Determining the age of single cells using scMLEAge

Area of Science:

• Gerontology
• Computational Biology
• Genomics

Background:

• Aging is a complex biological process involving physiological decline and increased disease vulnerability.
• Understanding cellular aging heterogeneity at single-cell resolution is crucial but challenging.
• Existing methods struggle to capture the nuances of aging across diverse cell types.

Purpose of the Study:

• To develop a robust statistical framework for predicting individual cell ages from transcriptomic profiles.
• To investigate organ- and cell-type-specific transcriptomic signatures of aging.
• To provide a tool for dissecting cellular heterogeneity in aging and age-related functional decline.

Main Methods:

• Developed a Bayesian statistical framework (scMLEAge) to estimate cell age based on transcriptomic read counts.
• Applied the scMLEAge model to transcriptomic data from the Tabula Muris Senis dataset.
• Evaluated predictive accuracy against standard regression-based methods.

Main Results:

• The scMLEAge framework demonstrated higher predictive accuracy compared to standard regression methods.
• Identified distinct transcriptomic signatures of aging across various organs and cell types.
• Successfully applied the model to dissect cellular heterogeneity in aging.

Conclusions:

• scMLEAge is a powerful and accurate tool for predicting cell age from transcriptomic data.
• The framework facilitates a deeper understanding of cellular aging heterogeneity.
• Enables the study of age-related functional decline at the single-cell level.