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

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

Abstract Background 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...

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Bibliographic Details
Main Authors: Bethany K. Hughes, Andrew Davis, Deborah Milligan, Ryan Wallis, Federica Mossa, Michael P. Philpott, Linda J. Wainwright, David A. Gunn, Cleo L. Bishop
Format: Article
Language:English
Published: BMC 2025-01-01
Series:Genome Medicine
Subjects:
3D organotypic culture
In vivo senescence detection
Living skin equivalent
Machine learning
scRNA-seq
Senescence
Online Access:https://doi.org/10.1186/s13073-024-01418-0
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https://doi.org/10.1186/s13073-024-01418-0

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