Zhou Wenli, Bai Yongyi, Chen Jianqiao, Li Huiying, Zhang Baohua, Liu Hongbin
Medical School of Chinese PLA, Beijing, China.
Department of Cardiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China.
Front Genet. 2022 May 23;13:900358. doi: 10.3389/fgene.2022.900358. eCollection 2022.
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 for clinical implications. To perform an unbiased and systematic analysis of the molecular regulatory mechanism of phenotypic switching of SMCs during AS in mice, we searched and included several publicly available single-cell datasets from the GEO database, resulting in an inclusion of more than 80,000 cells. Algorithms implemented in the Seurat package were used for cell clustering and cell atlas depiction. The pySCENIC and SCENIC packages were used to identify master regulators of interested cell groups. Monocle2 was used to perform pseudotime analysis. clusterProfiler was used for Gene Ontology enrichment analysis. 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. A total of 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 reprogramming was identified in mSMC3, including Dlx5, Sox9, and Runx2. 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.
动脉粥样硬化（AS）相关心血管疾病仍存在残余风险有待解决。考虑到平滑肌细胞（SMC）表型转换在AS中，尤其是在钙化过程中的关键作用，靶向SMC表型调节在临床应用方面具有巨大潜力。为了对小鼠AS过程中SMC表型转换的分子调控机制进行无偏且系统的分析，我们搜索并纳入了来自GEO数据库的几个公开可用的单细胞数据集，共纳入了超过80,000个细胞。使用Seurat软件包中实现的算法进行细胞聚类和细胞图谱描绘。使用pySCENIC和SCENIC软件包识别感兴趣细胞群的主调控因子。使用Monocle2进行伪时间分析。使用clusterProfiler进行基因本体富集分析。在降维和聚类后，进行了可靠的注释。正常动脉和AS病变细胞之间的比较分析显示，随着AS进展出现了三个聚类，分别命名为mSMC1、mSMC2和mSMC3。转录和功能富集分析建立了SMC向mSMC转分化的连续过渡模式，伪时间分析进一步支持了这一模式。共鉴定出237个调控子，其在不同细胞类型中的活性得分各不相同。构建了SMC和mSMC亚型的潜在核心调控网络。此外，模块分析揭示了特定细胞类型调控子的协同调控模式。有趣的是，与骨化相关转录和功能特征的获得一致，在mSMC3中鉴定出一小部分有助于骨软骨重编程的相应调控因子，包括Dlx5、Sox9和Runx2。基因调控网络推断表明调控模块的层次组织共同精细调节细胞状态。此处的分析提供了宝贵的资源，可为后续生物学实验提供指导。
