Revealing the Critical Regulators of Modulated Smooth Muscle Cells in Atherosclerosis in Mice - Zhou, Wenli; Bai, Yongyi; Chen, Jianqiao; Li, Huiying; Zhang, Baohua; Liu, Hongbin (Frontiers in Genetics, 2022)
Background: There are still residual risks for atherosclerosis (AS) associated cardiovascular diseases to be resolved. Considering the vital role of phenotypic switching of smooth muscle cells (SMCs) in AS, especially in calcification, targeting SMC phenotypic modulation holds great promise of clinical implications. Methods: To perform an unbiased and systematic analysis of molecular regulatory mechanism of phenotypic switching of SMCs during AS in mice, we searched and included several publicly available single cell datasets from GEO database, resulting in an inclusion of more than 80,000 cells. Algorithms implemented in Seurat package were used for cell clustering and cell atlas depiction. pySCENIC and SCENIC package were used to identify master regulators of interested cell groups. Monocle2 was used to perform pseudotime analysis. ClusterProfiler were used for Gene Ontology enrichment analysis. Results: After dimensionality reduction and clustering, reliable annotation was performed. Comparative analysis between cells from normal artery and AS lesions revealed that three clusters emerged as AS progression, designated as mSMC1, mSMC2 and mSMC3. Transcriptional and functional enrichment analysis established a continuous transitional mode of SMCs’ transdifferentiation to mSMCs, which is further supported by pseudotime analysis. 237 regulons were identified with varying activity scores across cell types. A potential core regulatory network was constructed for SMC and mSMC subtypes. In addition, module analysis revealed a coordinate regulatory mode of regulons for a specific cell type. Intriguingly, consistent with gain of ossification-related transcriptional and functional characteristics, a corresponding small set of regulators contributing to osteochondral reprograming was identified in mSMC3, including Dlx5, Sox9 and Runx2, etc. Conclusion: Gene regulatory network inference indicates a hierarchical organization of regulatory modules that work together in fine-tuning cellular states. The analysis here provides a valuable resource that can provide guidance for subsequent biological experiments.