ArticleGenome research2025
Dissecting multilayer cell-cell communications with signaling feedback loops from spatial transcriptomics data.
Article in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Inference of marker genes of subtle cell state changes via iLR: iterative logistic regression.Bioinformatics (Oxford, England) · 2026Article
- Advancing spatial cellular communication inference with ligand diffusion and transport model.Communications biology · 2026Article
- Spatiotemporal decoding of skin biology: development, aging, disease, and regeneration.Burns & trauma · 2026Article
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.Nature machine intelligence · 2026Article
- Cell-cell interactions as predictive and prognostic markers for drug responses in cancer.Genome medicine · 2025Review
- Cancer detection via one-shot learning: integrating gene expression and genomic mutation analysis.BMC bioinformatics · 2025Article
- Multiscale mathematical model-informed reinforcement learning optimizes combination treatment scheduling in glioblastoma evolution.Science advances · 2025Article
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
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Authors and funding
4 authors.
Funding
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Abstract
The emergence of spatial transcriptomics (ST) provides unprecedented opportunities to better decipher cell-cell communication (CCC). How to integrate spatial information and complex signaling mechanisms to infer functional CCC, however, remains a major challenge. Here, we present stMLnet, a method that takes into account spatial information and multilayer signaling regulation to identify signaling feedback loops within multilayer CCCs from ST data. To this end, stMLnet quantifies spatially dependent ligand-receptor signaling activity based on diffusion and mass action models, and maps it to intracellular targets. We benchmark stMLnet against seven representative methods and find that stMLnet performs better in both intercellular ligand-receptor inference and intracellular target gene prediction. We apply stMLnet to analyze data from diverse ST techniques like seqFISH+, Slide-seq v2, MERFISH, and Stereo-seq, verifying its robustness and scalability on ST data with varying spatial resolutions and gene coverages. Particularly, stMLnet reveals multilayer signaling feedback loops underlying the inflammatory response in ST data of COVID-19-infected lung tissue. Our study provides an effective tool for dissecting multilayer ligand/receptor-target regulation and multicellular signaling circuits from ST data, which can advance understanding of the mechanistic and functional roles of spatial CCC.
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