ArticleNature biotechnology2023
Modeling intercellular communication in tissues using spatial graphs of cells.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 2 of them syntheses that pooled it.
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Who cites it
139 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Spatial transcriptomics unveils the in situ cellular and molecular hallmarks of the lung in fatal COVID-19.Nature communications · 2026Article
- Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity.PLoS computational biology · 2026Article
- A meta-interaction basis for cell-cell communication in tissues.bioRxiv : the preprint server for biology · 2026Article
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Prenatal PMNature communications · 2026Article
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- LYNX: a deep generative model for linking spatial dynamics and cell interactions in multimodal spatial data.bioRxiv : the preprint server for biology · 2026Article
- SHIELD: A weakly supervised graph attention neural network for decoding disease-relevant cell-cell interactions.Patterns (New York, N.Y.) · 2026Article
- Spatial Autocorrelation Aware Resampling Improves Cell-Cell Interaction Inference in Spatial Transcriptomics Data.bioRxiv : the preprint server for biology · 2026Article
- Counterfactual Diffusion Modeling Enables Spatially Targeted Reprogramming of Tissue Microenvironments.Biology · 2026Article
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- CytoSignal detects locations and dynamics of ligand-receptor signaling at cellular resolution from spatial transcriptomic data.Nature genetics · 2026Article
- FAP-CD40 and PD1-IL2v combination therapy reprograms immunologically cold tumors through de novo intratumoral T cell-dendritic cell clusters.Journal for immunotherapy of cancer · 2026Article
- Deciphering microenvironmental heterogeneity by scalable Niche Guided Module Discovery.Communications biology · 2026Article
- Spatial imprints of emergent cardiomyocyte states in the pressure-overloaded heart.bioRxiv : the preprint server for biology · 2026Article
- Charting spatial ligand-target activity using Renoir.Nature communications · 2026Article
79 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor-ligand signaling and ignore spatial proximity in situ. We present node-centric expression modeling, a method based on graph neural networks that estimates the effects of niche composition on gene expression in an unbiased manner from spatial molecular profiling data. We recover signatures of molecular processes known to underlie cell communication.
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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.