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Recent advances in single-cell sequencing technologies offer an opportunity to explore cell–cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell–cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell–cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell–cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand–receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell–cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation. Key points CellChat is a software package for systematic inference, quantitative analysis and intuitive visualization of cell–cell communication in an easily interpretable way from single-cell transcriptomic data; it also enables comparative analysis of intercellular communication across different conditions. CellChat v2 is an updated version that includes additional functionalities for comparative analysis and an expanded database of ligand–receptor pairs along with rich functional annotations. CellChat enables systematic inference, quantitative analysis and intuitive visualization of cell–cell communication from single-cell transcriptomic data, as well as comparative analysis of intercellular communication across biological conditions.

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis. Proteomics of small sample sizes using data-independent acquisition methods achieves higher throughput with multiplexing.

Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling. Single cell expression data allows for inferring cell-cell communication between cells expressing ligands and those expressing their cognate receptors. Here the authors present an updated and curated database of ligand-receptor pairs and a Python-based toolkit to construct and analyse communication networks from single cell and bulk expression data.

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health 1 . The intesting has a length of over nine metres, along which there are differences in structure and function 2 . The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease. Intestinal cell types are organized into distinct neighbourhoods and communities within the healthy human intestine, with distinct immunological niches.

Inference of cell–cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell–cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell–cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche–phenotype relationships in health and disease. Cell–cell communication pathways are dissected at the level of individual cells.

Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens. Profiling of 53,193 individual epithelial cells from the mouse small intestine identifies previously unknown cell subtypes and corresponding gene markers, providing insight into gut homeostasis and response to pathogens. Surveying the stomach wall Intestinal epithelial cells can sense and respond to microbial stimuli to support their barrier function and coordinate appropriate immune responses, which range from tolerance to active immunity in cases of pathogen infection. In this study, Aviv Regev, Ramnik Xavier and colleagues used single-cell expression profiling to provide a comprehensive analysis of the epithelial cell composition of mouse small intestines when healthy and after infection, providing new markers, transcriptional programs and organizational principles of gut homeostasis and physiology.

High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment. Intratumoural heterogeneity in high-grade serous ovarian carcinoma (HGSOC) remains to be explored. Here, the authors perform spatial transcriptomics and reveal a high degree of subclonal heterogeneity in HGSOC.

Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial scales and molecular modalities are lacking. In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue. We developed a toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components and global patterning in each organoid and primary tissue. In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation. A multimodal atlas of retinal organoid development shows spatial interactions over time.

Single-molecule fluorescence in situ hybridization is performed to identify several landmark genes in the liver and their level of expression in single-cell RNA sequencing is used to spatially reconstruct the zonation of all liver genes. Division of labour in the liver A few important liver genes are differentially expressed in a radial fashion from the central vein outward, but the extent of such spatial division, or zonation, has remained unknown. Shalev Itzkovitz, Ido Amit and colleagues performed single-molecule fluorescence in situ hybridization against several landmark genes, and used their level of expression to spatially reconstruct the zonation of all liver genes. They find that around half of the genes in the liver are zonated, and surprisingly also find that several genes peak in the middle of the liver lobules. The succession of gene expression profiles matches the position of proteins in the bile-acid biosynthetic cascade. The mammalian liver consists of hexagon-shaped lobules that are radially polarized by blood flow and morphogens 1 , 2 , 3 , 4 . Key liver genes have been shown to be differentially expressed along the lobule axis, a phenomenon termed zonation 5 , 6 , but a detailed genome-wide reconstruction of this spatial division of labour has not been achieved. Here we measure the entire transcriptome of thousands of mouse liver cells and infer their lobule coordinates on the basis of a panel of zonated landmark genes, characterized with single-molecule fluorescence in situ hybridization 7 . Using this approach, we obtain the zonation profiles of all liver genes with high spatial resolution. We find that around 50% of liver genes are significantly zonated and uncover abundant non-monotonic profiles that peak at the mid-lobule layers. These include a spatial order of bile acid biosynthesis enzymes that matches their position in the enzymatic cascade. Our approach can facilitate the reconstruction of similar spatial genomic blueprints for other mammalian organs.

The expression profile and tissue coordinates of liver endothelial cells are determined using a paired-cell sequencing approach that extracts spatial information from attached hepatocytes. Spatially resolved single-cell RNA sequencing (scRNAseq) is a powerful approach for inferring connections between a cell's identity and its position in a tissue. We recently combined scRNAseq with spatially mapped landmark genes to infer the expression zonation of hepatocytes. However, determining zonation of small cells with low mRNA content, or without highly expressed landmark genes, remains challenging. Here we used paired-cell sequencing, in which mRNA from pairs of attached mouse cells were sequenced and gene expression from one cell type was used to infer the pairs' tissue coordinates. We applied this method to pairs of hepatocytes and liver endothelial cells (LECs). Using the spatial information from hepatocytes, we reconstructed LEC zonation and extracted a landmark gene panel that we used to spatially map LEC scRNAseq data. Our approach revealed the expression of both Wnt ligands and the Dkk3 Wnt antagonist in distinct pericentral LEC sub-populations. This approach can be used to reconstruct spatial expression maps of non-parenchymal cells in other tissues.