Single-cell RNA sequencing unveils unique transcriptomic signatures of endothelial cells and role of ENO1 in response to disturbed flow.
Li-Jing ChenJulie Yi-Shuan LiPhu NguyenMing HeZhen Bouman ChenShankar SubramaniamJohn Y-J ShyyShu ChienPublished in: Proceedings of the National Academy of Sciences of the United States of America (2024)
Flow patterns exert significant effects on vascular endothelial cells (ECs) to lead to the focal nature of atherosclerosis. Using a step flow chamber to investigate the effects of disturbed shear (DS) and pulsatile shear (PS) on ECs in the same flow channel, we conducted single-cell RNA sequencing analyses to explore the distinct transcriptomic profiles regulated by DS vs. PS. Integrated analysis identified eight cell clusters and demonstrated that DS induces EC transition from atheroprotective to proatherogenic phenotypes. Using an automated cell type annotation algorithm (SingleR), we showed that DS promoted endothelial-to-mesenchymal transition (EndMT) by inducing the transcriptional phenotypes for inflammation, hypoxia responses, transforming growth factor-beta (TGF-β) signaling, glycolysis, and fatty acid synthesis. Enolase 1 (ENO1), a key gene in glycolysis, was one of the top-ranked genes in the DS-induced EndMT cluster. Pseudotime trajectory analysis revealed that the kinetic expression of ENO1 was significantly associated with EndMT and that ENO1 silencing repressed the DS- and TGF-β-induced EC inflammation and EndMT. Consistent with these findings, ENO1 was highly expressed in ECs at the inner curvature of the mouse aortic arch (which is exposed to DS) and atherosclerotic lesions, suggesting its proatherogenic role in vivo. In summary, we present a comprehensive single-cell atlas of ECs in response to different flow patterns within the same flow channel. Among the DS-regulated genes, ENO1 plays an important role in DS-induced EC inflammation and EndMT. These results provide insights into how hemodynamic forces regulate vascular endothelium in health and disease.
Keyphrases
- single cell
- rna seq
- endothelial cells
- high glucose
- transforming growth factor
- high throughput
- oxidative stress
- diabetic rats
- epithelial mesenchymal transition
- transcription factor
- cardiovascular disease
- healthcare
- poor prognosis
- fatty acid
- nitric oxide
- bone marrow
- machine learning
- drug induced
- gene expression
- deep learning
- public health
- dna methylation
- data analysis