ArticleNature communications2025
Finding spatially variable ligand-receptor interactions with functional support from downstream genes.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).International journal of molecular medicine · 2026Review
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Spatial Immunology in Translation: Linking Immune Organisation to Therapeutic Outcome.Medical sciences (Basel, Switzerland) · 2026Review
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- Computational Decoding of Cell-Cell Communications in Heterogeneous Cellular Microenvironments Based on Spatial Transcriptomics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- CELLetter: leveraging large language model and dual-stream network to identify context-specific ligand-receptor interactions for cell-cell communication analysis.Briefings in bioinformatics · 2025Article
- Finding spatially variable ligand-receptor interactions with functional support from downstream genes.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics has emerged as a groundbreaking tool for the study of intercellular ligand-receptor interactions (LRIs) that exhibit spatial variability. To identify spatially variable LRIs with activation evidence, we present SPIDER, which constructs cell-cell interaction interfaces constrained by cellular interaction capacity, and profiles and identifies spatially variable interaction (SVI) signals with support from downstream transcript factors via multiple probabilistic models. SPIDER demonstrates superior performance regarding accuracy, specificity, and spatial variance relative to existing methods. Experiments of simulations and real datasets in bulk and single-cell resolutions validate SPIDER-identified SVIs by spatial autocorrelation and correlation with downstream target genes, and reveal their consistency across multiple biological replicates. Particularly, distinct SVIs on mouse datasets reveal the potential in representing regional and inter-cell type interactions. SPIDER groups SVIs with similar spatial distributions into SVI patterns that are supported by strong Pearson correlations on spot annotations, generating interaction-based sub-clusters within cell-type regions, and deriving enriched pathways.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.