ReviewBiochemical Society transactions2022
Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data.
Review in Biochemical Society transactions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- Article
- Diabetic Wound Vasculopathy and Neuropathy: Spotlight on Wound Lipid Signaling.Advances in wound care · 2026Review
- Advancing spatial cellular communication inference with ligand diffusion and transport model.Communications biology · 2026Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- RIDDEN: Data-driven inference of receptor activity from transcriptomic data.PLoS computational biology · 2025Article
- Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data.NAR genomics and bioinformatics · 2025Article
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.Nature protocols · 2025Article
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- A spatial portrait of the human sebaceous gland transcriptional program.The Journal of biological chemistry · 2024Article
- ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods.Genome research · 2023Article
- A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data.Methods in molecular biology (Clifton, N.J.) · 2023Article
- The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI.Frontiers in oncology · 2023Review
- The role of macrophages in rosacea: implications for targeted therapies.Frontiers in immunology · 2023Review
- scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction.Bioinformatics (Oxford, England) · 2022Article
- LRLoop: a method to predict feedback loops in cell-cell communication.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tissue development and homeostasis require coordinated cell-cell communication. Recent advances in single-cell sequencing technologies have emerged as a revolutionary method to reveal cellular heterogeneity with unprecedented resolution. This offers a great opportunity to explore cell-cell communication in tissues systematically and comprehensively, and to further identify signaling mechanisms driving cell fate decisions and shaping tissue phenotypes. Using gene expression information from single-cell transcriptomics, several computational tools have been developed for inferring cell-cell communication, greatly facilitating analysis and interpretation. However, in single-cell transcriptomics, spatial information of cells is inherently lost. Given that most cell signaling events occur within a limited distance in tissues, incorporating spatial information into cell-cell communication analysis is critical for understanding tissue organization and function. Spatial transcriptomics provides spatial location of cell subsets along with their gene expression, leading to new directions for leveraging spatial information to develop computational approaches for cell-cell communication inference and analysis. These computational approaches have been successfully applied to uncover previously unrecognized mechanisms of intercellular communication within various contexts and across organ systems, including the skin, a formidable model to study mechanisms of cell-cell communication due to the complex interactions between the different cell populations that comprise it. Here, we review emergent cell-cell communication inference tools using single-cell transcriptomics and spatial transcriptomics, and highlight the biological insights gained by applying these computational tools to exploring cellular communication in skin development, homeostasis, disease and aging, as well as discuss future potential research avenues.
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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.