Predictive modeling of molecular activity underlying physical cell-cell interactions

Abstract

Interactions between cells are central to tissue organization and function in health and disease. Labeling immune partnerships by sortagging intercellular contacts (LIPSTIC) quantitatively measures direct physical cell-cell interactions. Combined with single-cell RNA sequencing (scRNA-seq), it jointly profiles cell interaction intensities and intracellular transcriptomes. Here, we present group lasso on scRNA-seq (Gloss), a predictive modeling framework that systematically links gene and pathway activity to LIPSTIC-measured interaction strength. Across multiple datasets and benchmarks, Gloss outperforms correlation-based and standard regression approaches while remaining interpretable. We apply Gloss to characterize molecular features of myeloid-T cell interactions during anti-Ctla4 immunotherapy in mouse tumors and to describe interactions between different T cell subpopulations during viral infection. Gloss provides a general computational framework for analyzing LIPSTIC+scRNA-seq data and prioritizing genes and pathways driving cellular communication.

Keywords: CP: computational biology; CP: systems biology; LIPSTIC; cell-cell interactions; group lasso; machine learning; proximity labeling; scRNA-seq; single-cell transcriptomics.