Unveiling the cell-type-specific landscape of cellular senescence through single-cell transcriptomics using SenePy

Area of Science:

• Gerontology
• Cell Biology
• Bioinformatics

Background:

• Senescent cells accumulate with aging, stress, and disease, but identifying them is difficult due to varied signatures.
• Existing methods struggle with cell-type and tissue specificity in detecting cellular senescence.

Purpose of the Study:

• To develop an analytical algorithm for identifying cell-type-specific and universal signatures of cellular senescence.
• To create a platform, SenePy, for scoring cellular senescence across diverse cell types and tissues.
• To analyze senescent cell accumulation in aging and disease contexts.

Main Methods:

• Utilized 72 mouse and 64 human weighted single-cell transcriptomic signatures.
• Developed the SenePy scoring platform based on these signatures.
• Applied SenePy to map senescent cell kinetics in healthy aging and disease models (tumorigenesis, inflammation, myocardial infarction).

Main Results:

• SenePy signatures effectively recapitulate in vivo cellular senescence, outperforming in vitro-derived signatures.
• The platform successfully mapped senescent cell accumulation dynamics in various physiological and pathological states.
• Demonstrated SenePy's capability to characterize cell-type-specific in vivo senescence.

Conclusions:

• SenePy provides a robust method for identifying and analyzing cellular senescence in vivo.
• This platform can reveal cell-type-specific senescence patterns relevant to aging and disease progression.
• SenePy may facilitate the discovery of novel genes involved in senescence and disease mediation.