ICE: robust detection of cellular senescence from weak single-cell signatures using imputation-based marker...

Area of Science:

• Biotechnology
• Computational Biology
• Genomics

Background:

• Detecting senescent cells using single-cell RNA sequencing (scRNA-seq) is challenging due to weak and non-specific canonical marker expression.
• Expanding existing marker sets does not enhance detection accuracy for these low-signal cells.

Purpose of the Study:

• To develop a computational framework for accurate senescent cell detection from scRNA-seq data.
• To overcome the limitations of canonical markers in identifying senescent cell populations.

Main Methods:

• Developed ICE (Imputation-based Cell Enrichment), a novel computational framework.
• Integrated expression imputation with marker refinement strategies.
• Applied ICE to scRNA-seq data from pancreatic β cells and Alzheimer's disease microglia.

Main Results:

• ICE significantly improves the detection accuracy of senescent cells.
• Successfully identified senescent cell populations in pancreatic β cells.
• Demonstrated improved detection in microglia from Alzheimer's disease samples.

Conclusions:

• ICE provides a reliable method for identifying senescent cell populations.
• This tool facilitates detailed analyses of senescence-associated cell heterogeneity and dynamics.
• Enables deeper understanding of senescence in human tissues and disease contexts.