Protocol for classifying cellular senescence from single-cell and single-nucleus RNA sequencing data

Area of Science:

• Cell Biology
• Genomics
• Bioinformatics

Background:

• Cellular senescence is a stable cell-cycle arrest with significant molecular and functional alterations.
• Identifying senescent cells is crucial for understanding aging and disease.
• Existing methods may lack precision in classifying senescent cells from complex datasets.

Purpose of the Study:

• To present a novel protocol for classifying senescent cells.
• To enable senescence classification using single-cell or single-nucleus RNA sequencing data.
• To facilitate the analysis of senescence-associated transcriptional programs.

Main Methods:

• Gene expression activity scoring of a user-defined senescence gene set.
• Data-driven optimization of senescent cell proportions per cell type using enrichment testing.
• Differential gene expression analysis to identify senescence-associated transcriptional programs.

Main Results:

• A robust protocol for classifying senescent cells from scRNA-seq and snRNA-seq data.
• Method for optimizing the estimation of senescent cell populations.
• Identification of key transcriptional programs associated with senescence.

Conclusions:

• The presented protocol offers a standardized approach for senescent cell classification.
• This method aids in dissecting the role of senescent cells in various biological contexts.
• The protocol facilitates deeper investigation into the molecular underpinnings of senescence.