Resolving cell–cell interaction networks and their molecular logic in complex tissues

Abstract

Cells in complex organisms function through extensive interactions, yet mapping these interaction networks at scale remains challenging. Here we present CCI-seq, a high-throughput method to unbiasedly capture cell–cell interactions across a proximity continuum by combining cell clump combinatorial indexing with single-cell sequencing. CCI-seq identified known interactions and fine-grained cellular organization in mouse kidney and intestine, and uncovered aberrant interactions and disrupted spatial organization in adenomatous polyposis coli knockout (Apc-KO) intestines. Applied to human colorectal cancer, it revealed subtype-specific interactions linked to clinical classifications. By leveraging a single-cell RNA sequencing backbone, CCI-seq achieves deep transcriptome coverage, enabling analysis of molecular states arising from interactions. This functional resolution revealed that interacting cells associate with distinct transcriptional programs—driving ion transport in kidney, antigen presentation in Apc-KO villi and NF-κB inflammatory activation in colorectal cancer. Thus, CCI-seq provides a scalable platform to unveil large-scale cell–cell interaction networks and dissect their molecular and functional impacts, enhancing our understanding of complex multicellular systems. CCI-seq leverages combinatorial cell clump indexing and single-cell sequencing to capture large-scale cell–cell interaction networks.