601
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Yun J, Zheng X, Xu P, Zheng X, Xu J, Cao C, Fu Y, Xu B, Dai X, Wang Y, Liu H, Yi Q, Zhu Y, Wang J, Wang L, Dong Z, Huang L, Huang Y, Du W. Interfacial Nanoinjection-Based Nanoliter Single-Cell Analysis. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1903739. [PMID: 31565845 DOI: 10.1002/smll.201903739] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 09/08/2019] [Indexed: 06/10/2023]
Abstract
Single-cell analysis offers unprecedented resolution for the investigation of cellular heterogeneity and the capture of rare cells from large populations. Here, described is a simple method named interfacial nanoinjection (INJ), which can miniaturize various single-cell assays to be performed in nanoliter water-in-oil droplets on standard microwell plates. The INJ droplet handler can adjust droplet volumes for multistep reactions on demand with high precision and excellent monodispersity, and consequently enables a wide range of single-cell assays. Importantly, INJ can be coupled with fluorescence-activated cell sorting (FACS), which is currently the most effective and accurate single-cell sorting and isolation method. FACS-INJ pipelines for high-throughput plate well-based single-cell analyses, including single-cell proliferation, drug-resistance testing, polymerase chain reaction (PCR), reverse-transcription PCR, and whole-genome sequencing are introduced. This FACS-INJ pipeline is compatible with a wide range of samples and can be extended to various single-cell analysis applications in microbiology, cell biology, and biomedical diagnostics.
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Affiliation(s)
- Juanli Yun
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiaowei Zheng
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Peng Xu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xu Zheng
- State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jingyue Xu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Chen Cao
- Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), College of Engineering, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Yusi Fu
- Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), College of Engineering, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Bingxue Xu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Xin Dai
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yi Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Hongtao Liu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Qiaolian Yi
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Yaxin Zhu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jian Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Zhiyang Dong
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Li Huang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Yanyi Huang
- Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), College of Engineering, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Wenbin Du
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 100049, China
- Savaid Medical School, University of the Chinese Academy of Sciences, Beijing, 100049, China
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602
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Lin E, Mukherjee S, Kannan S. A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis. BMC Bioinformatics 2020; 21:64. [PMID: 32085701 PMCID: PMC7035735 DOI: 10.1186/s12859-020-3401-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 02/07/2020] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements). RESULTS To overcome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data. CONCLUSIONS Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.
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Affiliation(s)
- Eugene Lin
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA.,Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA.,Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Sudipto Mukherjee
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Sreeram Kannan
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA.
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603
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Abstract
Cells, the basic units of life, have striking differences at transcriptomic, proteomic and epigenomic levels across tissues, organs, organ systems and organisms. The coordination of individual immune cells is essential for the generation of effective immune responses to pathogens while immune tolerance is maintained to protect the host. In rheumatic diseases, when immune responses are dysregulated, pathologically important cells might represent only a small fraction of the immune system. Interrogation of the contributions of individual immune cells to pathogenesis and disease progression should therefore reveal important insights into the complicated aetiology of rheumatic diseases. Technological advances are enabling the high-dimensional dissection of single cells at multiple omics levels, which could facilitate the identification of dysregulated molecular mechanisms in patients with rheumatic diseases and the discovery of new therapeutic targets and biomarkers. The single-cell technologies that have been developed over the past decade and the experimental platforms that enable multi-omics integrative analyses have already made inroads into immunology-related fields of study and have potential for use in rheumatology. Layers of omics data derived from single cells are likely to fundamentally change our understanding of the molecular pathways that underpin the pathogenesis of rheumatic diseases.
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604
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CellTagging: combinatorial indexing to simultaneously map lineage and identity at single-cell resolution. Nat Protoc 2020; 15:750-772. [PMID: 32051617 DOI: 10.1038/s41596-019-0247-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 09/20/2019] [Indexed: 01/16/2023]
Abstract
Single-cell technologies are offering unparalleled insight into complex biology, revealing the behavior of rare cell populations that are masked in bulk population analyses. One current limitation of single-cell approaches is that lineage relationships are typically lost as a result of cell processing. We recently established a method, CellTagging, permitting the parallel capture of lineage information and cell identity via a combinatorial cell indexing approach. CellTagging integrates with high-throughput single-cell RNA sequencing, where sequential rounds of cell labeling enable the construction of multi-level lineage trees. Here, we provide a detailed protocol to (i) generate complex plasmid and lentivirus CellTag libraries for labeling of cells; (ii) sequentially CellTag cells over the course of a biological process; (iii) profile single-cell transcriptomes via high-throughput droplet-based platforms; and (iv) generate a CellTag expression matrix, followed by clone calling and lineage reconstruction. This lentiviral-labeling approach can be deployed in any organism or in vitro culture system that is amenable to viral transduction to simultaneously profile lineage and identity at single-cell resolution.
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605
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Zhang MJ, Ntranos V, Tse D. Determining sequencing depth in a single-cell RNA-seq experiment. Nat Commun 2020; 11:774. [PMID: 32034137 PMCID: PMC7005864 DOI: 10.1038/s41467-020-14482-y] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 12/13/2019] [Indexed: 11/25/2022] Open
Abstract
An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes. For single-cell RNA-seq experiments the sequencing budget is limited, and how it should be optimally allocated to maximize information is not clear. Here the authors develop a mathematical framework to show that, for estimating many gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene.
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Affiliation(s)
- Martin Jinye Zhang
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Vasilis Ntranos
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - David Tse
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
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606
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Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CSO, Aparicio S, Baaijens J, Balvert M, Barbanson BD, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo TH, Lelieveldt BP, Mandoiu II, Marioni JC, Marschall T, Mölder F, Niknejad A, Rączkowska A, Reinders M, Ridder JD, Saliba AE, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, McHardy AC, Raphael BJ, Shah SP, Schönhuth A. Eleven grand challenges in single-cell data science. Genome Biol 2020; 21:31. [PMID: 32033589 PMCID: PMC7007675 DOI: 10.1186/s13059-020-1926-6] [Citation(s) in RCA: 554] [Impact Index Per Article: 138.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/02/2020] [Indexed: 02/08/2023] Open
Abstract
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.
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Affiliation(s)
- David Lähnemann
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Department of Paediatric Oncology, Haematology and Immunology, Medical Faculty, Heinrich Heine University, University Hospital, Düsseldorf, Germany
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Johannes Köster
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - Ewa Szczurek
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Davis J. McCarthy
- Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Australia
- Melbourne Integrative Genomics, School of BioSciences–School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD USA
| | - Mark D. Robinson
- Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zürich, Zürich, Switzerland
| | - Catalina A. Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- The Alan Turing Institute, British Library, London, UK
| | - Kieran R. Campbell
- Department of Statistics, University of British Columbia, Vancouver, Canada
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Data Science Institute, University of British Columbia, Vancouver, Canada
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Alexandros Stamatakis
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
- Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Jasmijn Baaijens
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Marleen Balvert
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
| | - Buys de Barbanson
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Antonio Cappuccio
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
| | - Giacomo Corleone
- Department of Surgery and Cancer, The Imperial Centre for Translational and Experimental Medicine, Imperial College London, London, UK
| | - Bas E. Dutilh
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maria Florescu
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rens Holmer
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Thamar Jessurun Lobo
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Emma M. Keizer
- Biometris, Wageningen University & Research, Wageningen, The Netherlands
| | - Indu Khatri
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Szymon M. Kielbasa
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan O. Korbel
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Alexey M. Kozlov
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Tzu-Hao Kuo
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Boudewijn P.F. Lelieveldt
- PRB lab, Delft University of Technology, Delft, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ion I. Mandoiu
- Computer Science & Engineering Department, University of Connecticut, Storrs, USA
| | - John C. Marioni
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Felix Mölder
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Amir Niknejad
- Computation molecular design, Zuse Institute Berlin, Berlin, Germany
- Mathematics Department, Mount Saint Vincent, New York, USA
| | - Alicja Rączkowska
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Marcel Reinders
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Antoine-Emmanuel Saliba
- Helmholtz Institute for RNA-based Infection Research, Helmholtz-Center for Infection Research, Würzburg, Germany
| | - Antonios Somarakis
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center–DKFZ, Heidelberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Huan Yang
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research–LACDR–Leiden University, Leiden, The Netherlands
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, USA
- The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alice C. McHardy
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | - Sohrab P. Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Alexander Schönhuth
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
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607
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Foreman R, Wollman R. Mammalian gene expression variability is explained by underlying cell state. Mol Syst Biol 2020; 16:e9146. [PMID: 32043799 PMCID: PMC7011657 DOI: 10.15252/msb.20199146] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 12/04/2019] [Accepted: 01/07/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.
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Affiliation(s)
- Robert Foreman
- Institute for Quantitative and Computational BiosciencesUniversity of California, Los AngelesLos AngelesCAUSA
- Program in Bioinformatics and Systems BiologyUniversity of California, San DiegoSan DiegoCAUSA
| | - Roy Wollman
- Institute for Quantitative and Computational BiosciencesUniversity of California, Los AngelesLos AngelesCAUSA
- Program in Bioinformatics and Systems BiologyUniversity of California, San DiegoSan DiegoCAUSA
- Department of Integrative Biology and PhysiologyDepartment of Chemistry and BiochemistryUniversity of California, Los AngelesLos AngelesCAUSA
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608
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Stewart BJ, Ferdinand JR, Clatworthy MR. Using single-cell technologies to map the human immune system - implications for nephrology. Nat Rev Nephrol 2020; 16:112-128. [PMID: 31831877 DOI: 10.1038/s41581-019-0227-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2019] [Indexed: 02/02/2023]
Abstract
Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthy individuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology.
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Affiliation(s)
- Benjamin J Stewart
- Molecular Immunity Unit, University of Cambridge Department of Medicine, Cambridge, UK
- Cellular Genetics, Wellcome Sanger Institute, Cambridge, UK
- Cambridge NIHR Biomedical Research Centre, Cambridge, UK
| | - John R Ferdinand
- Molecular Immunity Unit, University of Cambridge Department of Medicine, Cambridge, UK
- Cellular Genetics, Wellcome Sanger Institute, Cambridge, UK
- Cambridge NIHR Biomedical Research Centre, Cambridge, UK
| | - Menna R Clatworthy
- Molecular Immunity Unit, University of Cambridge Department of Medicine, Cambridge, UK.
- Cellular Genetics, Wellcome Sanger Institute, Cambridge, UK.
- Cambridge NIHR Biomedical Research Centre, Cambridge, UK.
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609
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Orozco LD, Chen HH, Cox C, Katschke KJ, Arceo R, Espiritu C, Caplazi P, Nghiem SS, Chen YJ, Modrusan Z, Dressen A, Goldstein LD, Clarke C, Bhangale T, Yaspan B, Jeanne M, Townsend MJ, van Lookeren Campagne M, Hackney JA. Integration of eQTL and a Single-Cell Atlas in the Human Eye Identifies Causal Genes for Age-Related Macular Degeneration. Cell Rep 2020; 30:1246-1259.e6. [PMID: 31995762 DOI: 10.1016/j.celrep.2019.12.082] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/04/2019] [Accepted: 12/19/2019] [Indexed: 12/11/2022] Open
Abstract
Age-related macular degeneration (AMD) is a leading cause of vision loss. To better understand disease pathogenesis and identify causal genes in GWAS loci for AMD risk, we present a comprehensive database of human retina and retinal pigment epithelium (RPE). Our database comprises macular and non-macular RNA sequencing (RNA-seq) profiles from 129 donors, a genome-wide expression quantitative trait loci (eQTL) dataset that includes macula-specific retina and RPE/choroid, and single-nucleus RNA-seq (NucSeq) from human retina and RPE with subtype resolution from more than 100,000 cells. Using NucSeq, we find enriched expression of AMD candidate genes in RPE cells. We identify 15 putative causal genes for AMD on the basis of co-localization of genetic association signals for AMD risk and eye eQTL, including the genes TSPAN10 and TRPM1. These results demonstrate the value of our human eye database for elucidating genetic pathways and potential therapeutic targets for ocular diseases.
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Affiliation(s)
- Luz D Orozco
- Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA
| | - Hsu-Hsin Chen
- Department of Biomarker Discovery OMNI, Genentech, South San Francisco, CA 94080, USA
| | - Christian Cox
- Department of Immunology Discovery, Genentech, South San Francisco, CA 94080, USA
| | - Kenneth J Katschke
- Department of Immunology Discovery, Genentech, South San Francisco, CA 94080, USA
| | - Rommel Arceo
- Department of Pathology, Genentech, South San Francisco, CA 94080, USA
| | - Carmina Espiritu
- Department of Pathology, Genentech, South San Francisco, CA 94080, USA
| | - Patrick Caplazi
- Department of Pathology, Genentech, South San Francisco, CA 94080, USA
| | | | - Ying-Jiun Chen
- Department of Molecular Biology, Genentech, South San Francisco, CA 94080, USA
| | - Zora Modrusan
- Department of Molecular Biology, Genentech, South San Francisco, CA 94080, USA
| | - Amy Dressen
- Department of Human Genetics, Genentech, South San Francisco, CA 94080, USA
| | - Leonard D Goldstein
- Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA; Department of Molecular Biology, Genentech, South San Francisco, CA 94080, USA
| | - Christine Clarke
- Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA
| | - Tushar Bhangale
- Department of Human Genetics, Genentech, South San Francisco, CA 94080, USA
| | - Brian Yaspan
- Department of Human Genetics, Genentech, South San Francisco, CA 94080, USA
| | - Marion Jeanne
- Department of Immunology Discovery, Genentech, South San Francisco, CA 94080, USA
| | - Michael J Townsend
- Department of Biomarker Discovery OMNI, Genentech, South San Francisco, CA 94080, USA.
| | | | - Jason A Hackney
- Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080, USA.
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610
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Abstract
RNA sequencing is widely used to measure gene expression in biomedical research; therefore, improvements in the simplicity and accuracy of the technology are desirable. All existing RNA sequencing methods rely on the conversion of RNA into double-stranded DNA through reverse transcription followed by second-strand synthesis. The latter step requires additional enzymes and purification, and introduces sequence-dependent bias. Here, we show that Tn5 transposase, which randomly binds and cuts double-stranded DNA, can directly fragment and prime the RNA/DNA heteroduplexes generated by reverse transcription. The primed fragments are then subject to PCR amplification. This provides an approach for simple and accurate RNA characterization and quantification. Transcriptome profiling by RNA sequencing (RNA-seq) has been widely used to characterize cellular status, but it relies on second-strand complementary DNA (cDNA) synthesis to generate initial material for library preparation. Here we use bacterial transposase Tn5, which has been increasingly used in various high-throughput DNA analyses, to construct RNA-seq libraries without second-strand synthesis. We show that Tn5 transposome can randomly bind RNA/DNA heteroduplexes and add sequencing adapters onto RNA directly after reverse transcription. This method, Sequencing HEteRo RNA-DNA-hYbrid (SHERRY), is versatile and scalable. SHERRY accepts a wide range of starting materials, from bulk RNA to single cells. SHERRY offers a greatly simplified protocol and produces results with higher reproducibility and GC uniformity compared with prevailing RNA-seq methods.
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611
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Tsuyuzaki K, Sato H, Sato K, Nikaido I. Benchmarking principal component analysis for large-scale single-cell RNA-sequencing. Genome Biol 2020; 21:9. [PMID: 31955711 PMCID: PMC6970290 DOI: 10.1186/s13059-019-1900-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Accepted: 11/26/2019] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation time is long and consumes large amounts of memory. RESULTS In this work, we review the existing fast and memory-efficient PCA algorithms and implementations and evaluate their practical application to large-scale scRNA-seq datasets. Our benchmark shows that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, memory-efficient, and more accurate than the other algorithms. CONCLUSION We develop a guideline to select an appropriate PCA implementation based on the differences in the computational environment of users and developers.
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Affiliation(s)
- Koki Tsuyuzaki
- Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198 Japan
- Japan Science and Technology Agency, PRESTO, 5-3, Yonbancho, Chiyoda-ku, Tokyo, 102-8666 Japan
| | - Hiroyuki Sato
- Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Kenta Sato
- Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198 Japan
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Itoshi Nikaido
- Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198 Japan
- Bioinformatics Course, Master’s/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8577 Japan
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612
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Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, Chen J. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 2020; 21:12. [PMID: 31948481 PMCID: PMC6964114 DOI: 10.1186/s13059-019-1850-9] [Citation(s) in RCA: 479] [Impact Index Per Article: 119.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 10/03/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal. RESULTS We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression. CONCLUSION Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
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Affiliation(s)
- Hoa Thi Nhu Tran
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore
| | - Kok Siong Ang
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore
| | - Marion Chevrier
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore
| | - Xiaomeng Zhang
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore
| | - Nicole Yee Shin Lee
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore
| | - Michelle Goh
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore
| | - Jinmiao Chen
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore.
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613
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González-Silva L, Quevedo L, Varela I. Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies. Trends Cancer 2020; 6:13-19. [PMID: 31952776 DOI: 10.1016/j.trecan.2019.11.010] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 11/08/2019] [Accepted: 11/26/2019] [Indexed: 01/01/2023]
Abstract
Effective cancer treatment has been precluded by the presence of various forms of intratumoral complexity that drive treatment resistance and metastasis. Recent single-cell sequencing technologies are significantly facilitating the characterization of tumor internal architecture during disease progression. New applications and advances occurring at a fast pace predict an imminent broad application of these technologies in many research areas. As occurred with next-generation sequencing (NGS) technologies, once applied to clinical samples across tumor types, single-cell sequencing technologies could trigger an exponential increase in knowledge of the molecular pathways involved in cancer progression and contribute to the improvement of cancer treatment.
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Affiliation(s)
- Laura González-Silva
- Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria - CSIC, Santander, Spain
| | - Laura Quevedo
- Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria - CSIC, Santander, Spain
| | - Ignacio Varela
- Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria - CSIC, Santander, Spain.
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614
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Salk JJ, Kennedy SR. Next-Generation Genotoxicology: Using Modern Sequencing Technologies to Assess Somatic Mutagenesis and Cancer Risk. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2020; 61:135-151. [PMID: 31595553 PMCID: PMC7003768 DOI: 10.1002/em.22342] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 09/20/2019] [Accepted: 09/25/2019] [Indexed: 05/09/2023]
Abstract
Mutations have a profound effect on human health, particularly through an increased risk of carcinogenesis and genetic disease. The strong correlation between mutagenesis and carcinogenesis has been a driving force behind genotoxicity research for more than 50 years. The stochastic and infrequent nature of mutagenesis makes it challenging to observe and to study. Indeed, decades have been spent developing increasingly sophisticated assays and methods to study these low-frequency genetic errors, in hopes of better predicting which chemicals may be carcinogens, understanding their mode of action, and informing guidelines to prevent undue human exposure. While effective, widely used genetic selection-based technologies have a number of limitations that have hampered major advancements in the field of genotoxicity. Emerging new tools, in the form of enhanced next-generation sequencing platforms and methods, are changing this paradigm. In this review, we discuss rapidly evolving sequencing tools and technologies, such as error-corrected sequencing and single cell analysis, which we anticipate will fundamentally reshape the field. In addition, we consider a variety emerging applications for these new technologies, including the detection of DNA adducts, inference of mutational processes based on genomic site and local sequence contexts, and evaluation of genome engineering fidelity, as well as other cutting-edge challenges for the next 50 years of environmental and molecular mutagenesis research. Environ. Mol. Mutagen. 61:135-151, 2020. © 2019 The Authors. Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society.
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Affiliation(s)
- Jesse J. Salk
- Department of Medicine, Division of Medical OncologyUniversity of Washington School of MedicineSeattleWashington
- TwinStrand BiosciencesSeattleWashington
| | - Scott R. Kennedy
- Department of PathologyUniversity of WashingtonSeattleWashington
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615
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Capdevila C, Calderon RI, Bush EC, Sheldon-Collins K, Sims PA, Yan KS. Single-Cell Transcriptional Profiling of the Intestinal Epithelium. Methods Mol Biol 2020; 2171:129-153. [PMID: 32705639 DOI: 10.1007/978-1-0716-0747-3_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Emerging single-cell technologies, like single-cell RNA sequencing (scRNA-seq), enable the study of heterogeneous biological systems at cellular resolution. By profiling the set of expressed transcripts in each cell, single-cell transcriptomics has allowed for the cataloging of the cellular constituents of multiple organs and tissues, both in health and disease. In addition, these technologies have provided mechanistic insights into cellular function, cell state transitions, developmental trajectories and lineage relationships, as well as helped to dissect complex, population-level responses to environmental perturbations. scRNA-seq is particularly useful for characterizing the intestinal epithelium because it is a dynamic, rapidly self-renewing tissue comprised of more than a dozen specialized cell types. Here we discuss the fundamentals of single-cell transcriptomics of the murine small intestinal epithelium. We review the principles of proper experimental design and provide methods for the dissociation of the small intestinal epithelium into single cells followed by fluorescence-activated cell sorting (FACS) and for scRNA-seq using the 10× Genomics Chromium platform.
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Affiliation(s)
- Claudia Capdevila
- Columbia Center for Human Development, Columbia Stem Cell Initiative, Division of Digestive & Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, NY, USA
| | - Ruben I Calderon
- Columbia Center for Human Development, Columbia Stem Cell Initiative, Division of Digestive & Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, NY, USA
| | - Erin C Bush
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
| | - Kismet Sheldon-Collins
- Columbia Center for Human Development, Columbia Stem Cell Initiative, Division of Digestive & Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, NY, USA
| | - Peter A Sims
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
| | - Kelley S Yan
- Columbia Center for Human Development, Columbia Stem Cell Initiative, Division of Digestive & Liver Diseases, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Genetics & Development, Columbia University Irving Medical Center, New York, NY, USA.
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616
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Chintapula U, M Iqbal S, Kim YT. A compendium of single cell analysis in aging and disease. AIMS MOLECULAR SCIENCE 2020. [DOI: 10.3934/molsci.2020004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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617
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Ocasio J, Babcock B, Malawsky D, Weir SJ, Loo L, Simon JM, Zylka MJ, Hwang D, Dismuke T, Sokolsky M, Rosen EP, Vibhakar R, Zhang J, Saulnier O, Vladoiu M, El-Hamamy I, Stein LD, Taylor MD, Smith KS, Northcott PA, Colaneri A, Wilhelmsen K, Gershon TR. scRNA-seq in medulloblastoma shows cellular heterogeneity and lineage expansion support resistance to SHH inhibitor therapy. Nat Commun 2019; 10:5829. [PMID: 31863004 PMCID: PMC6925218 DOI: 10.1038/s41467-019-13657-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 11/14/2019] [Indexed: 01/23/2023] Open
Abstract
Targeting oncogenic pathways holds promise for brain tumor treatment, but inhibition of Sonic Hedgehog (SHH) signaling has failed in SHH-driven medulloblastoma. Cellular diversity within tumors and reduced lineage commitment can undermine targeted therapy by increasing the probability of treatment-resistant populations. Using single-cell RNA-seq and lineage tracing, we analyzed cellular diversity in medulloblastomas in transgenic, medulloblastoma-prone mice, and responses to the SHH-pathway inhibitor vismodegib. In untreated tumors, we find expected stromal cells and tumor-derived cells showing either a spectrum of neural progenitor-differentiation states or glial and stem cell markers. Vismodegib reduces the proliferative population and increases differentiation. However, specific cell types in vismodegib-treated tumors remain proliferative, showing either persistent SHH-pathway activation or stem cell characteristics. Our data show that even in tumors with a single pathway-activating mutation, diverse mechanisms drive tumor growth. This diversity confers early resistance to targeted inhibitor therapy, demonstrating the need to target multiple pathways simultaneously. Although the hedgehog (HH) pathway is known to be deregulated in medulloblastoma, inhibitors of the pathway have shown disappointing clinical benefit. Using single-cell sequencing in a mouse model of the disease, the authors show that the response to the HH pathway inhibitor vismodegib is cell-type specific.
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Affiliation(s)
- Jennifer Ocasio
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,UNC Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Benjamin Babcock
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Daniel Malawsky
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Seth J Weir
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Lipin Loo
- UNC Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Jeremy M Simon
- UNC Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,Carolina Institute for Developmental Disabilities, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Mark J Zylka
- UNC Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,Carolina Institute for Developmental Disabilities, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Duhyeong Hwang
- UNC Eshelman School of Pharmacy, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Taylor Dismuke
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Marina Sokolsky
- UNC Eshelman School of Pharmacy, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Elias P Rosen
- UNC Eshelman School of Pharmacy, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Rajeev Vibhakar
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.,Morgan Adams Foundation Pediatric Brain Tumor Research Program, Children's Hospital Colorado, Aurora, CO, USA
| | - Jiao Zhang
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, M5G 0A4, Canada
| | - Olivier Saulnier
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, M5G 0A4, Canada
| | - Maria Vladoiu
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, M5G 0A4, Canada
| | - Ibrahim El-Hamamy
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 0A4, Canada.,Program in Computational Biology, Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada
| | - Lincoln D Stein
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 0A4, Canada.,Program in Computational Biology, Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada
| | - Michael D Taylor
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, M5G 0A4, Canada.,Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, M5S 3E1, Canada
| | - Kyle S Smith
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Paul A Northcott
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Alejandro Colaneri
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.,Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Kirk Wilhelmsen
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA. .,Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA. .,Renaissance Computing Institute at UNC (RENCI), Chapel Hill, NC, 27517, USA.
| | - Timothy R Gershon
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA. .,UNC Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA. .,Carolina Institute for Developmental Disabilities, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA. .,Lineberger Comprehensive Cancer Center, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA.
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618
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Verboom K, Everaert C, Bolduc N, Livak KJ, Yigit N, Rombaut D, Anckaert J, Lee S, Venø MT, Kjems J, Speleman F, Mestdagh P, Vandesompele J. SMARTer single cell total RNA sequencing. Nucleic Acids Res 2019; 47:e93. [PMID: 31216024 PMCID: PMC6895261 DOI: 10.1093/nar/gkz535] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 05/05/2019] [Accepted: 06/04/2019] [Indexed: 12/18/2022] Open
Abstract
Single cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3' end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation and on two other cancer cell lines sorted in microplates. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.
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Affiliation(s)
- Karen Verboom
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
| | - Celine Everaert
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
| | | | | | - Nurten Yigit
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
| | - Dries Rombaut
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
| | - Jasper Anckaert
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
| | - Simon Lee
- Takara Bio USA, Mountain View, CA 94043, USA
| | - Morten T Venø
- Department of Molecular Biology and Genetics and Interdisciplinary Nanoscience Center, Aarhus University, Aarhus DK-8000, Denmark
| | - Jørgen Kjems
- Department of Molecular Biology and Genetics and Interdisciplinary Nanoscience Center, Aarhus University, Aarhus DK-8000, Denmark
| | - Frank Speleman
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
| | - Pieter Mestdagh
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
| | - Jo Vandesompele
- Center for Medical Genetics, Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent, Ghent, Belgium
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619
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Chaudhry F, Isherwood J, Bawa T, Patel D, Gurdziel K, Lanfear DE, Ruden DM, Levy PD. Single-Cell RNA Sequencing of the Cardiovascular System: New Looks for Old Diseases. Front Cardiovasc Med 2019; 6:173. [PMID: 31921894 PMCID: PMC6914766 DOI: 10.3389/fcvm.2019.00173] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 11/12/2019] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular disease encompasses a wide range of conditions, resulting in the highest number of deaths worldwide. The underlying pathologies surrounding cardiovascular disease include a vast and complicated network of both cellular and molecular mechanisms. Unique phenotypic alterations in specific cell types, visualized as varying RNA expression-levels (both coding and non-coding), have been identified as crucial factors in the pathology underlying conditions such as heart failure and atherosclerosis. Recent advances in single-cell RNA sequencing (scRNA-seq) have elucidated a new realm of cell subpopulations and transcriptional variations that are associated with normal and pathological physiology in a wide variety of diseases. This breakthrough in the phenotypical understanding of our cells has brought novel insight into cardiovascular basic science. scRNA-seq allows for separation of widely distinct cell subpopulations which were, until recently, simply averaged together with bulk-tissue RNA-seq. scRNA-seq has been used to identify novel cell types in the heart and vasculature that could be implicated in a variety of disease pathologies. Furthermore, scRNA-seq has been able to identify significant heterogeneity of phenotypes within individual cell subtype populations. The ability to characterize single cells based on transcriptional phenotypes allows researchers the ability to map development of cells and identify changes in specific subpopulations due to diseases at a very high throughput. This review looks at recent scRNA-seq studies of various aspects of the cardiovascular system and discusses their potential value to our understanding of the cardiovascular system and pathology.
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Affiliation(s)
- Farhan Chaudhry
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, United States
| | - Jenna Isherwood
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, United States
| | - Tejeshwar Bawa
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, United States
| | - Dhruvil Patel
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, United States
| | - Katherine Gurdziel
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, United States
| | - David E Lanfear
- Heart and Vascular Institute, Henry Ford Health System, Detroit, MI, United States
| | - Douglas M Ruden
- Department of Obstetrics and Gynecology, Center for Urban Responses to Environmental Stressors, Wayne State University, Detroit, MI, United States
| | - Phillip D Levy
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, United States
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620
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Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis. Proc Natl Acad Sci U S A 2019; 116:26980-26990. [PMID: 31806754 PMCID: PMC6936480 DOI: 10.1073/pnas.1911413116] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Single-cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell-type classification studies are unconstrained by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically and functionally identified neurons in 2 small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology. Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.
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621
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Dropout imputation and batch effect correction for single-cell RNA sequencing data. JOURNAL OF BIO-X RESEARCH 2019. [DOI: 10.1097/jbr.0000000000000053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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622
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Qi Z, Barrett T, Parikh AS, Tirosh I, Puram SV. Single-cell sequencing and its applications in head and neck cancer. Oral Oncol 2019; 99:104441. [PMID: 31689639 PMCID: PMC6981283 DOI: 10.1016/j.oraloncology.2019.104441] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 10/01/2019] [Indexed: 01/22/2023]
Abstract
Head and neck squamous cell carcinoma (HNSCC), like many tumors, is characterized by significant intra-tumoral heterogeneity, namely transcriptional, genetic, and epigenetic differences that define distinct cellular subpopulations. While it has been established that intra-tumoral heterogeneity may have prognostic significance in HNSCC, we are only beginning to describe and define such heterogeneity at a cellular resolution. Recent advances in single-cell sequencing technologies have been critical in this regard, opening new avenues in our understanding of more nuanced tumor biology by identifying distinct cellular subpopulations, dissecting signaling within the tumor microenvironment, and characterizing cellular genomic mutations and copy number aberrations. The combined effect of these insights is likely to be robust and meaningful changes in existing diagnostic and treatment algorithms through the application of novel biomarkers as well as targeted therapeutics. Here, we review single-cell technological and computational advances at the genomic, transcriptomic, and epigenomic levels, and discuss their applications in cancer research and clinical practice, with a specific focus on HNSCC.
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Affiliation(s)
- Zongtai Qi
- Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, USA; Department of Genetics, Washington University School of Medicine, St. Louis, USA
| | - Thomas Barrett
- Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, USA; Department of Genetics, Washington University School of Medicine, St. Louis, USA
| | - Anuraag S Parikh
- Department of Otolaryngology, Massachusetts Eye and Ear, Boston, MA, USA; Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
| | - Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sidharth V Puram
- Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, USA; Department of Genetics, Washington University School of Medicine, St. Louis, USA.
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623
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Chen S, Lake BB, Zhang K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat Biotechnol 2019; 37:1452-1457. [PMID: 31611697 PMCID: PMC6893138 DOI: 10.1038/s41587-019-0290-0] [Citation(s) in RCA: 379] [Impact Index Per Article: 75.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 09/12/2019] [Indexed: 12/27/2022]
Abstract
Single-cell RNA sequencing can reveal the transcriptional state of cells, yet provides little insight into the upstream regulatory landscape associated with open or accessible chromatin regions. Joint profiling of accessible chromatin and RNA within the same cells would permit direct matching of transcriptional regulation to its outputs. Here, we describe droplet-based single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq), a method that can link a cell's transcriptome with its accessible chromatin for sequencing at scale. Specifically, accessible sites are captured by Tn5 transposase in permeabilized nuclei to permit, within many droplets in parallel, DNA barcode tagging together with the mRNA molecules from the same cells. To demonstrate the utility of SNARE-seq, we generated joint profiles of 5,081 and 10,309 cells from neonatal and adult mouse cerebral cortices, respectively. We reconstructed the transcriptome and epigenetic landscapes of major and rare cell types, uncovered lineage-specific accessible sites, especially for low-abundance cells, and connected the dynamics of promoter accessibility with transcription level during neurogenesis.
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Affiliation(s)
- Song Chen
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Blue B Lake
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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624
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Matuła K, Rivello F, Huck WTS. Single-Cell Analysis Using Droplet Microfluidics. ACTA ACUST UNITED AC 2019; 4:e1900188. [PMID: 32293129 DOI: 10.1002/adbi.201900188] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 09/30/2019] [Indexed: 12/12/2022]
Abstract
Droplet microfluidics has revolutionized the study of single cells. The ability to compartmentalize cells within picoliter droplets in microfluidic devices has opened up a wide range of strategies to extract information at the genomic, transcriptomic, proteomic, or metabolomic level from large numbers of individual cells. Studying the different molecular landscapes at single-cell resolution has provided the authors with a detailed picture of intracellular heterogeneity and the resulting changes in cellular phenotypes. In addition, these technologies have aided in the discovery of rare cells in tumors or in the immune system, and left the authors with a deeper understanding of the fundamental biological processes that determine cell fate. This review aims to provide a detailed overview of the various droplet microfluidic strategies reported in the literature, taking into account the sometimes subtle differences in workflow or reagents that enable or improve certain protocols. Specifically, approaches to targeted- and whole-genome analysis, as well as whole-transcriptome profiling techniques, are reviewed. In addition, an up-to-date overview of new methods to characterize and quantify single-cell protein levels, and of developments to screen secreted molecules such as antibodies, cytokines, or metabolites at the single-cell level, is provided.
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Affiliation(s)
- Kinga Matuła
- Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525AJ, Nijmegen, The Netherlands
| | - Francesca Rivello
- Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525AJ, Nijmegen, The Netherlands
| | - Wilhelm T S Huck
- Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525AJ, Nijmegen, The Netherlands
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625
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Ku C, Sebé-Pedrós A. Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes. Philos Trans R Soc Lond B Biol Sci 2019; 374:20190098. [PMID: 31587645 PMCID: PMC6792447 DOI: 10.1098/rstb.2019.0098] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2019] [Indexed: 12/13/2022] Open
Abstract
Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue 'Single cell ecology'.
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Affiliation(s)
- Chuan Ku
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei 11529, Taiwan
| | - Arnau Sebé-Pedrós
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
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626
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Sagner A, Briscoe J. Establishing neuronal diversity in the spinal cord: a time and a place. Development 2019; 146:146/22/dev182154. [DOI: 10.1242/dev.182154] [Citation(s) in RCA: 137] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
ABSTRACT
The vertebrate spinal cord comprises multiple functionally distinct neuronal cell types arranged in characteristic positions. During development, these different types of neurons differentiate from transcriptionally distinct neural progenitors that are arrayed in discrete domains along the dorsal-ventral and anterior-posterior axes of the embryonic spinal cord. This organization arises in response to morphogen gradients acting upstream of a gene regulatory network, the architecture of which determines the spatial and temporal pattern of gene expression. In recent years, substantial progress has been made in deciphering the regulatory network that underlies the specification of distinct progenitor and neuronal cell identities. In this Review, we outline how distinct neuronal cell identities are established in response to spatial and temporal patterning systems, and outline novel experimental approaches to study the emergence and function of neuronal diversity in the spinal cord.
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627
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Hawkins LJ, Storey KB. Advances and applications of environmental stress adaptation research. Comp Biochem Physiol A Mol Integr Physiol 2019; 240:110623. [PMID: 31778815 DOI: 10.1016/j.cbpa.2019.110623] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/19/2019] [Accepted: 11/21/2019] [Indexed: 02/06/2023]
Abstract
Evolution has produced animals that survive extreme fluctuations in environmental conditions including freezing temperatures, anoxia, desiccating conditions, and prolonged periods without food. For example, the wood frog survives whole-body freezing every winter, arresting all gross physiological functions, but recovers functions upon thawing in the spring. Likewise, many small mammals hibernate for months at a time with minimal metabolic activity, organ perfusion, and movement, yet do not suffer significant muscle atrophy upon arousal. These conditions and the biochemical adaptations employed to deal with them can be viewed as Nature's answer to problems that humans wish to answer, particularly in a biomedical context. This review focuses on recent advances in the field of animal environmental stress adaptation, starting with an emphasis on new areas of research such as epigenetics and microRNA. We then examine new and emerging technologies such as genome editing, novel sequencing applications, and single cell analysis and how these can push us closer to a deeper understanding of biochemical adaptation. Next, evaluate the potential contributions of new high-throughput technologies (e.g. next-generation sequencing, mass spectrometry proteomics) to better understanding the adaptations that support these extreme phenotypes. Concluding, we examine some of the human applications that can be gained from understanding the principles of biochemical adaptation including organ preservation and treatments for conditions such as ischemic stroke and muscle disuse atrophy.
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Affiliation(s)
- Liam J Hawkins
- Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
| | - Kenneth B Storey
- Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada.
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628
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Li S, Kendall J, Park S, Wang Z, Alexander J, Moffitt A, Ranade N, Danyko C, Gegenhuber B, Fischer S, Robinson BD, Lepor H, Tollkuhn J, Gillis J, Brouzes E, Krasnitz A, Levy D, Wigler M. Copolymerization of single-cell nucleic acids into balls of acrylamide gel. Genome Res 2019; 30:49-61. [PMID: 31727682 PMCID: PMC6961581 DOI: 10.1101/gr.253047.119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 11/13/2019] [Indexed: 01/06/2023]
Abstract
We show the use of 5′-Acrydite oligonucleotides to copolymerize single-cell DNA or RNA into balls of acrylamide gel (BAGs). Combining this step with split-and-pool techniques for creating barcodes yields a method with advantages in cost and scalability, depth of coverage, ease of operation, minimal cross-contamination, and efficient use of samples. We perform DNA copy number profiling on mixtures of cell lines, nuclei from frozen prostate tumors, and biopsy washes. As applied to RNA, the method has high capture efficiency of transcripts and sufficient consistency to clearly distinguish the expression patterns of cell lines and individual nuclei from neurons dissected from the mouse brain. By using varietal tags (UMIs) to achieve sequence error correction, we show extremely low levels of cross-contamination by tracking source-specific SNVs. The method is readily modifiable, and we will discuss its adaptability and diverse applications.
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Affiliation(s)
- Siran Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Jude Kendall
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Sarah Park
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Zihua Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Joan Alexander
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Andrea Moffitt
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Nissim Ranade
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Cassidy Danyko
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Bruno Gegenhuber
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Brian D Robinson
- Department of Pathology and Laboratory Medicine, Weill Medical College of Cornell University, New York, New York 10021, USA
| | - Herbert Lepor
- Department of Urology, New York University Langone Medical Center, New York, New York 10017, USA
| | - Jessica Tollkuhn
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Eric Brouzes
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York 11794, USA
| | - Alex Krasnitz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Dan Levy
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Michael Wigler
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
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629
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The in vivo endothelial cell translatome is highly heterogeneous across vascular beds. Proc Natl Acad Sci U S A 2019; 116:23618-23624. [PMID: 31712416 DOI: 10.1073/pnas.1912409116] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Endothelial cells (ECs) are highly specialized across vascular beds. However, given their interspersed anatomic distribution, comprehensive characterization of the molecular basis for this heterogeneity in vivo has been limited. By applying endothelial-specific translating ribosome affinity purification (EC-TRAP) combined with high-throughput RNA sequencing analysis, we identified pan EC-enriched genes and tissue-specific EC transcripts, which include both established markers and genes previously unappreciated for their presence in ECs. In addition, EC-TRAP limits changes in gene expression after EC isolation and in vitro expansion, as well as rapid vascular bed-specific shifts in EC gene expression profiles as a result of the enzymatic tissue dissociation required to generate single-cell suspensions for fluorescence-activated cell sorting or single-cell RNA sequencing analysis. Comparison of our EC-TRAP with published single-cell RNA sequencing data further demonstrates considerably greater sensitivity of EC-TRAP for the detection of low abundant transcripts. Application of EC-TRAP to examine the in vivo host response to lipopolysaccharide (LPS) revealed the induction of gene expression programs associated with a native defense response, with marked differences across vascular beds. Furthermore, comparative analysis of whole-tissue and TRAP-selected mRNAs identified LPS-induced differences that would not have been detected by whole-tissue analysis alone. Together, these data provide a resource for the analysis of EC-specific gene expression programs across heterogeneous vascular beds under both physiologic and pathologic conditions.
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630
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Masgutova G, Harris A, Jacob B, Corcoran LM, Clotman F. Pou2f2 Regulates the Distribution of Dorsal Interneurons in the Mouse Developing Spinal Cord. Front Mol Neurosci 2019; 12:263. [PMID: 31787878 PMCID: PMC6853997 DOI: 10.3389/fnmol.2019.00263] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 10/16/2019] [Indexed: 12/20/2022] Open
Abstract
Spinal dorsal interneurons, which are generated during embryonic development, relay and process sensory inputs from the periphery to the central nervous system. Proper integration of these cells into neuronal circuitry depends on their correct positioning within the spinal parenchyma. Molecular cues that control neuronal migration have been extensively characterized but the genetic programs that regulate their production remain poorly investigated. Onecut (OC) transcription factors have been shown to control the migration of the dorsal interneurons (dINs) during spinal cord development. Here, we report that the OC factors moderate the expression of Pou2f2, a transcription factor essential for B-cell differentiation, in spinal dINs. Overexpression or inactivation of Pou2f2 leads to alterations in the differentiation of dI2, dI3 and Phox2a-positive dI5 populations and to defects in the distribution of dI2-dI6 interneurons. Thus, an OC-Pou2f2 genetic cascade regulates adequate diversification and distribution of dINs during embryonic development.
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Affiliation(s)
- Gauhar Masgutova
- Université catholique de Louvain, Institute of Neuroscience, Laboratory of Neural Differentiation, Brussels, Belgium
| | - Audrey Harris
- Université catholique de Louvain, Institute of Neuroscience, Laboratory of Neural Differentiation, Brussels, Belgium
| | - Benvenuto Jacob
- Université catholique de Louvain, Institute of Neuroscience, System and Cognition Division, Brussels, Belgium
| | - Lynn M Corcoran
- Molecular Immunology Division and Immunology Division, The Walter and Eliza Hall Institute, Parkville, VIC, Australia
| | - Frédéric Clotman
- Université catholique de Louvain, Institute of Neuroscience, Laboratory of Neural Differentiation, Brussels, Belgium
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631
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Zhu C, Yu M, Huang H, Juric I, Abnousi A, Hu R, Lucero J, Behrens MM, Hu M, Ren B. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat Struct Mol Biol 2019; 26:1063-1070. [PMID: 31695190 DOI: 10.1038/s41594-019-0323-x] [Citation(s) in RCA: 173] [Impact Index Per Article: 34.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 09/30/2019] [Indexed: 12/21/2022]
Abstract
Simultaneous profiling of transcriptome and chromatin accessibility within single cells is a powerful approach to dissect gene regulatory programs in complex tissues. However, current tools are limited by modest throughput. We now describe an ultra high-throughput method, Paired-seq, for parallel analysis of transcriptome and accessible chromatin in millions of single cells. We demonstrate the utility of Paired-seq for analyzing the dynamic and cell-type-specific gene regulatory programs in complex tissues by applying it to mouse adult cerebral cortex and fetal forebrain. The joint profiles of a large number of single cells allowed us to deconvolute the transcriptome and open chromatin landscapes in the major cell types within these brain tissues, infer putative target genes of candidate enhancers, and reconstruct the trajectory of cellular lineages within the developing forebrain.
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Affiliation(s)
- Chenxu Zhu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Miao Yu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Hui Huang
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.,Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Ivan Juric
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Armen Abnousi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Rong Hu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA, USA. .,Center for Epigenomics, Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, Moores Cancer Center, University of California San Diego, School of Medicine, La Jolla, CA, USA.
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632
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Copper and the brain noradrenergic system. J Biol Inorg Chem 2019; 24:1179-1188. [PMID: 31691104 DOI: 10.1007/s00775-019-01737-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 10/21/2019] [Indexed: 02/08/2023]
Abstract
Copper (Cu) plays an essential role in the development and function of the brain. In humans, genetic disorders of Cu metabolism may cause either severe Cu deficiency (Menkes disease) or excessive Cu accumulation (Wilson disease) in the brain tissue. In either case, the loss of Cu homeostasis results in catecholamine misbalance, abnormal myelination of neurons, loss of normal brain architecture, and a spectrum of neurologic and/or psychiatric manifestations. Several metabolic processes have been identified as particularly sensitive to Cu dis-homeostasis. This review focuses on the role of Cu in noradrenergic neurons and summarizes the current knowledge of mechanisms that maintain Cu homeostasis in these cells. The impact of Cu misbalance on catecholamine metabolism and functioning of noradrenergic system is discussed.
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633
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Immunology Driven by Large-Scale Single-Cell Sequencing. Trends Immunol 2019; 40:1011-1021. [DOI: 10.1016/j.it.2019.09.004] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/18/2019] [Accepted: 09/18/2019] [Indexed: 12/28/2022]
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634
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Ma Q, Bücking H, Gonzalez Hernandez JL, Subramanian S. Single-Cell RNA Sequencing of Plant-Associated Bacterial Communities. Front Microbiol 2019; 10:2452. [PMID: 31736899 PMCID: PMC6828647 DOI: 10.3389/fmicb.2019.02452] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 10/11/2019] [Indexed: 11/29/2022] Open
Abstract
Plants in soil are not solitary, hence continually interact with and obtain benefits from a community of microbes ("microbiome"). The meta-functional output from the microbiome results from complex interactions among the different community members with distinct taxonomic identities and metabolic capacities. Particularly, the bacterial communities of the root surface are spatially organized structures composed of root-attached biofilms and planktonic cells arranged in complex layers. With the distinct but coordinated roles among the different member cells, bacterial communities resemble properties of a multicellular organism. High throughput sequencing technologies have allowed rapid and large-scale analysis of taxonomic composition and metabolic capacities of bacterial communities. However, these methods are generally unable to reconstruct the assembly of these communities, or how the gene expression patterns in individual cells/species are coordinated within these communities. Single-cell transcriptomes of community members can identify how gene expression patterns vary among members of the community, including differences among different cells of the same species. This information can be used to classify cells based on functional gene expression patterns, and predict the spatial organization of the community. Here we discuss strategies for the isolation of single bacterial cells, mRNA enrichment, library construction, and analysis and interpretation of the resulting single-cell RNA-Seq datasets. Unraveling regulatory and metabolic processes at the single cell level is expected to yield an unprecedented discovery of mechanisms involved in bacterial recruitment, attachment, assembly, organization of the community, or in the specific interactions among the different members of these communities.
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Affiliation(s)
- Qin Ma
- Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Heike Bücking
- Biology and Microbiology Department, South Dakota State University, Brookings, SD, United States
| | - Jose L. Gonzalez Hernandez
- Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States
- Biology and Microbiology Department, South Dakota State University, Brookings, SD, United States
| | - Senthil Subramanian
- Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States
- Biology and Microbiology Department, South Dakota State University, Brookings, SD, United States
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635
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Selective demethylation of two CpG sites causes postnatal activation of the Dao gene and consequent removal of D-serine within the mouse cerebellum. Clin Epigenetics 2019; 11:149. [PMID: 31661019 PMCID: PMC6819446 DOI: 10.1186/s13148-019-0732-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 08/29/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Programmed epigenetic modifications occurring at early postnatal brain developmental stages may have a long-lasting impact on brain function and complex behavior throughout life. Notably, it is now emerging that several genes that undergo perinatal changes in DNA methylation are associated with neuropsychiatric disorders. In this context, we envisaged that epigenetic modifications during the perinatal period may potentially drive essential changes in the genes regulating brain levels of critical neuromodulators such as D-serine and D-aspartate. Dysfunction of this fine regulation may contribute to the genesis of schizophrenia or other mental disorders, in which altered levels of D-amino acids are found. We recently demonstrated that Ddo, the D-aspartate degradation gene, is actively demethylated to ultimately reduce D-aspartate levels. However, the role of epigenetics as a mechanism driving the regulation of appropriate D-ser levels during brain development has been poorly investigated to date. METHODS We performed comprehensive ultradeep DNA methylation and hydroxymethylation profiling along with mRNA expression and HPLC-based D-amino acids level analyses of genes controlling the mammalian brain levels of D-serine and D-aspartate. DNA methylation changes occurring in specific cerebellar cell types were also investigated. We conducted high coverage targeted bisulfite sequencing by next-generation sequencing and single-molecule bioinformatic analysis. RESULTS We report consistent spatiotemporal modifications occurring at the Dao gene during neonatal development in a specific brain region (the cerebellum) and within specific cell types (astrocytes) for the first time. Dynamic demethylation at two specific CpG sites located just downstream of the transcription start site was sufficient to strongly activate the Dao gene, ultimately promoting the complete physiological degradation of cerebellar D-serine a few days after mouse birth. High amount of 5'-hydroxymethylcytosine, exclusively detected at relevant CpG sites, strongly evoked the occurrence of an active demethylation process. CONCLUSION The present investigation demonstrates that robust and selective demethylation of two CpG sites is associated with postnatal activation of the Dao gene and consequent removal of D-serine within the mouse cerebellum. A single-molecule methylation approach applied at the Dao locus promises to identify different cell-type compositions and functions in different brain areas and developmental stages.
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636
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637
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Ren J, Isakova A, Friedmann D, Zeng J, Grutzner SM, Pun A, Zhao GQ, Kolluru SS, Wang R, Lin R, Li P, Li A, Raymond JL, Luo Q, Luo M, Quake SR, Luo L. Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei. eLife 2019; 8:e49424. [PMID: 31647409 PMCID: PMC6812963 DOI: 10.7554/elife.49424] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 10/12/2019] [Indexed: 12/11/2022] Open
Abstract
Serotonin neurons of the dorsal and median raphe nuclei (DR, MR) collectively innervate the entire forebrain and midbrain, modulating diverse physiology and behavior. To gain a fundamental understanding of their molecular heterogeneity, we used plate-based single-cell RNA-sequencing to generate a comprehensive dataset comprising eleven transcriptomically distinct serotonin neuron clusters. Systematic in situ hybridization mapped specific clusters to the principal DR, caudal DR, or MR. These transcriptomic clusters differentially express a rich repertoire of neuropeptides, receptors, ion channels, and transcription factors. We generated novel intersectional viral-genetic tools to access specific subpopulations. Whole-brain axonal projection mapping revealed that DR serotonin neurons co-expressing vesicular glutamate transporter-3 preferentially innervate the cortex, whereas those co-expressing thyrotropin-releasing hormone innervate subcortical regions in particular the hypothalamus. Reconstruction of 50 individual DR serotonin neurons revealed diverse and segregated axonal projection patterns at the single-cell level. Together, these results provide a molecular foundation of the heterogenous serotonin neuronal phenotypes.
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Affiliation(s)
- Jing Ren
- Department of Biology and Howard Hughes Medical InstituteStanford UniversityStanfordUnited States
| | - Alina Isakova
- Department of BioengineeringStanford UniversityStanfordUnited States
- Department of Applied PhysicsStanford UniversityStanfordUnited States
| | - Drew Friedmann
- Department of Biology and Howard Hughes Medical InstituteStanford UniversityStanfordUnited States
| | - Jiawei Zeng
- National Institute of Biological ScienceBeijingChina
| | - Sophie M Grutzner
- Department of Biology and Howard Hughes Medical InstituteStanford UniversityStanfordUnited States
| | - Albert Pun
- Department of Biology and Howard Hughes Medical InstituteStanford UniversityStanfordUnited States
| | - Grace Q Zhao
- Department of NeurobiologyStanford University School of MedicineStanfordUnited States
| | - Sai Saroja Kolluru
- Department of BioengineeringStanford UniversityStanfordUnited States
- Department of Applied PhysicsStanford UniversityStanfordUnited States
| | - Ruiyu Wang
- National Institute of Biological ScienceBeijingChina
| | - Rui Lin
- National Institute of Biological ScienceBeijingChina
| | - Pengcheng Li
- Britton Chance Center for Biomedical PhotonicsWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology (HUST)WuhanChina
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for BrainsmaticsSuzhouChina
| | - Anan Li
- Britton Chance Center for Biomedical PhotonicsWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology (HUST)WuhanChina
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for BrainsmaticsSuzhouChina
| | - Jennifer L Raymond
- Department of NeurobiologyStanford University School of MedicineStanfordUnited States
| | - Qingming Luo
- Britton Chance Center for Biomedical PhotonicsWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology (HUST)WuhanChina
| | - Minmin Luo
- National Institute of Biological ScienceBeijingChina
- School of Life ScienceTsinghua UniversityBeijingChina
| | - Stephen R Quake
- Department of BioengineeringStanford UniversityStanfordUnited States
- Department of Applied PhysicsStanford UniversityStanfordUnited States
- Chan Zuckerberg BiohubSan FranciscoUnited States
| | - Liqun Luo
- Department of Biology and Howard Hughes Medical InstituteStanford UniversityStanfordUnited States
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638
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Li J, Wang GZ. Application of Computational Biology to Decode Brain Transcriptomes. GENOMICS PROTEOMICS & BIOINFORMATICS 2019; 17:367-380. [PMID: 31655213 PMCID: PMC6943780 DOI: 10.1016/j.gpb.2019.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 02/21/2019] [Accepted: 03/15/2019] [Indexed: 01/03/2023]
Abstract
The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases, providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages. Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity. However, our knowledge of brain transcriptional characteristics remains very limited. With the immense efforts to generate high-quality brain transcriptome atlases, new computational approaches to analyze these high-dimensional multivariate data are greatly needed. In this review, we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field, which would aid in making new discoveries in brain development and disorders.
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Affiliation(s)
- Jie Li
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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639
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Chen W, Li S, Kulkarni AS, Huang L, Cao J, Qian K, Wan J. Single Cell Omics: From Assay Design to Biomedical Application. Biotechnol J 2019; 15:e1900262. [DOI: 10.1002/biot.201900262] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/11/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Wenyi Chen
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - Shumin Li
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - Anuja Shreeram Kulkarni
- School of Biomedical EngineeringMed‐X Research InstituteShanghai Jiao Tong University Shanghai 200030 P. R. China
| | - Lin Huang
- School of Biomedical EngineeringMed‐X Research InstituteShanghai Jiao Tong University Shanghai 200030 P. R. China
| | - Jing Cao
- School of Biomedical EngineeringMed‐X Research InstituteShanghai Jiao Tong University Shanghai 200030 P. R. China
| | - Kun Qian
- School of Biomedical EngineeringMed‐X Research InstituteShanghai Jiao Tong University Shanghai 200030 P. R. China
| | - Jingjing Wan
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
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640
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Systematic Identification of Cell-Cell Communication Networks in the Developing Brain. iScience 2019; 21:273-287. [PMID: 31677479 PMCID: PMC6838536 DOI: 10.1016/j.isci.2019.10.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 09/24/2019] [Accepted: 10/13/2019] [Indexed: 01/07/2023] Open
Abstract
Since the generation of cell-type specific knockout models, the importance of inter-cellular communication between neural, vascular, and microglial cells during neural development has been increasingly appreciated. However, the extent of communication between these major cell populations remains to be systematically mapped. Here, we describe EMBRACE (embryonic brain cell extraction using FACS), a method to simultaneously isolate neural, mural, endothelial, and microglial cells to more than 94% purity in ∼4 h. Utilizing EMBRACE we isolate, transcriptionally analyze, and build a cell-cell communication map of the developing mouse brain. We identify 1,710 unique ligand-receptor interactions between neural, endothelial, mural, and microglial cells in silico and experimentally confirm the APOE-LDLR, APOE-LRP1, VTN-KDR, and LAMA4-ITGB1 interactions in the E14.5 brain. We provide our data via the searchable “Brain interactome explorer”, available at https://mpi-ie.shinyapps.io/braininteractomeexplorer/. Together, this study provides a comprehensive map that reveals the richness of communication within the developing brain. Isolation of embryonic neural, mural, endothelial, and microglial cells to >94% purity Transcriptome analyses of neural, vascular, and microglial cells from E14.5 brain Generation of inter-cellular communication network with 1,710 unique interactions Established “Brain interactome explorer,” a searchable cell communication database
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641
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Vawter MP, Hamzeh AR, Muradyan E, Civelli O, Abbott GW, Alachkar A. Association of Myoinositol Transporters with Schizophrenia and Bipolar Disorder: Evidence from Human and Animal Studies. MOLECULAR NEUROPSYCHIATRY 2019; 5:200-211. [PMID: 31768373 PMCID: PMC6873027 DOI: 10.1159/000501125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 05/21/2019] [Indexed: 12/12/2022]
Abstract
Evidence from animal and human studies has linked myo-inositol (MI) with the pathophysiology and/or treatment of psychiatric disorders such as schizophrenia and bipolar disorder. However, there is still controversy surrounding the definitive role of MI in these disorders. Given that brain MI is differentially regulated by three transporters - SMIT1, SMIT2 and/or HMIT (encoded by the genes: SLC5A3, SLC5A11, and SLC2A13, respectively) - we used available datasets to describe the distribution in mouse and human brain of the different MI transporters and to examine changes in mRNA expression of these transporters in patients with schizophrenia and bipolar disorder. We found a differential distribution of the mRNA of each of the three MI transporters in both human and mouse brain regions. Interestingly, while individual neurons express SMIT1 and HMIT, non-neuronal cells express SMIT2, thus partially accounting for different uptake levels of MI and concordance to downstream second messenger signaling pathways. We also found that the expression of MI transporters is significantly changed in schizophrenia and bipolar disorder in a diagnostic-, brain region- and subtype-specific manner. We then examined the effects of germline deletion in mice of Slc5a3 on behavioral phenotypes related to schizophrenia and bipolar disorder. This gene deletion produces behavioral deficits that mirror some specific symptoms of schizophrenia and bipolar disorder. Finally, chronic administration of MI was able to reverse particular, but not all, behavioral deficits in Slc5a3 knockout mice; MI itself induced some behavioral deficits. Our data support a strong correlation between the expression of MI transporters and schizophrenia and bipolar disorder, and suggest that brain region-specific aberration of one or more of these transporters determines the partial behavioral phenotypes and/or symptomatic pattern of these disorders.
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Affiliation(s)
- Marquis P. Vawter
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, California, USA
| | - Abdul Rezzak Hamzeh
- John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Edgar Muradyan
- Department of Pharmacology, School of Medicine, University of California, Irvine, Irvine, California, USA
| | - Olivier Civelli
- Department of Pharmacology, School of Medicine, University of California, Irvine, Irvine, California, USA
- Department of Pharmaceutical Sciences, School of Medicine, University of California, Irvine, Irvine, California, USA
- Department of Developmental and Cell Biology, School of Medicine, University of California, Irvine, Irvine, California, USA
| | - Geoffrey W. Abbott
- Bioelectricity Laboratory, Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, Irvine, California, USA
| | - Amal Alachkar
- Department of Pharmacology, School of Medicine, University of California, Irvine, Irvine, California, USA
- Department of Pharmaceutical Sciences, School of Medicine, University of California, Irvine, Irvine, California, USA
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642
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van Kuijk K, Kuppe C, Betsholtz C, Vanlandewijck M, Kramann R, Sluimer JC. Heterogeneity and plasticity in healthy and atherosclerotic vasculature explored by single-cell sequencing. Cardiovasc Res 2019; 115:1705-1715. [PMID: 31350876 PMCID: PMC6873093 DOI: 10.1093/cvr/cvz185] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/05/2019] [Accepted: 07/23/2019] [Indexed: 12/18/2022] Open
Abstract
Cellular characteristics and their adjustment to a state of disease have become more evident due to recent advances in imaging, fluorescent reporter mice, and whole genome RNA sequencing. The uncovered cellular heterogeneity and/or plasticity potentially complicates experimental studies and clinical applications, as markers derived from whole tissue 'bulk' sequencing is unable to yield a subtype transcriptome and specific markers. Here, we propose definitions on heterogeneity and plasticity, discuss current knowledge thereof in the vasculature and how this may be improved by single-cell sequencing (SCS). SCS is emerging as an emerging technique, enabling researchers to investigate different cell populations in more depth than ever before. Cell selection methods, e.g. flow assisted cell sorting, and the quantity of cells can influence the choice of SCS method. Smart-Seq2 offers sequencing of the complete mRNA molecule on a low quantity of cells, while Drop-seq is possible on large numbers of cells on a more superficial level. SCS has given more insight in heterogeneity in healthy vasculature, where it revealed that zonation is crucial in gene expression profiles among the anatomical axis. In diseased vasculature, this heterogeneity seems even more prominent with discovery of new immune subsets in atherosclerosis as proof. Vascular smooth muscle cells and mesenchymal cells also share these plastic characteristics with the ability to up-regulate markers linked to stem cells, such as Sca-1 or CD34. Current SCS studies show some limitations to the number of replicates, quantity of cells used, or the loss of spatial information. Bioinformatical tools could give some more insight in current datasets, making use of pseudo-time analysis or RNA velocity to investigate cell differentiation or polarization. In this review, we discuss the use of SCS in unravelling heterogeneity in the vasculature, its current limitations and promising future applications.
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Affiliation(s)
- Kim van Kuijk
- Pathology Department, CARIM School for Cardiovascular Diseases, MUMC Maastricht, P. Debyelaan 25, Maastricht, the Netherlands
| | | | - Christer Betsholtz
- Integrated Cardio Metabolic Centre, Karolinska Institute Stockholm, Sweden
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Michael Vanlandewijck
- Integrated Cardio Metabolic Centre, Karolinska Institute Stockholm, Sweden
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Judith C Sluimer
- Pathology Department, CARIM School for Cardiovascular Diseases, MUMC Maastricht, P. Debyelaan 25, Maastricht, the Netherlands
- British Heart Foundation Centre for Cardiovascular Sciences (CVS), University of Edinburgh, Edinburgh, UK
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643
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Single-cell transcriptomic profiling of the aging mouse brain. Nat Neurosci 2019; 22:1696-1708. [PMID: 31551601 DOI: 10.1038/s41593-019-0491-3] [Citation(s) in RCA: 339] [Impact Index Per Article: 67.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 08/09/2019] [Indexed: 01/09/2023]
Abstract
The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain ) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.
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644
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Hrvatin S, Tzeng CP, Nagy MA, Stroud H, Koutsioumpa C, Wilcox OF, Assad EG, Green J, Harvey CD, Griffith EC, Greenberg ME. A scalable platform for the development of cell-type-specific viral drivers. eLife 2019; 8:e48089. [PMID: 31545165 PMCID: PMC6776442 DOI: 10.7554/elife.48089] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 09/16/2019] [Indexed: 12/22/2022] Open
Abstract
Enhancers are the primary DNA regulatory elements that confer cell type specificity of gene expression. Recent studies characterizing individual enhancers have revealed their potential to direct heterologous gene expression in a highly cell-type-specific manner. However, it has not yet been possible to systematically identify and test the function of enhancers for each of the many cell types in an organism. We have developed PESCA, a scalable and generalizable method that leverages ATAC- and single-cell RNA-sequencing protocols, to characterize cell-type-specific enhancers that should enable genetic access and perturbation of gene function across mammalian cell types. Focusing on the highly heterogeneous mammalian cerebral cortex, we apply PESCA to find enhancers and generate viral reagents capable of accessing and manipulating a subset of somatostatin-expressing cortical interneurons with high specificity. This study demonstrates the utility of this platform for developing new cell-type-specific viral reagents, with significant implications for both basic and translational research.
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Affiliation(s)
- Sinisa Hrvatin
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
| | | | - M Aurel Nagy
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
| | - Hume Stroud
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
| | - Charalampia Koutsioumpa
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
- BBS ProgramHarvard Medical SchoolBostonUnited States
| | - Oren F Wilcox
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
| | - Elena G Assad
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
| | - Jonathan Green
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
| | | | - Eric C Griffith
- Department of NeurobiologyHarvard Medical SchoolBostonUnited States
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645
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Bais AS, Kostka D. scds: computational annotation of doublets in single-cell RNA sequencing data. Bioinformatics 2019; 36:1150-1158. [PMID: 31501871 PMCID: PMC7703774 DOI: 10.1093/bioinformatics/btz698] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 08/16/2019] [Accepted: 09/05/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study's conclusions, and therefore computational strategies for the identification of doublets are needed. RESULTS With scds, we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scds presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. AVAILABILITY AND IMPLEMENTATION scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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646
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Supervised classification enables rapid annotation of cell atlases. Nat Methods 2019; 16:983-986. [PMID: 31501545 PMCID: PMC6791524 DOI: 10.1038/s41592-019-0535-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 50.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 07/12/2019] [Indexed: 01/29/2023]
Abstract
Single-cell molecular profiling technologies are gaining rapid traction, but the manual process by which resulting cell types are typically annotated is labor intensive and rate-limiting. We describe Garnett, a tool for rapidly annotating cell types in single-cell transcriptional profiling and single-cell chromatin accessibility datasets, based on an interpretable, hierarchical markup language of cell type-specific genes. Garnett successfully classifies cell types in tissue and whole organism datasets, as well as across species.
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647
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Aldinger KA, Timms AE, Thomson Z, Mirzaa GM, Bennett JT, Rosenberg AB, Roco CM, Hirano M, Abidi F, Haldipur P, Cheng CV, Collins S, Park K, Zeiger J, Overmann LM, Alkuraya FS, Biesecker LG, Braddock SR, Cathey S, Cho MT, Chung BHY, Everman DB, Zarate YA, Jones JR, Schwartz CE, Goldstein A, Hopkin RJ, Krantz ID, Ladda RL, Leppig KA, McGillivray BC, Sell S, Wusik K, Gleeson JG, Nickerson DA, Bamshad MJ, Gerrelli D, Lisgo SN, Seelig G, Ishak GE, Barkovich AJ, Curry CJ, Glass IA, Millen KJ, Doherty D, Dobyns WB. Redefining the Etiologic Landscape of Cerebellar Malformations. Am J Hum Genet 2019; 105:606-615. [PMID: 31474318 DOI: 10.1016/j.ajhg.2019.07.019] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 07/26/2019] [Indexed: 11/15/2022] Open
Abstract
Cerebellar malformations are diverse congenital anomalies frequently associated with developmental disability. Although genetic and prenatal non-genetic causes have been described, no systematic analysis has been performed. Here, we present a large-exome sequencing study of Dandy-Walker malformation (DWM) and cerebellar hypoplasia (CBLH). We performed exome sequencing in 282 individuals from 100 families with DWM or CBLH, and we established a molecular diagnosis in 36 of 100 families, with a significantly higher yield for CBLH (51%) than for DWM (16%). The 41 variants impact 27 neurodevelopmental-disorder-associated genes, thus demonstrating that CBLH and DWM are often features of monogenic neurodevelopmental disorders. Though only seven monogenic causes (19%) were identified in more than one individual, neuroimaging review of 131 additional individuals confirmed cerebellar abnormalities in 23 of 27 genetic disorders (85%). Prenatal risk factors were frequently found among individuals without a genetic diagnosis (30 of 64 individuals [47%]). Single-cell RNA sequencing of prenatal human cerebellar tissue revealed gene enrichment in neuronal and vascular cell types; this suggests that defective vasculogenesis may disrupt cerebellar development. Further, de novo gain-of-function variants in PDGFRB, a tyrosine kinase receptor essential for vascular progenitor signaling, were associated with CBLH, and this discovery links genetic and non-genetic etiologies. Our results suggest that genetic defects impact specific cerebellar cell types and implicate abnormal vascular development as a mechanism for cerebellar malformations. We also confirmed a major contribution for non-genetic prenatal factors in individuals with cerebellar abnormalities, substantially influencing diagnostic evaluation and counseling regarding recurrence risk and prognosis.
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Affiliation(s)
- Kimberly A Aldinger
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Andrew E Timms
- Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Zachary Thomson
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Ghayda M Mirzaa
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - James T Bennett
- Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - Alexander B Rosenberg
- Department of Electrical Engineering, University of Washington, Seattle, WA 98105, USA
| | - Charles M Roco
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Matthew Hirano
- Department of Electrical Engineering, University of Washington, Seattle, WA 98105, USA
| | - Fatima Abidi
- Greenwood Genetic Center, Greenwood, SC 29646, USA
| | - Parthiv Haldipur
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Chi V Cheng
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Sarah Collins
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Kaylee Park
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Jordan Zeiger
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Lynne M Overmann
- Institute of Genetic Medicine, Newcastle University, International Centre for life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Fowzan S Alkuraya
- Department of Genetics, King Faisal Specialist Hospital Research Center, Riyadh, 11211, Saudi Arabia
| | - Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892 USA
| | - Stephen R Braddock
- Department of Pediatrics, Saint Louis University School of Medicine, St. Louis, MO 63104, USA
| | - Sara Cathey
- Greenwood Genetic Center, Greenwood, SC 29646, USA
| | | | - Brian H Y Chung
- Department of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | | | - Yuri A Zarate
- Section of Genetics and Metabolism, University of Arkansas for Medical Sciences, Little Rock, AR, 72202, USA
| | | | | | - Amy Goldstein
- Mitochondrial Medicine Frontier Program, Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Robert J Hopkin
- Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Ian D Krantz
- The Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA; Division of Human Genetics and Molecular Biology, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104 USA
| | - Roger L Ladda
- Department of Pediatrics, Milton S Hershey Medical Center, Hershey, PA 17033, USA; Departments of Pathology, Milton S Hershey Medical Center, Hershey, PA 17033, USA
| | - Kathleen A Leppig
- Genetic Services, Kaiser Permanente Washington, Seattle, WA 98112, USA
| | - Barbara C McGillivray
- Department of Medical Genetics, Children's and Women's Health Centre of British Columbia, Vancouver, BC V6H 3N1, Canada
| | - Susan Sell
- Department of Pediatrics, Milton S Hershey Medical Center, Hershey, PA 17033, USA
| | - Katherine Wusik
- Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Joseph G Gleeson
- Department of Neurosciences, Howard Hughes Medical Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; University of Washington Center for Mendelian Genomics, Seattle, WA 98195, USA
| | - Michael J Bamshad
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; University of Washington Center for Mendelian Genomics, Seattle, WA 98195, USA
| | - Dianne Gerrelli
- University College London Institute of Child Health, London WC1N 1EH, UK
| | - Steven N Lisgo
- Institute of Genetic Medicine, Newcastle University, International Centre for life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Georg Seelig
- Department of Electrical Engineering, University of Washington, Seattle, WA 98105, USA; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Gisele E Ishak
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - A James Barkovich
- Departments of Radiology, Neurology, Pediatrics, and Neurosurgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Cynthia J Curry
- Genetic Medicine, Department of Pediatrics, University of California San Francisco, Fresno, CA, 93701, USA
| | - Ian A Glass
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - Kathleen J Millen
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - Dan Doherty
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - William B Dobyns
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Pediatrics, University of Washington, Seattle, WA 98105, USA; Department of Neurology, University of Washington, Seattle, WA 98105, USA.
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648
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Emerging Roles of Synapse Organizers in the Regulation of Critical Periods. Neural Plast 2019; 2019:1538137. [PMID: 31565044 PMCID: PMC6745111 DOI: 10.1155/2019/1538137] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 07/09/2019] [Accepted: 07/25/2019] [Indexed: 01/10/2023] Open
Abstract
Experience remodels cortical connectivity during developmental windows called critical periods. Experience-dependent regulation of synaptic strength during these periods establishes circuit functions that are stabilized as critical period plasticity wanes. These processes have been extensively studied in the developing visual cortex, where critical period opening and closure are orchestrated by the assembly, maturation, and strengthening of distinct synapse types. The synaptic specificity of these processes points towards the involvement of distinct molecular pathways. Attractive candidates are pre- and postsynaptic transmembrane proteins that form adhesive complexes across the synaptic cleft. These synapse-organizing proteins control synapse development and maintenance and modulate structural and functional properties of synapses. Recent evidence suggests that they have pivotal roles in the onset and closure of the critical period for vision. In this review, we describe roles of synapse-organizing adhesion molecules in the regulation of visual critical period plasticity and we discuss the potential they offer to restore circuit functions in amblyopia and other neurodevelopmental disorders.
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Vazquez J, Ong IM, Stanic AK. Single-cell technologies in reproductive immunology. Am J Reprod Immunol 2019; 82:e13157. [PMID: 31206899 PMCID: PMC6697222 DOI: 10.1111/aji.13157] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/06/2019] [Accepted: 06/07/2019] [Indexed: 11/29/2022] Open
Abstract
The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.
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Affiliation(s)
- Jessica Vazquez
- Division of Reproductive Sciences, University of Wisconsin-Madison, Madison, WI
| | - Irene M Ong
- Division of Reproductive Sciences, University of Wisconsin-Madison, Madison, WI
- Division of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI
| | - Aleksandar K. Stanic
- Division of Reproductive Sciences, University of Wisconsin-Madison, Madison, WI
- Division of Reproductive Endocrinology and Infertility, Departments of Obstetrics and Gynecology, University of Wisconsin-Madison, Madison, WI
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Mehmood S, Bilal M, Manzoor R, Iqbal H. Deciphering the adult brain development complexity by single-cell transcriptome analysis—a review. MATERIALS TODAY CHEMISTRY 2019. [DOI: 10.1016/j.mtchem.2019.04.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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