ArticlePLoS computational biology2022
Inferring a spatial code of cell-cell interactions across a whole animal body.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- A systematic review on the selection of reference genes for gene expression studies in rodents: are the classics the best choice?Molecular biology reports · 2024Pooled it
- Tensor-cell2cell v2 unravels coordinated dynamics of protein- and metabolite-mediated cell-cell communication.Bioinformatics (Oxford, England) · 2026Article
- Single-cell-resolved transcriptional dynamics of human subcutaneous adipose tissue during lifestyle- and bariatric surgery-induced weight loss.Nature metabolism · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Implications of Single-Cell RNA Sequencing in Cervical Cancer: Unravelling the Molecular Landscape.ACS omega · 2025Review
- Spatially Resolved Profiling of Compartmentalized Muscle and Brain Inflammation.European journal of immunology · 2025Review
- MEBOCOST maps metabolite-mediated intercellular communications using single-cell RNA-seq.Nucleic acids research · 2025Article
- Dissecting multilayer cell-cell communications with signaling feedback loops from spatial transcriptomics data.Genome research · 2025Article
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data with miRTalk.Genome biology · 2025Article
- Quantitative characterization of cell niches in spatially resolved omics data.Nature genetics · 2025Article
- Trajectory Inference with Cell-Cell Interactions (TICCI): intercellular communication improves the accuracy of trajectory inference methods.Bioinformatics (Oxford, England) · 2025Article
- The next frontier in lung development and regeneration research: harnessing iPSC models to illuminate notch signaling pathways.Frontiers in cell and developmental biology · 2025Review
- Molecular correlates for HPV-negative head and neck cancer engraftment prognosticate patient outcomes.Nature communications · 2024Article
- Single-cell and spatial proteo-transcriptomic profiling reveals immune infiltration heterogeneity associated with neuroendocrine features in small cell lung cancer.Cell discovery · 2024Article
- Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb.BMC biology · 2024Article
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Spatially resolved single-cell atlas of ascidian endostyle provides insight into the origin of vertebrate pharyngeal organs.Science advances · 2024Article
- Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues.Nature communications · 2023Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.
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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 Socratic graph.