Academic Journal
Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.
| Τίτλος: | Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats. |
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| Συγγραφείς: | Wu Y; Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, East Lansing 48824, MI, United States., Su H; Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, East Lansing 48824, MI, United States., Cui Y; Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, East Lansing 48824, MI, United States. |
| Πηγή: | Briefings in bioinformatics [Brief Bioinform] 2026 Sep 01; Vol. 27 (5). |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE |
| Imprint Name(s): | Publication: Oxford : Oxford University Press Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000- |
| Ιατρικοί όροι (MeSH): | Cell Communication*/genetics , Spatial Transcriptomics* , Transcriptome* , Models, Biological*, Computational Biology/methods ; Humans ; Signal Transduction ; Animals |
| Περίληψη: | Cell-cell communication (CCC) is essential for maintaining tissue organization and driving biological progression, yet its inference from transcriptomic data has long been limited by the absence of spatial context. Advances in spatial transcriptomics (ST) now enable mechanistically grounded analyses of CCC by preserving the physical organization of cells and their microenvironments. In this review, we examine recent methodological developments in CCC inference from ST data, focusing on how statistical, optimal transport, and deep learning frameworks incorporate spatial information to model ligand-receptor (LR) interactions and downstream signaling. We also summarize key mechanism-driven components shared across spatial and non-spatial CCC approaches. In addition, we discuss how tissue heterogeneity and spatial architecture can introduce context-dependent biases, particularly for permutation-based inference, and outline mechanistic considerations such as LR biochemistry, signal transduction, and condition-specific communication. We further highlight databases that curate intercellular conduction and intracellular signaling processes. By integrating spatial constraints with biochemical and computational principles, this review offers an integrated assessment of the opportunities and limitations of current approaches. We conclude by identifying key methodological challenges and future directions for developing robust, scalable, and mechanistically interpretable CCC inference as ST technologies continue to advance. (© The Author(s) 2026. Published by Oxford University Press.) |
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| Contributed Indexing: | Keywords: cell–cell communication; computational methods; ligand–receptor interactions; spatial transcriptomics; tissue heterogeneity |
| Entry Date(s): | Date Created: 20260907 Date Completed: 20260907 Latest Revision: 20260909 |
| Update Code: | 20260909 |
| PubMed Central ID: | PMC13548332 |
| DOI: | 10.1093/bib/bbag477 |
| PMID: | 42704267 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1477-4054 |
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| DOI: | 10.1093/bib/bbag477 |