bioRxivCell BiologyPreprint
Uncovering the transcriptional hallmarks of endothelial cell aging via integrated single-cell analysis
Sarah DobneriD, Lisa KleißliD, Fanni Tóth, Robert L. Paxton, Irena Yordanova, Kveta BrazdilovaiD, Cedric VanluyteniD, Aleksandra AizenshtadtiD, Hanna Toth, Jake BurtoniD, Aglaja KopfiD, Vera Belyaeva, Katharina Ferencevic, Dunja Al‐NuaimiiD, André F. RendeiroiD, Christoph BockiD, Joanna Maria KaluckaiD, Stefan Johannes Karl KraussiD, Selma Osmanagic‐MyersiD, Laurens Joseph CeulemansiD, Georg StaryiD, Abdel Rahman Abdel FattahiD, Laura P. M. H. de RooijiD
Abstract
Endothelial cells (ECs) are critical regulators of vascular function and exhibit specialized, organ-specific roles across tissues. During aging, these cells become dysfunctional, resulting in increased susceptibility to cardiovascular disease and its associated mortality. While single-cell transcriptomics studies have revealed extensive endothelial heterogeneity across tissues and conditions, a comprehensive atlas of human EC transcriptomes over the course of the adult human lifespan is still lacking. Here, we present the Human Aging Endothelial Cell Atlas (HAECA), a harmonized single-cell transcriptomic compendium of over 375,000 ECs from 12 human tissues throughout adulthood. Using HAECA, we identified age-associated transcriptional shifts, including a decline in angiogenic gene expression in venous ECs and widespread alterations in extracellular matrix (ECM)- and mechanotransduction-associated pathways. We validated these findings in aging human skin and further uncovered a p21-linked transcriptional program in ECs, confirmed in both in vitro and in vivo models and linked to cellular senescence. Together, our study provides a high-resolution transcriptome reference across spatial as well as temporal axes of the human endothelium.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 4
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