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

Abstract

Characterizing cellular aging is essential for understanding age-related diseases. While tissue-level studies reveal broad age-associated changes, they often reflect compositional shifts rather than cell-level reprogramming. The cellular damage hypothesis posits that aging involves the accumulation of DNA, chromatin, and other damage across molecular layers, increasing transcriptional entropy. Existing supervised methods for detecting cellular senescence yield cell type-specific senescence scores but rely on labeled data and lack generalizability. Here, we introduce a first-principles framework for quantifying transcriptional entropy in single cells as each cell's deviation from a transcriptomic manifold, capturing breakdown of transcriptional coordination. This unsupervised approach identifies aging-affected cell types and distinguishes two cellular aging mechanisms: loss of expression precision and activation of stress-response pathways in high entropy cells. Applied to Tabula Muris Senis and SenNet Multiome datasets, transcriptional entropy correlates with chromatin-based mitotic age and highlights regenerative tissue compartments as most affected by aging.