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Characterizing Cellular Heterogeneity and Transcriptomic Features of Senotype Using Deep Graph Representation Learning

ABSTRACT: Cellular senescence is a primordial driver of tissue and organ aging, and the accumulation of senescent cells (SnCs) has been implicated in numerous age-related diseases. A major barrier to studying senescence is the rarity and heterogeneity of SnCs, which are not a uniform population but instead comprise diverse senotypes shaped by cell-of-origin and microenvironmental context. Such heterogeneity exceeds what classical senescence hallmarks can resolve at single-cell resolution, motivating the need for computational frameworks that can capture senotype-level diversity intrinsically. Here, we introduce DeepSAS, a deep graph representation learning framework that robustly identifies cell-type-specific SnCs and their senescence-associated genes (SnGs). DeepSAS incorporates a heterogeneous graph that integrates intracellular transcriptional states with intercellular communication cues, enabling the joint inference of senescent cells and senescence-linked genes through attention-based contrastive learning. Applied to public healthy eye and lung atlases, DeepSAS identified SnCs whose proportions positively correlate with aging. From in-house idiopathic pulmonary fibrosis (IPF) patient scRNA-seq data, DeepSAS detected 1,678 SnCs (out of 24,125 cells) and 263 SnGs across 26 cell types, including 43 SnGs that are uniquely associated with a single cell type. We generated high-resolution Xenium spatial transcriptomics data to further validate SnGs in IPF, revealing NFE2L2 as a SnG specifically enriched in CTHRC1+ fibroblasts. Notably, the ex vivo bleomycin-induced senescence in human precision-cut lung slice (hPCLS) samples similarly identified NFE2L2 as an SnG in CTHRC1+ fibroblasts, albeit with stronger transcriptional signals, suggesting mechanistic differences in senescence cells associated with chronic and acute injury. Overall, DeepSAS uncovers distinct senescence programs and infers cell-type-specific SnGs that are difficult to resolve using existing marker-based approaches. We believe it offers a generalizable and translationally relevant strategy for advancing senescence biology and therapeutic development.

ORGANISM(S): Homo sapiens

PROVIDER: GSE331432 | GEO | 2026/05/29

REPOSITORIES: GEO

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Project description:Cellular senescence due to telomere dysfunction has been hypothesized to play a role in age-associated diseases including idiopathic pulmonary fibrosis (IPF). It has been postulated that paracrine mediators originating from senescent alveolar epithelia signal to surrounding mesenchymal cells and contribute to disease pathogenesis. However, murine models of telomere-induced alveolar epithelial senescence fail to display the canonical senescence-associated secretory phenotype (SASP) that is observed in senescent human cells. In an effort to understand human-specific responses to telomere dysfunction, we modelled telomere dysfunction-induced senescence in a human alveolar epithelial cell line. We hypothesized that this system would enable us to probe for differences in transcriptional and proteomic senescence pathways in vitro and to identify novel secreted protein (secretome) changes that potentially contribute to the pathogenesis of IPF. Following induction of telomere dysfunction, a robust senescence phenotype was observed. RNA-Seq analysis of the senescent cells revealed the SASP and comparisons to previous murine data highlighted species-specific responses to telomere dysfunction. We then conducted a proteomic analysis of the senescent cells using a novel biotin ligase capable of labeling secreted proteins. Candidate biomarkers selected from our transcriptional and secretome data were then evaluated in IPF and control patient plasma. Four novel proteins were found to be differentially expressed between the patient groups: stanniocalcin-1, contactin-1, tenascin C, and total inhibin. Our data show that human telomere-induced, alveolar epithelial senescence results in a transcriptional SASP that is distinct from that seen in analogous murine cells. Our findings suggest that studies in animal models should be carefully validated given the species-specific responses to telomere dysfunction. We also describe a pragmatic approach for the study of the consequences of telomere-induced alveolar epithelial cell senescence in humans.

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Characterizing Cellular Heterogeneity and Transcriptomic Features of Senotype Using Deep Graph Representation Learning

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