701
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Griss J, Viteri G, Sidiropoulos K, Nguyen V, Fabregat A, Hermjakob H. ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis. Mol Cell Proteomics 2020; 19:2115-2125. [PMID: 32907876 PMCID: PMC7710148 DOI: 10.1074/mcp.tir120.002155] [Citation(s) in RCA: 194] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/28/2020] [Indexed: 01/27/2023] Open
Abstract
Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.
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Affiliation(s)
- Johannes Griss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom; Department of Dermatology, Medical University of Vienna, Vienna, Austria.
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Konstantinos Sidiropoulos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Vy Nguyen
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Antonio Fabregat
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom.
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702
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Crowell HL, Soneson C, Germain PL, Calini D, Collin L, Raposo C, Malhotra D, Robinson MD. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat Commun 2020; 11:6077. [PMID: 33257685 PMCID: PMC7705760 DOI: 10.1038/s41467-020-19894-4] [Citation(s) in RCA: 232] [Impact Index Per Article: 46.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 11/05/2020] [Indexed: 02/06/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.
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Affiliation(s)
- Helena L Crowell
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Charlotte Soneson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Pierre-Luc Germain
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- D-HEST Institute for Neuroscience, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Daniela Calini
- F. Hoffmann-La Roche Ltd., Pharma Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Ludovic Collin
- F. Hoffmann-La Roche Ltd., Pharma Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Catarina Raposo
- F. Hoffmann-La Roche Ltd., Pharma Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Dheeraj Malhotra
- F. Hoffmann-La Roche Ltd., Pharma Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland.
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703
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Watson MI, Barabas P, McGahon M, McMahon M, Fuchs MA, Curtis TM, Simpson DA. Single-cell transcriptomic profiling provides insights into retinal endothelial barrier properties. Mol Vis 2020; 26:766-779. [PMID: 33380778 PMCID: PMC7765706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 11/25/2020] [Indexed: 11/08/2022] Open
Abstract
Purpose To better characterize retinal endothelial barrier properties through analysis of individual transcriptomes of primary bovine retinal microvascular endothelial cells (RMECs). Methods Individual RMECs were captured on the Fluidigm C1 system, cDNA libraries were prepared using a Nextera XT kit, and sequencing was performed on a NextSeq system (Illumina). Data analysis was performed using R packages Scater, SC3, and Seurat, and the browser application Automated Single-cell Analysis Pipeline (ASAP). Alternative splicing events in single cells were quantified with Outrigger. Cytoscape was used for network analyses. Results Application of a single-cell RNA sequencing (scRNA-seq) analysis workflow showed that RMECs form a relatively homogeneous population in culture, with the main differences related to proliferation status. Expression of markers from along the arteriovenous tree suggested that most cells originated from capillaries. Average gene expression levels across all cells were used to develop an in silico model of the inner blood-retina barrier incorporating junctional proteins not previously reported within the retinal vasculature. Correlation of barrier gene expression among individual cells revealed a subgroup of genes highly correlated with PECAM-1 at the center of the correlation network. Numerous alternative splicing events involving exons within microvascular barrier genes were observed, and in many cases, individual cells expressed one isoform exclusively. Conclusions We optimized a workflow for single-cell transcriptomics in primary RMECs. The results provide fundamental insights into the genes involved in formation of the retinal-microvascular barrier.
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Affiliation(s)
- Mark I. Watson
- Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland
| | - Peter Barabas
- Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland
| | - Mary McGahon
- Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland
| | - Megan McMahon
- Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland
| | - Marc A. Fuchs
- Genomics Core Technology Unit, Faculty of Medicine, Health & Life Sciences, Queen’s University Belfast, Belfast, Northern Ireland
| | - Tim M. Curtis
- Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland
| | - David A. Simpson
- Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland
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704
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Fehlmann T, Lehallier B, Schaum N, Hahn O, Kahraman M, Li Y, Grammes N, Geffers L, Backes C, Balling R, Kern F, Krüger R, Lammert F, Ludwig N, Meder B, Fromm B, Maetzler W, Berg D, Brockmann K, Deuschle C, von Thaler AK, Eschweiler GW, Milman S, Barziliai N, Reichert M, Wyss-Coray T, Meese E, Keller A. Common diseases alter the physiological age-related blood microRNA profile. Nat Commun 2020; 11:5958. [PMID: 33235214 PMCID: PMC7686493 DOI: 10.1038/s41467-020-19665-1] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 10/16/2020] [Indexed: 02/07/2023] Open
Abstract
Aging is a key risk factor for chronic diseases of the elderly. MicroRNAs regulate post-transcriptional gene silencing through base-pair binding on their target mRNAs. We identified nonlinear changes in age-related microRNAs by analyzing whole blood from 1334 healthy individuals. We observed a larger influence of the age as compared to the sex and provide evidence for a shift to the 5' mature form of miRNAs in healthy aging. The addition of 3059 diseased patients uncovered pan-disease and disease-specific alterations in aging profiles. Disease biomarker sets for all diseases were different between young and old patients. Computational deconvolution of whole-blood miRNAs into blood cell types suggests that cell intrinsic gene expression changes may impart greater significance than cell abundance changes to the whole blood miRNA profile. Altogether, these data provide a foundation for understanding the relationship between healthy aging and disease, and for the development of age-specific disease biomarkers.
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Affiliation(s)
- Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Benoit Lehallier
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Nicholas Schaum
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Oliver Hahn
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Mustafa Kahraman
- Chair for Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Yongping Li
- Chair for Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Nadja Grammes
- Chair for Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Lars Geffers
- Luxembourg Center for Systems Biomedicine, 4362, Esch-sur-Alzette, Luxemburg
| | - Christina Backes
- Chair for Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Rudi Balling
- Luxembourg Center for Systems Biomedicine, 4362, Esch-sur-Alzette, Luxemburg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), 1445, Strassen, Luxemburg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg, 1210, Luxembourg, Luxemburg
| | - Fabian Kern
- Chair for Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany
| | - Rejko Krüger
- Luxembourg Center for Systems Biomedicine, 4362, Esch-sur-Alzette, Luxemburg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), 1445, Strassen, Luxemburg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg, 1210, Luxembourg, Luxemburg
| | - Frank Lammert
- Internal Medicine, Saarland University, 66421, Homburg, Germany
| | - Nicole Ludwig
- Human Genetics, Saarland University, 66421, Homburg, Germany
| | - Benjamin Meder
- Internal Medicine, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Bastian Fromm
- Department of Molecular Biosciences, Stockholm University, 11418, Stockholm, Sweden
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-Universität zu Kiel, 24105, Kiel, Germany
| | - Daniela Berg
- Department of Neurology, Christian-Albrechts-Universität zu Kiel, 24105, Kiel, Germany
| | | | | | | | - Gerhard W Eschweiler
- Geriatric Center and the Department of Psychiatry and Psychotherapy, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Sofiya Milman
- The Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, 10461, USA
| | - Nir Barziliai
- The Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, 10461, USA
| | | | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Eckart Meese
- Human Genetics, Saarland University, 66421, Homburg, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123, Saarbrücken, Germany.
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA.
- Center for Bioinformatics, Saarland Informatics Campus, Saarland University, 66123, Saarbrücken, Germany.
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705
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Mižíková I, Thébaud B. Looking at the developing lung in single-cell resolution. Am J Physiol Lung Cell Mol Physiol 2020; 320:L680-L687. [PMID: 33205990 DOI: 10.1152/ajplung.00385.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Lung development is a complicated and delicate process, facilitated by spatially and temporarily coordinated cross talk of up to 40 cell types. Developmental origin and heterogeneity of lung cell lineages in context of lung development have been a focus of research efforts for decades. Bulk RNA and protein measurements, RNA and protein labeling, and lineage tracing techniques have been traditionally employed. However, the complex and heterogeneous nature of lung tissue presents a particular challenge when identifying subtle changes in gene expression in individual cell types. Rapidly developing single-cell RNA sequencing (scRNA-seq) techniques allow for unbiased and robust assessment of complex cellular dynamics during biological processes in unprecedented ways. Discovered a decade ago, scRNA-seq has been applied in respiratory research to understand lung cellular composition and to identify novel cell types. Still, very few studies to date have addressed the single-cell transcriptome in healthy or aberrantly developing lung. In this review, we discuss principal discoveries with scRNA-seq in the field of prenatal and postnatal lung development. In addition, we examine challenges and expectations, and propose future steps associated with the use of scRNA-seq to study developmental lung diseases.
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Affiliation(s)
- I Mižíková
- Sinclair Centre for Regenerative Medicine, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - B Thébaud
- Sinclair Centre for Regenerative Medicine, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada.,Department of Pediatrics, Children's Hospital of Eastern Ontario (CHEO) and CHEO Research Institute, University of Ottawa, Ottawa, Ontario, Canada
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706
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Prompsy P, Kirchmeier P, Marsolier J, Deloger M, Servant N, Vallot C. Interactive analysis of single-cell epigenomic landscapes with ChromSCape. Nat Commun 2020; 11:5702. [PMID: 33177523 PMCID: PMC7658988 DOI: 10.1038/s41467-020-19542-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 10/21/2020] [Indexed: 01/05/2023] Open
Abstract
Chromatin modifications orchestrate the dynamic regulation of gene expression during development and in disease. Bulk approaches have characterized the wide repertoire of histone modifications across cell types, detailing their role in shaping cell identity. However, these population-based methods do not capture cell-to-cell heterogeneity of chromatin landscapes, limiting our appreciation of the role of chromatin in dynamic biological processes. Recent technological developments enable the mapping of histone marks at single-cell resolution, opening up perspectives to characterize the heterogeneity of chromatin marks in complex biological systems over time. Yet, existing tools used to analyze bulk histone modifications profiles are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape, a user-friendly interactive Shiny/R application distributed as a Bioconductor package, that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets. Using ChromSCape, we deconvolve chromatin landscapes within the tumor micro-environment, identifying distinct H3K27me3 landscapes associated with cell identity and breast tumor subtype.
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Affiliation(s)
- Pacôme Prompsy
- CNRS UMR3244, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France.
- Translational Research Department, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France.
| | - Pia Kirchmeier
- CNRS UMR3244, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
- Translational Research Department, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
| | - Justine Marsolier
- CNRS UMR3244, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
- Translational Research Department, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
| | - Marc Deloger
- INSERM U900, Institut Curie, PSL Research University, Mines ParisTech, 26 rue d'Ulm, 75005, Paris, France
| | - Nicolas Servant
- INSERM U900, Institut Curie, PSL Research University, Mines ParisTech, 26 rue d'Ulm, 75005, Paris, France
| | - Céline Vallot
- CNRS UMR3244, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France.
- Translational Research Department, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France.
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707
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Single-Cell RNA Sequencing Unravels Heterogeneity of the Stromal Niche in Cutaneous Melanoma Heterogeneous Spheroids. Cancers (Basel) 2020; 12:cancers12113324. [PMID: 33182777 PMCID: PMC7697260 DOI: 10.3390/cancers12113324] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/03/2020] [Accepted: 11/08/2020] [Indexed: 12/21/2022] Open
Abstract
Heterogeneous spheroids have recently acquired a prominent position in melanoma research because they incorporate microenvironmental cues relevant for melanoma. In this study, we focused on the analysis of microenvironmental factors introduced in melanoma heterogeneous spheroids by different dermal fibroblasts. We aimed to map the fibroblast diversity resulting from previously acquired damage caused by exposure to extrinsic and intrinsic stimuli. To construct heterogeneous melanoma spheroids, we used normal dermal fibroblasts from the sun-protected skin of a juvenile donor. We compared them to the fibroblasts from the sun-exposed photodamaged skin of an adult donor. Further, we analysed the spheroids by single-cell RNA sequencing. To validate transcriptional data, we also compared the immunohistochemical analysis of heterogeneous spheroids to melanoma biopsies. We have distinguished three functional clusters in primary human fibroblasts from melanoma spheroids. These clusters differed in the expression of (a) extracellular matrix-related genes, (b) pro-inflammatory factors, and (c) TGFβ signalling superfamily. We observed a broader deregulation of gene transcription in previously photodamaged cells. We have confirmed that pro-inflammatory cytokine IL-6 significantly enhances melanoma invasion to the extracellular matrix in our model. This supports the opinion that the aspects of ageing are essential for reliable melanoma 3D modelling in vitro.
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708
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Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun 2020; 11:5650. [PMID: 33159064 PMCID: PMC7648640 DOI: 10.1038/s41467-020-19015-1] [Citation(s) in RCA: 243] [Impact Index Per Article: 48.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 09/16/2020] [Indexed: 01/05/2023] Open
Abstract
Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.
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709
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The bone marrow microenvironment of pre-B acute lymphoblastic leukemia at single-cell resolution. Sci Rep 2020; 10:19173. [PMID: 33154494 PMCID: PMC7645756 DOI: 10.1038/s41598-020-76157-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/23/2020] [Indexed: 01/06/2023] Open
Abstract
The bone marrow microenvironment (BMM) plays a key role in leukemia progression, but its molecular complexity in pre-B cell acute lymphoblastic leukemia (B-ALL), the most common cancer in children, remains poorly understood. To gain further insight, we used single-cell RNA sequencing to characterize the kinetics of the murine BMM during B-ALL progression. Normal pro- and pre-B cells were found to be the most affected at the earliest stages of disease and this was associated with changes in expression of genes regulated by the AP1-transcription factor complex and regulatory factors NELFE, MYC and BCL11A. Granulocyte–macrophage progenitors show reduced expression of the tumor suppressor long non-coding RNA Neat1 and disruptions in the rate of transcription. Intercellular communication networks revealed monocyte-dendritic precursors to be consistently active during B-ALL progression, with enriched processes including cytokine-mediated signaling pathway, neutrophil-mediated immunity and regulation of cell migration and proliferation. In addition, we confirmed that the hematopoietic stem and progenitor cell compartment was perturbed during leukemogenesis. These findings extend our understanding of the complexity of changes and molecular interactions among the normal cells of the BMM during B-ALL progression.
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710
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Ben-Othman R, Cai B, Liu AC, Varankovich N, He D, Blimkie TM, Lee AH, Gill EE, Novotny M, Aevermann B, Drissler S, Shannon CP, McCann S, Marty K, Bjornson G, Edgar RD, Lin DTS, Gladish N, Maclsaac J, Amenyogbe N, Chan Q, Llibre A, Collin J, Landais E, Le K, Reiss SM, Koff WC, Havenar-Daughton C, Heran M, Sangha B, Walt D, Krajden M, Crotty S, Sok D, Briney B, Burton DR, Duffy D, Foster LJ, Mohn WW, Kobor MS, Tebbutt SJ, Brinkman RR, Scheuermann RH, Hancock REW, Kollmann TR, Sadarangani M. Systems Biology Methods Applied to Blood and Tissue for a Comprehensive Analysis of Immune Response to Hepatitis B Vaccine in Adults. Front Immunol 2020; 11:580373. [PMID: 33250895 PMCID: PMC7672042 DOI: 10.3389/fimmu.2020.580373] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 09/24/2020] [Indexed: 12/26/2022] Open
Abstract
Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, in vitro T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches-NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design.
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Affiliation(s)
- Rym Ben-Othman
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada.,Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia
| | - Bing Cai
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Aaron C Liu
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Natallia Varankovich
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Daniel He
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Travis M Blimkie
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Amy H Lee
- Simon Fraser University, Burnaby, BC, Canada
| | - Erin E Gill
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Mark Novotny
- Department of Informatics, J. Craig Venter Institute (La Jolla), La Jolla, CA, United States
| | - Brian Aevermann
- Department of Informatics, J. Craig Venter Institute (La Jolla), La Jolla, CA, United States
| | | | - Casey P Shannon
- Prevention of Organ Failure (PROOF) Centre of Excellence and Centre for Heart Lung Innovation, St. Paul's Hospital, Vancouver, BC, Canada
| | - Sarah McCann
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Kim Marty
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Gordean Bjornson
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Rachel D Edgar
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - David Tse Shen Lin
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Nicole Gladish
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Julia Maclsaac
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Nelly Amenyogbe
- Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia
| | - Queenie Chan
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Alba Llibre
- Translational Immunology Lab, Institut Pasteur, Paris, France
| | - Joyce Collin
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
| | - Elise Landais
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA, United States
| | - Khoa Le
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA, United States
| | - Samantha M Reiss
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Wayne C Koff
- Human Vaccines Project, New York, NY, United States
| | - Colin Havenar-Daughton
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Manraj Heran
- Department of Radiology, BC Children's Hospital, Vancouver, BC, Canada
| | - Bippan Sangha
- Department of Radiology, BC Children's Hospital, Vancouver, BC, Canada
| | - David Walt
- Wyss Institute at Harvard University, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Mel Krajden
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Shane Crotty
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Devin Sok
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA, United States
| | - Bryan Briney
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
| | - Dennis R Burton
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
| | - Darragh Duffy
- Translational Immunology Lab, Institut Pasteur, Paris, France
| | - Leonard J Foster
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - William W Mohn
- Department of Microbiology and Immunology, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Scott J Tebbutt
- Prevention of Organ Failure (PROOF) Centre of Excellence and Centre for Heart Lung Innovation, St. Paul's Hospital, Vancouver, BC, Canada.,Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ryan R Brinkman
- Terry Fox Laboratory, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute (La Jolla), La Jolla, CA, United States.,Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Robert E W Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Tobias R Kollmann
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada.,Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia
| | - Manish Sadarangani
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
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711
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Chung NC. Statistical significance of cluster membership for unsupervised evaluation of cell identities. Bioinformatics 2020; 36:3107-3114. [PMID: 32142108 PMCID: PMC7214036 DOI: 10.1093/bioinformatics/btaa087] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 01/05/2020] [Accepted: 03/03/2020] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Single-cell RNA-sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts and environmental stimuli. Transcriptional heterogeneity may reflect phenotypes and molecular signatures that are often unmeasured or unknown a priori. Cell identities of samples derived from heterogeneous subpopulations are then determined by clustering of scRNA-seq data. These cell identities are used in downstream analyses. How can we examine if cell identities are accurately inferred? Unlike external measurements or labels for single cells, using clustering-based cell identities result in spurious signals and false discoveries. RESULTS We introduce non-parametric methods to evaluate cell identities by testing cluster memberships in an unsupervised manner. Diverse simulation studies demonstrate accuracy of the jackstraw test for cluster membership. We propose a posterior probability that a cell should be included in that clustering-based subpopulation. Posterior inclusion probabilities (PIPs) for cluster memberships can be used to select and visualize samples relevant to subpopulations. The proposed methods are applied on three scRNA-seq datasets. First, a mixture of Jurkat and 293T cell lines provides two distinct cellular populations. Second, Cell Hashing yields cell identities corresponding to eight donors which are independently analyzed by the jackstraw. Third, peripheral blood mononuclear cells are used to explore heterogeneous immune populations. The proposed P-values and PIPs lead to probabilistic feature selection of single cells that can be visualized using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and others. By learning uncertainty in clustering high-dimensional data, the proposed methods enable unsupervised evaluation of cluster membership. AVAILABILITY AND IMPLEMENTATION https://cran.r-project.org/package=jackstraw. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Neo Christopher Chung
- Institute of Informatics, Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw 02-097, Poland.,NHLBI Integrated Cardiovascular Data Science Training Program, University of California, Los Angeles, CA 90095, USA.,Departments of Physiology and Medicine (Cardiology), UCLA School of Medicine, Los Angeles, CA 90095, USA
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712
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Lotto J, Drissler S, Cullum R, Wei W, Setty M, Bell EM, Boutet SC, Nowotschin S, Kuo YY, Garg V, Pe'er D, Church DM, Hadjantonakis AK, Hoodless PA. Single-Cell Transcriptomics Reveals Early Emergence of Liver Parenchymal and Non-parenchymal Cell Lineages. Cell 2020; 183:702-716.e14. [PMID: 33125890 PMCID: PMC7643810 DOI: 10.1016/j.cell.2020.09.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 07/06/2020] [Accepted: 09/01/2020] [Indexed: 02/08/2023]
Abstract
The cellular complexity and scale of the early liver have constrained analyses examining its emergence during organogenesis. To circumvent these issues, we analyzed 45,334 single-cell transcriptomes from embryonic day (E)7.5, when endoderm progenitors are specified, to E10.5 liver, when liver parenchymal and non-parenchymal cell lineages emerge. Our data detail divergence of vascular and sinusoidal endothelia, including a distinct transcriptional profile for sinusoidal endothelial specification by E8.75. We characterize two distinct mesothelial cell types as well as early hepatic stellate cells and reveal distinct spatiotemporal distributions for these populations. We capture transcriptional profiles for hepatoblast specification and migration, including the emergence of a hepatomesenchymal cell type and evidence for hepatoblast collective cell migration. Further, we identify cell-cell interactions during the organization of the primitive sinusoid. This study provides a comprehensive atlas of liver lineage establishment from the endoderm and mesoderm through to the organization of the primitive sinusoid at single-cell resolution.
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Affiliation(s)
- Jeremy Lotto
- Terry Fox Laboratory, BC Cancer, Vancouver, BC V5Z 1L3, Canada; Cell and Developmental Biology Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Sibyl Drissler
- Terry Fox Laboratory, BC Cancer, Vancouver, BC V5Z 1L3, Canada; Cell and Developmental Biology Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Rebecca Cullum
- Terry Fox Laboratory, BC Cancer, Vancouver, BC V5Z 1L3, Canada
| | - Wei Wei
- Terry Fox Laboratory, BC Cancer, Vancouver, BC V5Z 1L3, Canada
| | - Manu Setty
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Erin M Bell
- Cell and Developmental Biology Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | | | - Sonja Nowotschin
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ying-Yi Kuo
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Vidur Garg
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Dana Pe'er
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Anna-Katerina Hadjantonakis
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Pamela A Hoodless
- Terry Fox Laboratory, BC Cancer, Vancouver, BC V5Z 1L3, Canada; Cell and Developmental Biology Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
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713
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Crowell HL, Chevrier S, Jacobs A, Sivapatham S, Tumor Profiler Consortium, Bodenmiller B, Robinson MD. An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data. F1000Res 2020; 9:1263. [PMID: 36072920 PMCID: PMC9411975 DOI: 10.12688/f1000research.26073.1] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 07/20/2023] Open
Abstract
Mass cytometry (CyTOF) has become a method of choice for in-depth characterization of tissue heterogeneity in health and disease, and is currently implemented in multiple clinical trials, where higher quality standards must be met. Currently, preprocessing of raw files is commonly performed in independent standalone tools, which makes it difficult to reproduce. Here, we present an R pipeline based on an updated version of CATALYST that covers all preprocessing steps required for downstream mass cytometry analysis in a fully reproducible way. This new version of CATALYST is based on Bioconductor's SingleCellExperiment class and fully unit tested. The R-based pipeline includes file concatenation, bead-based normalization, single-cell deconvolution, spillover compensation and live cell gating after debris and doublet removal. Importantly, this pipeline also includes different quality checks to assess machine sensitivity and staining performance while allowing also for batch correction. This pipeline is based on open source R packages and can be easily be adapted to different study designs. It therefore has the potential to significantly facilitate the work of CyTOF users while increasing the quality and reproducibility of data generated with this technology.
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Affiliation(s)
- Helena L. Crowell
- Institute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, 8057, Switzerland
| | - Stéphane Chevrier
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | - Andrea Jacobs
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | - Sujana Sivapatham
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | | | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | - Mark D. Robinson
- Institute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, 8057, Switzerland
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714
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Tekman M, Batut B, Ostrovsky A, Antoniewski C, Clements D, Ramirez F, Etherington GJ, Hotz HR, Scholtalbers J, Manning JR, Bellenger L, Doyle MA, Heydarian M, Huang N, Soranzo N, Moreno P, Mautner S, Papatheodorou I, Nekrutenko A, Taylor J, Blankenberg D, Backofen R, Grüning B. A single-cell RNA-sequencing training and analysis suite using the Galaxy framework. Gigascience 2020; 9:5931798. [PMID: 33079170 PMCID: PMC7574357 DOI: 10.1093/gigascience/giaa102] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/30/2020] [Indexed: 11/25/2022] Open
Abstract
Background The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. Results Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.
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Affiliation(s)
- Mehmet Tekman
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Bérénice Batut
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Alexander Ostrovsky
- Department of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Christophe Antoniewski
- ARTbio, Sorbonne Université, CNRS FR 3631, Inserm US 037, Paris, France.,Institut de Biologie Paris Seine, 9 Quai Saint-Bernard Université Pierre et Marie Curie, Campus Jussieu, Bâtiments A-B-C, 75005 Paris, France
| | - Dave Clements
- Department of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Fidel Ramirez
- Boehringer Ingelheim International GmbH, Binger Strasse 173, 55216 Ingelheim am Rhein, Biberach, Germany
| | | | - Hans-Rudolf Hotz
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Jelle Scholtalbers
- European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Jonathan R Manning
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Lea Bellenger
- ARTbio, Sorbonne Université, CNRS FR 3631, Inserm US 037, Paris, France
| | - Maria A Doyle
- Research Computing Facility, Peter MacCallum Cancer Centre, Melbourne, 305 Grattan Street, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia
| | - Mohammad Heydarian
- Department of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Ni Huang
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Nicola Soranzo
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Stefan Mautner
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anton Nekrutenko
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - James Taylor
- Department of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Daniel Blankenberg
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, NB21 Cleveland, OH 44195, USA
| | - Rolf Backofen
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Björn Grüning
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
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715
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Stary V, Pandey RV, Strobl J, Kleissl L, Starlinger P, Pereyra D, Weninger W, Fischer GF, Bock C, Farlik M, Stary G. A discrete subset of epigenetically primed human NK cells mediates antigen-specific immune responses. Sci Immunol 2020; 5:eaba6232. [PMID: 33067380 PMCID: PMC7615005 DOI: 10.1126/sciimmunol.aba6232] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 09/25/2020] [Indexed: 12/11/2022]
Abstract
Adaptive features of natural killer (NK) cells have been reported in various species with different underlying mechanisms. It is unclear, however, which NK cell populations are capable of mounting antigen-specific recall responses and how such functions are regulated at the molecular level. Here, we identify and characterize a discrete population of CD49a+CD16- NK cells in the human liver that displays increased epigenetic potential to elicit memory responses and has the functional properties to exert antigen-specific immunity in the skin as an effector site. Integrated chromatin-based epigenetic and transcriptomic profiling revealed unique characteristics of hepatic CD49a+CD16- NK cells when compared with conventional CD49a-CD16+ NK cells, thereby defining active genomic regions and molecules underpinning distinct NK cell reactivity. In contrast to conventional NK cells, our results suggest that adaptive CD49a+CD16- NK cells are able to bypass the KIR receptor-ligand system upon antigen-specific stimulation. Furthermore, these cells were highly migratory toward chemokine gradients expressed in epicutaneous patch test lesions as an effector site of adaptive immune responses in the skin. These results define pathways operative in human antigen-specific adaptive NK cells and provide a roadmap for harnessing this NK cell subset for specific therapeutic or prophylactic vaccine strategies.
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Affiliation(s)
- Victoria Stary
- Department of Visceral Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Ram Vinay Pandey
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Johanna Strobl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Lisa Kleissl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
| | - Patrick Starlinger
- Department of Visceral Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
- Division of Hepatobiliary and Pancreas Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - David Pereyra
- Department of Visceral Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
- Center for Physiology and Pharmacology, Department of Thrombosis Research and Vascular Biology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Weninger
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | | | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Matthias Farlik
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Georg Stary
- Department of Dermatology, Medical University of Vienna, Vienna, Austria.
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
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716
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Huang R, Soneson C, Ernst FG, Rue-Albrecht KC, Yu G, Hicks SC, Robinson MD. TreeSummarizedExperiment: a S4 class for data with hierarchical structure. F1000Res 2020; 9:1246. [PMID: 33274053 PMCID: PMC7684683 DOI: 10.12688/f1000research.26669.1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/06/2020] [Indexed: 09/12/2023] Open
Abstract
Data organized into hierarchical structures (e.g., phylogenies or cell types) arises in several biological fields. It is therefore of interest to have data containers that store the hierarchical structure together with the biological profile data, and provide functions to easily access or manipulate data at different resolutions. Here, we present TreeSummarizedExperiment, a R/S4 class that extends the commonly used SingleCellExperiment class by incorporating tree representations of rows and/or columns (represented by objects of the phylo class). It follows the convention of the SummarizedExperiment class, while providing links between the assays and the nodes of a tree to allow data manipulation at arbitrary levels of the tree. The package is designed to be extensible, allowing new functions on the tree (phylo) to be contributed. As the work is based on the SingleCellExperiment class and the phylo class, both of which are popular classes used in many R packages, it is expected to be able to interact seamlessly with many other tools.
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Affiliation(s)
- Ruizhu Huang
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Charlotte Soneson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Felix G.M. Ernst
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Kevin C. Rue-Albrecht
- MRC WIMM Centre for Computational Biology, University of Oxford, Oxford, OX3 9DS, UK
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical University, Guangzhou, Guangdong, China
- Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Mark D. Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
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717
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Huang R, Soneson C, Ernst FG, Rue-Albrecht KC, Yu G, Hicks SC, Robinson MD. TreeSummarizedExperiment: a S4 class for data with hierarchical structure. F1000Res 2020; 9:1246. [PMID: 33274053 PMCID: PMC7684683 DOI: 10.12688/f1000research.26669.2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/04/2021] [Indexed: 12/26/2022] Open
Abstract
Data organized into hierarchical structures (e.g., phylogenies or cell types) arises in several biological fields. It is therefore of interest to have data containers that store the hierarchical structure together with the biological profile data, and provide functions to easily access or manipulate data at different resolutions. Here, we present TreeSummarizedExperiment, a R/S4 class that extends the commonly used SingleCellExperiment class by incorporating tree representations of rows and/or columns (represented by objects of the phylo class). It follows the convention of the SummarizedExperiment class, while providing links between the assays and the nodes of a tree to allow data manipulation at arbitrary levels of the tree. The package is designed to be extensible, allowing new functions on the tree (phylo) to be contributed. As the work is based on the SingleCellExperiment class and the phylo class, both of which are popular classes used in many R packages, it is expected to be able to interact seamlessly with many other tools.
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Affiliation(s)
- Ruizhu Huang
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Charlotte Soneson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Felix G.M. Ernst
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Kevin C. Rue-Albrecht
- MRC WIMM Centre for Computational Biology, University of Oxford, Oxford, OX3 9DS, UK
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical University, Guangzhou, Guangdong, China
- Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Mark D. Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
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718
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Ravenscroft TA, Janssens J, Lee PT, Tepe B, Marcogliese PC, Makhzami S, Holmes TC, Aerts S, Bellen HJ. Drosophila Voltage-Gated Sodium Channels Are Only Expressed in Active Neurons and Are Localized to Distal Axonal Initial Segment-like Domains. J Neurosci 2020; 40:7999-8024. [PMID: 32928889 PMCID: PMC7574647 DOI: 10.1523/jneurosci.0142-20.2020] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 07/15/2020] [Accepted: 08/04/2020] [Indexed: 12/28/2022] Open
Abstract
In multipolar vertebrate neurons, action potentials (APs) initiate close to the soma, at the axonal initial segment. Invertebrate neurons are typically unipolar with dendrites integrating directly into the axon. Where APs are initiated in the axons of invertebrate neurons is unclear. Voltage-gated sodium (NaV) channels are a functional hallmark of the axonal initial segment in vertebrates. We used an intronic Minos-Mediated Integration Cassette to determine the endogenous gene expression and subcellular localization of the sole NaV channel in both male and female Drosophila, para Despite being the only NaV channel in the fly, we show that only 23 ± 1% of neurons in the embryonic and larval CNS express para, while in the adult CNS para is broadly expressed. We generated a single-cell transcriptomic atlas of the whole third instar larval brain to identify para expressing neurons and show that it positively correlates with markers of differentiated, actively firing neurons. Therefore, only 23 ± 1% of larval neurons may be capable of firing NaV-dependent APs. We then show that Para is enriched in an axonal segment, distal to the site of dendritic integration into the axon, which we named the distal axonal segment (DAS). The DAS is present in multiple neuron classes in both the third instar larval and adult CNS. Whole cell patch clamp electrophysiological recordings of adult CNS fly neurons are consistent with the interpretation that Nav-dependent APs originate in the DAS. Identification of the distal NaV localization in fly neurons will enable more accurate interpretation of electrophysiological recordings in invertebrates.SIGNIFICANCE STATEMENT The site of action potential (AP) initiation in invertebrates is unknown. We tagged the sole voltage-gated sodium (NaV) channel in the fly, para, and identified that Para is enriched at a distal axonal segment. The distal axonal segment is located distal to where dendrites impinge on axons and is the likely site of AP initiation. Understanding where APs are initiated improves our ability to model neuronal activity and our interpretation of electrophysiological data. Additionally, para is only expressed in 23 ± 1% of third instar larval neurons but is broadly expressed in adults. Single-cell RNA sequencing of the third instar larval brain shows that para expression correlates with the expression of active, differentiated neuronal markers. Therefore, only 23 ± 1% of third instar larval neurons may be able to actively fire NaV-dependent APs.
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Affiliation(s)
- Thomas A Ravenscroft
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030
| | - Jasper Janssens
- VIB Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium
- Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Pei-Tseng Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030
| | - Burak Tepe
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030
| | - Paul C Marcogliese
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030
| | - Samira Makhzami
- VIB Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium
- Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Todd C Holmes
- Department of Physiology and Biophysics, School of Medicine, University of California at Irvine, Irvine, California 92697
| | - Stein Aerts
- VIB Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium
- Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Hugo J Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Program in Developmental Biology, Baylor College of Medicine, Houston, Texas 77030
- Howard Hughes Medical Institute, Baylor College of Medicine, Houston, Texas 77030
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719
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Interleukin-6 trans-signaling is a candidate mechanism to drive progression of human DCCs during clinical latency. Nat Commun 2020; 11:4977. [PMID: 33020483 PMCID: PMC7536220 DOI: 10.1038/s41467-020-18701-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 09/03/2020] [Indexed: 02/07/2023] Open
Abstract
Although thousands of breast cancer cells disseminate and home to bone marrow until primary surgery, usually less than a handful will succeed in establishing manifest metastases months to years later. To identify signals that support survival or outgrowth in patients, we profile rare bone marrow-derived disseminated cancer cells (DCCs) long before manifestation of metastasis and identify IL6/PI3K-signaling as candidate pathway for DCC activation. Surprisingly, and similar to mammary epithelial cells, DCCs lack membranous IL6 receptor expression and mechanistic dissection reveals IL6 trans-signaling to regulate a stem-like state of mammary epithelial cells via gp130. Responsiveness to IL6 trans-signals is found to be niche-dependent as bone marrow stromal and endosteal cells down-regulate gp130 in premalignant mammary epithelial cells as opposed to vascular niche cells. PIK3CA activation renders cells independent from IL6 trans-signaling. Consistent with a bottleneck function of microenvironmental DCC control, we find PIK3CA mutations highly associated with late-stage metastatic cells while being extremely rare in early DCCs. Our data suggest that the initial steps of metastasis formation are often not cancer cell-autonomous, but also depend on microenvironmental signals. Metastatic dissemination in breast cancer patients occurs early in malignant transformation, raising questions about how disseminated cancer cells (DCC) progress at distant sites. Here, the authors show that DCCs in bone marrow are activated via IL6-trans-signaling and thereby acquire stemness traits relevant for metastasis formation.
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720
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Penkava F, Velasco-Herrera MDC, Young MD, Yager N, Nwosu LN, Pratt AG, Lara AL, Guzzo C, Maroof A, Mamanova L, Cole S, Efremova M, Simone D, Filer A, Brown CC, Croxford AL, Isaacs JD, Teichmann S, Bowness P, Behjati S, Hussein Al-Mossawi M. Single-cell sequencing reveals clonal expansions of pro-inflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis. Nat Commun 2020; 11:4767. [PMID: 32958743 PMCID: PMC7505844 DOI: 10.1038/s41467-020-18513-6] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 08/27/2020] [Indexed: 11/08/2022] Open
Abstract
Psoriatic arthritis (PsA) is a debilitating immune-mediated inflammatory arthritis of unknown pathogenesis commonly affecting patients with skin psoriasis. Here we use complementary single-cell approaches to study leukocytes from PsA joints. Mass cytometry demonstrates a 3-fold expansion of memory CD8 T cells in the joints of PsA patients compared to peripheral blood. Meanwhile, droplet-based and plate-based single-cell RNA sequencing of paired T cell receptor alpha and beta chain sequences show pronounced CD8 T cell clonal expansions within the joints. Transcriptome analyses find these expanded synovial CD8 T cells to express cycling, activation, tissue-homing and tissue residency markers. T cell receptor sequence comparison between patients identifies clonal convergence. Finally, chemokine receptor CXCR3 is upregulated in the expanded synovial CD8 T cells, while two CXCR3 ligands, CXCL9 and CXCL10, are elevated in PsA synovial fluid. Our data thus provide a quantitative molecular insight into the cellular immune landscape of psoriatic arthritis.
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MESH Headings
- Arthritis, Psoriatic/blood
- Arthritis, Psoriatic/immunology
- CD8-Positive T-Lymphocytes/immunology
- CD8-Positive T-Lymphocytes/metabolism
- Clonal Selection, Antigen-Mediated
- Gene Expression Profiling
- Humans
- Immunologic Memory
- Receptors, Antigen, T-Cell, alpha-beta/genetics
- Receptors, Antigen, T-Cell, alpha-beta/metabolism
- Receptors, Chemokine/metabolism
- Receptors, Lymphocyte Homing/genetics
- Receptors, Lymphocyte Homing/metabolism
- Single-Cell Analysis
- Synovial Fluid/immunology
- Synovial Membrane/immunology
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Affiliation(s)
- Frank Penkava
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | | | | | - Nicole Yager
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Lilian N Nwosu
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Arthur G Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE7 7DN, UK
| | - Alicia Lledo Lara
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | | | - Ash Maroof
- UCB Pharma, 216 Bath road, Slough, SL1 3WE, UK
| | | | | | | | - Davide Simone
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Andrew Filer
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Institute of Inflammation and Ageing, Birmingham, UK
| | - Chrysothemis C Brown
- Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Health, London, WC1N 1EH, UK
| | - Andrew L Croxford
- Idorsia Pharmaceuticals Ltd., Drug Discovery Immunology, Hegenheimermattweg 91, 4123, Allschwill, Switzerland
| | - John D Isaacs
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE7 7DN, UK
| | | | - Paul Bowness
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Sam Behjati
- Wellcome Sanger Institute, Hinxton, CB10 1SA, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK.
- Department of Paediatrics, University of Cambridge, Cambridge, CB2 0SP, UK.
| | - M Hussein Al-Mossawi
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
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721
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Zhang AW, Campbell KR. Computational modelling in single-cell cancer genomics: methods and future directions. Phys Biol 2020; 17:061001. [DOI: 10.1088/1478-3975/abacfe] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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722
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Frost HR. Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring. Nucleic Acids Res 2020; 48:e94. [PMID: 32633778 PMCID: PMC7498348 DOI: 10.1093/nar/gkaa582] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/10/2020] [Accepted: 06/26/2020] [Indexed: 12/26/2022] Open
Abstract
Statistical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis, which can mitigate sparsity and noise, and improve interpretation and power, by aggregating expression data to the pathway level. Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific techniques has been limited. Importantly, no existing methods support cell-level gene set inference. To address this challenge, we developed a new gene set testing method, Variance-adjusted Mahalanobis (VAM), that integrates with the Seurat framework and can accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both data visualization and statistical enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, both population and cell-level inference is supported. As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.
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Affiliation(s)
- Hildreth Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
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723
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Massri AJ, Schiebinger GR, Berrio A, Wang L, Wray GA, McClay DR. Methodologies for Following EMT In Vivo at Single Cell Resolution. Methods Mol Biol 2020; 2179:303-314. [PMID: 32939729 DOI: 10.1007/978-1-0716-0779-4_23] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An epithelial-mesenchymal transition (EMT) occurs in almost every metazoan embryo at the time mesoderm begins to differentiate. Several embryos have a long record as models for studying an EMT given that a known population of cells enters the EMT at a known time thereby enabling a detailed study of the process. Often, however, it is difficult to learn the molecular details of these model EMT systems because the transitioning cells are a minority of the population of cells in the embryo and in most cases there is an inability to isolate that population. Here we provide a method that enables an examination of genes expressed before, during, and after the EMT with a focus on just the cells that undergo the transition. Single cell RNA-seq (scRNA-seq) has advanced as a technology making it feasible to study the trajectory of gene expression specifically in the cells of interest, in vivo, and without the background noise of other cell populations. The sea urchin skeletogenic cells constitute only 5% of the total number of cells in the embryo yet with scRNA-seq it is possible to study the genes expressed by these cells without background noise. This approach, though not perfect, adds a new tool for uncovering the mechanism of EMT in this cell type.
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Affiliation(s)
| | | | | | - Lingyu Wang
- Department of Biology, Duke University, Durham, NC, USA
| | | | - David R McClay
- Department of Biology, Duke University, Durham, NC, USA.
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724
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Sayols S, Klassek J, Werner C, Möckel S, Ritz S, Mendez-Lago M, Soshnikova N. Signalling codes for the maintenance and lineage commitment of embryonic gastric epithelial progenitors. Development 2020; 147:dev.188839. [PMID: 32878924 DOI: 10.1242/dev.188839] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 08/11/2020] [Indexed: 12/16/2022]
Abstract
The identity of embryonic gastric epithelial progenitors is unknown. We used single-cell RNA-sequencing, genetic lineage tracing and organoid assays to assess whether Axin2- and Lgr5-expressing cells are gastric progenitors in the developing mouse stomach. We show that Axin2 + cells represent a transient population of embryonic epithelial cells in the forestomach. Lgr5 + cells generate both glandular corpus and squamous forestomach organoids ex vivo Only Lgr5 + progenitors give rise to zymogenic cells in culture. Modulating the activity of the WNT, BMP and Notch pathways in vivo and ex vivo, we found that WNTs are essential for the maintenance of Lgr5 + epithelial cells. Notch prevents differentiation of the embryonic epithelial cells along all secretory lineages and hence ensures their maintenance. Whereas WNTs promote differentiation of the embryonic progenitors along the zymogenic cell lineage, BMPs enhance their differentiation along the parietal lineage. In contrast, WNTs and BMPs are required to suppress differentiation of embryonic gastric epithelium along the pit cell lineage. Thus, coordinated action of the WNT, BMP and Notch pathways controls cell fate determination in the embryonic gastric epithelium.
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Affiliation(s)
- Sergi Sayols
- Institute of Molecular Biology gGmbH, Mainz 55128, Germany
| | - Jakub Klassek
- Institute of Molecular Biology gGmbH, Mainz 55128, Germany
| | - Clara Werner
- Institute of Molecular Biology gGmbH, Mainz 55128, Germany
| | | | - Sandra Ritz
- Institute of Molecular Biology gGmbH, Mainz 55128, Germany
| | | | - Natalia Soshnikova
- Institute for Molecular Medicine, University Medical Center of the Johannes Gutenberg-University, Mainz 55131, Germany
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725
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Perez-Shibayama C, Islander U, Lütge M, Cheng HW, Onder L, Ring SS, De Martin A, Novkovic M, Colston J, Gil-Cruz C, Ludewig B. Type I interferon signaling in fibroblastic reticular cells prevents exhaustive activation of antiviral CD8 + T cells. Sci Immunol 2020; 5:5/51/eabb7066. [PMID: 32917792 DOI: 10.1126/sciimmunol.abb7066] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 08/20/2020] [Indexed: 12/16/2022]
Abstract
Fibroblastic reticular cells (FRCs) are stromal cells that actively promote the induction of immune responses by coordinating the interaction of innate and adaptive immune cells. However, whether and to which extent immune cell activation is determined by lymph node FRC reprogramming during acute viral infection has remained unexplored. Here, we genetically ablated expression of the type I interferon-α receptor (Ifnar) in Ccl19-Cre+ cells and found that sensing of type I interferon imprints an antiviral state in FRCs and thereby preserves myeloid cell composition in lymph nodes of naive mice. During localized lymphocytic choriomeningitis virus infection, IFNAR signaling precipitated profound phenotypic adaptation of all FRC subsets enhancing antigen presentation, chemokine-driven immune cell recruitment, and immune regulation. The IFNAR-dependent shift of all FRC subsets toward an immunostimulatory state reduced exhaustive CD8+ T cell activation. In sum, these results unveil intricate circuits underlying type I IFN sensing in lymph node FRCs that enable protective antiviral immunity.
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Affiliation(s)
| | - Ulrika Islander
- Krefting Research Centre, Department of Internal Medicine and Clinical Nutrition, University of Gothenburg, Gothenburg, Sweden
| | - Mechthild Lütge
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Hung-Wei Cheng
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Lucas Onder
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Sandra S Ring
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Angelina De Martin
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Mario Novkovic
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Julia Colston
- Department of Medicine, Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, UK
| | - Cristina Gil-Cruz
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland. .,Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
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726
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Ando Y, Kwon ATJ, Shin JW. An era of single-cell genomics consortia. Exp Mol Med 2020; 52:1409-1418. [PMID: 32929222 PMCID: PMC8080593 DOI: 10.1038/s12276-020-0409-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 01/24/2020] [Accepted: 02/10/2020] [Indexed: 12/24/2022] Open
Abstract
The human body consists of 37 trillion single cells represented by over 50 organs that are stitched together to make us who we are, yet we still have very little understanding about the basic units of our body: what cell types and states make up our organs both compositionally and spatially. Previous efforts to profile a wide range of human cell types have been attempted by the FANTOM and GTEx consortia. Now, with the advancement in genomic technologies, profiling the human body at single-cell resolution is possible and will generate an unprecedented wealth of data that will accelerate basic and clinical research with tangible applications to future medicine. To date, several major organs have been profiled, but the challenges lie in ways to integrate single-cell genomics data in a meaningful way. In recent years, several consortia have begun to introduce harmonization and equity in data collection and analysis. Herein, we introduce existing and nascent single-cell genomics consortia, and present benefits to necessitate single-cell genomic consortia in a regional environment to achieve the universal human cell reference dataset.
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Affiliation(s)
- Yoshinari Ando
- RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-Cho, Tsurumi-Ku, Yokohama, 230-0045, Japan
| | - Andrew Tae-Jun Kwon
- RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-Cho, Tsurumi-Ku, Yokohama, 230-0045, Japan
| | - Jay W Shin
- RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-Cho, Tsurumi-Ku, Yokohama, 230-0045, Japan.
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727
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Schuijs MJ, Png S, Richard AC, Tsyben A, Hamm G, Stockis J, Garcia C, Pinaud S, Nicholls A, Ros XR, Su J, Eldridge MD, Riedel A, Serrao EM, Rodewald HR, Mack M, Shields JD, Cohen ES, McKenzie ANJ, Goodwin RJA, Brindle KM, Marioni JC, Halim TYF. ILC2-driven innate immune checkpoint mechanism antagonizes NK cell antimetastatic function in the lung. Nat Immunol 2020; 21:998-1009. [PMID: 32747815 PMCID: PMC7116357 DOI: 10.1038/s41590-020-0745-y] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Accepted: 06/23/2020] [Indexed: 12/23/2022]
Abstract
Metastasis constitutes the primary cause of cancer-related deaths, with the lung being a commonly affected organ. We found that activation of lung-resident group 2 innate lymphoid cells (ILC2s) orchestrated suppression of natural killer (NK) cell-mediated innate antitumor immunity, leading to increased lung metastases and mortality. Using multiple models of lung metastasis, we show that interleukin (IL)-33-dependent ILC2 activation in the lung is involved centrally in promoting tumor burden. ILC2-driven innate type 2 inflammation is accompanied by profound local suppression of interferon-γ production and cytotoxic function of lung NK cells. ILC2-dependent suppression of NK cells is elaborated via an innate regulatory mechanism, which is reliant on IL-5-induced lung eosinophilia, ultimately limiting the metabolic fitness of NK cells. Therapeutic targeting of IL-33 or IL-5 reversed NK cell suppression and alleviated cancer burden. Thus, we reveal an important function of IL-33 and ILC2s in promoting tumor metastasis via their capacity to suppress innate type 1 immunity.
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Affiliation(s)
| | - Shaun Png
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Arianne C Richard
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Anastasia Tsyben
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Gregory Hamm
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Julie Stockis
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Celine Garcia
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Silvain Pinaud
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Ashley Nicholls
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Xavier Romero Ros
- Bioscience Asthma, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Jing Su
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Angela Riedel
- MRC Cancer Unit, University of Cambridge, Cambridge, UK
| | - Eva M Serrao
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Hans-Reimer Rodewald
- Division of Cellular Immunology, German Cancer Research Center, Heidelberg, Germany
| | - Matthias Mack
- Department of Internal Medicine, University Hospital Regensburg, Regensburg, Germany
| | | | - E Suzanne Cohen
- Bioscience Asthma, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Richard J A Goodwin
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Kevin M Brindle
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - John C Marioni
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
- EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
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728
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Lee M, Lee E, Han SK, Choi YH, Kwon DI, Choi H, Lee K, Park ES, Rha MS, Joo DJ, Shin EC, Kim S, Kim JK, Lee YJ. Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells. Nat Commun 2020; 11:4367. [PMID: 32868763 PMCID: PMC7459300 DOI: 10.1038/s41467-020-18155-8] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 08/09/2020] [Indexed: 12/19/2022] Open
Abstract
Invariant natural killer T (iNKT), mucosal-associated invariant T (MAIT), and γδ T cells are innate T cells that acquire memory phenotype in the thymus and share similar biological characteristics. However, how their effector differentiation is developmentally regulated is still unclear. Here, we identify analogous effector subsets of these three innate T cell types in the thymus that share transcriptional profiles. Using single-cell RNA sequencing, we show that iNKT, MAIT and γδ T cells mature via shared, branched differentiation rather than linear maturation or TCR-mediated instruction. Simultaneous TCR clonotyping analysis reveals that thymic maturation of all three types is accompanied by clonal selection and expansion. Analyses of mice deficient of TBET, GATA3 or RORγt and additional in vivo experiments corroborate the predicted differentiation paths, while human innate T cells from liver samples display similar features. Collectively, our data indicate that innate T cells share effector differentiation processes in the thymus. Innate T cells such as iNKT, MAIT and γδ T cells all develop in the thymus, but their differentiation paths are still unclear. Here, the authors show, using single-cell RNA sequencing, that all three cell types develop via shared and branched differentiation paths that are corroborated by additional results from gene-deficient mice and human liver T cells.
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Affiliation(s)
- Minji Lee
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Eunmin Lee
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea
| | - Seong Kyu Han
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Yoon Ha Choi
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea
| | - Dong-Il Kwon
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Hyobeen Choi
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Kwanghwan Lee
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Eun Seo Park
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea
| | - Min-Seok Rha
- Laboratory of Immunology and Infectious Diseases, Graduate School of Medical Science and Engineering, KAIST, Daejeon, Korea
| | - Dong Jin Joo
- Department of Surgery, Yonsei University, College of Medicine, Seoul, Republic of Korea.,The Research Institute for Transplantation, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Eui-Cheol Shin
- Laboratory of Immunology and Infectious Diseases, Graduate School of Medical Science and Engineering, KAIST, Daejeon, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
| | - Jong Kyoung Kim
- Department of New Biology, DGIST, Daegu, 42988, Republic of Korea.
| | - You Jeong Lee
- Division of Integrative Biosciences and Biotechnology, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
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729
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Centonze A, Lin S, Tika E, Sifrim A, Fioramonti M, Malfait M, Song Y, Wuidart A, Van Herck J, Dannau A, Bouvencourt G, Dubois C, Dedoncker N, Sahay A, de Maertelaer V, Siebel CW, Van Keymeulen A, Voet T, Blanpain C. Heterotypic cell-cell communication regulates glandular stem cell multipotency. Nature 2020; 584:608-613. [PMID: 32848220 DOI: 10.1038/s41586-020-2632-y] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 05/20/2020] [Indexed: 12/15/2022]
Abstract
Glandular epithelia, including the mammary and prostate glands, are composed of basal cells (BCs) and luminal cells (LCs)1,2. Many glandular epithelia develop from multipotent basal stem cells (BSCs) that are replaced in adult life by distinct pools of unipotent stem cells1,3-8. However, adult unipotent BSCs can reactivate multipotency under regenerative conditions and upon oncogene expression3,9-13. This suggests that an active mechanism restricts BSC multipotency under normal physiological conditions, although the nature of this mechanism is unknown. Here we show that the ablation of LCs reactivates the multipotency of BSCs from multiple epithelia both in vivo in mice and in vitro in organoids. Bulk and single-cell RNA sequencing revealed that, after LC ablation, BSCs activate a hybrid basal and luminal cell differentiation program before giving rise to LCs-reminiscent of the genetic program that regulates multipotency during embryonic development7. By predicting ligand-receptor pairs from single-cell data14, we find that TNF-which is secreted by LCs-restricts BC multipotency under normal physiological conditions. By contrast, the Notch, Wnt and EGFR pathways were activated in BSCs and their progeny after LC ablation; blocking these pathways, or stimulating the TNF pathway, inhibited regeneration-induced BC multipotency. Our study demonstrates that heterotypic communication between LCs and BCs is essential to maintain lineage fidelity in glandular epithelial stem cells.
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Affiliation(s)
- Alessia Centonze
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Shuheng Lin
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Elisavet Tika
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Alejandro Sifrim
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.,Sanger Institute-EBI Single-Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Marco Fioramonti
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Milan Malfait
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Yura Song
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Aline Wuidart
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Jens Van Herck
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
| | - Anne Dannau
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Gaelle Bouvencourt
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Christine Dubois
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nina Dedoncker
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
| | - Amar Sahay
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, USA.,Harvard Stem Cell Institute, Cambridge, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,BROAD Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Christian W Siebel
- Department of Discovery Oncology, Genentech, South San Francisco, CA, USA
| | | | - Thierry Voet
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.,Sanger Institute-EBI Single-Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Cédric Blanpain
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles (ULB), Brussels, Belgium. .,WELBIO, Université Libre de Bruxelles (ULB), Brussels, Belgium.
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730
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Raimundo F, Vallot C, Vert JP. Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol 2020; 21:212. [PMID: 32831127 PMCID: PMC7444048 DOI: 10.1186/s13059-020-02128-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 08/03/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Many computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering, or differential analysis, often relying on default parameters. Yet, given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need. RESULTS Here, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it. CONCLUSIONS We find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA, and scVI can reach better performance but after parameter tuning.
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Affiliation(s)
| | - Celine Vallot
- CNRS UMR3244, Institut Curie, PSL Research University, Paris, 75005 France
- Translational Research Department, Institut Curie, PSL Research University, Paris, 75005 France
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731
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Yin C, Chen Z. Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning. Healthcare (Basel) 2020; 8:E291. [PMID: 32846941 PMCID: PMC7551840 DOI: 10.3390/healthcare8030291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/19/2020] [Accepted: 08/20/2020] [Indexed: 01/07/2023] Open
Abstract
Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled samples to achieve good classification performance. However, in the majority of the cases, labelled samples are hard to obtain, so the amount of training data are limited. However, many unclassified (unlabelled) sequences have been deposited in public databases, which may help the training procedure. This method is called semi-supervised learning and is very useful in many applications. Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in the training process. The deep forest method with the hyperparameter settings used in this paper can achieve excellent performance. Therefore, in this work, we propose a novel combined deep learning model and semi-supervised learning with self-training approach to improve the performance in disease classification, which utilizes unlabelled samples to update a mechanism designed to increase the number of high-confidence pseudo-labelled samples. The experimental results show that our proposed model can achieve good performance in disease classification and disease-causing gene identification.
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Affiliation(s)
- Chunwu Yin
- School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China;
| | - Zhanbo Chen
- School of Information and Statistics, Guangxi University of Finance and Economics, Nanning 530003, China
- Center of Guangxi Cooperative Innovation for Education Performance Assessment, Guangxi University of Finance and Economics, Nanning 530003, China
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732
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Cytlak U, Resteu A, Pagan S, Green K, Milne P, Maisuria S, McDonald D, Hulme G, Filby A, Carpenter B, Queen R, Hambleton S, Hague R, Lango Allen H, Thaventhiran JED, Doody G, Collin M, Bigley V. Differential IRF8 Transcription Factor Requirement Defines Two Pathways of Dendritic Cell Development in Humans. Immunity 2020; 53:353-370.e8. [PMID: 32735845 PMCID: PMC7447982 DOI: 10.1016/j.immuni.2020.07.003] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 12/27/2019] [Accepted: 07/02/2020] [Indexed: 01/10/2023]
Abstract
The formation of mammalian dendritic cells (DCs) is controlled by multiple hematopoietic transcription factors, including IRF8. Loss of IRF8 exerts a differential effect on DC subsets, including plasmacytoid DCs (pDCs) and the classical DC lineages cDC1 and cDC2. In humans, cDC2-related subsets have been described including AXL+SIGLEC6+ pre-DC, DC2 and DC3. The origin of this heterogeneity is unknown. Using high-dimensional analysis, in vitro differentiation, and an allelic series of human IRF8 deficiency, we demonstrated that cDC2 (CD1c+DC) heterogeneity originates from two distinct pathways of development. The lymphoid-primed IRF8hi pathway, marked by CD123 and BTLA, carried pDC, cDC1, and DC2 trajectories, while the common myeloid IRF8lo pathway, expressing SIRPA, formed DC3s and monocytes. We traced distinct trajectories through the granulocyte-macrophage progenitor (GMP) compartment showing that AXL+SIGLEC6+ pre-DCs mapped exclusively to the DC2 pathway. In keeping with their lower requirement for IRF8, DC3s expand to replace DC2s in human partial IRF8 deficiency.
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Affiliation(s)
- Urszula Cytlak
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Anastasia Resteu
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Sarah Pagan
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Kile Green
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Paul Milne
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Sheetal Maisuria
- Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds LS9 7TF, UK
| | - David McDonald
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Gillian Hulme
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Andrew Filby
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Benjamin Carpenter
- Oxford Genomics Centre, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Rachel Queen
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Sophie Hambleton
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Great North Children's Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK
| | - Rosie Hague
- Department of Paediatric Immunology and Infectious Diseases, Royal Hospital for Children, Glasgow G51 4TF, UK
| | - Hana Lango Allen
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0SP, UK; NIHR BioResource, Cambridge University Hospitals, Cambridge Biomedical Campus, Cambridge CB2 0SP, UK
| | - James E D Thaventhiran
- MRC Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge CB2 1QR, UK
| | - Gina Doody
- Leeds Institute of Medical Research, University of Leeds, Leeds LS9 7TF, UK
| | - Matthew Collin
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
| | - Venetia Bigley
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK.
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733
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Friedrich S, Sonnhammer ELL. Fusion transcript detection using spatial transcriptomics. BMC Med Genomics 2020; 13:110. [PMID: 32753032 PMCID: PMC7437936 DOI: 10.1186/s12920-020-00738-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 06/11/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes. METHOD We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5' gene and has an elevated number of poly(A) tails for the 3' gene. Its expression level is defined by the upstream promoter of the 5' gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score. RESULTS We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-score applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occurred in disease areas, e.g. inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands. CONCLUSIONS STfusion detects fusion transcripts in cancer cell line and clinical tissue data, and distinguishes chimeric transcripts from chimeras caused by trans-splicing events. With STfusion and the use of C-scores, fusion transcripts can be spatially localised in clinical tissue sections on almost single cell level.
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Affiliation(s)
- Stefanie Friedrich
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121, Solna, Sweden.
| | - Erik L L Sonnhammer
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121, Solna, Sweden
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734
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Wiseman DH, Baker SM, Dongre AV, Gurashi K, Storer JA, Somervaille TC, Batta K. Chronic myelomonocytic leukaemia stem cell transcriptomes anticipate disease morphology and outcome. EBioMedicine 2020; 58:102904. [PMID: 32763828 PMCID: PMC7403890 DOI: 10.1016/j.ebiom.2020.102904] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/30/2020] [Accepted: 07/07/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Chronic myelomonocytic leukaemia (CMML) is a clinically heterogeneous stem cell malignancy with overlapping features of myelodysplasia and myeloproliferation. Over 90% of patients carry mutations in epigenetic and/or splicing genes, typically detectable in the Lin-CD34+CD38- immunophenotypic stem cell compartment in which the leukaemia-initiating cells reside. Transcriptional dysregulation at the stem cell level is likely fundamental to disease onset and progression. METHODS We performed single-cell RNA sequencing on 6826 Lin-CD34+CD38-stem cells from CMML patients and healthy controls using the droplet-based, ultra-high-throughput 10x platform. FINDINGS We found substantial inter- and intra-patient heterogeneity, with CMML stem cells displaying distinctive transcriptional programs. Compared with normal controls, CMML stem cells exhibited transcriptomes characterized by increased expression of myeloid-lineage and cell cycle genes, and lower expression of genes selectively expressed by normal haematopoietic stem cells. Neutrophil-primed progenitor genes and a MYC transcription factor regulome were prominent in stem cells from CMML-1 patients, whereas CMML-2 stem cells exhibited strong expression of interferon-regulatory factor regulomes, including those associated with IRF1, IRF7 and IRF8. CMML-1 and CMML-2 stem cells (stages distinguished by proportion of downstream blasts and promonocytes) differed substantially in both transcriptome and pseudotime, indicating fundamentally different biology underpinning these disease states. Gene expression and pathway analyses highlighted potentially tractable therapeutic vulnerabilities for downstream investigation. Importantly, CMML patients harboured variably-sized subpopulations of transcriptionally normal stem cells, indicating a potential reservoir to restore functional haematopoiesis. INTERPRETATION Our findings provide novel insights into the CMML stem cell compartment, revealing an unexpected degree of heterogeneity and demonstrating that CMML stem cell transcriptomes anticipate disease morphology, and therefore outcome. FUNDING Project funding was supported by Oglesby Charitable Trust, Cancer Research UK, Blood Cancer UK, and UK Medical Research Council.
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Affiliation(s)
- Daniel H Wiseman
- Epigenetics of Haematopoiesis Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester M20 4GJ, UK.
| | - Syed M Baker
- Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, UK
| | - Arundhati V Dongre
- Epigenetics of Haematopoiesis Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester M20 4GJ, UK
| | - Kristian Gurashi
- Epigenetics of Haematopoiesis Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester M20 4GJ, UK
| | - Joanna A Storer
- Epigenetics of Haematopoiesis Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester M20 4GJ, UK
| | - Tim Cp Somervaille
- Leukaemia Biology Laboratory, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4GJ, UK
| | - Kiran Batta
- Epigenetics of Haematopoiesis Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester M20 4GJ, UK.
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735
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Paik DT, Cho S, Tian L, Chang HY, Wu JC. Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nat Rev Cardiol 2020; 17:457-473. [PMID: 32231331 PMCID: PMC7528042 DOI: 10.1038/s41569-020-0359-y] [Citation(s) in RCA: 198] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/24/2020] [Indexed: 02/08/2023]
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.
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Affiliation(s)
- David T Paik
- Stanford Cardiovascular Institute, Stanford, CA, USA.
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Sangkyun Cho
- Stanford Cardiovascular Institute, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Lei Tian
- Stanford Cardiovascular Institute, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford, CA, USA.
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
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736
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Ramachandran P, Matchett KP, Dobie R, Wilson-Kanamori JR, Henderson NC. Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis. Nat Rev Gastroenterol Hepatol 2020; 17:457-472. [PMID: 32483353 DOI: 10.1038/s41575-020-0304-x] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/08/2020] [Indexed: 12/19/2022]
Abstract
Liver disease is a major global health-care problem, affecting an estimated 844 million people worldwide. Despite this substantial burden, therapeutic options for liver disease remain limited, in part owing to a paucity of detailed analyses defining the cellular and molecular mechanisms that drive these conditions in humans. Single-cell transcriptomic technologies are transforming our understanding of cellular diversity and function in health and disease. In this Review, we discuss how these technologies have been applied in hepatology, advancing our understanding of cellular heterogeneity and providing novel insights into fundamental liver biology such as the metabolic zonation of hepatocytes, endothelial cells and hepatic stellate cells, and the cellular mechanisms underpinning liver regeneration. Application of these methodologies is also uncovering critical pathophysiological changes driving disease states such as hepatic fibrosis, where distinct populations of macrophages, endothelial cells and mesenchymal cells reside within a spatially distinct fibrotic niche and interact to promote scar formation. In addition, single-cell approaches are starting to dissect key cellular and molecular functions in liver cancer. In the near future, new techniques such as spatial transcriptomics and multiomic approaches will further deepen our understanding of disease pathogenesis, enabling the identification of novel therapeutic targets for patients across the spectrum of liver diseases.
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Affiliation(s)
- Prakash Ramachandran
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Kylie P Matchett
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Ross Dobie
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - John R Wilson-Kanamori
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Neil C Henderson
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK. .,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
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737
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Zhang Y, Li W, Zhou Y. Identification of hub genes in diabetic kidney disease via multiple-microarray analysis. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:997. [PMID: 32953797 PMCID: PMC7475500 DOI: 10.21037/atm-20-5171] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Background Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease; however, the underlying molecular mechanisms remain unclear. Recently, bioinformatics analysis has provided a comprehensive insight toward the molecular mechanisms of DKD. Here, we re-analyzed three mRNA microarray datasets including a single-cell RNA sequencing (scRNA-seq) dataset, with the aim of identifying crucial genes correlated with DKD and contribute to a better understanding of DKD pathogenesis. Methods Three datasets including GSE131882, GSE30122, and GSE30529 were utilized to find differentially expressed genes (DEGs). The potential functions of DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed, and hub genes were selected with the top three molecular complex detection (MCODE) score. A correlation analysis between hub genes and clinical indicators was also performed. Results In total, 84 upregulated DEGs and 49 downregulated DEGs were identified. Enriched pathways of the upregulated DEGs included extracellular matrix (ECM) receptor interaction, focal adhesion, human papillomavirus infection, malaria, and cell adhesion molecules. The downregulated DEGs were mainly enriched in ascorbate and aldarate metabolism, arginine and proline metabolism, endocrine- and other factor-regulated calcium reabsorption, mineral absorption and longevity regulating pathway, and multiple species signaling pathway. Seventeen hub genes were identified, and correlation analysis between unexplored hub genes and clinical features of DKD suggested that EGF, KNG1, GADD45B, and CDH2 might have reno-protective roles in DKD. Meanwhile, ATF3, B2M, VCAM1, CLDN4, SPP1, SOX9, JAG1, C3, and CD24 might promote the progression of DKD. Finally, most hub genes were found present in the immune cells of diabetic kidneys, which suggest the important role of inflammation infiltration in DKD pathogenesis. Conclusions In this study, we found seventeen hub genes using a scRNA-seq contained multiple-microarray analysis, which enriched the present understanding of molecular mechanisms underlying the pathogenesis of DKD in cells' level and provided candidate targets for diagnosis and treatment of DKD.
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Affiliation(s)
- Yumin Zhang
- Department of Endocrinology, Zhongda Hospital, Southeast University, Nanjing, China.,Institute of Diabetes, Medical School, Southeast University, Nanjing, China
| | - Wei Li
- Department of Endocrinology, Zhongda Hospital, Southeast University, Nanjing, China.,Institute of Diabetes, Medical School, Southeast University, Nanjing, China.,Suzhou Hospital Affiliated To Anhui Medical University, Suzhou, China
| | - Yunting Zhou
- Department of Endocrinology, Zhongda Hospital, Southeast University, Nanjing, China.,Institute of Diabetes, Medical School, Southeast University, Nanjing, China.,Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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738
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Brann DH, Tsukahara T, Weinreb C, Lipovsek M, Van den Berge K, Gong B, Chance R, Macaulay IC, Chou HJ, Fletcher RB, Das D, Street K, de Bezieux HR, Choi YG, Risso D, Dudoit S, Purdom E, Mill J, Hachem RA, Matsunami H, Logan DW, Goldstein BJ, Grubb MS, Ngai J, Datta SR. Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia. SCIENCE ADVANCES 2020; 6:eabc5801. [PMID: 32937591 PMCID: PMC10715684 DOI: 10.1126/sciadv.abc5801] [Citation(s) in RCA: 722] [Impact Index Per Article: 144.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/18/2020] [Indexed: 05/05/2023]
Abstract
Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.
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Affiliation(s)
- David H Brann
- Harvard Medical School Department of Neurobiology, Boston MA 02115 USA
| | - Tatsuya Tsukahara
- Harvard Medical School Department of Neurobiology, Boston MA 02115 USA
| | - Caleb Weinreb
- Harvard Medical School Department of Neurobiology, Boston MA 02115 USA
| | - Marcela Lipovsek
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London SE1 1UL, UK
| | - Koen Van den Berge
- Department of Statistics, University of California, Berkeley, CA 94720
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Boying Gong
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94720
| | - Rebecca Chance
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720
| | - Iain C Macaulay
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Hsin-Jung Chou
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720
| | - Russell B Fletcher
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720
- Present address: Surrozen, Inc., South San Francisco, CA 94080
| | - Diya Das
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720
- Berkeley Institute for Data Science, University of California, Berkeley
- Present address: Genentech, Inc., South San Francisco, CA 94080
| | - Kelly Street
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Hector Roux de Bezieux
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94720
- Center for Computational Biology, University of California, Berkeley, CA 94720
| | - Yoon-Gi Choi
- QB3 Functional Genomics Laboratory, University of California, Berkeley, CA 94720
| | - Davide Risso
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - Sandrine Dudoit
- Department of Statistics, University of California, Berkeley, CA 94720
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94720
| | - Elizabeth Purdom
- Department of Statistics, University of California, Berkeley, CA 94720
| | - Jonathan Mill
- University of Exeter Medical School, College of Medicine & Health, University of Exeter, Exeter EX2 5DW, UK
| | - Ralph Abi Hachem
- Duke University School of Medicine Department of Head and Neck Surgery & Communication Sciences, Durham, NC 27717 USA
| | - Hiroaki Matsunami
- Duke University School of Medicine Department of Molecular Genetics and Microbiology, Department of Neurobiology, Duke Institute for Brain Sciences, Durham, NC 27717 US
| | - Darren W Logan
- Waltham Petcare Science Institute, Leicestershire LE14 4RT, UK
| | - Bradley J Goldstein
- Duke University School of Medicine Department of Head and Neck Surgery & Communication Sciences, Durham, NC 27717 USA
| | - Matthew S Grubb
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London SE1 1UL, UK
| | - John Ngai
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720
- QB3 Functional Genomics Laboratory, University of California, Berkeley, CA 94720
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720
- Present address: National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
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739
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Aragona M, Sifrim A, Malfait M, Song Y, Van Herck J, Dekoninck S, Gargouri S, Lapouge G, Swedlund B, Dubois C, Baatsen P, Vints K, Han S, Tissir F, Voet T, Simons BD, Blanpain C. Mechanisms of stretch-mediated skin expansion at single-cell resolution. Nature 2020; 584:268-273. [PMID: 32728211 DOI: 10.1038/s41586-020-2555-7] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 06/19/2020] [Indexed: 02/06/2023]
Abstract
The ability of the skin to grow in response to stretching has been exploited in reconstructive surgery1. Although the response of epidermal cells to stretching has been studied in vitro2,3, it remains unclear how mechanical forces affect their behaviour in vivo. Here we develop a mouse model in which the consequences of stretching on skin epidermis can be studied at single-cell resolution. Using a multidisciplinary approach that combines clonal analysis with quantitative modelling and single-cell RNA sequencing, we show that stretching induces skin expansion by creating a transient bias in the renewal activity of epidermal stem cells, while a second subpopulation of basal progenitors remains committed to differentiation. Transcriptional and chromatin profiling identifies how cell states and gene-regulatory networks are modulated by stretching. Using pharmacological inhibitors and mouse mutants, we define the step-by-step mechanisms that control stretch-mediated tissue expansion at single-cell resolution in vivo.
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Affiliation(s)
- Mariaceleste Aragona
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Alejandro Sifrim
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.,Sanger Institute-EBI Single-Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Milan Malfait
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Yura Song
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Jens Van Herck
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.,Sanger Institute-EBI Single-Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Sophie Dekoninck
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Souhir Gargouri
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Gaëlle Lapouge
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Benjamin Swedlund
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Christine Dubois
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium
| | - Pieter Baatsen
- Electron Microscopy Platform of VIB Bio Imaging Core, Leuven, Belgium
| | - Katlijn Vints
- Electron Microscopy Platform of VIB Bio Imaging Core, Leuven, Belgium
| | - Seungmin Han
- The Wellcome Trust-Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK.,Wellcome Trust-Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Fadel Tissir
- Université Catholique de Louvain, Institute of Neuroscience, Developmental Neurobiology, Brussels, Belgium
| | - Thierry Voet
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.,Sanger Institute-EBI Single-Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Benjamin D Simons
- The Wellcome Trust-Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK. .,Wellcome Trust-Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK. .,Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK.
| | - Cédric Blanpain
- Laboratory of Stem Cells and Cancer, Université Libre de Bruxelles, Brussels, Belgium. .,WELBIO, Université Libre de Bruxelles, Brussels, Belgium.
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740
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Jacob F, Pather SR, Huang WK, Wong SZH, Zhou H, Zhang F, Cubitt B, Chen CZ, Xu M, Pradhan M, Zhang DY, Zheng W, Bang AG, Song H, de A Torre JC, Ming GL. Human Pluripotent Stem Cell-Derived Neural Cells and Brain Organoids Reveal SARS-CoV-2 Neurotropism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.07.28.225151. [PMID: 32766575 PMCID: PMC7402032 DOI: 10.1101/2020.07.28.225151] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
Neurological complications are common in patients with COVID-19. While SARS-CoV-2, the causal pathogen of COVID-19, has been detected in some patient brains, its ability to infect brain cells and impact their function are not well understood, and experimental models using human brain cells are urgently needed. Here we investigated the susceptibility of human induced pluripotent stem cell (hiPSC)-derived monolayer brain cells and region-specific brain organoids to SARS-CoV-2 infection. We found modest numbers of infected neurons and astrocytes, but greater infection of choroid plexus epithelial cells. We optimized a protocol to generate choroid plexus organoids from hiPSCs, which revealed productive SARS-CoV-2 infection that leads to increased cell death and transcriptional dysregulation indicative of an inflammatory response and cellular function deficits. Together, our results provide evidence for SARS-CoV-2 neurotropism and support use of hiPSC-derived brain organoids as a platform to investigate the cellular susceptibility, disease mechanisms, and treatment strategies for SARS-CoV-2 infection.
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741
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Ramos TAR, Maracaja-Coutinho V, Ortega JM, do Rêgo TG. CORAZON: a web server for data normalization and unsupervised clustering based on expression profiles. BMC Res Notes 2020; 13:338. [PMID: 32665017 PMCID: PMC7359491 DOI: 10.1186/s13104-020-05171-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/03/2020] [Indexed: 01/12/2023] Open
Abstract
Objective Data normalization and clustering are mandatory steps in gene expression and downstream analyses, respectively. However, user-friendly implementations of these methodologies are available exclusively under expensive licensing agreements, or in stand-alone scripts developed, reflecting on a great obstacle for users with less computational skills. Results We developed an online tool called CORAZON (Correlations Analyses Zipper Online), which implements three unsupervised learning methods to cluster gene expression datasets in a friendly environment. It allows the usage of eight gene expression normalization/transformation methodologies and the attribute’s influence. The normalizations requiring the gene length only could be performed to RNA-seq, meanwhile the others can be used with microarray and/or NanoString data. Clustering methodologies performances were evaluated through five models with accuracies between 92 and 100%. We applied our tool to obtain functional insights of non-coding RNAs (ncRNAs) based on Gene Ontology enrichment of clusters in a dataset generated by the ENCODE project. The clusters where the majority of transcripts are coding genes were enriched in Cellular, Metabolic, Transports, and Systems Development categories. Meanwhile, the ncRNAs were enriched in the Detection of Stimulus, Sensory Perception, Immunological System, and Digestion categories. CORAZON source-code is freely available at https://gitlab.com/integrativebioinformatics/corazon and the web-server can be accessed at http://corazon.integrativebioinformatics.me.
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Affiliation(s)
- Thaís A R Ramos
- Programa de Pós-Graduação em Bioinformática, Bioinformatics Multidisciplinary Environment (BioME), Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil.,Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
| | - Vinicius Maracaja-Coutinho
- Programa de Pós-Graduação em Bioinformática, Bioinformatics Multidisciplinary Environment (BioME), Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil. .,Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile. .,Instituto Vandique, João Pessoa, Brazil.
| | - J Miguel Ortega
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
| | - Thaís G do Rêgo
- Programa de Pós-Graduação em Bioinformática, Bioinformatics Multidisciplinary Environment (BioME), Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil. .,Departamento de Informática, Centro de Informática, Universidade Federal da Paraíba, João Pessoa, Brazil.
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742
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Ireland AS, Micinski AM, Kastner DW, Guo B, Wait SJ, Spainhower KB, Conley CC, Chen OS, Guthrie MR, Soltero D, Qiao Y, Huang X, Tarapcsák S, Devarakonda S, Chalishazar MD, Gertz J, Moser JC, Marth G, Puri S, Witt BL, Spike BT, Oliver TG. MYC Drives Temporal Evolution of Small Cell Lung Cancer Subtypes by Reprogramming Neuroendocrine Fate. Cancer Cell 2020; 38:60-78.e12. [PMID: 32473656 PMCID: PMC7393942 DOI: 10.1016/j.ccell.2020.05.001] [Citation(s) in RCA: 319] [Impact Index Per Article: 63.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 03/23/2020] [Accepted: 04/30/2020] [Indexed: 02/06/2023]
Abstract
Small cell lung cancer (SCLC) is a neuroendocrine tumor treated clinically as a single disease with poor outcomes. Distinct SCLC molecular subtypes have been defined based on expression of ASCL1, NEUROD1, POU2F3, or YAP1. Here, we use mouse and human models with a time-series single-cell transcriptome analysis to reveal that MYC drives dynamic evolution of SCLC subtypes. In neuroendocrine cells, MYC activates Notch to dedifferentiate tumor cells, promoting a temporal shift in SCLC from ASCL1+ to NEUROD1+ to YAP1+ states. MYC alternatively promotes POU2F3+ tumors from a distinct cell type. Human SCLC exhibits intratumoral subtype heterogeneity, suggesting that this dynamic evolution occurs in patient tumors. These findings suggest that genetics, cell of origin, and tumor cell plasticity determine SCLC subtype.
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Affiliation(s)
- Abbie S Ireland
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Alexi M Micinski
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - David W Kastner
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Bingqian Guo
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Sarah J Wait
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Kyle B Spainhower
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher C Conley
- Huntsman Cancer Institute Bioinformatic Analysis Shared Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Opal S Chen
- Huntsman Cancer Institute High-Throughput Genomics Shared Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Matthew R Guthrie
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Danny Soltero
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Yi Qiao
- Utah Center for Genetic Discovery, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Xiaomeng Huang
- Utah Center for Genetic Discovery, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Szabolcs Tarapcsák
- Utah Center for Genetic Discovery, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Siddhartha Devarakonda
- Division of Medical Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Milind D Chalishazar
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Jason Gertz
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Justin C Moser
- HonorHealth Research Institute, Scottsdale, AZ 85254, USA
| | - Gabor Marth
- Utah Center for Genetic Discovery, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Sonam Puri
- Department of Internal Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Benjamin L Witt
- Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA; ARUP Laboratories at University of Utah, Salt Lake City, UT 84108, USA
| | - Benjamin T Spike
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Trudy G Oliver
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA.
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743
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Zhong R, Chen D, Cao S, Li J, Han B, Zhong H. Immune cell infiltration features and related marker genes in lung cancer based on single-cell RNA-seq. Clin Transl Oncol 2020; 23:405-417. [PMID: 32656582 DOI: 10.1007/s12094-020-02435-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 06/18/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE Immune cells in the immune microenvironment of lung cancer have a great impact on the development of lung cancer. Our purpose was to analyze the immune cell infiltration features and related marker genes for lung cancer. METHODS Single cell RNA sequencing data of 11,485 lung cancer cells were retrieved from the Gene Expression Omnibus. After quality control and data normalization, cell clustering was performed using the Seurat package. Based on the marker genes of each cell type from the CellMarker database, each cell was divided into G1, G2M, and S phases. Then, differential expression and functional enrichment analyses were performed. CIBERSORT was used to reconstruct immune cell types. RESULTS Following cell filtering, highly variable genes were identified for all cells. 14 cell types were clustered. Among them, CD4 + T cell, B cell, plasma cell, natural killer cell and cancer stem cell were the top five cell types. Up-regulated genes were mainly enriched in immune-related biological processes and pathways. Using CIBERSORT, we identified the significantly higher fractions of naïve B cell, memory CD4 + T cell, T follicular helper cell, T regulatory helper cell and M1 macrophage in lung cancer tissues compared to normal tissues. Furthermore, the fractions of resting NK cell, monocyte, M0 macrophage, resting mast cell, eosinophil and neutrophil were significantly lower in tumor tissues than normal tissues. CONCLUSION Our findings dissected the immune cell infiltration features and related marker genes for lung cancer, which might provide novel insights for the immunotherapy of lung cancer.
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Affiliation(s)
- R Zhong
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Huaihai West Road No. 241, Shanghai, 200030, China
| | - D Chen
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Huaihai West Road No. 241, Shanghai, 200030, China
| | - S Cao
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Huaihai West Road No. 241, Shanghai, 200030, China
| | - J Li
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Huaihai West Road No. 241, Shanghai, 200030, China
| | - B Han
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Huaihai West Road No. 241, Shanghai, 200030, China
| | - H Zhong
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Huaihai West Road No. 241, Shanghai, 200030, China.
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744
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Liu J, Liu S, Gao H, Han L, Chu X, Sheng Y, Shou W, Wang Y, Liu Y, Wan J, Yang L. Genome-wide studies reveal the essential and opposite roles of ARID1A in controlling human cardiogenesis and neurogenesis from pluripotent stem cells. Genome Biol 2020; 21:169. [PMID: 32646524 PMCID: PMC7350744 DOI: 10.1186/s13059-020-02082-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 06/22/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Early human heart and brain development simultaneously occur during embryogenesis. Notably, in human newborns, congenital heart defects strongly associate with neurodevelopmental abnormalities, suggesting a common gene or complex underlying both cardiogenesis and neurogenesis. However, due to lack of in vivo studies, the molecular mechanisms that govern both early human heart and brain development remain elusive. RESULTS Here, we report ARID1A, a DNA-binding subunit of the SWI/SNF epigenetic complex, controls both neurogenesis and cardiogenesis from human embryonic stem cells (hESCs) through distinct mechanisms. Knockout-of-ARID1A (ARID1A-/-) leads to spontaneous differentiation of neural cells together with globally enhanced expression of neurogenic genes in undifferentiated hESCs. Additionally, when compared with WT hESCs, cardiac differentiation from ARID1A -/- hESCs is prominently suppressed, whereas neural differentiation is significantly promoted. Whole genome-wide scRNA-seq, ATAC-seq, and ChIP-seq analyses reveal that ARID1A is required to open chromatin accessibility on promoters of essential cardiogenic genes, and temporally associated with key cardiogenic transcriptional factors T and MEF2C during early cardiac development. However, during early neural development, transcription of most essential neurogenic genes is dependent on ARID1A, which can interact with a known neural restrictive silencer factor REST/NRSF. CONCLUSIONS We uncover the opposite roles by ARID1A to govern both early cardiac and neural development from pluripotent stem cells. Global chromatin accessibility on cardiogenic genes is dependent on ARID1A, whereas transcriptional activity of neurogenic genes is under control by ARID1A, possibly through ARID1A-REST/NRSF interaction.
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Affiliation(s)
- Juli Liu
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 W Walnut Street, R4 272, Indianapolis, IN, 46202, USA
| | - Sheng Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Hongyu Gao
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Lei Han
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 W Walnut Street, R4 272, Indianapolis, IN, 46202, USA
| | - Xiaona Chu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yi Sheng
- Department of Obstetrics, Gynecology & Reproductive Sciences, Magee-Women's Research Institute, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Weinian Shou
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 W Walnut Street, R4 272, Indianapolis, IN, 46202, USA
| | - Yue Wang
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University - Purdue University Indianapolis, Indianapolis, IN, 46202, USA
| | - Jun Wan
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University - Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
| | - Lei Yang
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 W Walnut Street, R4 272, Indianapolis, IN, 46202, USA.
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745
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Cole MB, Risso D, Wagner A, DeTomaso D, Ngai J, Purdom E, Dudoit S, Yosef N. Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq. Cell Syst 2020; 8:315-328.e8. [PMID: 31022373 DOI: 10.1016/j.cels.2019.03.010] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 02/07/2019] [Accepted: 03/15/2019] [Indexed: 01/17/2023]
Abstract
Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.
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Affiliation(s)
- Michael B Cole
- Department of Physics, University of California, Berkeley, CA, USA.
| | - Davide Risso
- Department of Statistical Sciences, University of Padova, Padova, Italy; Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA.
| | - Allon Wagner
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Center for Computational Biology, University of California, Berkeley, CA, USA
| | - David DeTomaso
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - John Ngai
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Elizabeth Purdom
- Center for Computational Biology, University of California, Berkeley, CA, USA; Department of Statistics, University of California, Berkeley, CA, USA
| | - Sandrine Dudoit
- Center for Computational Biology, University of California, Berkeley, CA, USA; Department of Statistics, University of California, Berkeley, CA, USA; Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
| | - Nir Yosef
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Center for Computational Biology, University of California, Berkeley, CA, USA.
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746
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Jalili V, Afgan E, Gu Q, Clements D, Blankenberg D, Goecks J, Taylor J, Nekrutenko A. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res 2020; 48:W395-W402. [PMID: 32479607 PMCID: PMC7319590 DOI: 10.1093/nar/gkaa434] [Citation(s) in RCA: 299] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/24/2020] [Accepted: 05/11/2020] [Indexed: 12/18/2022] Open
Abstract
Galaxy (https://galaxyproject.org) is a web-based computational workbench used by tens of thousands of scientists across the world to analyze large biomedical datasets. Since 2005, the Galaxy project has fostered a global community focused on achieving accessible, reproducible, and collaborative research. Together, this community develops the Galaxy software framework, integrates analysis tools and visualizations into the framework, runs public servers that make Galaxy available via a web browser, performs and publishes analyses using Galaxy, leads bioinformatics workshops that introduce and use Galaxy, and develops interactive training materials for Galaxy. Over the last two years, all aspects of the Galaxy project have grown: code contributions, tools integrated, users, and training materials. Key advances in Galaxy's user interface include enhancements for analyzing large dataset collections as well as interactive tools for exploratory data analysis. Extensions to Galaxy's framework include support for federated identity and access management and increased ability to distribute analysis jobs to remote resources. New community resources include large public servers in Europe and Australia, an increasing number of regional and local Galaxy communities, and substantial growth in the Galaxy Training Network.
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Affiliation(s)
- Vahid Jalili
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Enis Afgan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Qiang Gu
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Dave Clements
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel Blankenberg
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - James Taylor
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Anton Nekrutenko
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
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747
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Rath JA, Bajwa G, Carreres B, Hoyer E, Gruber I, Martínez-Paniagua MA, Yu YR, Nouraee N, Sadeghi F, Wu M, Wang T, Hebeisen M, Rufer N, Varadarajan N, Ho PC, Brenner MK, Gfeller D, Arber C. Single-cell transcriptomics identifies multiple pathways underlying antitumor function of TCR- and CD8αβ-engineered human CD4 + T cells. SCIENCE ADVANCES 2020; 6:eaaz7809. [PMID: 32923584 PMCID: PMC7455496 DOI: 10.1126/sciadv.aaz7809] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 05/26/2020] [Indexed: 06/11/2023]
Abstract
Transgenic coexpression of a class I-restricted tumor antigen-specific T cell receptor (TCR) and CD8αβ (TCR8) redirects antigen specificity of CD4+ T cells. Reinforcement of biophysical properties and early TCR signaling explain how redirected CD4+ T cells recognize target cells, but the transcriptional basis for their acquired antitumor function remains elusive. We, therefore, interrogated redirected human CD4+ and CD8+ T cells by single-cell RNA sequencing and characterized them experimentally in bulk and single-cell assays and a mouse xenograft model. TCR8 expression enhanced CD8+ T cell function and preserved less differentiated CD4+ and CD8+ T cells after tumor challenge. TCR8+CD4+ T cells were most potent by activating multiple transcriptional programs associated with enhanced antitumor function. We found sustained activation of cytotoxicity, costimulation, oxidative phosphorylation- and proliferation-related genes, and simultaneously reduced differentiation and exhaustion. Our study identifies molecular features of TCR8 expression that can guide the development of enhanced immunotherapies.
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Affiliation(s)
- Jan A. Rath
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Gagan Bajwa
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital and Texas Children’s Hospital, Houston, TX, USA
| | - Benoit Carreres
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Elisabeth Hoyer
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital and Texas Children’s Hospital, Houston, TX, USA
| | - Isabelle Gruber
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | | | - Yi-Ru Yu
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Nazila Nouraee
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital and Texas Children’s Hospital, Houston, TX, USA
| | - Fatemeh Sadeghi
- Department of Chemical and Biomolecular Engineering, University of Houston, TX, USA
| | - Mengfen Wu
- Biostatistics Shared Resource, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Tao Wang
- Biostatistics Shared Resource, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael Hebeisen
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Nathalie Rufer
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Navin Varadarajan
- Department of Chemical and Biomolecular Engineering, University of Houston, TX, USA
| | - Ping-Chih Ho
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Malcolm K. Brenner
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital and Texas Children’s Hospital, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - David Gfeller
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Caroline Arber
- Department of Oncology UNIL-CHUV, Lausanne University Hospital, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital and Texas Children’s Hospital, Houston, TX, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
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748
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Heterochromatin establishment during early mammalian development is regulated by pericentromeric RNA and characterized by non-repressive H3K9me3. Nat Cell Biol 2020; 22:767-778. [PMID: 32601371 PMCID: PMC7610380 DOI: 10.1038/s41556-020-0536-6] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 05/24/2020] [Indexed: 01/24/2023]
Abstract
Upon fertilization in mammals the gametes are reprogrammed to create a totipotent zygote, a process that involves de novo establishment of chromatin domains. A major feature occurring during preimplantation development is the dramatic remodeling of constitutive heterochromatin, although the functional relevance of this is unknown. Here we show that heterochromatin establishment relies on the stepwise expression and regulated activity of Suv39h enzymes. Enforcing precocious acquisition of constitutive heterochromatin results in compromised development and epigenetic reprogramming, demonstrating that heterochromatin remodeling is essential for natural reprogramming at fertilization. We find that de novo H3K9 trimethylation in the paternal pronucleus after fertilization is catalyzed by Suv39h2 and that pericentromeric RNAs inhibit Suv39h2 activity and reduce H3K9me3. De novo H3K9me3 is initially non-repressive for gene expression but instead can bookmark promoters for compaction. Overall, we uncover the functional importance for the restricted transmission of constitutive heterochromatin during reprogramming and a non-repressive role for H3K9me3.
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749
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Liang P, Yang W, Chen X, Long C, Zheng L, Li H, Zuo Y. Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 20:155-163. [PMID: 32169803 PMCID: PMC7066034 DOI: 10.1016/j.omtn.2020.02.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/27/2019] [Accepted: 02/05/2020] [Indexed: 12/21/2022]
Abstract
Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.
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Affiliation(s)
- Pengfei Liang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Wuritu Yang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Xing Chen
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Chunshen Long
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lei Zheng
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Hanshuang Li
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China.
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750
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Graf C, Wilgenbus P, Pagel S, Pott J, Marini F, Reyda S, Kitano M, Macher-Göppinger S, Weiler H, Ruf W. Myeloid cell-synthesized coagulation factor X dampens antitumor immunity. Sci Immunol 2020; 4:4/39/eaaw8405. [PMID: 31541031 DOI: 10.1126/sciimmunol.aaw8405] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 07/02/2019] [Accepted: 08/06/2019] [Indexed: 12/20/2022]
Abstract
Immune evasion in the tumor microenvironment (TME) is a crucial barrier for effective cancer therapy, and plasticity of innate immune cells may contribute to failures of targeted immunotherapies. Here, we show that rivaroxaban, a direct inhibitor of activated coagulation factor X (FX), promotes antitumor immunity by enhancing infiltration of dendritic cells and cytotoxic T cells at the tumor site. Profiling FX expression in the TME identifies monocytes and macrophages as crucial sources of extravascular FX. By generating mice with immune cells lacking the ability to produce FX, we show that myeloid cell-derived FX plays a pivotal role in promoting tumor immune evasion. In mouse models of cancer, we report that the efficacy of rivaroxaban is comparable with anti-programmed cell death ligand 1 (PD-L1) therapy and that rivaroxaban synergizes with anti-PD-L1 in improving antitumor immunity. Mechanistically, we demonstrate that FXa promotes immune evasion by signaling through protease-activated receptor 2 and that rivaroxaban specifically targets this cell-autonomous signaling pathway to reprogram tumor-associated macrophages. Collectively, our results have uncovered the importance of FX produced in the TME as a regulator of immune cell activation and suggest translational potential of direct oral anticoagulants to remove persisting roadblocks for immunotherapy and provide extravascular benefits in other diseases.
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Affiliation(s)
- Claudine Graf
- Center for Thrombosis and Hemostasis, Johannes Gutenberg University Medical Center, Mainz, Germany.,Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.,Department of Internal Medicine III, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Petra Wilgenbus
- Center for Thrombosis and Hemostasis, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Sven Pagel
- Center for Thrombosis and Hemostasis, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Jennifer Pott
- Center for Thrombosis and Hemostasis, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Federico Marini
- Center for Thrombosis and Hemostasis, Johannes Gutenberg University Medical Center, Mainz, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Sabine Reyda
- Center for Thrombosis and Hemostasis, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Maki Kitano
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | | | - Hartmut Weiler
- Blood Research Institute, Blood Center of Wisconsin, Milwaukee, WI, USA
| | - Wolfram Ruf
- Center for Thrombosis and Hemostasis, Johannes Gutenberg University Medical Center, Mainz, Germany. .,Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
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