51
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Huang P, Cai M, Lu X, McKennan C, Wang J. Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.15.532820. [PMID: 36993280 PMCID: PMC10055056 DOI: 10.1101/2023.03.15.532820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.
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Affiliation(s)
- Penghui Huang
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Manqi Cai
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chris McKennan
- Deparment of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
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52
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Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532468. [PMID: 36993743 PMCID: PMC10054947 DOI: 10.1101/2023.03.13.532468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Ming Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xuan Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Department of Psychiatry, Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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53
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Campbell KA, Colacino JA, Puttabyatappa M, Dou JF, Elkin ER, Hammoud SS, Domino SE, Dolinoy DC, Goodrich JM, Loch-Caruso R, Padmanabhan V, Bakulski KM. Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences. Commun Biol 2023; 6:264. [PMID: 36914823 PMCID: PMC10011423 DOI: 10.1038/s42003-023-04623-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
The placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue (n = 9 biological replicates) at term (n = 40,494 cells). We deconvoluted eight published microarray case-control studies of preeclampsia (n = 173 controls, 157 cases). Preeclampsia was associated with excess extravillous trophoblasts and fewer mesenchymal and Hofbauer cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (log2 fold-change cutoff = 0.1, FDR < 0.05) from 1154 to 0, whereas downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1 (37.8%, 95% CI [27.5%, 48.8%]), LEP (34.5%, 95% CI [26.0%, 44.9%]), and ENG (34.5%, 95% CI [25.0%, 45.3%]) overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia contributes to previously observed bulk gene expression differences. This deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.
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Affiliation(s)
- Kyle A Campbell
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Justin A Colacino
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | | | - John F Dou
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Elana R Elkin
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Saher S Hammoud
- Human Genetics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Urology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Steven E Domino
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Dana C Dolinoy
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jaclyn M Goodrich
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rita Loch-Caruso
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Vasantha Padmanabhan
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Pediatrics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Kelly M Bakulski
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
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54
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Song X, Ji J, Rothstein JH, Alexeeff SE, Sakoda LC, Sistig A, Achacoso N, Jorgenson E, Whittemore AS, Klein RJ, Habel LA, Wang P, Sieh W. MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer. Nat Commun 2023; 14:377. [PMID: 36690614 PMCID: PMC9871010 DOI: 10.1038/s41467-023-35888-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.
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Affiliation(s)
- Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Jiayi Ji
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph H Rothstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Alice S Whittemore
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert J Klein
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Pei Wang
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Weiva Sieh
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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55
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Deng W, Li B, Wang J, Jiang W, Yan X, Li N, Vukmirovic M, Kaminski N, Wang J, Zhao H. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. Brief Bioinform 2023; 24:bbac616. [PMID: 36631398 PMCID: PMC9851324 DOI: 10.1093/bib/bbac616] [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: 10/19/2022] [Revised: 11/28/2022] [Accepted: 12/14/2022] [Indexed: 01/13/2023] Open
Abstract
Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.
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Affiliation(s)
- Wenxuan Deng
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Bolun Li
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Jiawei Wang
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Xiting Yan
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ningshan Li
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Milica Vukmirovic
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College St., ON, Canada
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Jing Wang
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
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56
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Humphrey J, Venkatesh S, Hasan R, Herb JT, de Paiva Lopes K, Küçükali F, Byrska-Bishop M, Evani US, Narzisi G, Fagegaltier D, Sleegers K, Phatnani H, Knowles DA, Fratta P, Raj T. Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes. Nat Neurosci 2023; 26:150-162. [PMID: 36482247 DOI: 10.1038/s41593-022-01205-3] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/13/2022] [Indexed: 12/13/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. In this study, we investigated gene expression changes in ALS via RNA sequencing in 380 postmortem samples from cervical, thoracic and lumbar spinal cord segments from 154 individuals with ALS and 49 control individuals. We observed an increase in microglia and astrocyte gene expression, accompanied by a decrease in oligodendrocyte gene expression. By creating a gene co-expression network in the ALS samples, we identified several activated microglia modules that negatively correlate with retrospective disease duration. We mapped molecular quantitative trait loci and found several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how common genetic variants associated with splicing of C9orf72 act as proxies for the well-known repeat expansion, and we use the same mechanism to suggest ATXN3 as a putative risk gene.
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Affiliation(s)
- Jack Humphrey
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Sanan Venkatesh
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rahat Hasan
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jake T Herb
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Katia de Paiva Lopes
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | | | | | - Delphine Fagegaltier
- New York Genome Center, New York, NY, USA
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - David A Knowles
- New York Genome Center, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Pietro Fratta
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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57
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Okereke LC, Bello AU, Onwukwe EA. Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells. Cells 2022; 11:cells11223604. [PMID: 36429031 PMCID: PMC9688486 DOI: 10.3390/cells11223604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022] Open
Abstract
Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.
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Affiliation(s)
- Lois Chinwendu Okereke
- Department of Pure and Applied Mathematics, Mathematics Institute (Emerging Regional Centre of Excellence (ERCE) of the European Mathematical Society (EMS)), African University of Science and Technology, Abuja 900107, Nigeria
- Correspondence:
| | - Abdulmalik Usman Bello
- Department of Pure and Applied Mathematics, Mathematics Institute (Emerging Regional Centre of Excellence (ERCE) of the European Mathematical Society (EMS)), African University of Science and Technology, Abuja 900107, Nigeria
- Department of Mathematics, Federal University Dutsin-Ma, Dutsin-Ma 821101, Nigeria
| | - Emmanuel Akwari Onwukwe
- Department of Theoretical and Applied Physics, African University of Science and Technology, Abuja 900107, Nigeria
- Inspired Innovative Sustainable (IIS) Projects & Solutions Limited, Abuja 900107, Nigeria
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58
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Chatre L, Ducat A, Spradley FT, Palei AC, Chéreau C, Couderc B, Thomas KC, Wilson AR, Amaral LM, Gaillard I, Méhats C, Lagoutte I, Jacques S, Miralles F, Batteux F, Granger JP, Ricchetti M, Vaiman D. Increased NOS coupling by the metabolite tetrahydrobiopterin (BH4) reduces preeclampsia/IUGR consequences. Redox Biol 2022; 55:102406. [PMID: 35964341 PMCID: PMC9389306 DOI: 10.1016/j.redox.2022.102406] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/04/2022] [Accepted: 07/11/2022] [Indexed: 11/24/2022] Open
Abstract
Preeclampsia (PE) is a high-prevalence pregnancy disease characterized by placental insufficiency, gestational hypertension, and proteinuria. Overexpression of the A isoform of the STOX1 transcription factor (STOX1A) recapitulates PE in mice, and STOX1A overexpressing trophoblasts recapitulate PE patients hallmarks in terms of gene expression and pathophysiology. STOX1 overexpression induces nitroso-redox imbalance and mitochondrial hyper-activation. Here, by a thorough analysis on cell models, we show that STOX1 overexpression in trophoblasts alters inducible nitric oxide synthase (iNOS), nitric oxide (NO) content, the nitroso-redox balance, the antioxidant defense, and mitochondrial function. This is accompanied by specific alterations of the Krebs cycle leading to reduced l-malate content. By increasing NOS coupling using the metabolite tetrahydrobiopterin (BH4) we restore this multi-step pathway in vitro. Moving in vivo on two different rodent models (STOX1 mice and RUPP rats, alike early onset and late onset preeclampsia, respectively), we show by transcriptomics that BH4 directly reverts STOX1-deregulated gene expression including glutathione metabolism, oxidative phosphorylation, cholesterol metabolism, inflammation, lipoprotein metabolism and platelet activation, successfully treating placental hypotrophy, gestational hypertension, proteinuria and heart hypertrophy. In the RUPP rats we show that the major fetal issue of preeclampsia, Intra Uterine Growth Restriction (IUGR), is efficiently corrected. Our work posits on solid bases BH4 as a novel potential therapy for preeclampsia.
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Affiliation(s)
- Laurent Chatre
- Institut Pasteur, Department of Developmental & Stem Cell Biology, Stem Cell & Development, 25-28 Rue du Dr. Roux, Paris, France; UMR 3738 CNRS, 25 Rue du Dr. Roux, Paris, 75015, France
| | - Aurélien Ducat
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Frank T Spradley
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Ana C Palei
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Christiane Chéreau
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Betty Couderc
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Kamryn C Thomas
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Anna R Wilson
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Lorena M Amaral
- Department of Pharmacology & Toxicology, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Irène Gaillard
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Céline Méhats
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Isabelle Lagoutte
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Sébastien Jacques
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Francisco Miralles
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Frédéric Batteux
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Joey P Granger
- Department of Physiology & Biophysics, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Miria Ricchetti
- Institut Pasteur, Department of Developmental & Stem Cell Biology, Stem Cell & Development, 25-28 Rue du Dr. Roux, Paris, France; UMR 3738 CNRS, 25 Rue du Dr. Roux, Paris, 75015, France; Institut Pasteur, Molecular Mechanisms of Pathological and Physiological Ageing, 25-28 Rue du Dr. Roux, Paris, France
| | - Daniel Vaiman
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France.
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Shi C, Zhu J, Shen Y, Luo S, Zhu H, Song R. Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2110876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
| | | | - Ye Shen
- North Carolina State University
| | | | - Hongtu Zhu
- University of North Carolina at Chapel Hill
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Cai M, Yue M, Chen T, Liu J, Forno E, Lu X, Billiar T, Celedón J, McKennan C, Chen W, Wang J. Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution. Bioinformatics 2022; 38:3004-3010. [PMID: 35438146 PMCID: PMC9991889 DOI: 10.1093/bioinformatics/btac279] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/22/2022] [Accepted: 04/13/2022] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. RESULTS To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data. AVAILABILITY AND IMPLEMENTATION EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Molin Yue
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Tianmeng Chen
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Jinling Liu
- Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
- Department of Biological Sciences, Missouri University of Science and Technology, Rolla, MO 65409, USA
| | - Erick Forno
- Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Timothy Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Juan Celedón
- Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Wei Chen
- Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
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61
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Haeusler GM, Garnham AL, Li‐Wai‐Suen CSN, Clark JE, Babl FE, Allaway Z, Slavin MA, Mechinaud F, Smyth GK, Phillips B, Thursky KA, Pellegrini M, Doerflinger M. Blood transcriptomics identifies immune signatures indicative of infectious complications in childhood cancer patients with febrile neutropenia. Clin Transl Immunology 2022; 11:e1383. [PMID: 35602885 PMCID: PMC9113042 DOI: 10.1002/cti2.1383] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/03/2022] [Accepted: 03/10/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Febrile neutropenia (FN) is a major cause of treatment disruption and unplanned hospitalization in childhood cancer patients. This study investigated the transcriptome of peripheral blood mononuclear cells (PBMCs) in children with cancer and FN to identify potential predictors of serious infection. Methods Whole-genome transcriptional profiling was conducted on PBMCs collected during episodes of FN in children with cancer at presentation to the hospital (Day 1; n = 73) and within 8-24 h (Day 2; n = 28) after admission. Differentially expressed genes as well as gene pathways that correlated with clinical outcomes were defined for different infectious outcomes. Results Global differences in gene expression associated with specific immune responses in children with FN and documented infection, compared to episodes without documented infection, were identified at admission. These differences resolved over the subsequent 8-24 h. Distinct gene signatures specific for bacteraemia were identified both at admission and on Day 2. Differences in gene signatures between episodes with bacteraemia and episodes with bacterial infection, viral infection and clinically defined infection were also observed. Only subtle differences in gene expression profiles between non-bloodstream bacterial and viral infections were identified. Conclusion Blood transcriptome immune profiling analysis during FN episodes may inform monitoring and aid in defining adequate treatment for different infectious aetiologies in children with cancer.
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Affiliation(s)
- Gabrielle M Haeusler
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,The Victorian Paediatric Integrated Cancer ServiceVictoria State GovernmentMelbourneVICAustralia,Infection Diseases UnitDepartment of General MedicineRoyal Children's HospitalMelbourneVICAustralia
| | - Alexandra L Garnham
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
| | - Connie SN Li‐Wai‐Suen
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
| | - Julia E Clark
- Queensland Children's HospitalChild Health Research CentreThe University of QueenslandBrisbaneQLDAustralia
| | - Franz E Babl
- Department of Emergency MedicineRoyal Children's HospitalMelbourneVICAustralia,Murdoch Children's Research InstitutePaediatric Research in Emergency Departments International Collaborative (PREDICT)MelbourneVICAustralia,Murdoch Children's Research InstituteMelbourneVICAustralia,Department of PaediatricsFaculty of Medicine, Dentistry and Health SciencesUniversity of MelbourneMelbourneVICAustralia
| | - Zoe Allaway
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia
| | - Monica A Slavin
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Infection Diseases UnitDepartment of General MedicineRoyal Children's HospitalMelbourneVICAustralia,Victorian Infectious Diseases ServiceThe Peter Doherty Institute for Infection and ImmunityMelbourneVICAustralia
| | - Francoise Mechinaud
- Children's Cancer CentreThe Royal Children's HospitalMelbourneVICAustralia,Unité d'Hématologie Immunologie PédiatriqueHopital Robert DebréAPHP Nord Université de ParisParisFrance
| | - Gordon K Smyth
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,School of Mathematics and StatisticsUniversity of MelbourneMelbourneVICAustralia
| | - Bob Phillips
- Leeds Children's HospitalLeeds General InfirmaryLeedsUK
| | - Karin A Thursky
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Department of Infectious DiseasesNational Centre for Antimicrobial StewardshipUniversity of MelbourneMelbourneVICAustralia
| | - Marc Pellegrini
- NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
| | - Marcel Doerflinger
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
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62
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Haimbaugh A, Meyer D, Akemann C, Gurdziel K, Baker TR. Comparative Toxicotranscriptomics of Single Cell RNA-Seq and Conventional RNA-Seq in TCDD-Exposed Testicular Tissue. FRONTIERS IN TOXICOLOGY 2022; 4:821116. [PMID: 35615540 PMCID: PMC9126299 DOI: 10.3389/ftox.2022.821116] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/03/2022] [Indexed: 12/18/2022] Open
Abstract
In this report, we compare the outcomes and limitations of two methods of transcriptomic inquiry on adult zebrafish testes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) during sexual differentiation: conventional or bulk RNA-seq (bulk-seq) and single cell RNA sequencing (scRNA-seq) data. scRNA-seq has emerged as a valuable tool for uncovering cell type-specific transcriptome dynamics which exist in heterogeneous tissue. Our lab previously showed the toxicological value of the scRNA-seq pipeline to characterize the sequelae of TCDD exposure in testes, demonstrating that loss of spermatids and spermatozoa, but not other cell types, contributed to the pathology of infertility in adult male zebrafish exposed during sexual differentiation. To investigate the potential for technical artifacts in scRNA-seq such as cell dissociation effects and reduced transcriptome coverage, we compared bulk-sequenced and scRNA-seq-paired samples from control and TCDD-exposed samples to understand what is gained and lost in scRNA-seq vs bulk-seq, both transcriptomically and toxicologically. We hypothesized that the testes may be sensitive to tissue disruption as they contain multiple cell types under constant division and/or maturation, and that TCDD exposure may mediate the extent of sensitivity. Thus, we sought to understand the extent to which this dissociation impacts the toxicological value of data returned from scRNA-seq. We confirm that the required dissociation of individual cells from intact tissue has a significant impact on gene expression, affecting gene pathways with the potential to confound toxicogenomics studies on exposures if findings are not well-controlled and well-situated in context. Additionally, a common scRNA-seq method using cDNA amplified from the 3' end of mRNA under-detects low-expressing transcripts including transcription factors. We confirm this, and show TCDD-related genes may be overlooked by scRNA-seq, however, this under-detection effect is not mediated by TCDD exposure. Even so, scRNA-seq generally extracted toxicologically relevant information better than the bulk-seq method in the present study. This report aims to inform future experimental design for transcriptomic investigation in the growing field of toxicogenomics by demonstrating the differential information extracted from sequencing cells-despite being from the same tissue and exposure scheme-is influenced by the specific protocol used, with implications for the interpretation of exposure-induced risk.
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Affiliation(s)
- Alex Haimbaugh
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
| | - Danielle Meyer
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
- Institute of Environmental Health Sciences, Wayne State University, Detroit, MI, United States
- Department of Environmental and Global Health, University of Florida, Gainesville, FL, United States
| | - Camille Akemann
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
| | - Katherine Gurdziel
- Genome Sciences Core, Office of the Vice President for Research, Wayne State University, Detroit, MI, United States
| | - Tracie R. Baker
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
- Institute of Environmental Health Sciences, Wayne State University, Detroit, MI, United States
- Department of Environmental and Global Health, University of Florida, Gainesville, FL, United States
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63
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Studniberg SI, Ioannidis LJ, Utami RAS, Trianty L, Liao Y, Abeysekera W, Li‐Wai‐Suen CSN, Pietrzak HM, Healer J, Puspitasari AM, Apriyanti D, Coutrier F, Poespoprodjo JR, Kenangalem E, Andries B, Prayoga P, Sariyanti N, Smyth GK, Cowman AF, Price RN, Noviyanti R, Shi W, Garnham AL, Hansen DS. Molecular profiling reveals features of clinical immunity and immunosuppression in asymptomatic P. falciparum malaria. Mol Syst Biol 2022; 18:e10824. [PMID: 35475529 PMCID: PMC9045086 DOI: 10.15252/msb.202110824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 01/12/2023] Open
Abstract
Clinical immunity to P. falciparum malaria is non-sterilizing, with adults often experiencing asymptomatic infection. Historically, asymptomatic malaria has been viewed as beneficial and required to help maintain clinical immunity. Emerging views suggest that these infections are detrimental and constitute a parasite reservoir that perpetuates transmission. To define the impact of asymptomatic malaria, we pursued a systems approach integrating antibody responses, mass cytometry, and transcriptional profiling of individuals experiencing symptomatic and asymptomatic P. falciparum infection. Defined populations of classical and atypical memory B cells and a TH2 cell bias were associated with reduced risk of clinical malaria. Despite these protective responses, asymptomatic malaria featured an immunosuppressive transcriptional signature with upregulation of pathways involved in the inhibition of T-cell function, and CTLA-4 as a predicted regulator in these processes. As proof of concept, we demonstrated a role for CTLA-4 in the development of asymptomatic parasitemia in infection models. The results suggest that asymptomatic malaria is not innocuous and might not support the induction of immune processes to fully control parasitemia or efficiently respond to malaria vaccines.
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Affiliation(s)
- Stephanie I Studniberg
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Lisa J Ioannidis
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Retno A S Utami
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia,Eijkman Institute for Molecular BiologyJakartaIndonesia
| | - Leily Trianty
- Eijkman Institute for Molecular BiologyJakartaIndonesia
| | - Yang Liao
- Olivia Newton‐John Cancer Research InstituteHeidelbergVic.Australia
| | - Waruni Abeysekera
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Connie S N Li‐Wai‐Suen
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Halina M Pietrzak
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Julie Healer
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | | | - Dwi Apriyanti
- Eijkman Institute for Molecular BiologyJakartaIndonesia
| | | | | | | | | | - Pak Prayoga
- Papuan Health and Community FoundationPapuaIndonesia
| | | | - Gordon K Smyth
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Alan F Cowman
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Ric N Price
- Global and Tropical Health DivisionMenzies School of Health Research and Charles Darwin UniversityDarwinNTAustralia,Centre for Tropical Medicine and Global HealthNuffield Department of MedicineUniversity of OxfordOxfordUK,Mahidol‐Oxford Tropical Medicine Research UnitMahidol UniversityBangkokThailand
| | | | - Wei Shi
- Olivia Newton‐John Cancer Research InstituteHeidelbergVic.Australia
| | - Alexandra L Garnham
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Diana S Hansen
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
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64
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Girdhar K, Hoffman GE, Bendl J, Rahman S, Dong P, Liao W, Hauberg ME, Sloofman L, Brown L, Devillers O, Kassim BS, Wiseman JR, Park R, Zharovsky E, Jacobov R, Flatow E, Kozlenkov A, Gilgenast T, Johnson JS, Couto L, Peters MA, Phillips-Cremins JE, Hahn CG, Gur RE, Tamminga CA, Lewis DA, Haroutunian V, Dracheva S, Lipska BK, Marenco S, Kundakovic M, Fullard JF, Jiang Y, Roussos P, Akbarian S. Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains. Nat Neurosci 2022; 25:474-483. [PMID: 35332326 PMCID: PMC8989650 DOI: 10.1038/s41593-022-01032-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 02/09/2022] [Indexed: 12/19/2022]
Abstract
Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n = 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104-106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.
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Affiliation(s)
- Kiran Girdhar
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Gabriel E Hoffman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Rahman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pengfei Dong
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Will Liao
- New York Genome Center, New York, NY, USA
| | - Mads E Hauberg
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Laura Sloofman
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leanne Brown
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olivia Devillers
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bibi S Kassim
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer R Wiseman
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Royce Park
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elizabeth Zharovsky
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rivky Jacobov
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elie Flatow
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexey Kozlenkov
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Gilgenast
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica S Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lizette Couto
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Jennifer E Phillips-Cremins
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Chang-Gyu Hahn
- Department of Psychiatry, Vickie and Jack Farber Institute for Neuroscience, Jefferson University, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical School, Dallas, TX, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Stella Dracheva
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Barbara K Lipska
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Marija Kundakovic
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Biological Sciences, Fordham University, Bronx, NY, USA
| | - John F Fullard
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yan Jiang
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA.
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Schahram Akbarian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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65
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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: 64] [Impact Index Per Article: 21.3] [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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Brown AL, Wilkins OG, Keuss MJ, Kargbo-Hill SE, Zanovello M, Lee WC, Bampton A, Lee FCY, Masino L, Qi YA, Bryce-Smith S, Gatt A, Hallegger M, Fagegaltier D, Phatnani H, Newcombe J, Gustavsson EK, Seddighi S, Reyes JF, Coon SL, Ramos D, Schiavo G, Fisher EMC, Raj T, Secrier M, Lashley T, Ule J, Buratti E, Humphrey J, Ward ME, Fratta P. TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A. Nature 2022; 603:131-137. [PMID: 35197628 PMCID: PMC8891020 DOI: 10.1038/s41586-022-04436-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 01/18/2022] [Indexed: 12/12/2022]
Abstract
Variants of UNC13A, a critical gene for synapse function, increase the risk of amyotrophic lateral sclerosis and frontotemporal dementia1-3, two related neurodegenerative diseases defined by mislocalization of the RNA-binding protein TDP-434,5. Here we show that TDP-43 depletion induces robust inclusion of a cryptic exon in UNC13A, resulting in nonsense-mediated decay and loss of UNC13A protein. Two common intronic UNC13A polymorphisms strongly associated with amyotrophic lateral sclerosis and frontotemporal dementia risk overlap with TDP-43 binding sites. These polymorphisms potentiate cryptic exon inclusion, both in cultured cells and in brains and spinal cords from patients with these conditions. Our findings, which demonstrate a genetic link between loss of nuclear TDP-43 function and disease, reveal the mechanism by which UNC13A variants exacerbate the effects of decreased TDP-43 function. They further provide a promising therapeutic target for TDP-43 proteinopathies.
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Affiliation(s)
- Anna-Leigh Brown
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Oscar G Wilkins
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
| | - Matthew J Keuss
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Sarah E Kargbo-Hill
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Matteo Zanovello
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Weaverly Colleen Lee
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Alexander Bampton
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Flora C Y Lee
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
| | | | - Yue A Qi
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Sam Bryce-Smith
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Ariana Gatt
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Martina Hallegger
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
| | - Delphine Fagegaltier
- Center for Genomics of Neurodegenerative Disease, New York Genome Center (NYGC), New York, NY, USA
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center (NYGC), New York, NY, USA
| | - Jia Newcombe
- NeuroResource, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Emil K Gustavsson
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - Sahba Seddighi
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
- Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel F Reyes
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Steven L Coon
- Molecular Genomics Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
| | - Daniel Ramos
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Giampietro Schiavo
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- UK Dementia Research Institute, University College London, London, UK
| | - Elizabeth M C Fisher
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Towfique Raj
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria Secrier
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, UK
| | - Tammaryn Lashley
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jernej Ule
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
- Department of Molecular Biology and Nanobiotechnology, National Institute of Chemistry, Ljubljana, Slovenia
| | - Emanuele Buratti
- Molecular Pathology Lab, International Centre for Genetic Engineering and Biotechnology (ICGEB), Trieste, Italy
| | - Jack Humphrey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael E Ward
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA.
| | - Pietro Fratta
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK.
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Hasan R, Humphrey J, Bettencourt C, Newcombe J, Lashley T, Fratta P, Raj T. Transcriptomic analysis of frontotemporal lobar degeneration with TDP-43 pathology reveals cellular alterations across multiple brain regions. Acta Neuropathol 2022; 143:383-401. [PMID: 34961893 PMCID: PMC10725322 DOI: 10.1007/s00401-021-02399-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/07/2021] [Accepted: 12/10/2021] [Indexed: 12/28/2022]
Abstract
Frontotemporal lobar degeneration (FTLD) is a group of heterogeneous neurodegenerative disorders affecting the frontal and temporal lobes of the brain. Nuclear loss and cytoplasmic aggregation of the RNA-binding protein TDP-43 represents the major FTLD pathology, known as FTLD-TDP. To date, there is no effective treatment for FTLD-TDP due to an incomplete understanding of the molecular mechanisms underlying disease development. Here we compared postmortem tissue RNA-seq transcriptomes from the frontal cortex, temporal cortex, and cerebellum between 28 controls and 30 FTLD-TDP patients to profile changes in cell-type composition, gene expression and transcript usage. We observed downregulation of neuronal markers in all three regions of the brain, accompanied by upregulation of microglia, astrocytes, and oligodendrocytes, as well as endothelial cells and pericytes, suggesting shifts in both immune activation and within the vasculature. We validate our estimates of neuronal loss using neuropathological atrophy scores and show that neuronal loss in the cortex can be mainly attributed to excitatory neurons, and that increases in microglial and endothelial cell expression are highly correlated with neuronal loss. All our analyses identified a strong involvement of the cerebellum in the neurodegenerative process of FTLD-TDP. Altogether, our data provides a detailed landscape of gene expression alterations to help unravel relevant disease mechanisms in FTLD.
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Affiliation(s)
- Rahat Hasan
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jack Humphrey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Conceição Bettencourt
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Jia Newcombe
- NeuroResource, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Tammaryn Lashley
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Pietro Fratta
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Towfique Raj
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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68
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Danaher P, Kim Y, Nelson B, Griswold M, Yang Z, Piazza E, Beechem JM. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nat Commun 2022; 13:385. [PMID: 35046414 PMCID: PMC8770643 DOI: 10.1038/s41467-022-28020-5] [Citation(s) in RCA: 160] [Impact Index Per Article: 53.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 12/10/2021] [Indexed: 12/11/2022] Open
Abstract
Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.
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Affiliation(s)
| | | | | | | | - Zhi Yang
- NanoString Technologies, Seattle, WA, USA
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69
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Hoffman GE, Ma Y, Montgomery KS, Bendl J, Jaiswal MK, Kozlenkov A, Peters MA, Dracheva S, Fullard JF, Chess A, Devlin B, Sieberts SK, Roussos P. Sex Differences in the Human Brain Transcriptome of Cases With Schizophrenia. Biol Psychiatry 2022; 91:92-101. [PMID: 34154796 PMCID: PMC8463632 DOI: 10.1016/j.biopsych.2021.03.020] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND While schizophrenia differs between males and females in the age of onset, symptomatology, and disease course, the molecular mechanisms underlying these differences remain uncharacterized. METHODS To address questions about the sex-specific effects of schizophrenia, we performed a large-scale transcriptome analysis of RNA sequencing data from 437 controls and 341 cases from two distinct cohorts from the CommonMind Consortium. RESULTS Analysis across the cohorts identified a reproducible gene expression signature of schizophrenia that was highly concordant with previous work. Differential expression across sex was reproducible across cohorts and identified X- and Y-linked genes, as well as those involved in dosage compensation. Intriguingly, the sex expression signature was also enriched for genes involved in neurexin family protein binding and synaptic organization. Differential expression analysis testing a sex-by-diagnosis interaction effect did not identify any genome-wide signature after multiple testing corrections. Gene coexpression network analysis was performed to reduce dimensionality from thousands of genes to dozens of modules and elucidate interactions among genes. We found enrichment of coexpression modules for sex-by-diagnosis differential expression signatures, which were highly reproducible across the two cohorts and involved a number of diverse pathways, including neural nucleus development, neuron projection morphogenesis, and regulation of neural precursor cell proliferation. CONCLUSIONS Overall, our results indicate that the effect size of sex differences in schizophrenia gene expression signatures is small and underscore the challenge of identifying robust sex-by-diagnosis signatures, which will require future analyses in larger cohorts.
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Affiliation(s)
- Gabriel E Hoffman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Yixuan Ma
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Jaroslav Bendl
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Manoj Kumar Jaiswal
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - Alex Kozlenkov
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | | | - Stella Dracheva
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - John F Fullard
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrew Chess
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | | | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York.
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70
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Nadel BB, Oliva M, Shou BL, Mitchell K, Ma F, Montoya DJ, Mouton A, Kim-Hellmuth S, Stranger BE, Pellegrini M, Mangul S. Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples. Brief Bioinform 2021; 22:bbab265. [PMID: 34346485 PMCID: PMC8768458 DOI: 10.1093/bib/bbab265] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/07/2021] [Accepted: 06/21/2021] [Indexed: 11/13/2022] Open
Abstract
Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. Although these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare 12 cell type quantification tools and evaluate their performance while using each of 10 separate reference profiles. Specifically, we have run each tool on over 4000 samples with known cell type proportions, spanning both immune and stromal cell types. A total of 12 of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single-cell data. A final 3728 clinical samples have been collected from the Framingham cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool Estimating the Proportions of Immune and Cancer cells (EPIC) produces the highest correlation, whereas Gene Expression Deconvolution Interactive Tool (GEDIT) produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. We find that optimal reference depends on the tool used, and report suggested references to be used with each tool. Most tools return results within minutes, but on large datasets runtimes for CIBERSORT can exceed hours or even days. We conclude that deconvolution methods are capable of returning high-quality results, but that proper reference selection is critical.
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Affiliation(s)
- Brian B Nadel
- Corresponding authors: Brian B. Nadel, Tel: 310-963-7077; E-mail: ; Matteo Pellegrini, Tel: 310-825-0012, E-mail: ; Serghei Mangul, Tel: 323-442-0043, E-mail:
| | | | | | | | | | | | | | | | | | | | - Serghei Mangul
- Corresponding authors: Brian B. Nadel, Tel: 310-963-7077; E-mail: ; Matteo Pellegrini, Tel: 310-825-0012, E-mail: ; Serghei Mangul, Tel: 323-442-0043, E-mail:
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71
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Hess JL, Radonjić NV, Patak J, Glatt SJ, Faraone SV. Autophagy, apoptosis, and neurodevelopmental genes might underlie selective brain region vulnerability in attention-deficit/hyperactivity disorder. Mol Psychiatry 2021; 26:6643-6654. [PMID: 33339955 PMCID: PMC8760041 DOI: 10.1038/s41380-020-00974-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Large-scale brain imaging studies by the ENIGMA Consortium identified structural changes associated with attention-deficit/hyperactivity disorder (ADHD). It is not clear why some brain regions are impaired and others spared by the etiological risks for ADHD. We hypothesized that spatial variation in brain cell organization and/or pathway expression levels contribute to selective brain region vulnerability (SBRV) in ADHD. In this study, we used the largest available collection of magnetic resonance imaging (MRI) results from the ADHD ENIGMA Consortium (subcortical MRI n = 3242; cortical MRI n = 4180) along with high-resolution postmortem brain microarray data from Allen Brain Atlas (donors n = 6) from 22 brain regions to investigate our SBRV hypothesis. We performed deconvolution of the bulk transcriptomic data to determine abundances of neuronal and nonneuronal cells in the brain. We assessed the relationships between gene-set expression levels, cell abundance, and standardized effect sizes representing regional changes in brain sizes in cases of ADHD. Our analysis yielded significant correlations between apoptosis, autophagy, and neurodevelopment genes with smaller brain sizes in ADHD, along with associations to regional abundances of astrocytes and oligodendrocytes. The lack of enrichment of common genetic risk variants for ADHD within implicated gene sets suggests an environmental etiology to these differences. This work provides novel mechanistic clues about SBRV in ADHD.
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Affiliation(s)
- Jonathan L Hess
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Nevena V Radonjić
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Jameson Patak
- Department of Neuroscience, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen J Glatt
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen V Faraone
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
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72
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Qiu Y, Wang J, Lei J, Roeder K. Identification of cell-type-specific marker genes from co-expression patterns in tissue samples. Bioinformatics 2021; 37:3228-3234. [PMID: 33904573 PMCID: PMC8504631 DOI: 10.1093/bioinformatics/btab257] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 03/15/2021] [Accepted: 04/24/2021] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION Marker genes, defined as genes that are expressed primarily in a single-cell type, can be identified from the single-cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. RESULTS To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. AVAILABILITY AND IMPLEMENTATION We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yixuan Qiu
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jing Lei
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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73
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Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy. Sci Rep 2021; 11:19102. [PMID: 34580351 PMCID: PMC8476594 DOI: 10.1038/s41598-021-98534-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/09/2021] [Indexed: 12/30/2022] Open
Abstract
Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.
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74
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Zhang W, Xu H, Qiao R, Zhong B, Zhang X, Gu J, Zhang X, Wei L, Wang X. ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data. Brief Bioinform 2021; 23:6361035. [PMID: 34472588 DOI: 10.1093/bib/bbab362] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/13/2021] [Accepted: 08/16/2021] [Indexed: 11/12/2022] Open
Abstract
Quantifying cell proportions, especially for rare cell types in some scenarios, is of great value in tracking signals associated with certain phenotypes or diseases. Although some methods have been proposed to infer cell proportions from multicomponent bulk data, they are substantially less effective for estimating the proportions of rare cell types which are highly sensitive to feature outliers and collinearity. Here we proposed a new deconvolution algorithm named ARIC to estimate cell type proportions from gene expression or DNA methylation data. ARIC employs a novel two-step marker selection strategy, including collinear feature elimination based on the component-wise condition number and adaptive removal of outlier markers. This strategy can systematically obtain effective markers for weighted $\upsilon$-support vector regression to ensure a robust and precise rare proportion prediction. We showed that ARIC can accurately estimate fractions in both DNA methylation and gene expression data from different experiments. We further applied ARIC to the survival prediction of ovarian cancer and the condition monitoring of chronic kidney disease, and the results demonstrate the high accuracy and robustness as well as clinical potentials of ARIC. Taken together, ARIC is a promising tool to solve the deconvolution problem of bulk data where rare components are of vital importance.
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Affiliation(s)
- Wei Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Hanwen Xu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rong Qiao
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Bixi Zhong
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xianglin Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Jin Gu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Lei Wei
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
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75
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Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Brief Bioinform 2021; 22:6330938. [PMID: 34329375 DOI: 10.1093/bib/bbab259] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/14/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.
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Affiliation(s)
- Amarinder Singh Thind
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Isha Monga
- Columbia University, New York City, NY, USA
| | | | - Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | | | - Marie Ranson
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Bruce Ashford
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
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76
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Sun R, Cui C, Zhou Y, Cui Q. Comprehensive Analysis of RNA Expression Correlations between Biofluids and Human Tissues. Genes (Basel) 2021; 12:935. [PMID: 34207420 PMCID: PMC8234006 DOI: 10.3390/genes12060935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/12/2021] [Accepted: 06/15/2021] [Indexed: 11/25/2022] Open
Abstract
In recent years, biofluid has been considered a promising source of non-invasive biomarkers for health monitoring and disease diagnosis. However, the expression consistency between biofluid and human tissue, which is fundamental to RNA biomarker development, has not been fully evaluated. In this study, we collected expression profiles across 53 human tissues and five main biofluid types. Utilizing the above dataset, we uncovered a globally positive correlation pattern between various biofluids (including blood, urine, bile, saliva and stool) and human tissues. However, significantly varied biofluid-tissue similarity levels and tendencies were observed between mRNA and lncRNA. Moreover, a higher correlation was found between biofluid types and their functionally related and anatomically closer tissues. In particular, a highly specific correlation was discovered between urine and the prostate. The biological sex of the donor was also proved to be an important influencing factor in biofluid-tissue correlation. Moreover, genes enriched in basic biological processes were found to display low variability across biofluid types, while genes enriched in catabolism-associated pathways were identified as highly variable.
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Affiliation(s)
| | | | | | - Qinghua Cui
- Key Laboratory of Molecular Cardiovascular Sciences of the Ministry of Education, Center for Non-Coding RNA Medicine, Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China; (R.S.); (C.C.); (Y.Z.)
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77
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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: 49] [Impact Index Per Article: 12.3] [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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78
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Kang K, Huang C, Li Y, Umbach DM, Li L. CDSeqR: fast complete deconvolution for gene expression data from bulk tissues. BMC Bioinformatics 2021; 22:262. [PMID: 34030626 PMCID: PMC8142515 DOI: 10.1186/s12859-021-04186-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 05/12/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. RESULT We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.
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Affiliation(s)
- Kai Kang
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, 27709, USA.
| | - Caizhi Huang
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, 27709, USA
| | - Yuanyuan Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, 27709, USA
| | - David M Umbach
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, 27709, USA
| | - Leping Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, 27709, USA.
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79
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Amrhein L, Fuchs C. stochprofML: stochastic profiling using maximum likelihood estimation in R. BMC Bioinformatics 2021; 22:123. [PMID: 33722188 PMCID: PMC7958472 DOI: 10.1186/s12859-021-03970-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 01/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue. RESULTS We present the R package stochprofML which uses the maximum likelihood principle to parameterize heterogeneity from the cumulative expression of small random pools of cells. We evaluate the algorithm's performance in simulation studies and present further application opportunities. CONCLUSION Stochastic profiling outweighs the necessary demixing of mixed samples with a saving in experimental cost and effort and less measurement error. It offers possibilities for parameterizing heterogeneity, estimating underlying pool compositions and detecting differences between cell populations between samples.
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Affiliation(s)
- Lisa Amrhein
- Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
- Department of Mathematics, Technical University Munich, Boltzmannstrasse 3, 85748 Garching, Germany
| | - Christiane Fuchs
- Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
- Department of Mathematics, Technical University Munich, Boltzmannstrasse 3, 85748 Garching, Germany
- Faculty of Business Administration and Economics, Bielefeld University, Universitätsstrasse 25, 33615 Bielefeld, Germany
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80
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Hunt GJ, Gagnon-Bartsch JA. The role of scale in the estimation of cell-type proportions. Ann Appl Stat 2021. [DOI: 10.1214/20-aoas1395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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81
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Tai AS, Tseng GC, Hsieh WP. BayICE: A Bayesian hierarchical model for semireference-based deconvolution of bulk transcriptomic data. Ann Appl Stat 2021. [DOI: 10.1214/20-aoas1376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- An-Shun Tai
- Institute of Statistics, National Tsing Hua University
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82
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Nadel BB, Lopez D, Montoya DJ, Ma F, Waddel H, Khan MM, Mangul S, Pellegrini M. The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data. Gigascience 2021; 10:giab002. [PMID: 33590863 PMCID: PMC7931818 DOI: 10.1093/gigascience/giab002] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 01/07/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g., cell flow cytometry) are costly, time consuming and have potential to introduce bias. Computational approaches that use expression data to infer cell type abundance offer an alternative solution. While these methods have gained popularity, most fail to produce accurate predictions for the full range of platforms currently used by researchers or for the wide variety of tissue types often studied. RESULTS We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a flexible tool that utilizes gene expression data to accurately predict cell type abundances. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include multiple platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from 8 sources spanning a broad range of stromal and hematopoietic types in both human and mouse. GEDIT also accepts user-submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data are available. CONCLUSIONS GEDIT is a powerful method for evaluating the cell type composition of tissue samples and provides excellent accuracy and versatility compared to similar tools. The reference database provided here also allows users to obtain estimates for a wide variety of tissue samples without having to provide their own data.
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Affiliation(s)
- Brian B Nadel
- Bioinformatics Interdepartmental Degree Program, Molecular Biology Institute, Department of Molecular Cellular and Developmental Biology, and Institute for Genomics and Proteomics, University of California Los Angeles, 610 Charles E Young Dr S, Los Angeles, CA 90095, USA
| | - David Lopez
- Bioinformatics Interdepartmental Degree Program, Molecular Biology Institute, Department of Molecular Cellular and Developmental Biology, and Institute for Genomics and Proteomics, University of California Los Angeles, 610 Charles E Young Dr S, Los Angeles, CA 90095, USA
| | - Dennis J Montoya
- Bioinformatics Interdepartmental Degree Program, Molecular Biology Institute, Department of Molecular Cellular and Developmental Biology, and Institute for Genomics and Proteomics, University of California Los Angeles, 610 Charles E Young Dr S, Los Angeles, CA 90095, USA
| | - Feiyang Ma
- Bioinformatics Interdepartmental Degree Program, Molecular Biology Institute, Department of Molecular Cellular and Developmental Biology, and Institute for Genomics and Proteomics, University of California Los Angeles, 610 Charles E Young Dr S, Los Angeles, CA 90095, USA
| | - Hannah Waddel
- Department of Mathematics, University of Utah, 155 1400 E, Salt Lake City, UT 84112, USA
| | - Misha M Khan
- Departments of Biology and Computer Science, Swarthmore College, 500 College Ave, Swarthmore, PA 19081, USA
| | - Serghei Mangul
- Department of Clinical Pharmacy, USC School of Pharmacy, 1450 Alcazar Street Los Angeles, CA 90089, USA
| | - Matteo Pellegrini
- Bioinformatics Interdepartmental Degree Program, Molecular Biology Institute, Department of Molecular Cellular and Developmental Biology, and Institute for Genomics and Proteomics, University of California Los Angeles, 610 Charles E Young Dr S, Los Angeles, CA 90095, USA
- Department of Dermatology, David Geffen School of Medicine, University of California Los Angeles, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
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83
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Chen Z, Wu A. Progress and challenge for computational quantification of tissue immune cells. Brief Bioinform 2021; 22:6065002. [PMID: 33401306 DOI: 10.1093/bib/bbaa358] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/23/2020] [Accepted: 11/07/2020] [Indexed: 12/28/2022] Open
Abstract
Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.
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Affiliation(s)
- Ziyi Chen
- Suzhou Institute of Systems Medicine, Center for Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Jiangsu, Suzhou, China
| | - Aiping Wu
- Suzhou Institute of Systems Medicine, Center for Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Jiangsu, Suzhou, China
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84
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Bornholdt J, Broholm C, Chen Y, Rago A, Sloth S, Hendel J, Melsæther C, Müller CV, Juul Nielsen M, Strickertsson J, Engelholm L, Vitting-Seerup K, Jensen KB, Baker A, Sandelin A. Personalized B cell response to the Lactobacillus rhamnosus GG probiotic in healthy human subjects: a randomized trial. Gut Microbes 2020; 12:1-14. [PMID: 33274667 PMCID: PMC7722709 DOI: 10.1080/19490976.2020.1854639] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
The specific effects of administering live probiotics in the human gut are not well characterized. To this end, we investigated the immediate effect of Lactobacillus rhamnosus GG (LGG) in the jejunum of 27 healthy volunteers 2 h after ingestion using a combination of global RNA sequencing of human biopsies and bacterial DNA sequencing in a multi-visit, randomized, cross-over design (ClinicalTrials.gov number NCT03140878). While LGG was detectable in jejunum after 2 h in treated subjects, the gene expression response vs. placebo was subtle if assessed across all subjects. However, clustering analysis revealed that one-third of subjects exhibited a strong and consistent LGG response involving hundreds of genes, where genes related to B cell activation were upregulated, consistent with prior results in mice. Immunohistochemistry and single cell-based deconvolution analyses showed that this B cell signature likely is due to activation and proliferation of existing B cells rather than B cell immigration to the tissue. Our results indicate that the LGG strain has an immediate effect in the human gut in a subpopulation of individuals. In extension, our data strongly suggest that studies on in vivo probiotic effects in humans require large cohorts and must take individual variation into account.
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Affiliation(s)
- Jette Bornholdt
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen N, Denmark,Human Health Discovery, Hørsholm, Denmark
| | - Christa Broholm
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,Human Health Discovery, Hørsholm, Denmark
| | - Yun Chen
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen N, Denmark,Human Health Discovery, Hørsholm, Denmark
| | - Alfredo Rago
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen N, Denmark
| | - Stine Sloth
- Gastro Unit, Herlev Hospital, University of Copenhagen, Herlev, Denmark
| | - Jakob Hendel
- Gastro Unit, Herlev Hospital, University of Copenhagen, Herlev, Denmark
| | | | - Christina V. Müller
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark
| | - Maria Juul Nielsen
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark
| | - Jesper Strickertsson
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,Human Health Discovery, Hørsholm, Denmark
| | - Lars Engelholm
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,Finsen Laboratory, University of Copenhagen, Copenhagen N, Denmark
| | - Kristoffer Vitting-Seerup
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen N, Denmark,Danish Cancer Society, Copenhagen Ø, Denmark
| | - Kim B. Jensen
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, University of Copenhagen, Copenhagen N, Denmark,CONTACT Kim B. Jensen kim Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen NDK2200, Denmark
| | - Adam Baker
- Human Health Discovery, Hørsholm, Denmark,Adam Baker Human Health Discovery, Chr. Hansen A/S, Kogle Alle 6, Hørsholm2970, Denmark
| | - Albin Sandelin
- Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N, Denmark,The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen N, Denmark,Albin Sandelin The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen NDK2200, Denmark
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85
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Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun 2020; 11:5650. [PMID: 33159064 PMCID: PMC7648640 DOI: 10.1038/s41467-020-19015-1] [Citation(s) in RCA: 243] [Impact Index Per Article: 48.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 09/16/2020] [Indexed: 01/05/2023] Open
Abstract
Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.
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86
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Hauberg ME, Creus-Muncunill J, Bendl J, Kozlenkov A, Zeng B, Corwin C, Chowdhury S, Kranz H, Hurd YL, Wegner M, Børglum AD, Dracheva S, Ehrlich ME, Fullard JF, Roussos P. Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons. Nat Commun 2020; 11:5581. [PMID: 33149216 PMCID: PMC7643171 DOI: 10.1038/s41467-020-19319-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 10/08/2020] [Indexed: 01/04/2023] Open
Abstract
The chromatin landscape of human brain cells encompasses key information to understanding brain function. Here we use ATAC-seq to profile the chromatin structure in four distinct populations of cells (glutamatergic neurons, GABAergic neurons, oligodendrocytes, and microglia/astrocytes) from three different brain regions (anterior cingulate cortex, dorsolateral prefrontal cortex, and primary visual cortex) in human postmortem brain samples. We find that chromatin accessibility varies greatly by cell type and, more moderately, by brain region, with glutamatergic neurons showing the largest regional variability. Transcription factor footprinting implicates cell-specific transcriptional regulators and infers cell-specific regulation of protein-coding genes, long intergenic noncoding RNAs and microRNAs. In vivo transgenic mouse experiments validate the cell type specificity of several of these human-derived regulatory sequences. We find that open chromatin regions in glutamatergic neurons are enriched for neuropsychiatric risk variants, particularly those associated with schizophrenia. Integration of cell-specific chromatin data with a bulk tissue study of schizophrenia brains increases statistical power and confirms that glutamatergic neurons are most affected. These findings illustrate the utility of studying the cell-type-specific epigenome in complex tissues like the human brain, and the potential of such approaches to better understand the genetic basis of human brain function.
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Affiliation(s)
- Mads E Hauberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Jordi Creus-Muncunill
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jaroslav Bendl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alexey Kozlenkov
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Biao Zeng
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Chuhyon Corwin
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sarah Chowdhury
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Harald Kranz
- Gene Bridges, Im Neuenheimer Feld 584, 69120, Heidelberg, Germany
| | - Yasmin L Hurd
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Wegner
- Institut für Biochemie, Emil-Fischer-Zentrum, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anders D Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Stella Dracheva
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Michelle E Ehrlich
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John F Fullard
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA.
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87
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Li Z, Guo Z, Cheng Y, Jin P, Wu H. Robust partial reference-free cell composition estimation from tissue expression. Bioinformatics 2020; 36:3431-3438. [PMID: 32167531 DOI: 10.1093/bioinformatics/btaa184] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/05/2020] [Accepted: 03/10/2020] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION In the analysis of high-throughput omics data from tissue samples, estimating and accounting for cell composition have been recognized as important steps. High cost, intensive labor requirements and technical limitations hinder the cell composition quantification using cell-sorting or single-cell technologies. Computational methods for cell composition estimation are available, but they are either limited by the availability of a reference panel or suffer from low accuracy. RESULTS We introduce TOols for the Analysis of heterogeneouS Tissues TOAST/-P and TOAST/+P, two partial reference-free algorithms for estimating cell composition of heterogeneous tissues based on their gene expression profiles. TOAST/-P and TOAST/+P incorporate additional biological information, including cell-type-specific markers and prior knowledge of compositions, in the estimation procedure. Extensive simulation studies and real data analyses demonstrate that the proposed methods provide more accurate and robust cell composition estimation than existing methods. AVAILABILITY AND IMPLEMENTATION The proposed methods TOAST/-P and TOAST/+P are implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST. CONTACT ziyi.li@emory.edu or hao.wu@emory.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Zhenxing Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Ying Cheng
- Institute of Biomedical Research, Yunnan University, Kunming, China
| | - Peng Jin
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
| | - Hao Wu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
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88
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Li H, Sharma A, Ming W, Sun X, Liu H. A deconvolution method and its application in analyzing the cellular fractions in acute myeloid leukemia samples. BMC Genomics 2020; 21:652. [PMID: 32967610 PMCID: PMC7510109 DOI: 10.1186/s12864-020-06888-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 07/07/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The identification of cell type-specific genes (markers) is an essential step for the deconvolution of the cellular fractions, primarily, from the gene expression data of a bulk sample. However, the genes with significant changes identified by pair-wise comparisons cannot indeed represent the specificity of gene expression across multiple conditions. In addition, the knowledge about the identification of gene expression markers across multiple conditions is still paucity. RESULTS Herein, we developed a hybrid tool, LinDeconSeq, which consists of 1) identifying marker genes using specificity scoring and mutual linearity strategies across any number of cell types, and 2) predicting cellular fractions of bulk samples using weighted robust linear regression with the marker genes identified in the first stage. On multiple publicly available datasets, the marker genes identified by LinDeconSeq demonstrated better accuracy and reproducibility compared to MGFM and RNentropy. Among deconvolution methods, LinDeconSeq showed low average deviations (≤0.0958) and high average Pearson correlations (≥0.8792) between the predicted and actual fractions on the benchmark datasets. Importantly, the cellular fractions predicted by LinDeconSeq appear to be relevant in the diagnosis of acute myeloid leukemia (AML). The distinct cellular fractions in granulocyte-monocyte progenitor (GMP), lymphoid-primed multipotent progenitor (LMPP) and monocytes (MONO) were found to be closely associated with AML compared to the healthy samples. Moreover, the heterogeneity of cellular fractions in AML patients divided these patients into two subgroups, differing in both prognosis and mutation patterns. GMP fraction was the most pronounced between these two subgroups, particularly, in SubgroupA, which was strongly associated with the better AML prognosis and the younger population. Totally, the identification of marker genes by LinDeconSeq represents the improved feature for deconvolution. The data processing strategy with regard to the cellular fractions used in this study also showed potential for the diagnosis and prognosis of diseases. CONCLUSIONS Taken together, we developed a freely-available and open-source tool LinDeconSeq ( https://github.com/lihuamei/LinDeconSeq ), which includes marker identification and deconvolution procedures. LinDeconSeq is comparable to other current methods in terms of accuracy when applied to benchmark datasets and has broad application in clinical outcome and disease-specific molecular mechanisms.
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Affiliation(s)
- Huamei Li
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Amit Sharma
- Department of Ophthalmology, University Hospital Bonn, 53127, Bonn, Germany
| | - Wenglong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China.
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China.
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89
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Bortolomeazzi M, Keddar MR, Ciccarelli FD, Benedetti L. Identification of non-cancer cells from cancer transcriptomic data. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2020; 1863:194445. [PMID: 31654804 PMCID: PMC7346884 DOI: 10.1016/j.bbagrm.2019.194445] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/20/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Michele Bortolomeazzi
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK
| | - Mohamed Reda Keddar
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK.
| | - Lorena Benedetti
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK.
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90
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Li H, Sharma A, Luo K, Qin ZS, Sun X, Liu H. DeconPeaker, a Deconvolution Model to Identify Cell Types Based on Chromatin Accessibility in ATAC-Seq Data of Mixture Samples. Front Genet 2020; 11:392. [PMID: 32547592 PMCID: PMC7269180 DOI: 10.3389/fgene.2020.00392] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 03/30/2020] [Indexed: 12/26/2022] Open
Abstract
While our understanding of cellular and molecular processes has grown exponentially, issues related to the cell microenvironment and cellular heterogeneity have sparked a new debate concerning the cell identity. Cell composition (chromatin and nuclear architecture) poses a strong risk for dynamic changes in the diseased condition. Since chromatin accessibility patterns play a major role in human diseases, it is therefore anticipated that a deconvolution tool based on open chromatin data will provide better performance in identifying cell composition. Herein, we have designed the deconvolution tool "DeconPeaker," which can precisely define the uniqueness among subpopulations of cells using open chromatin datasets. Using this tool, we simultaneously evaluated chromatin accessibility and gene expression datasets to estimate cell types and their respective proportions in a mixture of samples. In comparison to other known deconvolution methods, we observed the lowest average root-mean-square error (RMSE = 0.042) and the highest average correlation coefficient (r = 0.919) between the prediction and "true" proportion. As a proof-of-concept, we also tested chromatin accessibility data from acute myeloid leukemia (AML) and successfully obtained unique cell types associated with AML progression. Furthermore, we showed that chromatin accessibility represents more essential characteristics in the identification of cell types than gene expression. Taken together, DeconPeaker as a powerful tool has the potential to combine different datasets (primarily, chromatin accessibility and gene expression) and define different cell types in mixtures. The Python package of DeconPeaker is now available at https://github.com/lihuamei/DeconPeaker.
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Affiliation(s)
- Huamei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Amit Sharma
- Department of Ophthalmology, University Hospital Bonn, Bonn, Germany
| | - Kun Luo
- Department of Neurosurgery, Xinjiang Evidence-Based Medicine Research Institute, First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China
| | - Zhaohui S. Qin
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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91
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EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data. Methods Mol Biol 2020; 2120:233-248. [PMID: 32124324 DOI: 10.1007/978-1-0716-0327-7_17] [Citation(s) in RCA: 323] [Impact Index Per Article: 64.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Gene expression profiling is nowadays routinely performed on clinically relevant samples (e.g., from tumor specimens). Such measurements are often obtained from bulk samples containing a mixture of cell types. Knowledge of the proportions of these cell types is crucial as they are key determinants of the disease evolution and response to treatment. Moreover, heterogeneity in cell type proportions across samples is an important confounding factor in downstream analyses.Many tools have been developed to estimate the proportion of the different cell types from bulk gene expression data. Here, we provide guidelines and examples on how to use these tools, with a special focus on our recent computational method EPIC (Estimating the Proportions of Immune and Cancer cells). EPIC includes RNA-seq-based gene expression reference profiles from immune cells and other nonmalignant cell types found in tumors. EPIC can additionally manage user-defined gene expression reference profiles. Some unique features of EPIC include the ability to account for an uncharacterized cell type, the introduction of a renormalization step to account for different mRNA content in each cell type, and the use of single-cell RNA-seq data to derive biologically relevant reference gene expression profiles. EPIC is available as a web application ( http://epic.gfellerlab.org ) and as an R-package ( https://github.com/GfellerLab/EPIC ).
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92
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Fang S, Xu T, Xiong M, Zhou X, Wang Y, Haydu LE, Ross MI, Gershenwald JE, Prieto VG, Cormier JN, Wargo J, Sui D, Wei Q, Amos CI, Lee JE. Role of Immune Response, Inflammation, and Tumor Immune Response-Related Cytokines/Chemokines in Melanoma Progression. J Invest Dermatol 2019; 139:2352-2358.e3. [PMID: 31176707 PMCID: PMC6814532 DOI: 10.1016/j.jid.2019.03.1158] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 03/07/2019] [Accepted: 03/24/2019] [Indexed: 01/12/2023]
Abstract
To investigate the role of tumor cytokines/chemokines in melanoma immune response, we estimated the proportions of immune cell subsets in melanoma tumors from The Cancer Genome Atlas, followed by evaluation of the association between cytokine/chemokine expression and these subsets. We then investigated the association of immune cell subsets, chemokines, and cytokines with patient survival. Finally, we evaluated the immune cell tumor-infiltrating lymphocyte (TIL) score for correlation with melanoma patient outcome in a separate cohort. There was good agreement between RNA sequencing estimation of T-cell subset and pathologist-determined TIL score. Expression levels of cytokines IL-12A, IFNG, and IL-10, and chemokines CXCL9 and CXCL10 were positively correlated with PDCD1, CTLA-4, and CD8+ T-cell subset, but negatively correlated with tumor purity (Bonferroni-corrected P < 0.05). In multivariable analysis, higher expression levels of cytokines IFN-γ and TGFB1, but not chemokines, were associated with improved overall survival. A higher expression level of CD8+ T-cell subset was also associated with improved overall survival (hazard ratio [HR] = 0.06, 95% confidence interval [CI] = 0.01-0.35, P = 0.002). Finally, multivariable analysis showed that patients with a brisk TIL score had improved melanoma-specific survival than those with a nonbrisk score (HR = 0.51, 95% CI = 0.27-0.98, P = 0.0423). These results suggest that the expression of specific tumor cytokines represents important biomarkers of melanoma immune response.
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Affiliation(s)
- Shenying Fang
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Tao Xu
- Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Momiao Xiong
- Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xinke Zhou
- The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Yuling Wang
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lauren E Haydu
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Merrick I Ross
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jeffrey E Gershenwald
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Victor G Prieto
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Janice N Cormier
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer Wargo
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Dawen Sui
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Qingyi Wei
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA; Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA
| | | | - Jeffrey E Lee
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
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