Work

Predictive modeling of molecular activity underlying physical cell-cell interactions

Year: 2026

Type: article

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.

Source: Cell Reports Methods

DOI: 10.1016/j.crmeth.2026.101301

Authors: Tamjeed Azad, Sarah K. Walker, Gabriel D. Victora, Yuri Pritykin

Institutions: Princeton University, Howard Hughes Medical Institute, Rockefeller University

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,824