons; n = 7, n = 7, and n = 3 biologically independent samples for ITA, RA, and RGA, respectively (b, d).

Source data

Extended Data Fig. 8 Inhibition of CREB5 could reduce fibrosis.

a. The expression of WNT10B and CREB5 in FB0-FB4. b. Work flow for CREB5 inhibition in vascular adventitial fibroblasts. c. qPCR for COL1A1 after 24 hours of CREB5 inhibition by ICG-001. n=4 per group. d. Immunostaining for COL1A1 after 24 hours of CREB5 inhibition by ICG-001. 3 independent experiments with n=8 per group. e. Quantification of COL1A1. n=8 per group. f-g. Scratch experiment after CREB5 inhibition. n=4 per group. Data are presented as mean ± s.e.m.; P values were calculated using one-way ANOVA unpaired multiple comparisons (c, e, g).

Source data

Extended Data Fig. 9 GDF10 induced fibrosis.

a. Work flow for GDF10 stimulation in vascular adventitial fibroblasts. b. qPCR for COL1A1 and E2F1 after 24 hours of GDF10 stimulation. n=4 per group. c. Immunostaining for COL1A1 after 24 hours of GDF10 stimulation. 3 independent experiments with n=8 per group. d. Quantification of COL1A1. n=8 per group. e-f. Scratch experiment after CREB5 inhibition. n=4 per group. g. The expression of GDF10 receptor in FB0-FB4. h. The expression of TGFB related genes in FB0-FB4. i. The effect of GDF10. Data are presented as mean ± s.e.m.; P values were calculated using one-way ANOVA unpaired multiple comparisons (b, d, f).

Source data

Extended Data Fig. 10 Multicolor immunofluorescence validation in CAs.

a. PTGS2 and SEMA3G in ECs of CAs. b. LDLR and CD36 in ECs of CAs. c. CACNA1C and KCNMA1 in VSMCs of CAs. d. GDF10 and CTHRC1 in FBs of CAs. 2 independent experiments obtained the similar results (a-d).

Supplementary information

Reporting Summary

Supplementary Tables 1–4

Supplementary Table 1. Patients’ clinical information.

Table 2. Gene set for the score analysis.

Table 3. Marker genes for the cell subtypes.

Table 4. Summary of major cell types in arterial grafts.

Source data

Source Data Fig. 2

Statistical source data for Fig. 2.

Source Data Fig. 3

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Source Data Fig. 4

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Source Data Fig. 5

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Source Data Fig. 7

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Source Data Fig. 8

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Source Data Extended Data Fig. 4

Statistical source data for Extended Data Fig. 4.

Source Data Extended Data Fig. 7

Statistical source data for Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Statistical source data for Extended Data Fig. 8.

Source Data Extended Data Fig. 9

Statistical source data for Extended Data Fig. 9.

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Hu, Z., Dai, M., Chang, Y. et al. Strategies for arterial graft optimization at the single-cell level. Nat Cardiovasc Res (2024). https://doi.org/10.1038/s44161-024-00464-6

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