Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.

/pdf/single-cell-transcriptomics-of-20-mouse-organs-creates-a-5ccrhqkhix.pdf

Single-cell transcriptomics of 20 mouse organs creates a "Tabula Muris"

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

https://www.embopress.org/doi/epdf/10.15252/msb.20188746

Current best practices in single-cell RNA-seq analysis: a tutorial.

There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, which has infected more than three million people worldwide1. Approximately 20% of patients with COVID-19 develop severe disease and 5% of patients require intensive care2. Severe disease has been associated with changes in peripheral immune activity, including increased levels of pro-inflammatory cytokines3,4 that may be produced by a subset of inflammatory monocytes5,6, lymphopenia7,8 and T cell exhaustion9,10. To elucidate pathways in peripheral immune cells that might lead to immunopathology or protective immunity in severe COVID-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells (PBMCs) from seven patients hospitalized for COVID-19, four of whom had acute respiratory distress syndrome, and six healthy controls. We identify reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene signature, HLA class II downregulation and a developing neutrophil population that appears closely related to plasmablasts appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, we found that peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines. Collectively, we provide a cell atlas of the peripheral immune response to severe COVID-19.

/pdf/a-single-cell-atlas-of-the-peripheral-immune-response-in-35b2elvqci.pdf

A single-cell atlas of the peripheral immune response in patients with severe COVID-19.

Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.

Challenges in unsupervised clustering of single-cell RNA-seq data.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

/pdf/eleven-grand-challenges-in-single-cell-data-science-3ras49kp2m.pdf

Eleven grand challenges in single-cell data science

Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

/pdf/comparison-of-clustering-tools-in-r-for-medium-sized-10x-4efxnyq1um.pdf

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data.

Pathological changes in the biomechanical environment are implicated in the progression of idiopathic pulmonary fibrosis (IPF). Stiffened matrix augments fibroblast proliferation and differentiation and activates TGF-β1 (transforming growth factor-β1). Stiffened matrix impairs the synthesis of the antifibrogenic lipid mediator prostaglandin E2 (PGE2) and reduces the expression of the rate-limiting prostanoid biosynthetic enzyme cyclooxygenase-2 (COX-2). We now show that prostaglandin E synthase (PTGES), the final enzyme in the PGE2 biosynthetic pathway, is expressed at lower levels in the lungs of patients with IPF. We also show substantial induction of COX-2, PTGES, prostaglandin E receptor 4 (EP4), and cytosolic phospholipase A2 (cPLA2) expression in human lung fibroblasts cultured in soft collagen hydrogels or in spheroids compared with conventional culture on stiff plastic culture plates. Induction of COX-2, cPLA2, and PTGES expression in spheroid cultures was moderately inhibited by the p38 mitogen-activated protein kinase inhibitor SB203580. The induction of prostanoid biosynthetic enzyme expression was accompanied by an increase in PGE2 levels only in non-IPF-derived fibroblast spheroids. Our study reveals an extensive dysregulation of prostanoid biosynthesis and signaling pathways in IPF-derived fibroblasts, which are only partially abrogated by culture in soft microenvironments.

Cellular Microenvironment Stiffness Regulates Eicosanoid Production and Signaling Pathways

The commercially available 10X Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method offers most accuracy. Answering this question is complicated by the fact that 10X Genomics data lack cell labels that would allow a direct performance evaluation. Thus in this review, we focused on comparing clustering solutions of a dozen methods for three datasets on human peripheral mononuclear cells generated with the 10X Genomics technology. While clustering solutions appeared robust, we found that solutions produced by different methods have little in common with each other. They also failed to replicate cell type assignment generated with supervised labeling approaches. Furthermore, we demonstrate that all clustering methods tested clustered cells to a large degree according to the amount of ribosomal RNA in each cell.

https://www.biorxiv.org/content/biorxiv/early/2017/10/17/203752.full.pdf

Ingrid Lönnstedt

Papers

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data.

Cellular Microenvironment Stiffness Regulates Eicosanoid Production and Signaling Pathways

Cluster Headache: Comparing Clustering Tools for 10X Single Cell Sequencing Data