Reconstructing co-dependent cellular crosstalk in lung adenocarcinoma using REMI

Abstract

Cellular crosstalk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCI) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA-seq dataset, then leverage single-cell transcriptomics data to increase its resolution and identify LR prognostic signatures. We experimentally confirmed colocalization of CTGF:LRP6 as an interaction predicted to be associated with LUAD progression. Our work presents a novel way to reconstruct interactomes and a new approach to identify clinically-relevant cell-cell interactions.