Deborah Milligan

Jan 14, 2025 | genomemedicine.biomedcentral.com | Bethany Hughes |Andrew Davis |Deborah Milligan |Ryan Wallis |Federica Mossa |Michael Philpott | +3 more

ReferencesHernandez-Segura A, Nehme J, Demaria M. Hallmarks of cellular senescence. Trends Cell Biol. 2018;28:436–53. Article  PubMed  Google Scholar  González-Gualda E, Baker AG, Fruk L, Muñoz-Espín D. A guide to assessing cellular senescence in vitro and in vivo. FEBS J. 2021;288:56–80. Article  PubMed  Google Scholar  Huang W, Hickson LJ, Eirin A, Kirkland JL, Lerman LO. Cellular senescence: the good, the bad and the unknown. Nat Rev Nephrol. 2022;18:611–27.

Oct 23, 2023 | biorxiv.org | Bethany Hughes |Andrew Davis |Ryan Wallis |Deborah Milligan

AbstractSenescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required for senescent cell identification. However, emerging scRNA-seq datasets have enabled increased understanding of the heterogeneity of senescence. Here we present SenPred, a machine-learning pipeline which can identify senescence based on single-cell transcriptomics.