A Manifold-Based Measure of Transcriptional Entropy for Quantifying Aging in Single Cells

Area of Science:

• Molecular Biology
• Genomics
• Aging Research

Background:

• Cellular aging is crucial for understanding age-related diseases.
• Tissue-level studies often mask cell-specific aging processes.
• Existing methods for senescence detection are limited by data requirements and generalizability.

Purpose of the Study:

• To develop a first-principles framework for quantifying transcriptional entropy in single cells.
• To identify cellular aging mechanisms and affected cell types using an unsupervised approach.
• To assess the correlation between transcriptional entropy and established aging markers.

Main Methods:

• Developed a framework to measure transcriptional entropy as deviation from a transcriptomic manifold.
• Utilized an unsupervised approach to analyze single-cell transcriptomic data.
• Applied the framework to Tabula Muris Senis and SenNet Multiome datasets.

Main Results:

• The framework quantifies transcriptional entropy, reflecting a breakdown in transcriptional coordination.
• Identified two aging mechanisms: loss of expression precision and activation of stress-response pathways.
• Transcriptional entropy correlates with chromatin-based mitotic age, particularly in regenerative tissues.

Conclusions:

• Transcriptional entropy provides a novel, unsupervised measure of cellular aging.
• This approach reveals distinct cellular aging mechanisms and identifies vulnerable tissue compartments.
• The findings advance our understanding of cellular reprogramming during aging and age-related disease.