Abstract
Single-cell RNA sequencing (scRNA-seq) improves our understanding of cellular heterogeneity in healthy and pathological states. However, most scRNA-seq analyses remain confined to single cells or distinct cell populations, limiting their clinical applicability. Addressing the need to translate single-cell insights into a patient-level disease understanding, we introduce mcBERT, a new method that leverages scRNA-seq data and a transformer-based model to generate integrative patient representations using a self-supervised learning phase followed by contrastive learning to refine these representations. Our evaluations of mcBERT across 7 million cells from 1223 individuals encompassing diverse disease states in heart, kidney, blood cell, and lung tissues show that learned representations facilitate a robust identification of disease cohorts and enable comparisons of patient similarity. Moreover, our findings indicate that mcBERT can accurately classify disease phenotypes, also in previously unseen biospecimens and patients. Independent of the specific tissue, mcBERT extends the utility of scRNA-seq data from cellular analysis to potentially actionable patient-centric applications.Competing Interest StatementS.H. declares consulting for Turbine.AI and reports funding from Novo Nordisk and Askbio GmbH and is a co-founder and shareholder of Sequantrix GmbH. R.K. is a founder, shareholder and board member of Sequantrix GmbH, a member of the scientific advisory board of Hybridize Therapeutics, has received honoraria for advisory boards and talks from Bayer, Chugai, Pfizer, Roche, Genentech, Lilly and GSK and has research funding from Travere Therapeutics, Galapagos, Novo Nordisk and AskBio. All other authors indicated that no competing interests exist.
Johannes Lohmöller
Researcher of Computer Science
My research interests include privacy-preserving methods for confidential computing.