The Human Cell Atlas
Howard Hughes Medical Institute1, Massachusetts Institute of Technology2, Broad Institute3, European Bioinformatics Institute4, Wellcome Trust Sanger Institute5, University of Cambridge6, Harvard University7, Weizmann Institute of Science8, University of Zurich9, Laboratory of Molecular Biology10, Utrecht University11, École Polytechnique Fédérale de Lausanne12, University of Pennsylvania13, German Cancer Research Center14, Heidelberg University15, Ludwig Maximilian University of Munich16, John Radcliffe Hospital17, Newcastle University18, Stanford University19, University of Oxford20, University of California, San Francisco21, Allen Institute for Brain Science22, Karolinska Institutet23, Royal Institute of Technology24, Icahn School of Medicine at Mount Sinai25, University of Cape Town26, University Medical Center Groningen27, Radboud University Nijmegen28, Kettering University29, University of Edinburgh30, Babraham Institute31, New York University32, Netherlands Cancer Institute33, Ragon Institute of MGH, MIT and Harvard34, University of Texas Health Science Center at Houston35, Technische Universität München36, Technical University of Denmark37, University of California, Berkeley38, King's College London39, California Institute of Technology40
TL;DR: An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease.
Abstract: The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.
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TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
7,892 citations
Cites background from "The Human Cell Atlas"
...Recent advances in molecular biology, microfluidics, and computation have transformed the growing field of single-cell sequencing beyond routine transcriptomic profiling with singlecell RNA sequencing (scRNA-seq) (Svensson et al., 2018; Tanay and Regev, 2017; Stuart and Satija, 2019)....
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TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
7,741 citations
Additional excerpts
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TL;DR: This work presents Scanpy, a scalable toolkit for analyzing single-cell gene expression data that includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks, and AnnData, a generic class for handling annotated data matrices.
Abstract: Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (
https://github.com/theislab/Scanpy
). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (
https://github.com/theislab/anndata
).
3,343 citations
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Abstract: 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 ~106 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. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
2,459 citations
TL;DR: On a compendium of single-cell data from tumors and brain, it is demonstrated that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states.
Abstract: We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenicaertslaborg) On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity
2,277 citations
References
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TL;DR: The derivation of a number of tissue culture cell lines which secrete anti-sheep red blood cell (SRBC) antibodies is described here, made by fusion of a mouse myeloma and mouse spleen cells from an immunised donor.
Abstract: THE manufacture of predefined specific antibodies by means of permanent tissue culture cell lines is of general interest. There are at present a considerable number of permanent cultures of myeloma cells1,2 and screening procedures have been used to reveal antibody activity in some of them. This, however, is not a satisfactory source of monoclonal antibodies of predefined specificity. We describe here the derivation of a number of tissue culture cell lines which secrete anti-sheep red blood cell (SRBC) antibodies. The cell lines are made by fusion of a mouse myeloma and mouse spleen cells from an immunised donor. To understand the expression and interactions of the Ig chains from the parental lines, fusion experiments between two known mouse myeloma lines were carried out.
19,053 citations
"The Human Cell Atlas" refers background in this paper
...Immunohistochemistry, pioneered in the 1940s (Arthur, 2016) and accelerated by the advent of monoclonal antibodies (Köhler and Milstein, 1975) and Fluorescence-Activated Cell Sorting (FACS; Dittrich and Göhde, 1971; Fulwyler, 1965) in the 1970s, made it possible to detect the presence and levels…...
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...Immunohistochemistry, pioneered in the 1940s (Arthur, 2016) and accelerated by the advent of monoclonal antibodies (Kohler and Milstein, 1975) and FluorescenceActivated Cell Sorting (FACS) (Dittrich and Göhde, 1971; Fulwyler, 1965) in the 1970s, made it possible to detect the presence and levels of specific proteins....
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TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
Abstract: We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
13,519 citations
"The Human Cell Atlas" refers methods in this paper
...…approaches could then allow iterative refinement of cellular characterization based on both a cell’s molecular profile and information about its neighborhood; methods perfected in the analysis of networks could pro- vide a helpful starting point (Blondel et al., 2008; Rosvall and Bergstrom, 2008)....
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...Computational approaches could then allow iterative refinement of cellular characterization based on both a cell’s molecular profile and information about its neighborhood; methods perfected in the analysis of networks could provide a helpful starting point (Blondel et al., 2008; Rosvall and Bergstrom, 2008)....
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Harvard University1, Vanderbilt University2, Netherlands Cancer Institute3, University of Colorado Denver4, Institut Gustave Roussy5, University of Duisburg-Essen6, University of Mannheim7, University of Utah8, VU University Amsterdam9, Mount Sinai Hospital10, Washington University in St. Louis11, University Hospital Southampton NHS Foundation Trust12, University of Paris13, Technische Universität München14, Loyola University Chicago15, Memorial Sloan Kettering Cancer Center16, University of South Florida17, Medarex18, Bristol-Myers Squibb19
TL;DR: Ipilimumab, with or without a gp100 peptide vaccine, as compared with gp100 alone, improved overall survival in patients with previously treated metastatic melanoma.
Abstract: Background An improvement in overall survival among patients with metastatic melanoma has been an elusive goal. In this phase 3 study, ipilimumab — which blocks cytotoxic T-lymphocyte–associated antigen 4 to potentiate an antitumor T-cell response — administered with or without a glycoprotein 100 (gp100) peptide vaccine was compared with gp100 alone in patients with previously treated metastatic melanoma. Methods A total of 676 HLA-A*0201–positive patients with unresectable stage III or IV melanoma, whose disease had progressed while they were receiving therapy for metastatic disease, were randomly assigned, in a 3:1:1 ratio, to receive ipilimumab plus gp100 (403 patients), ipilimumab alone (137), or gp100 alone (136). Ipilimumab, at a dose of 3 mg per kilogram of body weight, was administered with or without gp100 every 3 weeks for up to four treatments (induction). Eligible patients could receive reinduction therapy. The primary end point was overall survival. Results The median overall survival was 10.0 months among patients receiving ipilimumab plus gp100, as compared with 6.4 months among patients receiving gp100 alone (hazard ratio for death, 0.68; P<0.001). The median overall survival with ipilimumab alone was 10.1 months (hazard ratio for death in the comparison with gp100 alone, 0.66; P = 0.003). No difference in overall survival was detected between the ipilimumab groups (hazard ratio with ipilimumab plus gp100, 1.04; P = 0.76). Grade 3 or 4 immune-related adverse events occurred in 10 to 15% of patients treated with ipilimumab and in 3% treated with gp100 alone. There were 14 deaths related to the study drugs (2.1%), and 7 were associated with immune-related adverse events. Conclusions Ipilimumab, with or without a gp100 peptide vaccine, as compared with gp100 alone, improved overall survival in patients with previously treated metastatic melanoma. Adverse events can be severe, long-lasting, or both, but most are reversible with appropriate treatment. (Funded by Medarex and Bristol-Myers Squibb; ClinicalTrials.gov number, NCT00094653.)
13,081 citations
TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.
Abstract: We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.
11,078 citations
TL;DR: In this paper, a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level.
Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.
9,745 citations