CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data
7 affiliations
- 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- 2University of Texas Health Science Center at Houston School of Dentistry, Houston, TX, USA
- 3Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
- 4Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA
- 5Department of Medicine, Emory University, Atlanta, GA, USA
- 6Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. [email protected]
- 7Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA. [email protected]
Abstract
Nearby cells within tissues communicate through ligand-receptor signaling interactions. Emerging spatial transcriptomic technologies provide a tremendous opportunity to systematically detect ligand-receptor signaling, but no method operates at cellular resolution in the spatial context. We developed CytoSignal to infer the locations and dynamics of cell-cell communication at cellular resolution from spatial transcriptomic data. CytoSignal is based on the simple insight that signaling is a protein-protein interaction that occurs at a specific tissue location when ligand and receptor are expressed in close spatial proximity. Our cellular-resolution, spatially-resolved signaling scores allow several novel types of analyses: we identify spatial gradients in signaling strength; separately quantify the locations of contact-dependent and diffusible interactions; and detect signaling-associated differentially expressed genes. Additionally, we can predict the temporal dynamics of a signaling interaction at each spatial location. CytoSignal is compatible with nearly every kind of spatial transcriptomic technology including FISH-based protocols and spot-based protocols without deconvolution. We experimentally validate our results in situ by proximity ligation assay, confirming that CytoSignal scores closely match the tissue locations of ligand-receptor protein-protein interactions. Our work 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.
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BibTeX
@article{liu2026cytosignal,
title = {{CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data}},
author = {Jialin Liu and Hiroaki Manabe and Weizhou Qian and Shion Orikasa and Javid Ghaemmaghami and Yichen Wang and Yichen Gu and Angel Ka Yan Chu and Gaurav T. Gadhvi and Yuxuan Song and Patrick McClinden and Vibha N. Lama and Noriaki Ono and Joshua D. Welch},
journal = {Nature Genetics},
year = {2026},
month = jun,
doi = {10.1038/s41588-026-02624-9},
url = {https://doi.org/10.1038/s41588-026-02624-9},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data
AU - Jialin Liu
AU - Hiroaki Manabe
AU - Weizhou Qian
AU - Shion Orikasa
AU - Javid Ghaemmaghami
AU - Yichen Wang
AU - Yichen Gu
AU - Angel Ka Yan Chu
AU - Gaurav T. Gadhvi
AU - Yuxuan Song
AU - Patrick McClinden
AU - Vibha N. Lama
AU - Noriaki Ono
AU - Joshua D. Welch
PY - 2026
DA - 2026/06/01
JO - Nature Genetics
DO - 10.1038/s41588-026-02624-9
UR - https://doi.org/10.1038/s41588-026-02624-9
AB - Nearby cells within tissues communicate through ligand-receptor signaling interactions. Emerging spatial transcriptomic technologies provide a tremendous opportunity to systematically detect ligand-receptor signaling, but no method operates at cellular resolution in the spatial context. We developed CytoSignal to infer the locations and dynamics of cell-cell communication at cellular resolution from spatial transcriptomic data. CytoSignal is based on the simple insight that signaling is a protein-protein interaction that occurs at a specific tissue location when ligand and receptor are expressed in close spatial proximity. Our cellular-resolution, spatially-resolved signaling scores allow several novel types of analyses: we identify spatial gradients in signaling strength; separately quantify the locations of contact-dependent and diffusible interactions; and detect signaling-associated differentially expressed genes. Additionally, we can predict the temporal dynamics of a signaling interaction at each spatial location. CytoSignal is compatible with nearly every kind of spatial transcriptomic technology including FISH-based protocols and spot-based protocols without deconvolution. We experimentally validate our results in situ by proximity ligation assay, confirming that CytoSignal scores closely match the tissue locations of ligand-receptor protein-protein interactions. Our work 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.
SN - 1061-4036
SN - 1546-1718
ER -