Multiscale Analysis of Cellular Senescence through Ripley's Functions and Functional Statistics.

Abstract

Cellular senescence is a heterogeneous and evolving process involved in development, tissue repair, aging, and age-related diseases. Although senescence burden in tissues has been widely studied, its spatial organization remains poorly understood, particularly in vivo. Senescence encompasses a spectrum of distinct states, with cells differing in molecular signatures, secretory activity, persistence, and interactions with their microenvironment depending on the inducing stimulus and tissue context. This heterogeneity suggests that spatial organization may reflect underlying processes such as tissue repair, regeneration, or maladaptive remodeling, providing insight into senescence function and its pathological roles. Here, we propose a quantitative, multi-scale framework to characterize the spatial organization of senescent cell populations in post-infarction mouse hearts. By combining a senescence-signature scoring strategy with spatial statistical methods and functional data analysis, we assess whether senescent cells exhibit clustered or dispersed patterns, and how these spatial distributions evolve over time following infarction. This approach aims to provide new insights into the spatiotemporal dynamics of senescence in vivo and to identify spatial features that may inform therapeutic strategies targeting age-related and tissue repair-associated pathologies.