A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging.

Single-cell RNA sequencing has enabled the study of aging at a molecular scale. While substantial progress has been made in measuring age-related gene expression, the underlying patterns and mechanisms of aging transcriptomes remain poorly understood. To address this gap, we propose a physics-inspired, data-analysis approach to extract additional insights from single-cell RNA sequencing data. By considering the genome as a many-body interacting system, we leverage central idea of the Renormalization Group to construct an approach to hierarchically describe aging across a spectrum of scales for the gene expresion. This framework provides a quantitative language to study the multiscale patterns of aging transcriptomes. Overall, our study demonstrates the value of leveraging theoretical physics concepts like the Renormalization Group to gain new biological insights from complex high-dimensional single-cell data.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Li, Mani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)