Robust senescence evaluation by transcriptome-based hUSI to facilitate characterizing cellular senescence under various conditions

Abstract

Abstract Despite the manifestation and contribution of cellular senescence to tissue aging and aging-related disease, the identification of in vivo senescent cells and the recognition of senescence-specific communication still remain challenging. Current senescence evaluation methods rely greatly on expression level of well-known senescence markers, enrichment of aging-related gene sets or weighted sum of curated genes. However, focusing on limited senescence aspects, these methods could not adequately capture the comprehensive senescence features. To evaluate senescence in a more general and unbiased way from the most common and easily accessible transcriptome data, we developed human universal senescence index (hUSI) to quantify human cellular senescence based on a series of weighted genes learned from representative senescence RNA-seq profiles using a machine learning algorithm. hUSI demonstrated its superior performance in distinguishing senescent samples under various conditions and robustness in handling batch effects and sparse profiles. hUSI could uncover the accumulation of senescent cells of various cell types in complex pathological conditions, and reflected the increasing senescence burden of patients and provided potential senotherapeutic targets. Furthermore, combined with gaussian mixture model, hUSI successfully inferred senescent tumor cells in melanoma and identified key target signaling pathways that are beneficial for patient prognosis. Overall, hUSI provides a valuable choice to improve our ability in characterizing cellular senescence under various conditions, illustrating promising implications in aging studies and clinical situations.