Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics. Zhang Ze,Xiong Danyi,Wang Xinlei,Liu Hongyu,Wang Tao Nature methods Many experimental and bioinformatics approaches have been developed to characterize the human T cell receptor (TCR) repertoire. However, the unknown functional relevance of TCR profiling hinders unbiased interpretation of the biology of T cells. To address this inadequacy, we developed tessa, a tool to integrate TCRs with gene expression of T cells to estimate the effect that TCRs confer on the phenotypes of T cells. Tessa leveraged techniques combining single-cell RNA-sequencing with TCR sequencing. We validated tessa and showed its superiority over existing approaches that investigate only the TCR sequences. With tessa, we demonstrated that TCR similarity constrains the phenotypes of T cells to be similar and dictates a gradient in antigen targeting efficiency of T cell clonotypes with convergent TCRs. We showed this constraint could predict a functional dichotomization of T cells postimmunotherapy treatment and is weakened in tumor contexts. 10.1038/s41592-020-01020-3
    Fast, sensitive and accurate integration of single-cell data with Harmony. Korsunsky Ilya,Millard Nghia,Fan Jean,Slowikowski Kamil,Zhang Fan,Wei Kevin,Baglaenko Yuriy,Brenner Michael,Loh Po-Ru,Raychaudhuri Soumya Nature methods The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~10 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. 10.1038/s41592-019-0619-0
    Integrative single-cell analysis. Stuart Tim,Satija Rahul Nature reviews. Genetics The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement. 10.1038/s41576-019-0093-7
    Determining cell type abundance and expression from bulk tissues with digital cytometry. Newman Aaron M,Steen Chloé B,Liu Chih Long,Gentles Andrew J,Chaudhuri Aadel A,Scherer Florian,Khodadoust Michael S,Esfahani Mohammad S,Luca Bogdan A,Steiner David,Diehn Maximilian,Alizadeh Ash A Nature biotechnology Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells. 10.1038/s41587-019-0114-2
    Cell composition analysis of bulk genomics using single-cell data. Frishberg Amit,Peshes-Yaloz Naama,Cohn Ofir,Rosentul Diana,Steuerman Yael,Valadarsky Liran,Yankovitz Gal,Mandelboim Michal,Iraqi Fuad A,Amit Ido,Mayo Lior,Bacharach Eran,Gat-Viks Irit Nature methods Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data ('scBio' CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues. 10.1038/s41592-019-0355-5
    Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Chen Geng,Ning Baitang,Shi Tieliu Frontiers in genetics Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies. 10.3389/fgene.2019.00317
    Trajectory-based differential expression analysis for single-cell sequencing data. Van den Berge Koen,Roux de Bézieux Hector,Street Kelly,Saelens Wouter,Cannoodt Robrecht,Saeys Yvan,Dudoit Sandrine,Clement Lieven Nature communications Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. 10.1038/s41467-020-14766-3
    Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Andrews Tallulah S,Kiselev Vladimir Yu,McCarthy Davis,Hemberg Martin Nature protocols Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website ( https://scrnaseq-course.cog.sanger.ac.uk/website/index.html ), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods. 10.1038/s41596-020-00409-w
    Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Haghverdi Laleh,Lun Aaron T L,Morgan Michael D,Marioni John C Nature biotechnology Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells. 10.1038/nbt.4091
    Orchestrating single-cell analysis with Bioconductor. Amezquita Robert A,Lun Aaron T L,Becht Etienne,Carey Vince J,Carpp Lindsay N,Geistlinger Ludwig,Marini Federico,Rue-Albrecht Kevin,Risso Davide,Soneson Charlotte,Waldron Levi,Pagès Hervé,Smith Mike L,Huber Wolfgang,Morgan Martin,Gottardo Raphael,Hicks Stephanie C Nature methods Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users. 10.1038/s41592-019-0654-x