Inference and analysis of cell-cell communication using CellChat

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Feb. 17, 2021

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses those links. We construct a database interactions ligands, receptors their cofactors that accurately represent known heteromeric molecular complexes. then develop CellChat, tool is able to quantitatively infer analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major inputs outputs for how signals coordinate functions using network analysis pattern recognition approaches. Through manifold learning quantitative contrasts, classifies pathways delineates conserved context-specific across different datasets. Applying mouse human skin datasets shows its ability extract complex patterns. Our versatile easy-to-use toolkit web-based Explorer ( http://www.cellchat.org/ ) will help discover novel build atlases in diverse tissues.

Language: Английский

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Deciphering cell–cell interactions and communication from gene expression

Nature Reviews Genetics, Journal Year: 2020, Volume and Issue: 22(2), P. 71 - 88

Published: Nov. 9, 2020

Language: Английский

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Single‐cell RNA sequencing technologies and applications: A brief overview

Clinical and Translational Medicine, Journal Year: 2022, Volume and Issue: 12(3)

Published: March 1, 2022

Language: Английский

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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Nature Reviews Genetics, Journal Year: 2021, Volume and Issue: 22(10), P. 627 - 644

Published: June 18, 2021

Language: Английский

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Best practices for single-cell analysis across modalities

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 550 - 572

Published: March 31, 2023

Language: Английский

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An introduction to spatial transcriptomics for biomedical research

Genome Medicine, Journal Year: 2022, Volume and Issue: 14(1)

Published: June 27, 2022

Abstract Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact viable from tissue. This precluded many cell types study largely destroys spatial context that could otherwise inform analyses of identity function. An increasing number platforms now facilitate spatially resolved, high-dimensional assessment gene transcription, known as ‘spatial transcriptomics’. Here, we introduce different classes method, which either record locations hybridized mRNA molecules tissue, image positions themselves prior assessment, or employ arrays probes pre-determined location. We review sizes tissue area can assessed, their resolution, genes profiled. discuss if preservation influences choice platform, provide guidance on whether specific may better suited discovery screens hypothesis testing. Finally, bioinformatic methods analysing transcriptomic data, including pre-processing, integration with existing inference cell-cell interactions. Spatial -omics are already improving our understanding human tissues research, diagnostic, therapeutic settings. To build upon these recent advancements, entry-level those seeking own research.

Language: Английский

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Spatiotemporal analysis of human intestinal development at single-cell resolution

Cell, Journal Year: 2021, Volume and Issue: 184(3), P. 810 - 826.e23

Published: Jan. 7, 2021

Development of the human intestine is not well understood. Here, we link single-cell RNA sequencing and spatial transcriptomics to characterize intestinal morphogenesis through time. We identify 101 cell states including epithelial mesenchymal progenitor populations programs linked key morphogenetic milestones. describe principles crypt-villus axis formation; neural, vascular, morphogenesis, immune population developing gut. differentiation hierarchies fibroblast myofibroblast subtypes diverse functions for these as vascular niche cells. pinpoint origins Peyer's patches gut-associated lymphoid tissue (GALT) location-specific programs. use our resource present an unbiased analysis morphogen gradients that direct sequential waves cellular define cells locations rare developmental disorders. compile a publicly available online resource, spatio-temporal fetal development (STAR-FINDer), facilitate further work.

Language: Английский

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Applications of single-cell sequencing in cancer research: progress and perspectives

Journal of Hematology & Oncology, Journal Year: 2021, Volume and Issue: 14(1)

Published: June 9, 2021

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics is a powerful tool to decipher the cellular molecular landscape at single-cell resolution, unlike bulk which provides averaged data. The use of sequencing in cancer research has revolutionized our understanding biological characteristics dynamics within lesions. In this review, we summarize emerging technologies recent progress obtained by information related landscapes malignant cells immune cells, tumor heterogeneity, circulating underlying mechanisms behaviors. Overall, prospects facilitating diagnosis, targeted therapy prognostic prediction among spectrum tumors are bright. near future, advances will undoubtedly improve highlight potential precise therapeutic targets for patients.

Language: Английский

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stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2020, Volume and Issue: unknown

Published: May 31, 2020

ABSTRACT Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in undissociated tissue. Integrating these three types data creates a vast potential for deciphering novel biology cell their native morphological context. Here we developed innovative integrative analysis approaches utilise all first find types, then reconstruct type evolution within tissue, search regions with high cell-to-cell interactions. First, normalisation gene expression, compute distance measure using similarity neighbourhood smoothing. The normalised used clusters represent profiles specific cellular phenotypes. Clusters are further sub-clustered if spatially separated. Analysing anatomical mouse brain sections 12 human datasets, found clustering method more accurate sensitive than other methods. Second, introduce calculate states by pseudo-space-time (PST) distance. PST function physical (spatial distance) expression (pseudotime estimate pairwise between among We transition gradients connected locally cluster, or globally clusters, directed minimum spanning tree optimisation approach algorithm could model from non-invasive invasive breast cancer dataset. Third, information identify locations where there both ligand-receptor interaction activity diverse co-localisation. These predicted be hotspots cell-cell interactions likely occur. detected pairs significantly enriched compared background distribution across Together, algorithms, implemented comprehensive Python software stLearn, allow elucidation biological processes healthy diseased tissues.

Language: Английский

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Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: June 9, 2022

Abstract The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference cell-cell communication. Many computational tools were developed for this purpose. Each them consists a resource intercellular interactions prior knowledge and method to predict potential communication events. Yet impact choice on resulting predictions is largely unknown. To shed light this, we systematically compare 16 resources 7 methods, plus consensus between methods’ predictions. Among resources, find few unique interactions, varying degree overlap, uneven coverage specific pathways tissue-enriched proteins. We then examine all possible combinations methods show that both strongly influence predicted interactions. Finally, assess agreement with spatial colocalisation, cytokine activities, receptor protein abundance are generally coherent those data modalities. facilitate use described work, provide LIANA, LIgand-receptor ANalysis frAmework as open-source interface methods.

Language: Английский

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