ArticleBioinformatics (Oxford, England)2022
LRLoop: a method to predict feedback loops in cell-cell communication.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
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- Lignature provides a curated resource of ligand-induced transcriptomic signatures for signaling inference.Genome research · 2026Article
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
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- CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data.Nature protocols · 2025Review
- MetaLigand provides a prior-knowledge-guided framework for predicting non-peptide ligand mediated cell-cell communication.Cell reports methods · 2025Article
- Molecular and genetic landscapes of retina and brain microglia in neurodegenerative diseases.Genome research · 2025Article
- Cutting-edge technologies in neural regeneration.Cell regeneration (London, England) · 2025Review
- Single-cell profiling of trabecular meshwork identifies mitochondrial dysfunction in a glaucoma model that is protected by vitamin B3 treatment.bioRxiv : the preprint server for biology · 2025Article
- CausalCCC: a web server to explore intracellular causal pathways enabling cell-cell communication.Nucleic acids research · 2025Article
- Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data.Nature protocols · 2025Review
- Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data.NAR genomics and bioinformatics · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Intercellular communication atlas reveals Oprm1 as a neuroprotective factor for retinal ganglion cells.Nature communications · 2024Article
- The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics.Seminars in cancer biology · 2023Review
- Review
- Article
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7 authors.
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Abstract
motivationIntercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops.
resultsHere, we describe ligand-receptor loop (LRLoop), a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis and/or cell fate specification. AVAILABILITY AND IMPLEMENTATION: An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figshare (https://doi.org/10.6084/m9.figshare.20126021.v1). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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