SC3: consensus clustering of single-cell RNA-seq data

Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.V.Y.K., T.A., A.Y. and M.H. are supported by Wellcome Trust Grants. K.N.N. is supported by the Wellcome Trust Strategic Award 'Single cell genomics of mouse gastrulation'. M.T.S. acknowledges support from FRS-FNRS; the Belgian Network DYSCO (Dynamical Systems, Control and Optimisation), funded by the Interuniversity Attraction Poles Programme initiated by the Belgian State Science Policy Office; and the ARC (Action de Recherche Concerte) on Mining and Optimization of Big Data Models, funded by the Wallonia-Brussels Federation. M.B. acknowledges support from EPSRC (grant EP/N014529/1). T.C. was funded through a core funded fellowship by the Sanger Institute and a Chancellor′s fellowship from the University of Edinburgh. K.K. and A.R.G. are supported by Bloodwise (grant ref. 13003), the Wellcome Trust (grant ref. 104710/Z/14/Z), the Medical Research Council, the Kay Kendall Leukaemia Fund, the Cambridge NIHR Biomedical Research Center, the Cambridge Experimental Cancer Medicine Centre, the Leukemia and Lymphoma Society of America (grant ref. 07037) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute. W.R. was supported by BBSRC (grant ref. BB/K010867/1), the Wellcome Trust (grant ref. 095645/Z/11/Z), EU BLUEPRINT and EpiGeneSys

Clustering of small - sample single - cell RNA - seq data via feature clustering and selection

We present FeatClust, a software tool for clustering small sample size single-cell RNA-Seq datasets. The FeatClust approach is based on feature selection. It divides features into several groups by performing agglomerative hierarchical clustering and then iteratively clustering the samples and removing features belonging to groups with the least variance across samples. The optimal number of feature groups is selected based on silhouette analysis on the clustered data, i.e., selecting the clustering with the highest average silhouette coefficient. FeatClust also allows one to visually choose the number of clusters if it is not known, by generating silhouette plot for a chosen number of groupings of the dataset. We cluster five small sample single-cell RNA-seq datasets and use the adjusted rand index metric to compare the results with other clustering packages. The results are promising and show the effectiveness of FeatClust on small sample size datasets

Murine hematopoietic stem cell activity is derived from pre-circulation embryos but not yolk sacs.

The embryonic site of definitive hematopoietic stem cell (dHSC) origination has been debated for decades. Although an intra-embryonic origin is well supported, the yolk sac (YS) contribution to adult hematopoiesis remains controversial. The same developmental origin makes it difficult to identify specific markers that discern between an intraembryonic versus YS-origin using a lineage trace approach. Additionally, the highly migratory nature of blood cells and the inability of pre-circulatory embryonic cells (i.e., 5-7 somite pairs (sp)) to robustly engraft in transplantation, even after culture, has precluded scientists from properly answering these questions. Here we report robust, multi-lineage and serially transplantable dHSC activity from cultured 2-7sp murine embryonic explants (Em-Ex). dHSC are undetectable in 2-7sp YS explants. Additionally, the engraftment from Em-Ex is confined to an emerging CD31+CD45+c-Kit+CD41- population. In sum, our work supports a model in which the embryo, not the YS, is the major source of lifelong definitive hematopoiesis

Single-cell multi-omics analysis of the immune response in COVID-19

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy

Collagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis

Collagen-producing cells maintain the complex architecture of the lung and drive pathologic scarring in pulmonary fibrosis. Here we perform single-cell RNA-sequencing to identify all collagen-producing cells in normal and fibrotic lungs. We characterize multiple collagen-producing subpopulations with distinct anatomical localizations in different compartments of murine lungs. One subpopulation, characterized by expression of Cthrc1 (collagen triple helix repeat containing 1), emerges in fibrotic lungs and expresses the highest levels of collagens. Single-cell RNA-sequencing of human lungs, including those from idiopathic pulmonary fibrosis and scleroderma patients, demonstrate similar heterogeneity and CTHRC1-expressing fibroblasts present uniquely in fibrotic lungs. Immunostaining and in situ hybridization show that these cells are concentrated within fibroblastic foci. We purify collagen-producing subpopulations and find disease-relevant phenotypes of Cthrc1-expressing fibroblasts in in vitro and adoptive transfer experiments. Our atlas of collagen-producing cells provides a roadmap for studying the roles of these unique populations in homeostasis and pathologic fibrosis

Single cell RNA-seq reveals profound transcriptional similarity between Barrett's oesophagus and oesophageal submucosal glands

Barrett’s oesophagus is a precursor of oesophageal adenocarcinoma. In this common condition, squamous epithelium in the oesophagus is replaced by columnar epithelium in response to acid reflux. Barrett’s oesophagus is highly heterogeneous and its relationships to normal tissues are unclear. Here we investigate the cellular complexity of Barrett’s oesophagus and the upper gastrointestinal tract using RNA-sequencing of single cells from multiple biopsies from six patients with Barrett’s oesophagus and two patients without oesophageal pathology. We find that cell populations in Barrett’s oesophagus, marked by LEFTY1 and OLFM4, exhibit a profound transcriptional overlap with oesophageal submucosal gland cells, but not with gastric or duodenal cells. Additionally, SPINK4 and ITLN1 mark cells that precede morphologically identifiable goblet cells in colon and Barrett’s oesophagus, potentially aiding the identification of metaplasia. Our findings reveal striking transcriptional relationships between normal tissue populations and cells in a premalignant condition, with implications for clinical practice

A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology-the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation

Sampling time-dependent artifacts in single-cell genomics studies

Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.HH is a Miguel Servet (CP14/00229) researcher funded by the Spanish Institute of Health Carlos III (ISCIII). CM and MK are supported by AECC postdoctoral fellowships. This work has received funding from the Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P; AEI/FEDER, UE). This study was further funded by the Spanish Ministry of Economy and Competitiveness (grant number: IPT-010000-2010- 36, cofunded by the European Regional Development Fund). Core funding is from the ISCIII and the Generalitat de Catalunya. We acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, the CERCA Programme/Generalitat de Catalunya, the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) through the Instituto de Salud Carlos III and the Generalitat de Catalunya through Departament de Salut and Departament d’Empresa i Coneixement. We also acknowledge the Co-financing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014–2020 Smart Growth Operating Program. We acknowledge the Generalitat de Catalunya Suport Grups de Recerca AGAUR 2017-SGR-736 (to JIMS) and 2017-SGR-1142 (to EC), and CIBERONC (CB16/12/00225 and CB16/12/00334). EC is an ICREA Academia Researcher. This project received support from the European Commission under the projects DocTIS (H2020, SEP-210574908). This publication is part of a project (BCLLATLAS) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 810287