1
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Wang C, Lin Y, Li S, Guan J. Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data. BMC Genomics 2024; 25:875. [PMID: 39294558 PMCID: PMC11409548 DOI: 10.1186/s12864-024-10728-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 08/20/2024] [Indexed: 09/20/2024] Open
Abstract
BACKGROUND The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential. RESULTS We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies. CONCLUSIONS DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples.
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Affiliation(s)
- Chenqi Wang
- Department of Automation, Xiamen University, Xiamen, China
| | - Yifan Lin
- Department of Automation, Xiamen University, Xiamen, China
| | - Shuchao Li
- Department of Automation, Xiamen University, Xiamen, China
| | - Jinting Guan
- Department of Automation, Xiamen University, Xiamen, China.
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
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2
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Hoedjes KM, Grath S, Posnien N, Ritchie MG, Schlötterer C, Abbott JK, Almudi I, Coronado-Zamora M, Durmaz Mitchell E, Flatt T, Fricke C, Glaser-Schmitt A, González J, Holman L, Kankare M, Lenhart B, Orengo DJ, Snook RR, Yılmaz VM, Yusuf L. From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology. Mol Ecol 2024:e17382. [PMID: 38856653 DOI: 10.1111/mec.17382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/09/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
RNA sequencing (RNAseq) methodology has experienced a burst of technological developments in the last decade, which has opened up opportunities for studying the mechanisms of adaptation to environmental factors at both the organismal and cellular level. Selecting the most suitable experimental approach for specific research questions and model systems can, however, be a challenge and researchers in ecology and evolution are commonly faced with the choice of whether to study gene expression variation in whole bodies, specific tissues, and/or single cells. A wide range of sometimes polarised opinions exists over which approach is best. Here, we highlight the advantages and disadvantages of each of these approaches to provide a guide to help researchers make informed decisions and maximise the power of their study. Using illustrative examples of various ecological and evolutionary research questions, we guide the readers through the different RNAseq approaches and help them identify the most suitable design for their own projects.
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Affiliation(s)
- Katja M Hoedjes
- Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sonja Grath
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Nico Posnien
- Department of Developmental Biology, Göttingen Center for Molecular Biosciences (GZMB), University of Göttingen, Göttingen, Germany
| | - Michael G Ritchie
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
| | | | | | - Isabel Almudi
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | | | - Esra Durmaz Mitchell
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Functional Genomics and Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Thomas Flatt
- Department of Biology, University of Fribourg, Fribourg, Switzerland
| | - Claudia Fricke
- Institute for Zoology/Animal Ecology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | | | - Josefa González
- Institute of Evolutionary Biology, CSIC, UPF, Barcelona, Spain
| | - Luke Holman
- School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK
| | - Maaria Kankare
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Benedict Lenhart
- Department of Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Dorcas J Orengo
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | - Rhonda R Snook
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Vera M Yılmaz
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Leeban Yusuf
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
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3
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Lee MK, Azizgolshani N, Shapiro JA, Nguyen LN, Kolling FW, Zanazzi GJ, Frost HR, Christensen BC. Identifying tumor type and cell type-specific gene expression alterations in pediatric central nervous system tumors. Nat Commun 2024; 15:3634. [PMID: 38688897 PMCID: PMC11061189 DOI: 10.1038/s41467-024-47712-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/09/2024] [Indexed: 05/02/2024] Open
Abstract
Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collect single nuclei RNA-seq data from 84,700 nuclei of 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues and characterize tumor heterogeneity and transcriptomic alterations. We distinguish cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observe pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identify transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. Here we address current gaps in understanding single nuclei gene expression profiles of previously under-investigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.
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Affiliation(s)
- Min Kyung Lee
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Joshua A Shapiro
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Bala Cynwyd, PA, USA
| | - Lananh N Nguyen
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | | | - George J Zanazzi
- Dartmouth Cancer Center, Lebanon, NH, USA
- Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Hildreth Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
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4
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. Genome Med 2024; 16:65. [PMID: 38685057 PMCID: PMC11057104 DOI: 10.1186/s13073-024-01338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
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5
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Lee MK, Azizgolshani N, Shapiro JA, Nguyen LN, Kolling FW, Zanazzi GJ, Frost HR, Christensen BC. Tumor type and cell type-specific gene expression alterations in diverse pediatric central nervous system tumors identified using single nuclei RNA-seq. RESEARCH SQUARE 2023:rs.3.rs-2517703. [PMID: 36865335 PMCID: PMC9980204 DOI: 10.21203/rs.3.rs-2517703/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collected single nuclei RNA-seq data from 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues (84,700 nuclei) and characterized tumor heterogeneity and transcriptomic alterations. We distinguished cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observed pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identified transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. In this study, we address current gaps in understanding single nuclei gene expression profiles of previously uninvestigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.
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Affiliation(s)
- Min Kyung Lee
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Cardiothoracic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Joshua A Shapiro
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Bala Cynwyd, PA, USA
| | - Lananh N Nguyen
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | | | - George J Zanazzi
- Dartmouth Cancer Center, Lebanon, NH, USA
- Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Hildreth Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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6
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Brandenburg C, Griswold AJ, Van Booven DJ, Kilander MBC, Frei JA, Nestor MW, Dykxhoorn DM, Pericak-Vance MA, Blatt GJ. Transcriptomic analysis of isolated and pooled human postmortem cerebellar Purkinje cells in autism spectrum disorders. Front Genet 2022; 13:944837. [PMID: 36437953 PMCID: PMC9683032 DOI: 10.3389/fgene.2022.944837] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 08/11/2022] [Indexed: 11/29/2023] Open
Abstract
At present, the neuronal mechanisms underlying the diagnosis of autism spectrum disorder (ASD) have not been established. However, studies from human postmortem ASD brains have consistently revealed disruptions in cerebellar circuitry, specifically reductions in Purkinje cell (PC) number and size. Alterations in cerebellar circuitry would have important implications for information processing within the cerebellum and affect a wide range of human motor and non-motor behaviors. Laser capture microdissection was performed to obtain pure PC populations from a cohort of postmortem control and ASD cases and transcriptional profiles were compared. The 427 differentially expressed genes were enriched for gene ontology biological processes related to developmental organization/connectivity, extracellular matrix organization, calcium ion response, immune function and PC signaling alterations. Given the complexity of PCs and their far-ranging roles in response to sensory stimuli and motor function regulation, understanding transcriptional differences in this subset of cerebellar cells in ASD may inform on convergent pathways that impact neuronal function.
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Affiliation(s)
- Cheryl Brandenburg
- Hussman Institute for Autism, Baltimore, MD, United States
- University of Maryland School of Medicine, Baltimore, MD, United States
| | - Anthony J. Griswold
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, United States
| | - Derek J. Van Booven
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, United States
| | | | | | | | - Derek M. Dykxhoorn
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, United States
| | | | - Gene J. Blatt
- Hussman Institute for Autism, Baltimore, MD, United States
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7
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Lam KHB, Diamandis P. Niche deconvolution of the glioblastoma proteome reveals a distinct infiltrative phenotype within the proneural transcriptomic subgroup. Sci Data 2022; 9:596. [PMID: 36182941 PMCID: PMC9526702 DOI: 10.1038/s41597-022-01716-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 09/07/2022] [Indexed: 11/23/2022] Open
Abstract
Glioblastoma is often subdivided into three transcriptional subtypes (classical, proneural, mesenchymal) based on bulk RNA signatures that correlate with distinct genetic and clinical features. Potential cellular-level differences of these subgroups, such as the relative proportions of glioblastoma’s hallmark histopathologic features (e.g. brain infiltration, microvascular proliferation), may provide insight into their distinct phenotypes but are, however, not well understood. Here we leverage machine learning and reference proteomic profiles derived from micro-dissected samples of these major histomorphologic glioblastoma features to deconvolute and estimate niche proportions in an independent proteogenomically-characterized cohort. This approach revealed a strong association of the proneural transcriptional subtype with a diffusely infiltrating phenotype. Similarly, enrichment of a microvascular proliferation proteomic signature was seen within the mesenchymal subtype. This study is the first to link differences in the cellular pathology signatures and transcriptional profiles of glioblastoma, providing potential new insights into the genetic drivers and poor treatment response of specific subsets of glioblastomas.
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Affiliation(s)
- K H Brian Lam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada. .,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada. .,Laboratory Medicine Program, University Health Network, 200 Elizabeth Street, Toronto, ON, Toronto, Ontario, M5G 2C4, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.
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8
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Chen D, Li S, Wang X. GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA. FOUNDATIONS OF DATA SCIENCE (SPRINGFIELD, MO.) 2022; 4:441-466. [PMID: 38250319 PMCID: PMC10798655 DOI: 10.3934/fods.2022013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Complete deconvolution analysis for bulk RNA-seq data is important and helpful to distinguish whether the differences of disease-associated GEPs (gene expression profiles) in tissues of patients and normal controls are due to changes in cellular composition of tissue samples, or due to GEPs changes in specific cells. One of the major techniques to perform complete deconvolution is nonnegative matrix factorization (NMF), which also has a wide-range of applications in the machine learning community. However, the NMF is a well-known strongly ill-posed problem, so a direct application of NMF to RNA-seq data will suffer severe difficulties in the interpretability of solutions. In this paper, we develop an NMF-based mathematical model and corresponding computational algorithms to improve the solution identifiability of deconvoluting bulk RNA-seq data. In our approach, we combine the biological concept of marker genes with the solvability conditions of the NMF theories, and develop a geometric structures guided optimization model. In this strategy, the geometric structure of bulk tissue data is first explored by the spectral clustering technique. Then, the identified information of marker genes is integrated as solvability constraints, while the overall correlation graph is used as manifold regularization. Both synthetic and biological data are used to validate the proposed model and algorithms, from which solution interpretability and accuracy are significantly improved.
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Affiliation(s)
- Duan Chen
- Department of Mathematics and Statistics School of Data Science University of North Carolina at Charlotte, USA
| | - Shaoyu Li
- Department of Mathematics and Statistics University of North Carolina at Charlotte, USA
| | - Xue Wang
- Department of Quantitative Health Sciences Mayo Clinic, Florida, 32224, USA
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9
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Comprehensive evaluation of deconvolution methods for human brain gene expression. Nat Commun 2022; 13:1358. [PMID: 35292647 PMCID: PMC8924248 DOI: 10.1038/s41467-022-28655-4] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/28/2022] [Indexed: 11/08/2022] Open
Abstract
Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing. Transcriptome deconvolution aims to estimate cellular composition based on gene expression data. Here the authors evaluate deconvolution methods for human brain transcriptome and conclude that partial deconvolution algorithms work best, but that appropriate cell-type signatures are also important.
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10
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Boldina G, Fogel P, Rocher C, Bettembourg C, Luta G, Augé F. A2Sign: Agnostic Algorithms for Signatures-a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution. Bioinformatics 2022; 38:1015-1021. [PMID: 34788798 DOI: 10.1093/bioinformatics/btab773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 09/17/2021] [Accepted: 11/09/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Molecular signatures are critical for inferring the proportions of cell types from bulk transcriptomics data. However, the identification of these signatures is based on a methodology that relies on prior biological knowledge of the cell types being studied. When working with less known biological material, a data-driven approach is required to uncover the underlying classes and generate ad hoc signatures from healthy or pathogenic tissue. RESULTS We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization (NTF) strategy that allows us to identify cell-type-specific molecular signatures, greatly reduce collinearities and also account for inter-individual variability. We propose a global framework that can be applied to uncover molecular signatures for cell-type deconvolution in arbitrary tissues using bulk transcriptome data. We also present two new molecular signatures for deconvolution of up to 16 immune cell types using microarray or RNA-seq data. AVAILABILITY AND IMPLEMENTATION All steps of our analysis were implemented in annotated Python notebooks (https://github.com/paulfogel/A2SIGN). To perform NTF, we used the NMTF package, which can be downloaded using Python pip install. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Galina Boldina
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
| | - Paul Fogel
- Consultant, F-75006 Paris, France.,Advestis, F-75008 Paris, France.,Quinten, F-75017 Paris, France
| | - Corinne Rocher
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
| | - Charles Bettembourg
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
| | - George Luta
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC 20057, USA
| | - Franck Augé
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
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11
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Haas E, Incebacak RD, Hentrich T, Huridou C, Schmidt T, Casadei N, Maringer Y, Bahl C, Zimmermann F, Mills JD, Aronica E, Riess O, Schulze-Hentrich JM, Hübener-Schmid J. A Novel SCA3 Knock-in Mouse Model Mimics the Human SCA3 Disease Phenotype Including Neuropathological, Behavioral, and Transcriptional Abnormalities Especially in Oligodendrocytes. Mol Neurobiol 2022; 59:495-522. [PMID: 34716557 PMCID: PMC8786755 DOI: 10.1007/s12035-021-02610-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/19/2021] [Indexed: 12/28/2022]
Abstract
Spinocerebellar ataxia type 3 is the most common autosomal dominant inherited ataxia worldwide, caused by a CAG repeat expansion in the Ataxin-3 gene resulting in a polyglutamine (polyQ)-expansion in the corresponding protein. The disease is characterized by neuropathological, phenotypical, and specific transcriptional changes in affected brain regions. So far, there is no mouse model available representing all the different aspects of the disease, yet highly needed for a better understanding of the disease pathomechanisms. Here, we characterized a novel Ataxin-3 knock-in mouse model, expressing a heterozygous or homozygous expansion of 304 CAACAGs in the murine Ataxin-3 locus using biochemical, behavioral, and transcriptomic approaches. We compared neuropathological, and behavioral features of the new knock-in model with the in SCA3 research mostly used YAC84Q mouse model. Further, we compared transcriptional changes found in cerebellar samples of the SCA3 knock-in mice and post-mortem human SCA3 patients. The novel knock-in mouse is characterized by the expression of a polyQ-expansion in the murine Ataxin-3 protein, leading to aggregate formation, especially in brain regions known to be vulnerable in SCA3 patients, and impairment of Purkinje cells. Along these neuropathological changes, the mice showed a reduction in body weight accompanied by gait and balance instability. Transcriptomic analysis of cerebellar tissue revealed age-dependent differential expression, enriched for genes attributed to myelinating oligodendrocytes. Comparing these changes with those found in cerebellar tissue of SCA3 patients, we discovered an overlap of differentially expressed genes pointing towards similar gene expression perturbances in several genes linked to myelin sheaths and myelinating oligodendrocytes.
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Affiliation(s)
- Eva Haas
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Rana D Incebacak
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Thomas Hentrich
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Chrisovalantou Huridou
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Thorsten Schmidt
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Nicolas Casadei
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
- DFG NGS Competence Center Tübingen, Tübingen, Germany
| | - Yacine Maringer
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Carola Bahl
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Frank Zimmermann
- Interfaculty Biomedical Facility (IBF) Biotechnology lab, University of Heidelberg, Heidelberg, Germany
| | - James D Mills
- Department of (Neuro)Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Eleonora Aronica
- Department of (Neuro)Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Olaf Riess
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
- DFG NGS Competence Center Tübingen, Tübingen, Germany
| | - Julia M Schulze-Hentrich
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Jeannette Hübener-Schmid
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany.
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12
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Wang XL, Li L. Cell type-specific potential pathogenic genes and functional pathways in Alzheimer's Disease. BMC Neurol 2021; 21:381. [PMID: 34600516 PMCID: PMC8487122 DOI: 10.1186/s12883-021-02407-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/28/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a pervasive age-related and highly heritable neurodegenerative disorder but has no effective therapy. The complex cellular microenvironment in the AD brain impedes our understanding of pathogenesis. Thus, a comprehensive investigation of cell type-specific responses in AD is crucial to provide precise molecular and cellular targets for therapeutic development. METHODS Here, we integrated analyzed 4,441 differentially expressed genes (DEGs) that were identified from 263,370 single-cells in cortex samples by single-nucleus RNA sequencing (snRNA-seq) between 42 AD-pathology subjects and 39 normal controls within 3 studies. DEGs were analyzed in microglia, astrocytes, oligodendrocytes, excitatory neurons, inhibitory neurons, and endothelial cells, respectively. In each cell type, we identified both common DEGs which were observed in all 3 studies, and overlapping DEGs which have been seen in at least 2 studies. Firstly, we showed the common DEGs expression and explained the biological functions by comparing with existing literature or multil-omics signaling pathways knowledgebase. We then determined the significant modules and hub genes, and explored the biological processes using the overlapping DEGs. Finally, we identified the common and distinct dysregulated pathways using overall DEGs and overlapping DEGs in a cell type-specific manner. RESULTS Up-regulated LINGO1 has been seen in both oligodendrocytes and excitatory neurons across 3 studies. Interestingly, genes enriched in the mitochondrial module were up-regulated across all cell types, which indicates mitochondrial dysfunction in the AD brain. The estrogen signaling pathway seems to be the most common pathway that is disrupted in AD. CONCLUSION Together, these analyses provide detailed information of cell type-specific and overall transcriptional changes and pathways underlying the human AD-pathology. These findings may provide important insights for drug development to tackle this disease.
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Affiliation(s)
- Xiao-Lan Wang
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Université de Strasbourg, Strasbourg, France
| | - Lianjian Li
- Department of Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430061 China
- Hubei Province Academy of Traditional Chinese Medicine, Wuhan, 430076 China
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13
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Zucha D, Kubista M, Valihrach L. Tutorial: Guidelines for Single-Cell RT-qPCR. Cells 2021; 10:cells10102607. [PMID: 34685587 PMCID: PMC8534298 DOI: 10.3390/cells10102607] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 01/05/2023] Open
Abstract
Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.
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Affiliation(s)
- Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology CAS, 252 50 Vestec, Czech Republic; (D.Z.); (M.K.)
- Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, 166 28 Prague, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology CAS, 252 50 Vestec, Czech Republic; (D.Z.); (M.K.)
- TATAA Biocenter AB, 411 03 Gothenburg, Sweden
| | - Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology CAS, 252 50 Vestec, Czech Republic; (D.Z.); (M.K.)
- Correspondence:
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14
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Aliee H, Theis FJ. AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution. Cell Syst 2021; 12:706-715.e4. [PMID: 34293324 DOI: 10.1016/j.cels.2021.05.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 07/31/2020] [Accepted: 05/07/2021] [Indexed: 12/25/2022]
Abstract
Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. Hence, several deconvolution methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles and closely correlated cell types highly depends on the set of genes undergoing deconvolution. In this work, we introduce AutoGeneS, a platform that automatically extracts discriminative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. AutoGeneS can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).
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Affiliation(s)
- Hananeh Aliee
- Institute of Computational Biology, Helmholtz Centre, Munich, Bayern 85764, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Centre, Munich, Bayern 85764, Germany; Department of Mathematics, Technical University of Munich, Munich, Bayern 85748, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
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15
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Data-driven detection of subtype-specific differentially expressed genes. Sci Rep 2021; 11:332. [PMID: 33432005 PMCID: PMC7801594 DOI: 10.1038/s41598-020-79704-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 12/11/2020] [Indexed: 11/08/2022] Open
Abstract
Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular complex tissues. Classic differential analysis assumes a null hypothesis whose test statistic is not subtype-specific, thus can produce a high false positive rate and/or lower detection power. Here we first introduce a One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. We then propose a scaled test statistic (OVE-sFC) for assessing the statistical significance of SDEGs that applies a mixture null distribution model and a tailored permutation test. The OVE-FC/sFC test was validated on both type 1 error rate and detection power using extensive simulation data sets generated from real gene expression profiles of purified subtype samples. The OVE-FC/sFC test was then applied to two benchmark gene expression data sets of purified subtype samples and detected many known or previously unknown SDEGs. Subsequent supervised deconvolution results on synthesized bulk expression data, obtained using the SDEGs detected from the independent purified expression data by the OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.
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16
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Bordone MC, Barbosa-Morais NL. Unraveling Targetable Systemic and Cell-Type-Specific Molecular Phenotypes of Alzheimer's and Parkinson's Brains With Digital Cytometry. Front Neurosci 2020; 14:607215. [PMID: 33362460 PMCID: PMC7756021 DOI: 10.3389/fnins.2020.607215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/17/2020] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most common neurodegenerative disorders worldwide, with age being their major risk factor. The increasing worldwide life expectancy, together with the scarcity of available treatment choices, makes it thus pressing to find the molecular basis of AD and PD so that the causing mechanisms can be targeted. To study these mechanisms, gene expression profiles have been compared between diseased and control brain tissues. However, this approach is limited by mRNA expression profiles derived for brain tissues highly reflecting their degeneration in cellular composition but not necessarily disease-related molecular states. We therefore propose to account for cell type composition when comparing transcriptomes of healthy and diseased brain samples, so that the loss of neurons can be decoupled from pathology-associated molecular effects. This approach allowed us to identify genes and pathways putatively altered systemically and in a cell-type-dependent manner in AD and PD brains. Moreover, using chemical perturbagen data, we computationally identified candidate small molecules for specifically targeting the profiled AD/PD-associated molecular alterations. Our approach therefore not only brings new insights into the disease-specific and common molecular etiologies of AD and PD but also, in these realms, foster the discovery of more specific targets for functional and therapeutic exploration.
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Affiliation(s)
- Marie C Bordone
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Nuno L Barbosa-Morais
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
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17
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Schmidt M, Maié T, Dahl E, Costa IG, Wagner W. Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites. BMC Biol 2020; 18:178. [PMID: 33234153 PMCID: PMC7687708 DOI: 10.1186/s12915-020-00910-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Background The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand—methylated or non-methylated—and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing. Results We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a “FibroScore” correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types. Conclusion This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.
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Affiliation(s)
- Marco Schmidt
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, 52074, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, 52074, Aachen, Germany
| | - Tiago Maié
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany
| | - Edgar Dahl
- RWTH centralized Biomaterial Bank (RWTH cBMB), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Ivan G Costa
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany
| | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, 52074, Aachen, Germany. .,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, 52074, Aachen, Germany.
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18
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Wang X, Allen M, Li S, Quicksall ZS, Patel TA, Carnwath TP, Reddy JS, Carrasquillo MM, Lincoln SJ, Nguyen TT, Malphrus KG, Dickson DW, Crook JE, Asmann YW, Ertekin-Taner N. Deciphering cellular transcriptional alterations in Alzheimer's disease brains. Mol Neurodegener 2020; 15:38. [PMID: 32660529 PMCID: PMC7359236 DOI: 10.1186/s13024-020-00392-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 06/27/2020] [Indexed: 02/06/2023] Open
Abstract
Large-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimer's disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.
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Affiliation(s)
- Xue Wang
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, FL, USA.
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Shaoyu Li
- Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Zachary S Quicksall
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Tulsi A Patel
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Troy P Carnwath
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Joseph S Reddy
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, FL, USA
| | | | - Sarah J Lincoln
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Thuy T Nguyen
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | | | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Julia E Crook
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Yan W Asmann
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA.
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA.
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19
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Lavery LA, Ure K, Wan YW, Luo C, Trostle AJ, Wang W, Jin H, Lopez J, Lucero J, Durham MA, Castanon R, Nery JR, Liu Z, Goodell M, Ecker JR, Behrens MM, Zoghbi HY. Losing Dnmt3a dependent methylation in inhibitory neurons impairs neural function by a mechanism impacting Rett syndrome. eLife 2020; 9:e52981. [PMID: 32159514 PMCID: PMC7065908 DOI: 10.7554/elife.52981] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 02/20/2020] [Indexed: 12/11/2022] Open
Abstract
Methylated cytosine is an effector of epigenetic gene regulation. In the brain, Dnmt3a is the sole 'writer' of atypical non-CpG methylation (mCH), and MeCP2 is the only known 'reader' for mCH. We asked if MeCP2 is the sole reader for Dnmt3a dependent methylation by comparing mice lacking either protein in GABAergic inhibitory neurons. Loss of either protein causes overlapping and distinct features from the behavioral to molecular level. Loss of Dnmt3a causes global loss of mCH and a subset of mCG sites resulting in more widespread transcriptional alterations and severe neurological dysfunction than MeCP2 loss. These data suggest that MeCP2 is responsible for reading only part of the Dnmt3a dependent methylation in the brain. Importantly, the impact of MeCP2 on genes differentially expressed in both models shows a strong dependence on mCH, but not Dnmt3a dependent mCG, consistent with mCH playing a central role in the pathogenesis of Rett Syndrome.
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Affiliation(s)
- Laura A Lavery
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
| | - Kerstin Ure
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
| | - Ying-Wooi Wan
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
| | - Chongyuan Luo
- Genomic Analysis Laboratory, The Salk Institute for Biological StudiesLa JollaUnited States
- Howard Hughes Medical Institute, The Salk Institute for Biological StudiesLa JollaUnited States
| | - Alexander J Trostle
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Pediatrics, Baylor College of MedicineHoustonUnited States
| | - Wei Wang
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
| | - Haijing Jin
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of MedicineHoustonUnited States
| | - Joanna Lopez
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological StudiesLa JollaUnited States
| | - Mark A Durham
- Program in Developmental Biology, Baylor College of MedicineHoustonUnited States
- Medical Scientist Training Program, Baylor College of MedicineHoustonUnited States
| | - Rosa Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological StudiesLa JollaUnited States
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological StudiesLa JollaUnited States
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of MedicineHoustonUnited States
| | - Margaret Goodell
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
- Program in Developmental Biology, Baylor College of MedicineHoustonUnited States
- Center for Cell and Gene Therapy, Baylor College of MedicineHoustonUnited States
- Stem Cells and Regenerative Medicine Center, Baylor College of MedicineHoustonUnited States
- Department Molecular and Cellular Biology, Baylor College of MedicineHoustonUnited States
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological StudiesLa JollaUnited States
- Howard Hughes Medical Institute, The Salk Institute for Biological StudiesLa JollaUnited States
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological StudiesLa JollaUnited States
- Department of Psychiatry, University of California San DiegoLa JollaUnited States
| | - Huda Y Zoghbi
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s HospitalHoustonUnited States
- Department of Molecular and Human Genetics, Baylor College of MedicineHoustonUnited States
- Department of Pediatrics, Baylor College of MedicineHoustonUnited States
- Program in Developmental Biology, Baylor College of MedicineHoustonUnited States
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Howard Hughes Medical Institute, Baylor College of MedicineHoustonUnited States
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20
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Reynolds RH, Hardy J, Ryten M, Gagliano Taliun SA. Informing disease modelling with brain-relevant functional genomic annotations. Brain 2019; 142:3694-3712. [PMID: 31603214 PMCID: PMC6885670 DOI: 10.1093/brain/awz295] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 07/05/2019] [Accepted: 07/29/2019] [Indexed: 12/13/2022] Open
Abstract
The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.
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Affiliation(s)
- Regina H Reynolds
- Department of Neurodegenerative Disease, University College London (UCL) Institute of Neurology, London, UK
| | - John Hardy
- Department of Neurodegenerative Disease, University College London (UCL) Institute of Neurology, London, UK
- UK Dementia Research Institute at University College London (UCL), London, UK
| | - Mina Ryten
- Department of Neurodegenerative Disease, University College London (UCL) Institute of Neurology, London, UK
| | - Sarah A Gagliano Taliun
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
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21
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Russo I, Kaganovich A, Ding J, Landeck N, Mamais A, Varanita T, Biosa A, Tessari I, Bubacco L, Greggio E, Cookson MR. Transcriptome analysis of LRRK2 knock-out microglia cells reveals alterations of inflammatory- and oxidative stress-related pathways upon treatment with α-synuclein fibrils. Neurobiol Dis 2019; 129:67-78. [PMID: 31102768 PMCID: PMC6749993 DOI: 10.1016/j.nbd.2019.05.012] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/05/2019] [Accepted: 05/14/2019] [Indexed: 11/19/2022] Open
Abstract
Several previous studies have linked the Parkinson's disease (PD) gene LRRK2 to the biology of microglia cells. However, the precise ways in which LRRK2 affects microglial function have not been fully resolved. Here, we used the RNA-Sequencing to obtain transcriptomic profiles of LRRK2 wild-type (WT) and knock-out (KO) microglia cells treated with α-synuclein pre-formed fibrils (PFFs) or lipopolysaccharide (LPS) as a general inflammatory insult. We observed that, although α-synuclein PFFs and LPS mediate overlapping gene expression profiles in microglia, there are also distinct responses to each stimulus. α-Synuclein PFFs trigger alterations of oxidative stress-related pathways with the mitochondrial dismutase Sod2 as a strongly differentially regulated gene. We validated SOD2 at mRNA and protein levels. Furthermore, we found that LRRK2 KO microglia cells reported attenuated induction of mitochondrial SOD2 in response to α-synuclein PFFs, indicating a potential contribution of LRRK2 to oxidative stress-related pathways. We validate several genes in vivo using single-cell RNA-Seq from acutely isolated microglia after striatal injection of LPS into the mouse brain. Overall, these results suggest that microglial LRRK2 may contribute to the pathogenesis of PD via altered oxidative stress signaling.
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Affiliation(s)
- Isabella Russo
- Department of Biology, University of Padova, Padova 35131, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia 25123, Italy.
| | - Alice Kaganovich
- Department of Biology, University of Padova, Padova 35131, Italy; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Jinhui Ding
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Natalie Landeck
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Adamantios Mamais
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Tatiana Varanita
- Department of Biology, University of Padova, Padova 35131, Italy.
| | - Alice Biosa
- Department of Biology, University of Padova, Padova 35131, Italy.
| | - Isabella Tessari
- Department of Biology, University of Padova, Padova 35131, Italy.
| | - Luigi Bubacco
- Department of Biology, University of Padova, Padova 35131, Italy.
| | - Elisa Greggio
- Department of Biology, University of Padova, Padova 35131, Italy.
| | - Mark R Cookson
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
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22
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Avila Cobos F, Vandesompele J, Mestdagh P, De Preter K. Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 2019; 34:1969-1979. [PMID: 29351586 DOI: 10.1093/bioinformatics/bty019] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/10/2018] [Indexed: 12/22/2022] Open
Abstract
Summary Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results. Contact katleen.depreter@ugent.be. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francisco Avila Cobos
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Jo Vandesompele
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Pieter Mestdagh
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Katleen De Preter
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
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23
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Landry AP, Balas M, Spears J, Zador Z. Microenvironment of ruptured cerebral aneurysms discovered using data driven analysis of gene expression. PLoS One 2019; 14:e0220121. [PMID: 31329646 PMCID: PMC6645676 DOI: 10.1371/journal.pone.0220121] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 07/09/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND It is well known that ruptured intracranial aneurysms are associated with substantial morbidity and mortality, yet our understanding of the genetic mechanisms of rupture remains poor. We hypothesize that applying novel techniques to the genetic analysis of aneurysmal tissue will yield key rupture-associated mechanisms and novel drug candidates for the prevention of rupture. METHODS We applied weighted gene co-expression networks (WGCNA) and population-specific gene expression analysis (PSEA) to transcriptomic data from 33 ruptured and unruptured aneurysm domes. Mechanisms were annotated using Gene Ontology, and gene network/population-specific expression levels correlated with rupture state. We then used computational drug repurposing to identify plausible drug candidates for the prevention of aneurysm rupture. RESULTS Network analysis of bulk tissue identified multiple immune mechanisms to be associated with aneurysm rupture. Targeting these processes with computational drug repurposing revealed multiple candidates for preventing rupture including Btk inhibitors and modulators of hypoxia inducible factor. In the macrophage-specific analysis, we identify rupture-associated mechanisms MHCII antigen processing, cholesterol efflux, and keratan sulfate catabolism. These processes map well onto several of highly ranked drug candidates, providing further validation. CONCLUSIONS Our results are the first to demonstrate population-specific expression levels and intracranial aneurysm rupture, and propose novel drug candidates based on network-based transcriptomics.
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Affiliation(s)
- Alexander P. Landry
- Division of Neurosurgery, Department of Surgery, St. Michael’s Hospital, Toronto, ON, Canada
| | - Michael Balas
- Division of Neurosurgery, Department of Surgery, St. Michael’s Hospital, Toronto, ON, Canada
| | - Julian Spears
- Division of Neurosurgery, Department of Surgery, St. Michael’s Hospital, Toronto, ON, Canada
| | - Zsolt Zador
- Division of Neurosurgery, Department of Surgery, St. Michael’s Hospital, Toronto, ON, Canada
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24
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Guelfi S, Botia JA, Thom M, Ramasamy A, Perona M, Stanyer L, Martinian L, Trabzuni D, Smith C, Walker R, Ryten M, Reimers M, Weale ME, Hardy J, Matarin M. Transcriptomic and genetic analyses reveal potential causal drivers for intractable partial epilepsy. Brain 2019; 142:1616-1630. [DOI: 10.1093/brain/awz074] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 12/10/2018] [Accepted: 01/31/2019] [Indexed: 01/05/2023] Open
Affiliation(s)
- Sebastian Guelfi
- Department of Molecular Neuroscience, UCL, Institute of Neurology, Queen Square, London, UK
| | - Juan A. Botia
- Department of Molecular Neuroscience, UCL, Institute of Neurology, Queen Square, London, UK
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Maria Thom
- Division of Neuropathology, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | | | - Marina Perona
- Department of Radiobiology (CAC), National Atomic Energy Commission (CNEA), National Scientific and Technical Research Council (CONICET), Argentina
| | - Lee Stanyer
- Department of Molecular Neuroscience, UCL, Institute of Neurology, Queen Square, London, UK
| | - Lillian Martinian
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Daniah Trabzuni
- Department of Molecular Neuroscience, UCL, Institute of Neurology, Queen Square, London, UK
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Colin Smith
- Academic Department of Neuropathology, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Robert Walker
- Academic Department of Neuropathology, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mina Ryten
- Department of Molecular Neuroscience, UCL, Institute of Neurology, Queen Square, London, UK
| | - Mark Reimers
- Neuroscience Program and Biomedical Engineering, Michigan State University, East Lansing, MI, USA
| | - Michael E. Weale
- Department Medical and Molecular Genetics, King’s College London, London, UK
| | - John Hardy
- Department of Molecular Neuroscience, UCL, Institute of Neurology, Queen Square, London, UK
| | - Mar Matarin
- Department of Molecular Neuroscience, UCL, Institute of Neurology, Queen Square, London, UK
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, Queen Square, London, WC1N 3, UK
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25
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Du X, Wei C, Hejazi Pastor DP, Rao ER, Li Y, Grasselli G, Godfrey J, Palmenberg AC, Andrade J, Hansel C, Gomez CM. α1ACT Is Essential for Survival and Early Cerebellar Programming in a Critical Neonatal Window. Neuron 2019; 102:770-785.e7. [PMID: 30922876 DOI: 10.1016/j.neuron.2019.02.036] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 12/17/2018] [Accepted: 02/20/2019] [Indexed: 10/27/2022]
Abstract
Postnatal cerebellar development is a precisely regulated process involving well-orchestrated expression of neural genes. Neurological phenotypes associated with CACNA1A gene defects have been increasingly recognized, yet the molecular principles underlying this association remain elusive. By characterizing a dose-dependent CACNA1A gene deficiency mouse model, we discovered that α1ACT, as a transcription factor and secondary protein of CACNA1A mRNA, drives dynamic gene expression networks within cerebellar Purkinje cells and is indispensable for neonatal survival. Perinatal loss of α1ACT leads to motor dysfunction through disruption of neurogenesis and synaptic regulatory networks. However, its elimination in adulthood has minimal effect on the cerebellum. These findings shed light on the critical role of α1ACT in facilitating neuronal development in both mice and humans and support a rationale for gene therapies for calcium-channel-associated cerebellar disorders. Finally, we show that bicistronic expression may be common to the voltage-gated calcium channel (VGCC) gene family and may help explain complex genetic syndromes.
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Affiliation(s)
- Xiaofei Du
- Department of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Cenfu Wei
- Department of Neurology, University of Chicago, Chicago, IL 60637, USA
| | | | - Eshaan R Rao
- Department of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Yan Li
- Center for Research Informatics, University of Chicago, Chicago, IL 60637, USA
| | - Giorgio Grasselli
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA; Center for Synaptic Neuroscience and Technology, Italian Institute of Technology (IIT), L.go R. Benzi 10, 16132 Genova, Italy
| | - Jack Godfrey
- Department of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Ann C Palmenberg
- Institute for Molecular Virology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jorge Andrade
- Center for Research Informatics, University of Chicago, Chicago, IL 60637, USA; Department of Pediatrics, University of Chicago, Chicago, IL 60637, USA
| | - Christian Hansel
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
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26
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Kelley KW, Nakao-Inoue H, Molofsky AV, Oldham MC. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat Neurosci 2018; 21:1171-1184. [PMID: 30154505 PMCID: PMC6192711 DOI: 10.1038/s41593-018-0216-z] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 07/10/2018] [Indexed: 02/08/2023]
Abstract
It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7,221 intact CNS samples from 840 neurotypical individuals, representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer's disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource ( http://oldhamlab.ctec.ucsf.edu/ ) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.
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Affiliation(s)
- Kevin W Kelley
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program and Neuroscience Graduate Program, University of California at San Francisco, San Francisco, CA, USA
| | - Hiromi Nakao-Inoue
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Anna V Molofsky
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Michael C Oldham
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA.
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA.
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA.
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27
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Inference of cell type content from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis. PLoS One 2018; 13:e0200003. [PMID: 30016334 PMCID: PMC6049916 DOI: 10.1371/journal.pone.0200003] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 06/18/2018] [Indexed: 01/01/2023] Open
Abstract
Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type content for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package BrainInABlender (validated and publicly-released on github). Using this method, we found that the principal components of variation in the datasets strongly correlated with the predicted neuronal/glial content of the samples. This variability was not simply due to dissection–the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.
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28
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Brain Cell Type Specific Gene Expression and Co-expression Network Architectures. Sci Rep 2018; 8:8868. [PMID: 29892006 PMCID: PMC5995803 DOI: 10.1038/s41598-018-27293-5] [Citation(s) in RCA: 258] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 05/31/2018] [Indexed: 01/08/2023] Open
Abstract
Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell “signatures,” which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.
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29
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Wang N, Chen L, Wang Y. Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues. Methods Mol Biol 2018; 1751:223-236. [PMID: 29508301 DOI: 10.1007/978-1-4939-7710-9_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised methods to deconvolve tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we develop a novel unsupervised deconvolution method, Convex Analysis of Mixtures (CAM), within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tissue samples. To facilitate the utility of this method, we implement an R-Java CAM package that provides comprehensive analytic functions and graphic user interface (GUI).
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Affiliation(s)
- Niya Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.
| | - Lulu Chen
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
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30
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Transcriptomic profiling of the human brain reveals that altered synaptic gene expression is associated with chronological aging. Sci Rep 2017; 7:16890. [PMID: 29203886 PMCID: PMC5715102 DOI: 10.1038/s41598-017-17322-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 11/22/2017] [Indexed: 11/23/2022] Open
Abstract
Aging is a biologically universal event, and yet the key events that drive aging are still poorly understood. One approach to generate new hypotheses about aging is to use unbiased methods to look at change across lifespan. Here, we have examined gene expression in the human dorsolateral frontal cortex using RNA- Seq to populate a whole gene co-expression network analysis. We show that modules of co-expressed genes enriched for those encoding synaptic proteins are liable to change with age. We extensively validate these age-dependent changes in gene expression across several datasets including the publically available GTEx resource which demonstrated that gene expression associations with aging vary between brain regions. We also estimated the extent to which changes in cellular composition account for age associations and find that there are independent signals for cellularity and aging. Overall, these results demonstrate that there are robust age-related alterations in gene expression in the human brain and that genes encoding for neuronal synaptic function may be particularly sensitive to the aging process.
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31
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Scott EY, Penedo MCT, Murray JD, Finno CJ. Defining Trends in Global Gene Expression in Arabian Horses with Cerebellar Abiotrophy. CEREBELLUM (LONDON, ENGLAND) 2017; 16:462-472. [PMID: 27709457 PMCID: PMC5336519 DOI: 10.1007/s12311-016-0823-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Equine cerebellar abiotrophy (CA) is a hereditary neurodegenerative disease that affects the Purkinje neurons of the cerebellum and causes ataxia in Arabian foals. Signs of CA are typically first recognized either at birth to any time up to 6 months of age. CA is inherited as an autosomal recessive trait and is associated with a single nucleotide polymorphism (SNP) on equine chromosome 2 (13074277G>A), located in the fourth exon of TOE1 and in proximity to MUTYH on the antisense strand. We hypothesize that unraveling the functional consequences of the CA SNP using RNA-seq will elucidate the molecular pathways underlying the CA phenotype. RNA-seq (100 bp PE strand-specific) was performed in cerebellar tissue from four CA-affected and five age-matched unaffected horses. Three pipelines for differential gene expression (DE) analysis were used (Tophat2/Cuffdiff2, Kallisto/EdgeR, and Kallisto/Sleuth) with 151 significant DE genes identified by all three pipelines in CA-affected horses. TOE1 (Log2(foldchange) = 0.92, p = 0.66) and MUTYH (Log2(foldchange) = 1.13, p = 0.66) were not differentially expressed. Among the major pathways that were differentially expressed, genes associated with calcium homeostasis and specifically expressed in Purkinje neurons, CALB1 (Log2(foldchange) = -1.7, p < 0.01) and CA8 (Log2(foldchange) = -0.97, p < 0.01), were significantly down-regulated, confirming loss of Purkinje neurons. There was also a significant up-regulation of markers for microglial phagocytosis, TYROBP (Log2(foldchange) = 1.99, p < 0.01) and TREM2 (Log2(foldchange) = 2.02, p < 0.01). These findings reaffirm a loss of Purkinje neurons in CA-affected horses along with a potential secondary loss of granular neurons and activation of microglial cells.
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Affiliation(s)
- E Y Scott
- Department of Animal Science, University of California, Davis, USA
| | - M C T Penedo
- Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California, Davis, USA
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, USA
| | - J D Murray
- Department of Animal Science, University of California, Davis, USA.
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, USA.
| | - C J Finno
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, USA.
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32
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Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues. Sci Rep 2016; 6:18909. [PMID: 26739359 PMCID: PMC4703969 DOI: 10.1038/srep18909] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 11/23/2015] [Indexed: 01/18/2023] Open
Abstract
Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.
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33
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Capurro A, Bodea LG, Schaefer P, Luthi-Carter R, Perreau VM. Computational deconvolution of genome wide expression data from Parkinson's and Huntington's disease brain tissues using population-specific expression analysis. Front Neurosci 2015; 8:441. [PMID: 25620908 PMCID: PMC4288238 DOI: 10.3389/fnins.2014.00441] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Accepted: 12/15/2014] [Indexed: 01/09/2023] Open
Abstract
The characterization of molecular changes in diseased tissues gives insight into pathophysiological mechanisms and is important for therapeutic development. Genome-wide gene expression analysis has proven valuable for identifying biological processes in neurodegenerative diseases using post mortem human brain tissue and numerous datasets are publically available. However, many studies utilize heterogeneous tissue samples consisting of multiple cell types, all of which contribute to global gene expression values, confounding biological interpretation of the data. In particular, changes in numbers of neuronal and glial cells occurring in neurodegeneration confound transcriptomic analyses, particularly in human brain tissues where sample availability and controls are limited. To identify cell specific gene expression changes in neurodegenerative disease, we have applied our recently published computational deconvolution method, population specific expression analysis (PSEA). PSEA estimates cell-type-specific expression values using reference expression measures, which in the case of brain tissue comprises mRNAs with cell-type-specific expression in neurons, astrocytes, oligodendrocytes and microglia. As an exercise in PSEA implementation and hypothesis development regarding neurodegenerative diseases, we applied PSEA to Parkinson's and Huntington's disease (PD, HD) datasets. Genes identified as differentially expressed in substantia nigra pars compacta neurons by PSEA were validated using external laser capture microdissection data. Network analysis and Annotation Clustering (DAVID) identified molecular processes implicated by differential gene expression in specific cell types. The results of these analyses provided new insights into the implementation of PSEA in brain tissues and additional refinement of molecular signatures in human HD and PD.
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Affiliation(s)
- Alberto Capurro
- Department of Cell Physiology and Pharmacology, University of Leicester Leicester, UK
| | - Liviu-Gabriel Bodea
- Neural Regeneration Unit, Institute of Reconstructive Neurobiology, University of Bonn Bonn, Germany ; Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland St Lucia, QLD, Australia
| | | | - Ruth Luthi-Carter
- Department of Cell Physiology and Pharmacology, University of Leicester Leicester, UK
| | - Victoria M Perreau
- The Bioinformatics Core and The Synaptic Neurobiology Laboratory, The Florey Institute of Neuroscience and Mental Health Parkville, VIC, Australia
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34
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Kuhn A. Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89. BMC Bioinformatics 2014; 15:347. [PMID: 25431099 PMCID: PMC4245730 DOI: 10.1186/s12859-014-0347-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 10/07/2014] [Indexed: 12/02/2022] Open
Abstract
Computational expression deconvolution aims to estimate the contribution of individual cell populations to expression profiles measured in samples of heterogeneous composition. Zhong et al. recently proposed Digital Sorting Algorithm (BMC Bioinformatics 2013 Mar 7;14:89) and showed that they could accurately estimate population-specific expression levels and expression differences between two populations. They compared DSA with Population-Specific Expression Analysis (PSEA), a previous deconvolution method that we developed to detect expression changes occurring within the same population between two conditions (e.g. disease versus non-disease). However, Zhong et al. compared PSEA-derived specific expression levels across different cell populations. Specific expression levels obtained with PSEA cannot be directly compared across different populations as they are on a relative scale. They are accurate as we demonstrate by deconvolving the same dataset used by Zhong et al. and, importantly, allow for comparison of population-specific expression across conditions.
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Affiliation(s)
- Alexandre Kuhn
- Microfluidics Systems Biology Lab, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Proteos Building, Room #03-04, 61 Biopolis Drive, Singapore 138673, Singapore.
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35
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Bettencourt C, Ryten M, Forabosco P, Schorge S, Hersheson J, Hardy J, Houlden H. Insights from cerebellar transcriptomic analysis into the pathogenesis of ataxia. JAMA Neurol 2014; 71:831-9. [PMID: 24862029 PMCID: PMC4469030 DOI: 10.1001/jamaneurol.2014.756] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
IMPORTANCE The core clinical and neuropathological feature of the autosomal dominant spinocerebellar ataxias (SCAs) is cerebellar degeneration. Mutations in the known genes explain only 50% to 60% of SCA cases. To date, no effective treatments exist, and the knowledge of drug-treatable molecular pathways is limited. The examination of overlapping mechanisms and the interpretation of how ataxia genes interact will be important in the discovery of potential disease-modifying agents. OBJECTIVES To address the possible relationships among known SCA genes, predict their functions, identify overlapping pathways, and provide a framework for candidate gene discovery using whole-transcriptome expression data. DESIGN, SETTING, AND PARTICIPANTS We have used a systems biology approach based on whole-transcriptome gene expression analysis. As part of the United Kingdom Brain Expression Consortium, we analyzed the expression profile of 788 brain samples obtained from 101 neuropathologically healthy individuals (10 distinct brain regions each). Weighted gene coexpression network analysis was used to cluster 24 SCA genes into gene coexpression modules in an unsupervised manner. The overrepresentation of SCA transcripts in modules identified in the cerebellum was assessed. Enrichment analysis was performed to infer the functions and molecular pathways of genes in biologically relevant modules. MAIN OUTCOMES AND MEASURES Molecular functions and mechanisms implicating SCA genes, as well as lists of relevant coexpressed genes as potential candidates for novel SCA causative or modifier genes. RESULTS Two cerebellar gene coexpression modules were statistically enriched in SCA transcripts (P = .021 for the tan module and P = 2.87 × 10-5 for the light yellow module) and contained established granule and Purkinje cell markers, respectively. One module includes genes involved in the ubiquitin-proteasome system and contains SCA genes usually associated with a complex phenotype, while the other module encloses many genes important for calcium homeostasis and signaling and contains SCA genes associated mostly with pure ataxia. CONCLUSIONS AND RELEVANCE Using normal gene expression in the human brain, we identified significant cell types and pathways in SCA pathogenesis. The overrepresentation of genes involved in calcium homeostasis and signaling may indicate an important target for therapy in the future. Furthermore, the gene networks provide new candidate genes for ataxias or novel genes that may be critical for cerebellar function.
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Affiliation(s)
| | - Mina Ryten
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, England2Department of Medical and Molecular Genetics, King's College London, London, England
| | - Paola Forabosco
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Cagliari, Italy
| | - Stephanie Schorge
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, England
| | - Joshua Hersheson
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, England
| | - John Hardy
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, England
| | - Henry Houlden
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, England
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Wockner LF, Noble EP, Lawford BR, Young RM, Morris CP, Whitehall VLJ, Voisey J. Genome-wide DNA methylation analysis of human brain tissue from schizophrenia patients. Transl Psychiatry 2014; 4:e339. [PMID: 24399042 PMCID: PMC3905221 DOI: 10.1038/tp.2013.111] [Citation(s) in RCA: 213] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2013] [Accepted: 10/29/2013] [Indexed: 01/08/2023] Open
Abstract
Recent studies suggest that genetic and environmental factors do not account for all the schizophrenia risk, and epigenetics also has a role in disease susceptibility. DNA methylation is a heritable epigenetic modification that can regulate gene expression. Genome-wide DNA methylation analysis was performed on post-mortem human brain tissue from 24 patients with schizophrenia and 24 unaffected controls. DNA methylation was assessed at over 485,000 CpG sites using the Illumina Infinium HumanMethylation450 Bead Chip. After adjusting for age and post-mortem interval, 4641 probes corresponding to 2929 unique genes were found to be differentially methylated. Of those genes, 1291 were located in a CpG island and 817 were in a promoter region. These include NOS1, AKT1, DTNBP1, DNMT1, PPP3CC and SOX10, which have previously been associated with schizophrenia. More than 100 of these genes overlap with a previous DNA methylation study of peripheral blood from schizophrenia patients in which 27,000 CpG sites were analysed. Unsupervised clustering analysis of the top 3000 most variable probes revealed two distinct groups with significantly more people with schizophrenia in cluster one compared with controls (P=1.74 × 10(-4)). The first cluster composed of 88% of patients with schizophrenia and only 12% controls, whereas the second cluster composed of 27% of patients with schizophrenia and 73% controls. These results strongly suggest that differential DNA methylation is important in schizophrenia etiology and add support for the use of DNA methylation profiles as a future prognostic indicator of schizophrenia.
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Affiliation(s)
- L F Wockner
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - E P Noble
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - B R Lawford
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia,Alcohol and Drug Service, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - R McD Young
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - C P Morris
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - V L J Whitehall
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - J Voisey
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia,Institute of Health and Biomedical Innovation, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia. E-mail:
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