151
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Tirier SM, Park J, Preußer F, Amrhein L, Gu Z, Steiger S, Mallm JP, Krieger T, Waschow M, Eismann B, Gut M, Gut IG, Rippe K, Schlesner M, Theis F, Fuchs C, Ball CR, Glimm H, Eils R, Conrad C. Pheno-seq - linking visual features and gene expression in 3D cell culture systems. Sci Rep 2019; 9:12367. [PMID: 31451731 PMCID: PMC6710272 DOI: 10.1038/s41598-019-48771-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 08/12/2019] [Indexed: 12/22/2022] Open
Abstract
Patient-derived 3D cell culture systems are currently advancing cancer research since they potentiate the molecular analysis of tissue-like properties and drug response under well-defined conditions. However, our understanding of the relationship between the heterogeneity of morphological phenotypes and the underlying transcriptome is still limited. To address this issue, we here introduce "pheno-seq" to directly link visual features of 3D cell culture systems with profiling their transcriptome. As prototypic applications breast and colorectal cancer (CRC) spheroids were analyzed by pheno-seq. We identified characteristic gene expression signatures of epithelial-to-mesenchymal transition that are associated with invasive growth behavior of clonal breast cancer spheroids. Furthermore, we linked long-term proliferative capacity in a patient-derived model of CRC to a lowly abundant PROX1-positive cancer stem cell subtype. We anticipate that the ability to integrate transcriptome analysis and morphological patho-phenotypes of cancer cells will provide novel insight on the molecular origins of intratumor heterogeneity.
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Affiliation(s)
- Stephan M Tirier
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Chromatin Networks, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeongbin Park
- Digital Health Center, Berlin Institute of Health (BIH)/Charité-Universitätsmedizin Berlin, Berlin, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Friedrich Preußer
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Max Delbrück Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Amrhein
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Munich, Germany
| | - Zuguang Gu
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany
| | - Simon Steiger
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan-Philipp Mallm
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Chromatin Networks, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany
| | - Teresa Krieger
- Digital Health Center, Berlin Institute of Health (BIH)/Charité-Universitätsmedizin Berlin, Berlin, Germany
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marcel Waschow
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Björn Eismann
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marta Gut
- CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Ivo G Gut
- CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Karsten Rippe
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Chromatin Networks, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias Schlesner
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fabian Theis
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Munich, Germany
| | - Christiane Fuchs
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Munich, Germany
- Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
| | - Claudia R Ball
- Department of Translational Oncology, NCT Dresden, University Hospital, Carl Gustav Carus, Technische Universität Dresden, Dresden and DKFZ, Heidelberg, Germany
| | - Hanno Glimm
- Department of Translational Oncology, NCT Dresden, University Hospital, Carl Gustav Carus, Technische Universität Dresden, Dresden and DKFZ, Heidelberg, Germany
- German Cancer Consortium, Heidelberg, Germany
| | - Roland Eils
- Digital Health Center, Berlin Institute of Health (BIH)/Charité-Universitätsmedizin Berlin, Berlin, Germany
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany
- Health Data Science Unit, University Hospital Heidelberg, Heidelberg, Germany
| | - Christian Conrad
- Digital Health Center, Berlin Institute of Health (BIH)/Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany.
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany.
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152
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Vodiasova EA, Chelebieva ES, Kuleshova ON. The new technologies of high-throughput single-cell RNA sequencing. Vavilovskii Zhurnal Genet Selektsii 2019. [DOI: 10.18699/vj19.520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
A wealth of genome and transcriptome data obtained using new generation sequencing (NGS) technologies for whole organisms could not answer many questions in oncology, immunology, physiology, neurobiology, zoology and other fields of science and medicine. Since the cell is the basis for the living of all unicellular and multicellular organisms, it is necessary to study the biological processes at its level. This understanding gave impetus to the development of a new direction – the creation of technologies that allow working with individual cells (single-cell technology). The rapid development of not only instruments, but also various advanced protocols for working with single cells is due to the relevance of these studies in many fields of science and medicine. Studying the features of various stages of ontogenesis, identifying patterns of cell differentiation and subsequent tissue development, conducting genomic and transcriptome analyses in various areas of medicine (especially in demand in immunology and oncology), identifying cell types and states, patterns of biochemical and physiological processes using single cell technologies, allows the comprehensive research to be conducted at a new level. The first RNA-sequencing technologies of individual cell transcriptomes (scRNA-seq) captured no more than one hundred cells at a time, which was insufficient due to the detection of high cell heterogeneity, existence of the minor cell types (which were not detected by morphology) and complex regulatory pathways. The unique techniques for isolating, capturing and sequencing transcripts of tens of thousands of cells at a time are evolving now. However, new technologies have certain differences both at the sample preparation stage and during the bioinformatics analysis. In the paper we consider the most effective methods of multiple parallel scRNA-seq using the example of 10XGenomics, as well as the specifics of such an experiment, further bioinformatics analysis of the data, future outlook and applications of new high-performance technologies.
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Affiliation(s)
- E. A. Vodiasova
- A.O. Kovalevsky Institute of Biology of the Southern Seas, RAS
| | | | - O. N. Kuleshova
- A.O. Kovalevsky Institute of Biology of the Southern Seas, RAS
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153
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Kulkarni A, Anderson AG, Merullo DP, Konopka G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr Opin Biotechnol 2019; 58:129-136. [PMID: 30978643 PMCID: PMC6710112 DOI: 10.1016/j.copbio.2019.03.001] [Citation(s) in RCA: 174] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/13/2019] [Accepted: 03/01/2019] [Indexed: 12/23/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a promising approach to study the transcriptomes of individual cells in the brain and the central nervous system (CNS). This technology acts as a bridge between neuroscience, computational biology, and systems biology, enabling an unbiased and novel understanding of the cellular composition of the brain and CNS. Gene expression at the single cell resolution is often noisy, sparse, and high-dimensional, creating challenges for computational analysis of such data. In this review, we overview fundamental sample preparation and data analysis processes of scRNA-seq and provide a comparative perspective for analyzing and visualizing these data.
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Affiliation(s)
| | - Ashley G Anderson
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Devin P Merullo
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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154
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Abstract
Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.
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155
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scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 2019; 571:419-423. [DOI: 10.1038/s41586-019-1369-y] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 06/10/2019] [Indexed: 11/08/2022]
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156
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Bradley RA, Shireman J, McFalls C, Choi J, Canfield SG, Dong Y, Liu K, Lisota B, Jones JR, Petersen A, Bhattacharyya A, Palecek SP, Shusta EV, Kendziorski C, Zhang SC. Regionally specified human pluripotent stem cell-derived astrocytes exhibit different molecular signatures and functional properties. Development 2019; 146:dev.170910. [PMID: 31189664 DOI: 10.1242/dev.170910] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 06/03/2019] [Indexed: 01/10/2023]
Abstract
Astrocytes display diverse morphologies in different regions of the central nervous system. Whether astrocyte diversity is attributable to developmental processes and bears functional consequences, especially in humans, is unknown. RNA-seq of human pluripotent stem cell-derived regional astrocytes revealed distinct transcript profiles, suggesting differential functional properties. This was confirmed by differential calcium signaling as well as effects on neurite growth and blood-brain barrier formation. Distinct transcriptional profiles and functional properties of human astrocytes generated from regionally specified neural progenitors under the same conditions strongly implicate the developmental impact on astrocyte diversity. These findings provide a rationale for renewed examination of regional astrocytes and their role in the pathogenesis of psychiatric and neurological disorders.
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Affiliation(s)
- Robert A Bradley
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA.,Cellular and Molecular Biology Program, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Jack Shireman
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Caya McFalls
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Jeea Choi
- Department of Statistics, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Scott G Canfield
- Department of Chemical and Biological Engineering, University of Wisconsin - Madison, Madison, WI 53705, USA.,Department of Cellular and Integrative Physiology, School of Medicine, Indiana University - Terre Haute, IN 47885, USA
| | - Yi Dong
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Katie Liu
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Brianne Lisota
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Jeffery R Jones
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Andrew Petersen
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Anita Bhattacharyya
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Sean P Palecek
- Department of Chemical and Biological Engineering, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Eric V Shusta
- Department of Chemical and Biological Engineering, University of Wisconsin - Madison, Madison, WI 53705, USA
| | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI 53792, USA
| | - Su-Chun Zhang
- Department of Neuroscience, Waisman Center, University of Wisconsin - Madison, Madison, WI 53705, USA .,Cellular and Molecular Biology Program, University of Wisconsin - Madison, Madison, WI 53705, USA.,Department of Neuroscience, Department of Neurology, School of Medicine and Public Health, University of Wisconsin - Madison, Madison, WI 53705, USA.,Program in Neuroscience & Behavioral Disorders, Duke-NUS Medical School, Singapore 169857
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157
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Marques S, van Bruggen D, Castelo-Branco G. Single-Cell RNA Sequencing of Oligodendrocyte Lineage Cells from the Mouse Central Nervous System. Methods Mol Biol 2019; 1936:1-21. [PMID: 30820890 DOI: 10.1007/978-1-4939-9072-6_1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Single-cell RNA sequencing has emerged as a powerful technique for the identification of distinct cell states/populations in complex tissues. We have recently used this technology to investigate heterogeneity of cells of the oligodendrocyte lineage in the mouse central nervous system. In this chapter, we describe methods to perform single-cell RNA sequencing on this glial cell lineage, and discuss experimental and computational approaches to explore the potential and to tackle hurdles associated with this technology.
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Affiliation(s)
- Sueli Marques
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - David van Bruggen
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. .,Ming Wai Lau Centre for Reparative Medicine, Stockholm Node, Karolinska Institutet, Stockholm, Sweden.
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158
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Abstract
Integrating prior knowledge of pathway-level information can enhance power and facilitate interpretation of gene expression data analyses. Here, we provide a practical demonstration of the value of gene set or pathway enrichment testing and extend such techniques to identify and characterize transcriptional subpopulations from single-cell RNA-sequencing data using pathway and gene set overdispersion analysis (PAGODA).
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159
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A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 2019; 10:2832. [PMID: 31249312 PMCID: PMC6597610 DOI: 10.1038/s41467-019-10861-2] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 05/31/2019] [Indexed: 12/17/2022] Open
Abstract
Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples. Single-cell studies in solid tissues remain challenging and have benefited from the development of single-nuclei RNA sequencing strategies. Here Lake et al. apply single-nucleus RNA sequencing to human kidney tissues to provide a comprehensive molecular and cellular atlas of the human kidney, with potential implications for the understanding of kidney physiology and disease.
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160
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Tritschler S, Büttner M, Fischer DS, Lange M, Bergen V, Lickert H, Theis FJ. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 2019; 146. [DOI: 10.1242/dev.170506] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
ABSTRACT
Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.
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Affiliation(s)
- Sophie Tritschler
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85353 Freising, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - David S. Fischer
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85353 Freising, Germany
| | - Marius Lange
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Volker Bergen
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- German Center for Diabetes Research, 85764 Neuherberg, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
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161
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Soldatov R, Kaucka M, Kastriti ME, Petersen J, Chontorotzea T, Englmaier L, Akkuratova N, Yang Y, Häring M, Dyachuk V, Bock C, Farlik M, Piacentino ML, Boismoreau F, Hilscher MM, Yokota C, Qian X, Nilsson M, Bronner ME, Croci L, Hsiao WY, Guertin DA, Brunet JF, Consalez GG, Ernfors P, Fried K, Kharchenko PV, Adameyko I. Spatiotemporal structure of cell fate decisions in murine neural crest. Science 2019; 364:364/6444/eaas9536. [DOI: 10.1126/science.aas9536] [Citation(s) in RCA: 238] [Impact Index Per Article: 47.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 12/12/2018] [Accepted: 04/10/2019] [Indexed: 12/11/2022]
Abstract
Neural crest cells are embryonic progenitors that generate numerous cell types in vertebrates. With single-cell analysis, we show that mouse trunk neural crest cells become biased toward neuronal lineages when they delaminate from the neural tube, whereas cranial neural crest cells acquire ectomesenchyme potential dependent on activation of the transcription factor Twist1. The choices that neural crest cells make to become sensory, glial, autonomic, or mesenchymal cells can be formalized as a series of sequential binary decisions. Each branch of the decision tree involves initial coactivation of bipotential properties followed by gradual shifts toward commitment. Competing fate programs are coactivated before cells acquire fate-specific phenotypic traits. Determination of a specific fate is achieved by increased synchronization of relevant programs and concurrent repression of competing fate programs.
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162
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Menon V. Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data. Brief Funct Genomics 2019; 17:240-245. [PMID: 29236955 DOI: 10.1093/bfgp/elx044] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies aiming to identify transcriptomic cell types in various biological systems. There are multiple experimental approaches to isolate and profile single cells, which provide different levels of cellular and tissue coverage. In addition, multiple computational strategies have been proposed to identify putative cell types from single-cell data. From a data generation perspective, recent single-cell studies can be classified into two groups: those that distribute reads shallowly over large numbers of cells and those that distribute reads more deeply over a smaller cell population. Although there are advantages to both approaches in terms of cellular and tissue coverage, it is unclear whether different computational cell type identification methods are better suited to one or the other experimental paradigm. This study reviews three cell type clustering algorithms, each representing one of three broad approaches, and finds that PCA-based algorithms appear most suited to low read depth data sets, whereas gene clustering-based and biclustering algorithms perform better on high read depth data sets. In addition, highly related cell classes are better distinguished by higher-depth data, given the same total number of reads; however, simultaneous discovery of distinct and similar types is better served by lower-depth, higher cell number data. Overall, this study suggests that the depth of profiling should be determined by initial assumptions about the diversity of cells in the population, and that the selection of clustering algorithm(s) subsequently based on the depth of profiling will allow for better identification of putative transcriptomic cell types.
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Affiliation(s)
- Vilas Menon
- Howard Hughes Medical Institute, Janelia Research Campus, 19700 Helix Drive, Ashburn, VA, USA
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163
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Abstract
Precision medicine is emerging as a cornerstone of future cancer care with the objective of providing targeted therapies based on the molecular phenotype of each individual patient. Traditional bulk-level molecular phenotyping of tumours leads to significant information loss, as the molecular profile represents an average phenotype over large numbers of cells, while cancer is a disease with inherent intra-tumour heterogeneity at the cellular level caused by several factors, including clonal evolution, tissue hierarchies, rare cells and dynamic cell states. Single-cell sequencing provides means to characterize heterogeneity in a large population of cells and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and probability of treatment response. Single-cell sequencing methods are now reliable enough to be used in many research laboratories, and we are starting to see applications of these technologies for characterization of human primary cancer cells. In this review, we provide an overview of studies that have applied single-cell sequencing to characterize human cancers at the single-cell level, and we discuss some of the current challenges in the field.
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Affiliation(s)
- Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, Stockholm, Sweden
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164
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Ji F, Sadreyev RI. Single‐Cell RNA‐seq: Introduction to Bioinformatics Analysis. ACTA ACUST UNITED AC 2019; 127:e92. [DOI: 10.1002/cpmb.92] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Fei Ji
- Department of Molecular BiologyMassachusetts General Hospital Boston Massachusetts
- Department of GeneticsHarvard Medical School Boston Massachusetts
| | - Ruslan I. Sadreyev
- Department of Molecular BiologyMassachusetts General Hospital Boston Massachusetts
- Department of PathologyMassachusetts General Hospital and Harvard Medical School Boston Massachusetts
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165
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Koh W, Wu A, Penland L, Treutlein B, Neff NF, Mantalas GL, Blumenfeld YJ, El-Sayed YY, Stevenson DK, Shaw GM, Quake SR. Single Cell Transcriptomes Derived from Human Cervical and Uterine Tissue during Pregnancy. ACTA ACUST UNITED AC 2019; 3:e1800336. [PMID: 32648692 DOI: 10.1002/adbi.201800336] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 03/16/2019] [Indexed: 12/20/2022]
Abstract
This work presents the workflow for generating single cell transcriptomes derived from primary human uterine and cervical tissue obtained during planned cesarean hysterectomies. In total, a catalogue of 310 single cell transcriptomes are obtained, cell types present in these biopsies are inferred, and specific genes defining each of the cellular types present in the tissue are identified. Further validation of the inferred cell identity is also demonstrated via meta-analysis of independent repositories in literature generated by bulk sequenced data of fluorescence-activated cell sorting sorted cells.
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Affiliation(s)
- Winston Koh
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.,Molecular Engineering Lab, Agency of Science, Technology & Research, 61 Biopolis Drive, #03-13 Proteos, Singapore, 138673, Singapore
| | - Angela Wu
- Division of Life Science and Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Lolita Penland
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Barbara Treutlein
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Norma F Neff
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Gary L Mantalas
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Yair J Blumenfeld
- Division of Maternal-Fetal Medicine and Obstetrics, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Yasser Y El-Sayed
- Division of Maternal-Fetal Medicine and Obstetrics, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - David K Stevenson
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Gary M Shaw
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Stephen R Quake
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
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166
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Tracing the origin of adult intestinal stem cells. Nature 2019; 570:107-111. [PMID: 31092921 DOI: 10.1038/s41586-019-1212-5] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Accepted: 04/15/2019] [Indexed: 12/14/2022]
Abstract
Adult intestinal stem cells are located at the bottom of crypts of Lieberkühn, where they express markers such as LGR51,2 and fuel the constant replenishment of the intestinal epithelium1. Although fetal LGR5-expressing cells can give rise to adult intestinal stem cells3,4, it remains unclear whether this population in the patterned epithelium represents unique intestinal stem-cell precursors. Here we show, using unbiased quantitative lineage-tracing approaches, biophysical modelling and intestinal transplantation, that all cells of the mouse intestinal epithelium-irrespective of their location and pattern of LGR5 expression in the fetal gut tube-contribute actively to the adult intestinal stem cell pool. Using 3D imaging, we find that during fetal development the villus undergoes gross remodelling and fission. This brings epithelial cells from the non-proliferative villus into the proliferative intervillus region, which enables them to contribute to the adult stem-cell niche. Our results demonstrate that large-scale remodelling of the intestinal wall and cell-fate specification are closely linked. Moreover, these findings provide a direct link between the observed plasticity and cellular reprogramming of differentiating cells in adult tissues following damage5-9, revealing that stem-cell identity is an induced rather than a hardwired property.
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167
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Velte EK, Niedenberger BA, Serra ND, Singh A, Roa-DeLaCruz L, Hermann BP, Geyer CB. Differential RA responsiveness directs formation of functionally distinct spermatogonial populations at the initiation of spermatogenesis in the mouse. Development 2019; 146:dev.173088. [PMID: 31023878 DOI: 10.1242/dev.173088] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 04/16/2019] [Indexed: 12/22/2022]
Abstract
In the mammalian testis, sustained spermatogenesis relies on spermatogonial stem cells (SSCs); their progeny either remain as stem cells (self-renewal) or proliferate and differentiate to enter meiosis in response to retinoic acid (RA). Here, we sought to uncover elusive mechanisms regulating a key switch fundamental to spermatogonial fate: the capacity of spermatogonia to respond to RA. Using the developing mouse testis as a model, we found that spermatogonia and precursor prospermatogonia exhibit a heterogeneous capacity to respond to RA with at least two underlying causes. First, progenitor spermatogonia are prevented from responding to RA by catabolic activity of cytochrome P450 family 26 enzymes. Second, a smaller subset of undifferentiated spermatogonia enriched for SSCs exhibit catabolism-independent RA insensitivity. Moreover, for the first time, we observed that precursor prospermatogonia are heterogeneous and comprise subpopulations that exhibit the same differential RA responsiveness found in neonatal spermatogonia. We propose a novel model by which mammalian prospermatogonial and spermatogonial fates are regulated by their intrinsic capacity to respond (or not) to the differentiation signal provided by RA before, and concurrent with, the initiation of spermatogenesis.
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Affiliation(s)
- Ellen K Velte
- Departments of Anatomy and Cell Biology, Brody School of Medicine, East Carolina Diabetes and Obesity Institute, East Carolina University, Greenville, NC 27834, USA
| | - Bryan A Niedenberger
- Departments of Anatomy and Cell Biology, Brody School of Medicine, East Carolina Diabetes and Obesity Institute, East Carolina University, Greenville, NC 27834, USA
| | - Nicholas D Serra
- Departments of Anatomy and Cell Biology, Brody School of Medicine, East Carolina Diabetes and Obesity Institute, East Carolina University, Greenville, NC 27834, USA
| | - Anukriti Singh
- Department of Biology, University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Lorena Roa-DeLaCruz
- Department of Biology, University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Brian P Hermann
- Department of Biology, University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Christopher B Geyer
- Departments of Anatomy and Cell Biology, Brody School of Medicine, East Carolina Diabetes and Obesity Institute, East Carolina University, Greenville, NC 27834, USA .,East Carolina Diabetes and Obesity Institute, East Carolina University, Greenville, NC 27834, USA
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168
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Allodi I, Nijssen J, Benitez JA, Schweingruber C, Fuchs A, Bonvicini G, Cao M, Kiehn O, Hedlund E. Modeling Motor Neuron Resilience in ALS Using Stem Cells. Stem Cell Reports 2019; 12:1329-1341. [PMID: 31080111 PMCID: PMC6565614 DOI: 10.1016/j.stemcr.2019.04.009] [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: 08/16/2018] [Revised: 04/05/2019] [Accepted: 04/08/2019] [Indexed: 12/17/2022] Open
Abstract
Oculomotor neurons, which regulate eye movement, are resilient to degeneration in the lethal motor neuron disease amyotrophic lateral sclerosis (ALS). It would be highly advantageous if motor neuron resilience could be modeled in vitro. Toward this goal, we generated a high proportion of oculomotor neurons from mouse embryonic stem cells through temporal overexpression of PHOX2A in neuronal progenitors. We demonstrate, using electrophysiology, immunocytochemistry, and RNA sequencing, that in vitro-generated neurons are bona fide oculomotor neurons based on their cellular properties and similarity to their in vivo counterpart in rodent and man. We also show that in vitro-generated oculomotor neurons display a robust activation of survival-promoting Akt signaling and are more resilient to the ALS-like toxicity of kainic acid than spinal motor neurons. Thus, we can generate bona fide oculomotor neurons in vitro that display a resilience similar to that seen in vivo. Bona fide oculomotor neurons can be derived from stem cells by PHOX2A overexpression In vitro- and in vivo-generated oculomotor neurons are transcriptionally similar Stem cell-derived oculomotor neurons display a robust activation of Akt signaling In vitro-generated oculomotor neurons are relatively resilient to ALS-like toxicity
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Affiliation(s)
- Ilary Allodi
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jik Nijssen
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Andrea Fuchs
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Gillian Bonvicini
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ming Cao
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ole Kiehn
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | - Eva Hedlund
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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169
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Single-Cell Transcriptomics Uncovers Glial Progenitor Diversity and Cell Fate Determinants during Development and Gliomagenesis. Cell Stem Cell 2019; 24:707-723.e8. [PMID: 30982771 DOI: 10.1016/j.stem.2019.03.006] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 11/30/2018] [Accepted: 03/05/2019] [Indexed: 11/20/2022]
Abstract
The identity and degree of heterogeneity of glial progenitors and their contributions to brain tumor malignancy remain elusive. By applying lineage-targeted single-cell transcriptomics, we uncover an unanticipated diversity of glial progenitor pools with unique molecular identities in developing brain. Our analysis identifies distinct transitional intermediate states and their divergent developmental trajectories in astroglial and oligodendroglial lineages. Moreover, intersectional analysis uncovers analogous intermediate progenitors during brain tumorigenesis, wherein oligodendrocyte-progenitor intermediates are abundant, hyper-proliferative, and progressively reprogrammed toward a stem-like state susceptible to further malignant transformation. Similar actively cycling intermediate progenitors are prominent components in human gliomas with distinct driver mutations. We further unveil lineage-driving networks underlying glial fate specification and identify Zfp36l1 as necessary for oligodendrocyte-astrocyte lineage transition and glioma growth. Together, our results resolve the dynamic repertoire of common and divergent glial progenitors during development and tumorigenesis and highlight Zfp36l1 as a molecular nexus for balancing glial cell-fate decision and controlling gliomagenesis.
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170
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Abstract
Background:
The recently developed single-cell RNA sequencing (scRNA-seq) has
attracted a great amount of attention due to its capability to interrogate expression of individual
cells, which is superior to traditional bulk cell sequencing that can only measure mean gene
expression of a population of cells. scRNA-seq has been successfully applied in finding new cell
subtypes. New computational challenges exist in the analysis of scRNA-seq data.
Objective:
We provide an overview of the features of different similarity calculation and clustering
methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We
would also like to show that feature selection methods are important to improve clustering
performance.
Results:
We first described similarity measurement methods, followed by reviewing some new
clustering methods, as well as their algorithmic details. This analysis revealed several new
questions, including how to automatically estimate the number of clustering categories, how to
discover novel subpopulation, and how to search for new marker genes by using feature selection
methods.
Conclusion:
Without prior knowledge about the number of cell types, clustering or semisupervised
learning methods are important tools for exploratory analysis of scRNA-seq data.</P>
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Affiliation(s)
- Xiaoshu Zhu
- School of Computer Science and Engineering, Central South University, 410083, Changsha, Hunan, China
| | - Hong-Dong Li
- School of Computer Science and Engineering, Central South University, 410083, Changsha, Hunan, China
| | - Lilu Guo
- School of Computer Science and Engineering, Yulin Normal University, 537000, Yulin, Guangxi, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, 410083, Changsha, Hunan, China
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171
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Chen G, Ning B, Shi T. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front Genet 2019; 10:317. [PMID: 31024627 PMCID: PMC6460256 DOI: 10.3389/fgene.2019.00317] [Citation(s) in RCA: 495] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 03/21/2019] [Indexed: 12/15/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.
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Affiliation(s)
- Geng Chen
- Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China
| | - Baitang Ning
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR, United States
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China
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172
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Lukoseviciute M, Gavriouchkina D, Williams RM, Hochgreb-Hagele T, Senanayake U, Chong-Morrison V, Thongjuea S, Repapi E, Mead A, Sauka-Spengler T. From Pioneer to Repressor: Bimodal foxd3 Activity Dynamically Remodels Neural Crest Regulatory Landscape In Vivo. Dev Cell 2019; 47:608-628.e6. [PMID: 30513303 PMCID: PMC6286384 DOI: 10.1016/j.devcel.2018.11.009] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 08/15/2018] [Accepted: 10/31/2018] [Indexed: 02/06/2023]
Abstract
The neural crest (NC) is a transient embryonic stem cell-like population characterized by its multipotency and broad developmental potential. Here, we perform NC-specific transcriptional and epigenomic profiling of foxd3-mutant cells in vivo to define the gene regulatory circuits controlling NC specification. Together with global binding analysis obtained by foxd3 biotin-ChIP and single cell profiles of foxd3-expressing premigratory NC, our analysis shows that, during early steps of NC formation, foxd3 acts globally as a pioneer factor to prime the onset of genes regulating NC specification and migration by re-arranging the chromatin landscape, opening cis-regulatory elements and reshuffling nucleosomes. Strikingly, foxd3 then gradually switches from an activator to its well-described role as a transcriptional repressor and potentially uses differential partners for each role. Taken together, these results demonstrate that foxd3 acts bimodally in the neural crest as a switch from “permissive” to “repressive” nucleosome and chromatin organization to maintain multipotency and define cell fates. FoxD3 primes neural crest specification by modulating distal enhancers FoxD3 represses a number of neural crest migration and differentiation genes In neural crest, FoxD3 acts to switch chromatin from “permissive” to “repressive” Distinctive gene regulatory mechanisms underlie the bimodal action of FoxD3
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Affiliation(s)
- Martyna Lukoseviciute
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Daria Gavriouchkina
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Ruth M Williams
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Tatiana Hochgreb-Hagele
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Upeka Senanayake
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Vanessa Chong-Morrison
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Supat Thongjuea
- Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Emmanouela Repapi
- MRC WIMM Centre for Computational Biology Research Group, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Adam Mead
- Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Tatjana Sauka-Spengler
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK.
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173
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Newton PT, Li L, Zhou B, Schweingruber C, Hovorakova M, Xie M, Sun X, Sandhow L, Artemov AV, Ivashkin E, Suter S, Dyachuk V, El Shahawy M, Gritli-Linde A, Bouderlique T, Petersen J, Mollbrink A, Lundeberg J, Enikolopov G, Qian H, Fried K, Kasper M, Hedlund E, Adameyko I, Sävendahl L, Chagin AS. A radical switch in clonality reveals a stem cell niche in the epiphyseal growth plate. Nature 2019; 567:234-238. [PMID: 30814736 DOI: 10.1038/s41586-019-0989-6] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 02/01/2019] [Indexed: 12/22/2022]
Abstract
Longitudinal bone growth in children is sustained by growth plates, narrow discs of cartilage that provide a continuous supply of chondrocytes for endochondral ossification1. However, it remains unknown how this supply is maintained throughout childhood growth. Chondroprogenitors in the resting zone are thought to be gradually consumed as they supply cells for longitudinal growth1,2, but this model has never been proved. Here, using clonal genetic tracing with multicolour reporters and functional perturbations, we demonstrate that longitudinal growth during the fetal and neonatal periods involves depletion of chondroprogenitors, whereas later in life, coinciding with the formation of the secondary ossification centre, chondroprogenitors acquire the capacity for self-renewal, resulting in the formation of large, stable monoclonal columns of chondrocytes. Simultaneously, chondroprogenitors begin to express stem cell markers and undergo symmetric cell division. Regulation of the pool of self-renewing progenitors involves the hedgehog and mammalian target of rapamycin complex 1 (mTORC1) signalling pathways. Our findings indicate that a stem cell niche develops postnatally in the epiphyseal growth plate, which provides a continuous supply of chondrocytes over a prolonged period.
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Affiliation(s)
- Phillip T Newton
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden. .,Department of Women's and Children's Health, Karolinska Institutet and Pediatric Endocrinology Unit, Karolinska University Hospital, Stockholm, Sweden.
| | - Lei Li
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Baoyi Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | | | - Maria Hovorakova
- Department of Developmental Biology, Institute of Experimental Medicine, The Czech Academy of Sciences, Prague, Czech Republic
| | - Meng Xie
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Xiaoyan Sun
- Department of Biosciences and Nutrition and Center for Innovative Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Lakshmi Sandhow
- Center for Hematology and Regenerative Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Artem V Artemov
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.,Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Evgeny Ivashkin
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Simon Suter
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Vyacheslav Dyachuk
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.,National Scientific Center of Marine Biology, Far Eastern Branch, Russian Academy of Sciences, Vladivostok, Russian Federation
| | - Maha El Shahawy
- Department of Oral Biochemistry, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Amel Gritli-Linde
- Department of Oral Biochemistry, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Thibault Bouderlique
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Julian Petersen
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.,Department of Molecular Neurosciences, Center for Brain Research, Medical University Vienna, Vienna, Austria
| | - Annelie Mollbrink
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Grigori Enikolopov
- Center for Developmental Genetics and Department of Anesthesiology, Stony Brook University, Stony Brook, NY, USA
| | - Hong Qian
- Center for Hematology and Regenerative Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Kaj Fried
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Maria Kasper
- Department of Biosciences and Nutrition and Center for Innovative Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Eva Hedlund
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Igor Adameyko
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.,Department of Molecular Neurosciences, Center for Brain Research, Medical University Vienna, Vienna, Austria
| | - Lars Sävendahl
- Department of Women's and Children's Health, Karolinska Institutet and Pediatric Endocrinology Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Andrei S Chagin
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden. .,Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow, Russian Federation.
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174
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Shah PT, Stratton JA, Stykel MG, Abbasi S, Sharma S, Mayr KA, Koblinger K, Whelan PJ, Biernaskie J. Single-Cell Transcriptomics and Fate Mapping of Ependymal Cells Reveals an Absence of Neural Stem Cell Function. Cell 2019; 173:1045-1057.e9. [PMID: 29727663 DOI: 10.1016/j.cell.2018.03.063] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 12/13/2017] [Accepted: 03/23/2018] [Indexed: 12/15/2022]
Abstract
Ependymal cells are multi-ciliated cells that form the brain's ventricular epithelium and a niche for neural stem cells (NSCs) in the ventricular-subventricular zone (V-SVZ). In addition, ependymal cells are suggested to be latent NSCs with a capacity to acquire neurogenic function. This remains highly controversial due to a lack of prospective in vivo labeling techniques that can effectively distinguish ependymal cells from neighboring V-SVZ NSCs. We describe a transgenic system that allows for targeted labeling of ependymal cells within the V-SVZ. Single-cell RNA-seq revealed that ependymal cells are enriched for cilia-related genes and share several stem-cell-associated genes with neural stem or progenitors. Under in vivo and in vitro neural-stem- or progenitor-stimulating environments, ependymal cells failed to demonstrate any suggestion of latent neural-stem-cell function. These findings suggest remarkable stability of ependymal cell function and provide fundamental insights into the molecular signature of the V-SVZ niche.
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Affiliation(s)
- Prajay T Shah
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Jo A Stratton
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Morgan Gail Stykel
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Sepideh Abbasi
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Sandeep Sharma
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Kyle A Mayr
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Kathrin Koblinger
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Patrick J Whelan
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Jeff Biernaskie
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
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175
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Bang S, Son S, Kim S, Shin H. Disease Pathway Cut for Multi-Target drugs. BMC Bioinformatics 2019; 20:74. [PMID: 30760209 PMCID: PMC6483058 DOI: 10.1186/s12859-019-2638-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 01/18/2019] [Indexed: 02/07/2023] Open
Abstract
Background Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression. Methods In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway. If the pathway network is completely disconnected, development of disease will not further go on. The genes corresponding to the end points of the cutting edges are identified as candidate target genes for a multi-target drug. Results and conclusions The proposed method was applied to 10 disease pathways. In total, thirty candidate genes were suggested. The result was validated with gene set enrichment analysis software, PubMed literature review and de facto drug targets.
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Affiliation(s)
- Sunjoo Bang
- Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea
| | - Sangjoon Son
- Department of Psychiatry, Ajou University School of Medicine, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea
| | - Sooyoung Kim
- Department of Surgery, Thyroid Cancer Center, Gangnam Severance Hospital, Institute of Refractory Thyroid Cancer, Yonsei University College of Medicine, 211, Eonju-ro, Gangnam-gu, Seoul, Republic of Korea
| | - Hyunjung Shin
- Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.
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176
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Blecher-Gonen R, Bost P, Hilligan KL, David E, Salame TM, Roussel E, Connor LM, Mayer JU, Bahar Halpern K, Tóth B, Itzkovitz S, Schwikowski B, Ronchese F, Amit I. Single-Cell Analysis of Diverse Pathogen Responses Defines a Molecular Roadmap for Generating Antigen-Specific Immunity. Cell Syst 2019; 8:109-121.e6. [PMID: 30772378 DOI: 10.1016/j.cels.2019.01.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/20/2018] [Accepted: 01/14/2019] [Indexed: 12/11/2022]
Abstract
The immune system generates pathogen-tailored responses. The precise innate immune cell types and pathways that direct robust adaptive immune responses have not been fully characterized. By using fluorescent pathogens combined with massively parallel single-cell RNA-seq, we comprehensively characterized the initial 48 h of the innate immune response to diverse pathogens. We found that across all pathogens tested, most of the lymph node cell types and states showed little pathogen specificity. In contrast, the rare antigen-positive cells displayed pathogen-specific transcriptional programs as early as 24 h after immunization. In addition, mycobacteria activated a specific NK-driven IFNγ response. Depletion of NK cells and IFNγ showed that IFNγ initiated a monocyte-specific signaling cascade, leading to the production of major chemokines and cytokines that promote Th1 development. Our systems immunology approach sheds light on early events in innate immune responses and may help further development of safe and efficient vaccines.
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Affiliation(s)
| | - Pierre Bost
- Department of Immunology, Weizmann Institute of Science, Rehovot 76100, Israel; Systems Biology Group, Center for Bioinformatics, Biostatistics, and Integrative Biology (C3BI) and USR 3756, Institut Pasteur CNRS, Paris 75015, France; Sorbonne Universite, Complexite du vivant, F-75005 Paris, France
| | - Kerry L Hilligan
- Malaghan Institute of Medical Research, Wellington 6201, New Zealand; Department of Pathology and Molecular Medicine, University of Otago Wellington, Wellington 6201, New Zealand
| | - Eyal David
- Department of Immunology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Tomer Meir Salame
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Elsa Roussel
- Malaghan Institute of Medical Research, Wellington 6201, New Zealand
| | - Lisa M Connor
- Malaghan Institute of Medical Research, Wellington 6201, New Zealand
| | - Johannes U Mayer
- Malaghan Institute of Medical Research, Wellington 6201, New Zealand
| | - Keren Bahar Halpern
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Beáta Tóth
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Shalev Itzkovitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Benno Schwikowski
- Systems Biology Group, Center for Bioinformatics, Biostatistics, and Integrative Biology (C3BI) and USR 3756, Institut Pasteur CNRS, Paris 75015, France
| | - Franca Ronchese
- Malaghan Institute of Medical Research, Wellington 6201, New Zealand.
| | - Ido Amit
- Department of Immunology, Weizmann Institute of Science, Rehovot 76100, Israel.
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177
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Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds. Nat Commun 2019; 10:650. [PMID: 30737373 PMCID: PMC6368572 DOI: 10.1038/s41467-018-08247-x] [Citation(s) in RCA: 310] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Accepted: 12/19/2018] [Indexed: 01/11/2023] Open
Abstract
During wound healing in adult mouse skin, hair follicles and then adipocytes regenerate. Adipocytes regenerate from myofibroblasts, a specialized contractile wound fibroblast. Here we study wound fibroblast diversity using single-cell RNA-sequencing. On analysis, wound fibroblasts group into twelve clusters. Pseudotime and RNA velocity analyses reveal that some clusters likely represent consecutive differentiation states toward a contractile phenotype, while others appear to represent distinct fibroblast lineages. One subset of fibroblasts expresses hematopoietic markers, suggesting their myeloid origin. We validate this finding using single-cell western blot and single-cell RNA-sequencing on genetically labeled myofibroblasts. Using bone marrow transplantation and Cre recombinase-based lineage tracing experiments, we rule out cell fusion events and confirm that hematopoietic lineage cells give rise to a subset of myofibroblasts and rare regenerated adipocytes. In conclusion, our study reveals that wounding induces a high degree of heterogeneity among fibroblasts and recruits highly plastic myeloid cells that contribute to adipocyte regeneration. The diversity of fibroblasts contributing to wound healing is unclear. Here, the authors use single-cell RNA-sequencing to identify heterogeneity among murine fibroblasts in the wound and find that recruited myeloid cells contribute to adipocyte regeneration during healing.
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178
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Singer M, Anderson AC. Revolutionizing Cancer Immunology: The Power of Next-Generation Sequencing Technologies. Cancer Immunol Res 2019; 7:168-173. [PMID: 30709909 PMCID: PMC6366855 DOI: 10.1158/2326-6066.cir-18-0281] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
It has long been appreciated that tumors are diverse, varying in mutational status, composition of cellular infiltrate, and organizational architecture. For the most part, the information embedded in this diversity has gone untapped due to the limited resolution and dimensionality of assays for analyzing nucleic acid expression in cells. The advent of high-throughput, next-generation sequencing (NGS) technologies that measure nucleic acids, particularly at the single-cell level, is fueling the characterization of the many components that comprise the tumor microenvironment (TME), with a strong focus on immune composition. Understanding the immune and nonimmune components of the TME, how they interact, and how this shapes their functional properties requires the development of novel computational methods and, eventually, the application of systems-based approaches. The continued development and application of NGS technologies holds great promise for accelerating discovery in the cancer immunology field.
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Affiliation(s)
- Meromit Singer
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ana C Anderson
- Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, Massachusetts.
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179
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Differentiation and maturation of oligodendrocytes in human three-dimensional neural cultures. Nat Neurosci 2019; 22:484-491. [PMID: 30692691 PMCID: PMC6788758 DOI: 10.1038/s41593-018-0316-9] [Citation(s) in RCA: 207] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 12/05/2018] [Indexed: 02/07/2023]
Abstract
Investigating human oligodendrogenesis and the interaction of oligodendrocytes with neurons and astrocytes would accelerate our understanding of the mechanisms underlying white matter disorders. However, this is challenging due to limited accessibility of functional human brain tissue. Here, we developed a novel differentiation method of human induced pluripotent stem cells (hiPS cells) to generate three-dimensional (3D) neural spheroids that contain oligodendrocytes as well as neurons and astrocytes, called human oligodendrocyte spheroids (hOLS). We demonstrate that oligodendrocyte-lineage cells derived in hOLS transition through developmental stages similar to primary human oligodendrocytes and that the migration of oligodendrocyte-lineage cells and their susceptibility to lysolecithin exposure can be captured by live imaging. Moreover, their morphology changes as they mature over time in vitro and start myelinating neurons. We anticipate that this method can be used to study oligodendrocyte development, myelination, and interactions with other major cell types in the central nervous system.
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180
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Chan TE, Stumpf MPH, Babtie AC. Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions. Methods Mol Biol 2019; 1975:211-238. [PMID: 31062312 DOI: 10.1007/978-1-4939-9224-9_10] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)-and the associated software package NetworkInference.jl-to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.
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Affiliation(s)
- Thalia E Chan
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, UK
| | - Michael P H Stumpf
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, UK
| | - Ann C Babtie
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, UK.
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181
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Kashima Y, Suzuki A, Suzuki Y. An Informative Approach to Single-Cell Sequencing Analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1129:81-96. [PMID: 30968362 DOI: 10.1007/978-981-13-6037-4_6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Recent advances in sequencing technologies enable us to obtain genome, epigenome and transcriptome data in individual cells. In this review, we describe various platforms for single-cell sequencing analysis across multiple layers. We mainly introduce an automated single-cell RNA-seq platform, the Chromium Single Cell 3' RNA-seq system, and its technical features and compare it with other single-cell RNA-seq systems. We also describe computational methods for analyzing large, complex single-cell datasets. Due to the insufficient depth of single-cell RNA-seq data, resulting in a critical lack of transcriptome information for low-expressed genes, it is occasionally difficult to interpret the data as is. To overcome the analytical problems for such sparse datasets, there are many bioinformatics reports that provide informative approaches, including imputation, correction of batch effects, dimensional reduction and clustering.
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Affiliation(s)
- Yukie Kashima
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Ayako Suzuki
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
| | - Yutaka Suzuki
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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182
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Wu Y, Tamayo P, Zhang K. Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding. Cell Syst 2018; 7:656-666.e4. [PMID: 30528274 PMCID: PMC6311449 DOI: 10.1016/j.cels.2018.10.015] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 08/15/2018] [Accepted: 10/29/2018] [Indexed: 01/19/2023]
Abstract
High-throughput single-cell gene expression profiling has enabled the definition of new cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and a popular method is t-stochastic neighbor embedding (t-SNE), which visualizes local patterns well but distorts global structure, such as distances between clusters. We developed similarity weighted nonnegative embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states on the visualization alongside the cells and maintains fidelity when visualizing local and global structure for both developmental trajectories and discrete cell types. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors; embeds the cells, genes, and factors in a 2D visualization; and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single-cell RNA-seq data from hematopoietic progenitors and human brain cells. SWNE is available as an R package at github.com/yanwu2014/swne.
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Affiliation(s)
- Yan Wu
- Department of Bioengineering, University of California, San Diego, San Diego, CA, USA
| | - Pablo Tamayo
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA; School of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, San Diego, CA, USA.
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183
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Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods 2018; 15:1053-1058. [PMID: 30504886 PMCID: PMC6289068 DOI: 10.1038/s41592-018-0229-2] [Citation(s) in RCA: 837] [Impact Index Per Article: 139.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 10/26/2018] [Indexed: 12/29/2022]
Abstract
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task.
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Affiliation(s)
- Romain Lopez
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Jeffrey Regier
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Michael B Cole
- Department of Physics, University of California, Berkeley, Berkeley, CA, USA
| | - Michael I Jordan
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
| | - Nir Yosef
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA.
- Chan Zuckerberg BioHub, San Francisco, CA, USA.
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184
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Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nat Commun 2018; 9:4931. [PMID: 30467425 PMCID: PMC6250721 DOI: 10.1038/s41467-018-07261-3] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 10/17/2018] [Indexed: 12/30/2022] Open
Abstract
Chemo-resistance is one of the major causes of cancer-related deaths. Here we used single-cell transcriptomics to investigate divergent modes of chemo-resistance in tumor cells. We observed that higher degree of phenotypic intra-tumor heterogeneity (ITH) favors selection of pre-existing drug-resistant cells, whereas phenotypically homogeneous cells engage covert epigenetic mechanisms to trans-differentiate under drug-selection. This adaptation was driven by selection-induced gain of H3K27ac marks on bivalently poised resistance-associated chromatin, and therefore not expressed in the treatment-naïve setting. Mechanistic interrogation of this phenomenon revealed that drug-induced adaptation was acquired upon the loss of stem factor SOX2, and a concomitant gain of SOX9. Strikingly we observed an enrichment of SOX9 at drug-induced H3K27ac sites, suggesting that tumor evolution could be driven by stem cell-switch-mediated epigenetic plasticity. Importantly, JQ1 mediated inhibition of BRD4 could reverse drug-induced adaptation. These results provide mechanistic insights into the modes of therapy-induced cellular plasticity and underscore the use of epigenetic inhibitors in targeting tumor evolution. Drug resistance is one of the major causes of cancer-related deaths. Here, the authors using single cell RNA-seq of oral squamous cell carcinoma patient samples pre- and post-cisplatin treatment show that phenotypically homogenous cell populations display cell state plasticity, with poised chromatin marks at mesenchymal genes in epithelial cells, and that the loss of stem factor Sox2 but gain of Sox9 expression (with de novo gain of H3K27ac sites) is associated with drug-induced adaptation.
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185
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Lafzi A, Moutinho C, Picelli S, Heyn H. Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies. Nat Protoc 2018; 13:2742-2757. [DOI: 10.1038/s41596-018-0073-y] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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186
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Chen M, Zhou X. VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies. Genome Biol 2018; 19:196. [PMID: 30419955 PMCID: PMC6233584 DOI: 10.1186/s13059-018-1575-1] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 10/30/2018] [Indexed: 11/27/2022] Open
Abstract
We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.
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Affiliation(s)
- Mengjie Chen
- Department of Medicine, University of Chicago, Chicago, IL, 60637, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
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187
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Zhu H, Zhang L, Wu Y, Dong B, Guo W, Wang M, Yang L, Fan X, Tang Y, Liu N, Lei X, Wu H. T-ALL leukemia stem cell 'stemness' is epigenetically controlled by the master regulator SPI1. eLife 2018; 7:38314. [PMID: 30412053 PMCID: PMC6251627 DOI: 10.7554/elife.38314] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 11/09/2018] [Indexed: 12/17/2022] Open
Abstract
Leukemia stem cells (LSCs) are regarded as the origins and key therapeutic targets of leukemia, but limited knowledge is available on the key determinants of LSC 'stemness'. Using single-cell RNA-seq analysis, we identify a master regulator, SPI1, the LSC-specific expression of which determines the molecular signature and activity of LSCs in the murine Pten-null T-ALL model. Although initiated by PTEN-controlled β-catenin activation, Spi1 expression and LSC 'stemness' are maintained by a β-catenin-SPI1-HAVCR2 regulatory circuit independent of the leukemogenic driver mutation. Perturbing any component of this circuit either genetically or pharmacologically can prevent LSC formation or eliminate existing LSCs. LSCs lose their 'stemness' when Spi1 expression is silenced by DNA methylation, but Spi1 expression can be reactivated by 5-AZ treatment. Importantly, similar regulatory mechanisms may be also present in human T-ALL.
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Affiliation(s)
- Haichuan Zhu
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Liuzhen Zhang
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Yilin Wu
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Bingjie Dong
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Weilong Guo
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Mei Wang
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Lu Yang
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Xiaoying Fan
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Yuliang Tang
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Department of Chemical Biology, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Ningshu Liu
- Drug Discovery Oncology, Bayer Pharmaceuticals, Berlin, Germany
| | - Xiaoguang Lei
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Department of Chemical Biology, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Hong Wu
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.,Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
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188
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Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, Leeson R, Kanodia A, Mei S, Lin JR, Wang S, Rabasha B, Liu D, Zhang G, Margolais C, Ashenberg O, Ott PA, Buchbinder EI, Haq R, Hodi FS, Boland GM, Sullivan RJ, Frederick DT, Miao B, Moll T, Flaherty KT, Herlyn M, Jenkins RW, Thummalapalli R, Kowalczyk MS, Cañadas I, Schilling B, Cartwright ANR, Luoma AM, Malu S, Hwu P, Bernatchez C, Forget MA, Barbie DA, Shalek AK, Tirosh I, Sorger PK, Wucherpfennig K, Van Allen EM, Schadendorf D, Johnson BE, Rotem A, Rozenblatt-Rosen O, Garraway LA, Yoon CH, Izar B, Regev A. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell 2018; 175:984-997.e24. [PMID: 30388455 PMCID: PMC6410377 DOI: 10.1016/j.cell.2018.09.006] [Citation(s) in RCA: 780] [Impact Index Per Article: 130.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 06/18/2018] [Accepted: 09/05/2018] [Indexed: 12/12/2022]
Abstract
Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.
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Affiliation(s)
| | - Parin Shah
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Mei-Ju Su
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center for Cancer Precision Medicine of Dana-Farber Cancer Institute, Boston, MA, USA
| | - Johannes C Melms
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Rachel Leeson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center for Cancer Precision Medicine of Dana-Farber Cancer Institute, Boston, MA, USA
| | - Abhay Kanodia
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center for Cancer Precision Medicine of Dana-Farber Cancer Institute, Boston, MA, USA
| | - Shaolin Mei
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Shu Wang
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Bokang Rabasha
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - David Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gao Zhang
- Molecular & Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA
| | - Claire Margolais
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Orr Ashenberg
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Rizwan Haq
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - F Stephen Hodi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Ryan J Sullivan
- Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | | | - Benchun Miao
- Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | - Tabea Moll
- Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | | | - Meenhard Herlyn
- Molecular & Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA
| | - Russell W Jenkins
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | - Rohit Thummalapalli
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Monika S Kowalczyk
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Celsius Therapeutics, Cambridge, MA, USA
| | - Israel Cañadas
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bastian Schilling
- Department of Dermatology, University Hospital Essen, West German Cancer Center, University Duisburg-Essen and the German Cancer Consortium, Essen, Germany; Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Adam N R Cartwright
- Center for Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adrienne M Luoma
- Center for Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Shruti Malu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Patrick Hwu
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chantale Bernatchez
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marie-Andrée Forget
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David A Barbie
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alex K Shalek
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Itay Tirosh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Peter K Sorger
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Kai Wucherpfennig
- Center for Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, West German Cancer Center, University Duisburg-Essen and the German Cancer Consortium, Essen, Germany
| | - Bruce E Johnson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center for Cancer Precision Medicine of Dana-Farber Cancer Institute, Boston, MA, USA
| | - Asaf Rotem
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center for Cancer Precision Medicine of Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Levi A Garraway
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center for Cancer Precision Medicine of Dana-Farber Cancer Institute, Boston, MA, USA; Ludwig Center for Cancer Research at Harvard, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Charles H Yoon
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Brigham and Women's Hospital, Department of Surgical Oncology, Boston, MA, USA
| | - Benjamin Izar
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center for Cancer Precision Medicine of Dana-Farber Cancer Institute, Boston, MA, USA; Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA; Center for Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA; Ludwig Center for Cancer Research at Harvard, Boston, MA, USA.
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA; Ludwig Center for Cancer Research at MIT, Boston, MA, USA; Massachusetts Institute of Technology, Department of Biology, Cambridge, MA, USA
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189
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Stein-O'Brien GL, Arora R, Culhane AC, Favorov AV, Garmire LX, Greene CS, Goff LA, Li Y, Ngom A, Ochs MF, Xu Y, Fertig EJ. Enter the Matrix: Factorization Uncovers Knowledge from Omics. Trends Genet 2018; 34:790-805. [PMID: 30143323 PMCID: PMC6309559 DOI: 10.1016/j.tig.2018.07.003] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 06/01/2018] [Accepted: 07/16/2018] [Indexed: 12/20/2022]
Abstract
Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.
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Affiliation(s)
- Genevieve L Stein-O'Brien
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Raman Arora
- Department of Computer Science, Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD, USA
| | - Aedin C Culhane
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Alexander V Favorov
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Vavilov Institute of General Genetics, Moscow, Russia
| | | | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, PA, USA; Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, PA, USA
| | - Loyal A Goff
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Yifeng Li
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON, Canada
| | - Aloune Ngom
- School of Computer Science, University of Windsor, Windsor, ON, Canada
| | - Michael F Ochs
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Elana J Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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190
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Forget A, Martignetti L, Puget S, Calzone L, Brabetz S, Picard D, Montagud A, Liva S, Sta A, Dingli F, Arras G, Rivera J, Loew D, Besnard A, Lacombe J, Pagès M, Varlet P, Dufour C, Yu H, Mercier AL, Indersie E, Chivet A, Leboucher S, Sieber L, Beccaria K, Gombert M, Meyer FD, Qin N, Bartl J, Chavez L, Okonechnikov K, Sharma T, Thatikonda V, Bourdeaut F, Pouponnot C, Ramaswamy V, Korshunov A, Borkhardt A, Reifenberger G, Poullet P, Taylor MD, Kool M, Pfister SM, Kawauchi D, Barillot E, Remke M, Ayrault O. Aberrant ERBB4-SRC Signaling as a Hallmark of Group 4 Medulloblastoma Revealed by Integrative Phosphoproteomic Profiling. Cancer Cell 2018; 34:379-395.e7. [PMID: 30205043 DOI: 10.1016/j.ccell.2018.08.002] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 05/12/2018] [Accepted: 08/03/2018] [Indexed: 12/20/2022]
Abstract
The current consensus recognizes four main medulloblastoma subgroups (wingless, Sonic hedgehog, group 3 and group 4). While medulloblastoma subgroups have been characterized extensively at the (epi-)genomic and transcriptomic levels, the proteome and phosphoproteome landscape remain to be comprehensively elucidated. Using quantitative (phospho)-proteomics in primary human medulloblastomas, we unravel distinct posttranscriptional regulation leading to highly divergent oncogenic signaling and kinase activity profiles in groups 3 and 4 medulloblastomas. Specifically, proteomic and phosphoproteomic analyses identify aberrant ERBB4-SRC signaling in group 4. Hence, enforced expression of an activated SRC combined with p53 inactivation induces murine tumors that resemble group 4 medulloblastoma. Therefore, our integrative proteogenomics approach unveils an oncogenic pathway and potential therapeutic vulnerability in the most common medulloblastoma subgroup.
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Affiliation(s)
- Antoine Forget
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France.
| | - Loredana Martignetti
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Stéphanie Puget
- Department of Pediatric Neurosurgery, Necker University Hospital, University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Laurence Calzone
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Sebastian Brabetz
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Daniel Picard
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Arnau Montagud
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Stéphane Liva
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Alexandre Sta
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Florent Dingli
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Guillaume Arras
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Jaime Rivera
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Damarys Loew
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Aurore Besnard
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Joëlle Lacombe
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Mélanie Pagès
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Pascale Varlet
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Christelle Dufour
- Department of Pediatric and Adolescent Oncology, Gustave Roussy, Rue Edouard Vaillant, 94805 Villejuif, France
| | - Hua Yu
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Audrey L Mercier
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Emilie Indersie
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Anaïs Chivet
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Sophie Leboucher
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Institut Curie, Centre de Recherche, Plateforme d'Histologie, Orsay 91405, France
| | - Laura Sieber
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Kevin Beccaria
- Department of Pediatric Neurosurgery, Necker University Hospital, University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Michael Gombert
- Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Frauke D Meyer
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Nan Qin
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Jasmin Bartl
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Lukas Chavez
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Konstantin Okonechnikov
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Tanvi Sharma
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Venu Thatikonda
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Franck Bourdeaut
- Paris-Sciences-Lettres Research University, Institut Curie Research Center, SiRIC, Laboratory of Translational Research in Pediatric Oncology, Paris 75005, France; Paris-Sciences-Lettres Research University, Institut Curie Research Center, INSERM U830, Laboratory of Biology and Genetics of Cancers, Paris 75005, France
| | - Celio Pouponnot
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Vijay Ramaswamy
- Division of Haematology/Oncology, Hospital for Sick Children and Department of Paediatrics, Hospital for Sick Children, Toronto, ON, Canada
| | - Andrey Korshunov
- Clinical Cooperation Unit Neuropathology (G380), German Cancer Research Center (DKFZ), and Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Arndt Borkhardt
- Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Guido Reifenberger
- Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Patrick Poullet
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Michael D Taylor
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada; Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, Canada; Departments of Surgery, Laboratory Medicine and Pathobiology, and Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Marcel Kool
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Stefan M Pfister
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany; Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Daisuke Kawauchi
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany.
| | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France.
| | - Marc Remke
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany.
| | - Olivier Ayrault
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France.
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191
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Xie F, Zhou M, Xu Y. BayCount: A Bayesian decomposition method for inferring tumor heterogeneity using RNA-Seq counts. Ann Appl Stat 2018. [DOI: 10.1214/17-aoas1123] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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192
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Boldog E, Bakken TE, Hodge RD, Novotny M, Aevermann BD, Baka J, Bordé S, Close JL, Diez-Fuertes F, Ding SL, Faragó N, Kocsis ÁK, Kovács B, Maltzer Z, McCorrison JM, Miller JA, Molnár G, Oláh G, Ozsvár A, Rózsa M, Shehata SI, Smith KA, Sunkin SM, Tran DN, Venepally P, Wall A, Puskás LG, Barzó P, Steemers FJ, Schork NJ, Scheuermann RH, Lasken RS, Lein ES, Tamás G. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat Neurosci 2018; 21:1185-1195. [PMID: 30150662 PMCID: PMC6130849 DOI: 10.1038/s41593-018-0205-2] [Citation(s) in RCA: 159] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 06/14/2018] [Indexed: 11/29/2022]
Abstract
We describe convergent evidence from transcriptomics, morphology, and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single-nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a group of human interneurons with anatomical features never described in rodents, having large 'rosehip'-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1+CCK+, CNR1-SST-CALB2-PVALB-) matching a single transcriptomically defined cell type whose specific molecular marker signature is not seen in mouse cortex. Rosehip cells in layer 1 make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons, and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.
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Affiliation(s)
- Eszter Boldog
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | | | | | | | | | - Judith Baka
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | - Sándor Bordé
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | | | | | | | - Nóra Faragó
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | - Ágnes K Kocsis
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | - Balázs Kovács
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | - Zoe Maltzer
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Gábor Molnár
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | - Gáspár Oláh
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | - Attila Ozsvár
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | - Márton Rózsa
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary
| | | | | | | | | | | | - Abby Wall
- Allen Institute for Brain Science, Seattle, WA, USA
| | - László G Puskás
- Laboratory of Functional Genomics, Department of Genetics, Biological Research Center, Hungarian Academy of Sciences, Szeged, Hungary
| | | | - Frank J Steemers
- Department of Neurosurgery, University of Szeged, Szeged, Hungary
| | | | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Department of Pathology, University of California, San Diego, CA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Gábor Tamás
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary.
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193
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Todorov H, Saeys Y. Computational approaches for high‐throughput single‐cell data analysis. FEBS J 2018; 286:1451-1467. [DOI: 10.1111/febs.14613] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 06/04/2018] [Accepted: 07/25/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Helena Todorov
- Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium
- Department of Applied Mathematics, Computer Science and Statistics Ghent University Belgium
- Centre International de Recherche en Infectiologie Inserm U1111, Université Claude Bernard Lyon 1 CNRS, UMR5308 École Normale Supérieure de Lyon Univ Lyon France
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium
- Department of Applied Mathematics, Computer Science and Statistics Ghent University Belgium
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194
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Xu L, Deng C, Pang B, Zhang X, Liu W, Liao G, Yuan H, Cheng P, Li F, Long Z, Yan M, Zhao T, Xiao Y, Li X. TIP: A Web Server for Resolving Tumor Immunophenotype Profiling. Cancer Res 2018; 78:6575-6580. [DOI: 10.1158/0008-5472.can-18-0689] [Citation(s) in RCA: 356] [Impact Index Per Article: 59.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/20/2018] [Accepted: 08/20/2018] [Indexed: 11/16/2022]
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195
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Swapna LS, Molinaro AM, Lindsay-Mosher N, Pearson BJ, Parkinson J. Comparative transcriptomic analyses and single-cell RNA sequencing of the freshwater planarian Schmidtea mediterranea identify major cell types and pathway conservation. Genome Biol 2018; 19:124. [PMID: 30143032 PMCID: PMC6109357 DOI: 10.1186/s13059-018-1498-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 08/01/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In the Lophotrochozoa/Spiralia superphylum, few organisms have as high a capacity for rapid testing of gene function and single-cell transcriptomics as the freshwater planaria. The species Schmidtea mediterranea in particular has become a powerful model to use in studying adult stem cell biology and mechanisms of regeneration. Despite this, systematic attempts to define gene complements and their annotations are lacking, restricting comparative analyses that detail the conservation of biochemical pathways and identify lineage-specific innovations. RESULTS In this study we compare several transcriptomes and define a robust set of 35,232 transcripts. From this, we perform systematic functional annotations and undertake a genome-scale metabolic reconstruction for S. mediterranea. Cross-species comparisons of gene content identify conserved, lineage-specific, and expanded gene families, which may contribute to the regenerative properties of planarians. In particular, we find that the TRAF gene family has been greatly expanded in planarians. We further provide a single-cell RNA sequencing analysis of 2000 cells, revealing both known and novel cell types defined by unique signatures of gene expression. Among these are a novel mesenchymal cell population as well as a cell type involved in eye regeneration. Integration of our metabolic reconstruction further reveals the extent to which given cell types have adapted energy and nucleotide biosynthetic pathways to support their specialized roles. CONCLUSIONS In general, S. mediterranea displays a high level of gene and pathway conservation compared with other model systems, rendering it a viable model to study the roles of these pathways in stem cell biology and regeneration.
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Affiliation(s)
| | - Alyssa M Molinaro
- Hospital for Sick Children, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Nicole Lindsay-Mosher
- Hospital for Sick Children, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Bret J Pearson
- Hospital for Sick Children, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. .,Ontario Institute for Cancer Research, Toronto, ON, Canada.
| | - John Parkinson
- Hospital for Sick Children, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. .,Department of Biochemistry, University of Toronto, Toronto, ON, Canada.
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196
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Marques S, van Bruggen D, Vanichkina DP, Floriddia EM, Munguba H, Väremo L, Giacomello S, Falcão AM, Meijer M, Björklund ÅK, Hjerling-Leffler J, Taft RJ, Castelo-Branco G. Transcriptional Convergence of Oligodendrocyte Lineage Progenitors during Development. Dev Cell 2018; 46:504-517.e7. [PMID: 30078729 PMCID: PMC6104814 DOI: 10.1016/j.devcel.2018.07.005] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 05/23/2018] [Accepted: 07/03/2018] [Indexed: 01/31/2023]
Abstract
Pdgfra+ oligodendrocyte precursor cells (OPCs) arise in distinct specification waves during embryogenesis in the central nervous system (CNS). It is unclear whether there is a correlation between these waves and different oligodendrocyte (OL) states at adult stages. Here, we present bulk and single-cell transcriptomics resources providing insights on how transitions between these states occur. We found that post-natal OPCs from brain and spinal cord present similar transcriptional signatures. Moreover, post-natal OPC progeny of E13.5 Pdgfra+ cells present electrophysiological and transcriptional profiles similar to OPCs derived from subsequent specification waves, indicating that Pdgfra+ pre-OPCs rewire their transcriptional network during development. Single-cell RNA-seq and lineage tracing indicates that a subset of E13.5 Pdgfra+ cells originates cells of the pericyte lineage. Thus, our results indicate that embryonic Pdgfra+ cells in the CNS give rise to distinct post-natal cell lineages, including OPCs with convergent transcriptional profiles in different CNS regions. OPCs arising from different parts of the CNS are highly similar to one another Pre-OPCs converge into similar transcriptional and electrophysiological OPC states E13.5 Pdgfra+ cells give rise mainly to OPCs in the post-natal CNS E13.5 Pdgfra+ cells also give rise to cells of the pericyte lineage
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Affiliation(s)
- Sueli Marques
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden
| | - David van Bruggen
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden
| | - Darya Pavlovna Vanichkina
- Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Camperdown, NSW 2050, Australia; Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD 4067, Australia
| | - Elisa Mariagrazia Floriddia
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden
| | - Hermany Munguba
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden
| | - Leif Väremo
- Science for Life Laboratory, Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Göteborg 412 96, Sweden
| | - Stefania Giacomello
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden
| | - Ana Mendanha Falcão
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden
| | - Mandy Meijer
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden
| | - Åsa Kristina Björklund
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, 75237 Uppsala, Sweden
| | - Jens Hjerling-Leffler
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ryan James Taft
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD 4067, Australia; Illumina, Inc., San Diego, CA 92122, USA
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, Stockholm 17177, Sweden.
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197
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Gardeux V, David FPA, Shajkofci A, Schwalie PC, Deplancke B. ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data. Bioinformatics 2018; 33:3123-3125. [PMID: 28541377 PMCID: PMC5870842 DOI: 10.1093/bioinformatics/btx337] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 05/21/2017] [Indexed: 12/11/2022] Open
Abstract
Motivation Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets. Results We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types. Availability and implementation The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vincent Gardeux
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
| | - Fabrice P A David
- Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland.,Bioinformatics and Biostatistics Core Facility, EPFL, Lausanne CH-1015, Switzerland
| | - Adrian Shajkofci
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Petra C Schwalie
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
| | - Bart Deplancke
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
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198
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Rambow F, Rogiers A, Marin-Bejar O, Aibar S, Femel J, Dewaele M, Karras P, Brown D, Chang YH, Debiec-Rychter M, Adriaens C, Radaelli E, Wolter P, Bechter O, Dummer R, Levesque M, Piris A, Frederick DT, Boland G, Flaherty KT, van den Oord J, Voet T, Aerts S, Lund AW, Marine JC. Toward Minimal Residual Disease-Directed Therapy in Melanoma. Cell 2018; 174:843-855.e19. [PMID: 30017245 DOI: 10.1016/j.cell.2018.06.025] [Citation(s) in RCA: 422] [Impact Index Per Article: 70.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 04/13/2018] [Accepted: 06/12/2018] [Indexed: 01/05/2023]
Abstract
Many patients with advanced cancers achieve dramatic responses to a panoply of therapeutics yet retain minimal residual disease (MRD), which ultimately results in relapse. To gain insights into the biology of MRD, we applied single-cell RNA sequencing to malignant cells isolated from BRAF mutant patient-derived xenograft melanoma cohorts exposed to concurrent RAF/MEK-inhibition. We identified distinct drug-tolerant transcriptional states, varying combinations of which co-occurred within MRDs from PDXs and biopsies of patients on treatment. One of these exhibited a neural crest stem cell (NCSC) transcriptional program largely driven by the nuclear receptor RXRG. An RXR antagonist mitigated accumulation of NCSCs in MRD and delayed the development of resistance. These data identify NCSCs as key drivers of resistance and illustrate the therapeutic potential of MRD-directed therapy. They also highlight how gene regulatory network architecture reprogramming may be therapeutically exploited to limit cellular heterogeneity, a key driver of disease progression and therapy resistance.
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Affiliation(s)
- Florian Rambow
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Aljosja Rogiers
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Oskar Marin-Bejar
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Sara Aibar
- Laboratory of Computational Biology, VIB Center for Brain & Disease Research, KU Leuven, Leuven, Belgium; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Julia Femel
- Department of Cell, Developmental, and Cancer Biology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Michael Dewaele
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Panagiotis Karras
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Daniel Brown
- Laboratory of reproductive genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA
| | - Maria Debiec-Rychter
- Laboratory for Genetics of Malignant Disorders, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Carmen Adriaens
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Enrico Radaelli
- Comparative Pathology Core, University of Pennsylvania, Department of Pathobiology, Philadelphia, PA, USA
| | - Pascal Wolter
- Department of General Medical Oncology, UZ Leuven, Leuven, Belgium
| | - Oliver Bechter
- Department of General Medical Oncology, UZ Leuven, Leuven, Belgium
| | - Reinhard Dummer
- Department of Dermatology, University of Zürich Hospital, Zürich, Switzerland
| | - Mitchell Levesque
- Department of Dermatology, University of Zürich Hospital, Zürich, Switzerland
| | - Adriano Piris
- Department of Surgical Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Dennie T Frederick
- Department of Surgical Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve Boland
- Department of Surgical Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Keith T Flaherty
- Department of Medical Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Joost van den Oord
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, UZ Leuven, Leuven, Belgium
| | - Thierry Voet
- Laboratory of reproductive genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Stein Aerts
- Laboratory of Computational Biology, VIB Center for Brain & Disease Research, KU Leuven, Leuven, Belgium; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Amanda W Lund
- Department of Cell, Developmental, and Cancer Biology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
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199
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Vegh P, Haniffa M. The impact of single-cell RNA sequencing on understanding the functional organization of the immune system. Brief Funct Genomics 2018; 17:265-272. [PMID: 29547972 PMCID: PMC6063276 DOI: 10.1093/bfgp/ely003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Application of single-cell genomics technologies has revolutionized our approach to study the immune system. Unravelling the functional diversity of immune cells and their coordinated response is key to understanding immunity. Single-cell transcriptomics technologies provide high-dimensional assessment of the transcriptional states of immune cells and have been successfully applied to discover new immune cell types, reveal haematopoietic lineages, identify gene modules dictating immune responses and investigate lymphocyte antigen receptor diversity. In this review, we discuss the impact and applications of single-cell RNA sequencing technologies in immunology.
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Affiliation(s)
- Peter Vegh
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK
| | - Muzlifah Haniffa
- Department of Dermatology, Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK
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200
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Fiers MWEJ, Minnoye L, Aibar S, Bravo González-Blas C, Kalender Atak Z, Aerts S. Mapping gene regulatory networks from single-cell omics data. Brief Funct Genomics 2018; 17:246-254. [PMID: 29342231 PMCID: PMC6063279 DOI: 10.1093/bfgp/elx046] [Citation(s) in RCA: 137] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.
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Affiliation(s)
- Mark W E J Fiers
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
| | - Liesbeth Minnoye
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Sara Aibar
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Zeynep Kalender Atak
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Stein Aerts
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
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