Protein expression derived from scRNA-seq reveals lupus-induced acceleration of immune aging

Single-cell RNA sequencing (scRNA-seq) is widely established and excels at capturing transcriptional heterogeneity and signaling states. However, it lacks direct quantification of surface proteins, which are key determinants of immune cell identity and function. To enable protein-based characterization of immunophenotypic diversity in scRNA-seq data, we developed scEN, which employs a regularized Elastic Net regression model to predict protein expression from gene expressions. We trained scEN on Cellular Indexing Transcriptomes with Epitopes Sequencing (CITE-seq) data containing paired surface-protein and transcriptomic measurements from bone marrow. When applied to scRNA-seq from peripheral blood of healthy donors scEN generated robust protein predictions aligned with known immunophenotypes at single-cell resolution and the predicted protein expression enabled cytometry-style manual gating to resolve immune-cell subsets associated with physiological immune aging. When applied to scRNA-seq data from lupus patients, the predicted protein expression captured shifts among immune-cell subsets, revealing lupus-induced acceleration of immune aging.