SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden.

Bethany K Hughes, Andrew Davis, Deborah Milligan, Ryan Wallis, Federica Mossa, Michael P Philpott, Linda J Wainwright, David A Gunn, Cleo L Bishop

Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK.Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK. · pubmed

Paper

Abstract

Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity.

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Research summary

The paper addresses the classification of senescent cells, which is a key aspect of understanding cellular aging and its implications for longevity research. By utilizing single-cell RNA sequencing and machine learning, it aims to improve the detection of senescent cells, potentially contributing to the understanding of senescence as a root cause of aging. However, while the research is solid, it appears to be an incremental advance rather than a groundbreaking discovery, limiting its overall impact on the field.

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