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SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types
SenCat: Cataloging human cell aging by analyzing multiple types of aged primary cells using various molecular profiles
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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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