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.

Journal: Genome medicine
PMID: 39810225

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 senescent cell heterogeneity.

Authors

  • Bethany K Hughes
    Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK.
  • Andrew Davis
    Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States.
  • Deborah Milligan
    Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK.
  • Ryan Wallis
    Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK. r.wallis@qmul.ac.uk.
  • Federica Mossa
    Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK.
  • Michael P Philpott
    Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK.
  • Linda J Wainwright
    Unilever R&D, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK.
  • David A Gunn
    Unilever King's Biosciences Innovation Hub, King's College London, London, UK.
  • Cleo L Bishop
    Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.

Keywords

External Resources

Popular Topics
Recent Journals