Faculty, Staff and Student Publications

Language

English

Publication Date

6-1-2026

Journal

Nature Genetics

DOI

10.1038/s41588-026-02624-9

PMID

42271087

PMCID

PMC13263138

PubMedCentral® Posted Date

6-10-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Cells communicate through ligand-receptor (LR) signaling interactions, but identifying when and where these interactions are active remains challenging. We developed CytoSignal to infer the locations and dynamics of cell-cell communication at cellular resolution from spatial transcriptomic data. Here we show that our cellular resolution, spatially resolved signaling scores enable several important analyses-identifying spatial gradients in signaling strength, quantifying the locations of contact-dependent and diffusible interactions, detecting signaling-associated genes and identifying differential signaling across multisample data. Additionally, we can predict the temporal dynamics of a signaling interaction at each spatial location. We experimentally validate our results in situ by proximity ligation assay, confirming that CytoSignal predicts the locations of LR interactions more accurately than previous approaches. This study addresses the field's current need for a robust and scalable tool to detect cell-cell signaling interactions and their dynamics at cellular resolution from spatial transcriptomic data.

Keywords

Animals, Humans, Cell Communication, Ligands, Signal Transduction, Spatial Transcriptomics, Transcriptome, Computational biology and bioinformatics, Organogenesis

Published Open-Access

yes

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