Werner syndrome (WS) is a premature aging disorder caused by WRN protein deficiency. Here, we report on the generation of a human WS model in human embryonic stem cells (ESCs). Differentiation of WRN-null ESCs to mesenchymal stem cells (MSCs) recapitulates features of premature cellular aging, a global loss of H3K9me3, and changes in heterochromatin architecture. We show that WRN associates with heterochromatin proteins SUV39H1 and HP1α and nuclear lamina-heterochromatin anchoring protein LAP2β. Targeted knock-in of catalytically inactive SUV39H1 in wild-type MSCs recapitulates accelerated cellular senescence, resembling WRN-deficient MSCs. Moreover, decrease in WRN and heterochromatin marks are detected in MSCs from older individuals. Our observations uncover a role for WRN in maintaining heterochromatin stability and highlight heterochromatin disorganization as a potential determinant of human aging.

Background: Smooth muscle cells (SMC) play significant roles in atherosclerosis via phenotypic switching, a pathological process in which SMC dedifferentiation, migration and transdifferentiation into other cell types. Yet, how SMC contribute to pathophysiology of atherosclerosis remains elusive. Methods: To reveal the trajectories of SMC transdifferentiation during atherosclerosis and to identify molecular targets for disease therapy, we combined SMC fate mapping and single-cell RNA sequencing of both mouse and human atherosclerotic plaques. We also performed cell biology experiments on isolated SMC-derived cells, conducted integrative human genomics, and employed pharmacological studies targeting SMC-derived cells both in vivo and in vitro . Results: We found that SMC transitioned to an intermediate cell state during atherosclerosis, which was also found in human atherosclerotic plaques of carotid and coronary arteries. SMC-derived intermediate cells, termed "SEM" cells, were multipotent and could differentiate into macrophage-like and fibrochondrocyte-like cells, as well as return towards SMC phenotype. Retinoic acid (RA) signaling was identified as a regulator of SMC to SEM cell transition and RA signaling was dysregulated in symptomatic human atherosclerosis. Human genomics revealed enrichment of genome wide association study (GWAS) signals for coronary artery disease (CAD) in RA signaling target gene loci and correlation between CAD risk alleles and repressed expression of these genes. Activation of RA signaling by all-trans retinoic acid (ATRA), an anti-cancer drug for acute promyelocytic leukemia, blocked SMC transition to SEM cells, reduced atherosclerotic burden and promoted fibrous cap stability. Conclusions: Integration of cell-specific fate mapping, single-cell genomics and human genetics adds novel insights into the complexity of SMC biology and reveals regulatory pathways for therapeutic targeting of SMC transitions in atherosclerotic cardiovascular disease.

SIRT6 belongs to the mammalian homologs of Sir2 histone NAD+-dependent deacylase family. In rodents, SIRT6 deficiency leads to aging-associated degeneration of mesodermal tissues. It remains unknown whether human SIRT6 has a direct role in maintaining the homeostasis of mesodermal tissues. To this end, we generated SIRT6 knockout human mesenchymal stem cells (hMSCs) by targeted gene editing. SIRT6-deficient hMSCs exhibited accelerated functional decay, a feature distinct from typical premature cellular senescence. Rather than compromised chromosomal stability, SIRT6-null hMSCs were predominately characterized by dysregulated redox metabolism and increased sensitivity to the oxidative stress. In addition, we found SIRT6 in a protein complex with both nuclear factor erythroid 2-related factor 2 (NRF2) and RNA polymerase II, which was required for the transactivation of NRF2-regulated antioxidant genes, including heme oxygenase 1 (HO-1). Overexpression of HO-1 in SIRT6-null hMSCs rescued premature cellular attrition. Our study uncovers a novel function of SIRT6 in maintaining hMSC homeostasis by serving as a NRF2 coactivator, which represents a new layer of regulation of oxidative stress-associated stem cell decay.

Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.