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SenCat: Cataloging human cell aging by analyzing multiple types of aged primary cells using various molecular profiles

Updated

Abstract

A catalog named 'SenCat' was created, profiling the transcriptomes and proteomes of 14 different primary human cell types undergoing over 30 senescence paradigms.

  • Senescent cells from various primary tissue types do not share a single unique marker.
  • Shared specific metabolic and damage-response pathways are activated in senescent cells, which are linked to tissue repair.
  • Machine learning analysis of the SenCat datasets identified independent sets of senescent human cells.
  • The analysis also revealed senescent-like cell dynamics in mouse lung and kidney.

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Full Text

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Funding

Competing interests

DECLARATION OF INTERESTS Z.L.’s participation in this project was part of a competitive contract awarded to DataTecnica LLC by the National Institutes of Health to support open science research.
PubMed

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