Cell-type resolved transcriptional network analysis of in vivo cellular senescence following injury

Alda Sabalic, Victoria Moiseeva, Andres Cisneros, Oleg Deryagin, Eusebio Perdiguero, Pura Muñoz-Cánoves and Jordi Garcia-Ojalvo

PLOS Computational Biology, 2026, vol. 22, issue 6, 1-22

Abstract: Identifying the genetic correlates of complex phenotypes is a challenging task. Methods coming from the field of complex networks can help finding such molecular patterns, by revealing statistical associations among groups of genes that correlate with the phenotype. Here we study cellular senescence, a complex cell state whose molecular underpinnings are still under active investigation. We analyze cell type–resolved RNA sequencing data obtained from injured muscle tissue in mice, with a network-based approach that merges eigenvector centrality feature selection and community detection. Our analysis identifies genetic markers that had not been associated with senescence so far, which are validated with existing single-cell RNA sequencing data in a different type of tissue. The identified key genes belong to transcriptional pathways associated with established hallmarks of senescence, and thus can be interpreted as molecular correlates of such hallmarks. The method proposed here could be applied to any complex cellular phenotype even when only bulk RNA sequencing is available, provided the data is resolved by cell type.Author summary: Senescence is a key manifestation of aging at the cellular level, caused by damage incurred by cells in time. In spite of their wide-ranging implications on how our multicellular bodies age, senescent cells are very challenging to identify due to their complex nature: many different aspects of cells are affected by this cellular state. This complicates defining clear criteria that help us decide whether a cell is senescent or not. In this paper, we propose a computational pipeline that enables us to identify a small subset of genes associated with senescence. The method combines two approaches commonly used in the study of networks, community detection and node centrality, and applies them to gene expression data obtained from the muscle tissue of mice after damage. The results obtained can contribute to establish the molecular correlates of a complex cellular state such as senescence.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014429

DOI: 10.1371/journal.pcbi.1014429

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