Yu, Grace

BACKGROUND Cellular senescence, an irreversible cell cycle arrest with secretory phenotype, is a hallmark of skin aging. Regenerative exosome-based approaches, such as topical human platelet extract (HPE), are emerging to target age-related skin dysfunction. OBJECTIVE To evaluate the cellular and molecular effects of topical HPE for skin rejuvenation after 12 weeks of twice daily use. METHODS Skin biopsies were obtained for histological evaluation of senescence markers, p16INK4a and p21CIP1/WAF1. Telomere-associated foci, coassociation of telomeres, and DNA damage marker, γH2AX, were assessed. RNA sequencing evaluated senescence associated secretory phenotype (SASP) and extracellular matrix pathways. RESULTS p16INK4a and p21CIP1/WAF1 staining in senescent skin cells revealed low and high expression subgroups that did not correspond to chronological age. Topical HPE significantly reduced high p16INK4a cells in the dermis (p = .02). There was also a decrease in telomere damage after topical HPE (p = .03). In patients with high senescent cells at baseline, there was a 40% reduction in proinflammatory SASP. Extracellular matrix remodeling pathways, including collagen and elastic fibers, were up-regulated. CONCLUSION Topical HPE, applied on intact skin, reduced senescence signaling and senescence-associated telomere damage after 12 weeks of twice daily use, targeting a path for skin longevity or healthy skin aging.

Abstract Single‐cell RNA sequencing and spatial transcriptomics enable unprecedented insight into cellular and molecular pathways implicated in human skin aging and regeneration. Senescent cells are individual cells that are irreversibly cell cycle arrested and can accumulate across the human lifespan due to cell‐intrinsic and ‐extrinsic stressors. With an atlas of single‐cell RNA‐sequencing and spatial transcriptomics, epidermal and dermal senescence and its effects were investigated, with a focus on melanocytes and fibroblasts. Photoaging due to ultraviolet light exposure was associated with higher burdens of senescent cells, a sign of biological aging, compared to chronological aging. A skin‐specific cellular senescence gene set, termed SenSkin™, was curated and confirmed to be elevated in the context of photoaging, chronological aging, and non‐replicating CDKN1A + (p21) cells. In the epidermis, senescent melanocytes were associated with elevated melanin synthesis, suggesting haphazard pigmentation, while in the dermis, senescent reticular dermal fibroblasts were associated with decreased collagen and elastic fiber synthesis. Spatial analysis revealed the tendency for senescent cells to cluster, particularly in photoaged skin. This work proposes a strategy for characterizing age‐related skin dysfunction through the lens of cellular senescence and suggests a role for senescent epidermal cells (i.e., melanocytes) and senescent dermal cells (i.e., reticular dermal fibroblasts) in age‐related skin sequelae.

AbstractBackgroundChronic wounds have been associated with an elevated burden of cellular senescence, a state of essentially irreversible cell cycle arrest, resistance to apoptosis, and a secretory phenotype. However, whether senescent cells contribute to wound chronicity in humans remains unclear. The objective of this article is to assess the role of clinicopathological characteristics and cellular senescence in the time‐to‐healing of chronic wounds.MethodsA cohort of 79 patients with chronic wounds was evaluated in a single‐center academic practice from February 1, 2005, to February 28, 2015, and followed for up to 36 months. Clinical characteristics and wound biopsies were obtained at baseline, and time‐to‐healing was assessed. Wound biopsies were analyzed histologically for pathological characteristics and molecularly for markers of cellular senescence. In addition, biopsy slides were stained for p16INK4a expression.ResultsNo clinical or pathological characteristics were found to have significant associations with time‐to‐healing. A Cox proportional hazard ratio model revealed increased CDKN1A (p21CIP1/WAF1) expression to predict longer time‐to‐healing, and a model adjusted for gender and epidermal hyperplasia revealed increased CDKN1A expression and decreased PAPPA expression to predict longer time‐to‐healing. Increased p16INK4a staining was observed in diabetic wounds compared to non‐diabetic wounds, and the same association was observed in the context of high dermal fibrosis.ConclusionsThe findings of this pilot study suggest that senescent cells contribute to wound chronicity in humans, especially in diabetic wounds.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.