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

/pdf/inference-and-analysis-of-cell-cell-communication-using-4esvnmfzge.pdf

Inference and analysis of cell-cell communication using CellChat.

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build a cell-cell communication atlas in diverse tissues.

/pdf/inference-and-analysis-of-cell-cell-communication-using-411i2tc0eu.pdf

Inference and analysis of cell-cell communication using CellChat

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions—including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization. This review describes the state of spatial transcriptomics technologies and analysis tools that are being used to generate biological insights in diverse areas of biology.

Exploring tissue architecture using spatial transcriptomics.

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

/pdf/deciphering-cell-cell-interactions-and-communication-from-19hzux0gfi.pdf

Deciphering cell-cell interactions and communication from gene expression.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.

/pdf/giotto-a-toolbox-for-integrative-analysis-and-visualization-2ul3pqtiad.pdf

Giotto: a toolbox for integrative analysis and visualization of spatial expression data

The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.

https://www.biorxiv.org/content/biorxiv/early/2020/05/30/701680.full.pdf

Giotto, a toolbox for integrative analysis and visualization of spatial expression data

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Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Giotto, a toolbox for integrative analysis and visualization of spatial expression data