Yu, Grace T

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.