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Görtler F, Mensching-Buhr M, Skaar Ø, Schrod S, Sterr T, Schäfer A, Beißbarth T, Joshi A, Zacharias HU, Grellscheid SN, Altenbuchinger M. Adaptive digital tissue deconvolution. Bioinformatics 2024; 40:i100-i109. [PMID: 38940181 PMCID: PMC11256946 DOI: 10.1093/bioinformatics/btae263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. RESULTS We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. AVAILABILITY AND IMPLEMENTATION A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362).
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Affiliation(s)
- Franziska Görtler
- Computational Biology Unit, Department of Biological Sciences, University of Bergen, N-5008 Bergen, Norway
- Department of Oncology and Medical Physics, Haukeland University Hospital, 5021 Bergen, Norway
| | - Malte Mensching-Buhr
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Ørjan Skaar
- Department of Informatics, Computational Biology Unit, University of Bergen, N-5008 Bergen, Norway
| | - Stefan Schrod
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Thomas Sterr
- Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany
| | - Andreas Schäfer
- Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany
| | - Tim Beißbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Anagha Joshi
- Department of Clinical Science, Computational Biology Unit, University of Bergen, N-5008 Bergen, Norway
| | - Helena U Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany
| | | | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany
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2
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Minvielle Moncla LH, Briend M, Sokhna Sylla M, Mathieu S, Rufiange A, Bossé Y, Mathieu P. Mendelian randomization reveals interactions of the blood proteome and immunome in mitral valve prolapse. COMMUNICATIONS MEDICINE 2024; 4:108. [PMID: 38844506 PMCID: PMC11156961 DOI: 10.1038/s43856-024-00530-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Mitral valve prolapse (MVP) is a common heart disorder characterized by an excessive production of proteoglycans and extracellular matrix in mitral valve leaflets. Large-scale genome-wide association study (GWAS) underlined that MVP is heritable. The molecular underpinnings of the disease remain largely unknown. METHODS We interrogated cross-modality data totaling more than 500,000 subjects including GWAS, 4809 molecules of the blood proteome, and genome-wide expression of mitral valves to identify candidate drivers of MVP. Data were investigated through Mendelian randomization, network analysis, ligand-receptor inference and digital cell quantification. RESULTS In this study, Mendelian randomization identify that 33 blood proteins, enriched in networks for immunity, are associated with the risk of MVP. MVP- associated blood proteins are enriched in ligands for which their cognate receptors are differentially expressed in mitral valve leaflets during MVP and enriched in cardiac endothelial cells and macrophages. MVP-associated blood proteins are involved in the renewal-polarization of macrophages and regulation of adaptive immune response. Cytokine activity profiling and digital cell quantification show in MVP a shift toward cytokine signature promoting M2 macrophage polarization. Assessment of druggability identify CSF1R, CX3CR1, CCR6, IL33, MMP8, ENPEP and angiotensin receptors as actionable targets in MVP. CONCLUSIONS Hence, integrative analysis identifies networks of candidate molecules and cells involved in immune control and remodeling of the extracellular matrix, which drive the risk of MVP.
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Affiliation(s)
| | - Mewen Briend
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Mame Sokhna Sylla
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Samuel Mathieu
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Anne Rufiange
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Yohan Bossé
- Department of Molecular Medicine, Laval University, Quebec City, QC, Canada
| | - Patrick Mathieu
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada.
- Department of Surgery, Laval University, Quebec City, QC, Canada.
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3
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Azuma I, Mizuno T, Kusuhara H. GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution. Brief Bioinform 2024; 25:bbae315. [PMID: 38982642 PMCID: PMC11233176 DOI: 10.1093/bib/bbae315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/21/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024] Open
Abstract
Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.
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Affiliation(s)
- Iori Azuma
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
| | - Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
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4
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Nguyen H, Nguyen H, Tran D, Draghici S, Nguyen T. Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges. Nucleic Acids Res 2024; 52:4761-4783. [PMID: 38619038 PMCID: PMC11109966 DOI: 10.1093/nar/gkae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Duc Tran
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, USA
- Advaita Bioinformatics, Ann Arbor, MI, USA
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
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5
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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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6
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Azuma I, Mizuno T, Morita K, Suzuki Y, Kusuhara H. Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset. NAR Genom Bioinform 2024; 6:lqad111. [PMID: 38187088 PMCID: PMC10768887 DOI: 10.1093/nargab/lqad111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/31/2023] [Accepted: 12/16/2023] [Indexed: 01/09/2024] Open
Abstract
Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. Here, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. We prepared seven mouse liver injury models using small-molecule compounds and established a benchmark dataset with corresponding liver bulk RNA-Seq and immune cell proportions. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus to provide a reference. We found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods and established a liver-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling is highly extrapolatable. We expect that this approach will enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.
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Affiliation(s)
- Iori Azuma
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
| | - Tadahaya Mizuno
- To whom correspondence should be addressed. Tel: +81 3 5841 4771; Fax: +81 3 5841 4766;
| | - Katsuhisa Morita
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
| | - Yutaka Suzuki
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
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7
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Merotto L, Sturm G, Dietrich A, List M, Finotello F. Making mouse transcriptomics deconvolution accessible with immunedeconv. BIOINFORMATICS ADVANCES 2024; 4:vbae032. [PMID: 38464974 PMCID: PMC10924280 DOI: 10.1093/bioadv/vbae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/01/2024] [Accepted: 02/26/2024] [Indexed: 03/12/2024]
Abstract
Summary Transcriptome deconvolution has emerged as a reliable technique to estimate cell-type abundances from bulk RNA sequencing data. Unlike their human equivalents, methods to quantify the cellular composition of complex tissues from murine transcriptomics are sparse and sometimes not easy to use. We extended the immunedeconv R package to facilitate the deconvolution of mouse transcriptomics, enabling the quantification of murine immune-cell types using 13 different methods. Through immunedeconv, we further offer the possibility of tweaking cell signatures used by deconvolution methods, providing custom annotations tailored for specific cell types and tissues. These developments strongly facilitate the study of the immune-cell composition of mouse models and further open new avenues in the investigation of the cellular composition of other tissues and organisms. Availability and implementation The R package and the documentation are available at https://github.com/omnideconv/immunedeconv.
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Affiliation(s)
- Lorenzo Merotto
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck 6020, Austria
| | - Gregor Sturm
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck 6020, Austria
- Boehringer Ingelheim International Pharma GmbH & Co KG, Biberach 88400, Germany
| | - Alexander Dietrich
- Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Markus List
- Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Francesca Finotello
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck 6020, Austria
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8
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Cai M, Zhou J, McKennan C, Wang J. scMD facilitates cell type deconvolution using single-cell DNA methylation references. Commun Biol 2024; 7:1. [PMID: 38168620 PMCID: PMC10762261 DOI: 10.1038/s42003-023-05690-5] [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: 08/14/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA, USA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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9
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Stumpff JP, Kim SY, McFadden MI, Nishida A, Shirazi R, Steuerman Y, Gat-Viks I, Forero A, Nair MG, Morrison J. Pleural macrophages translocate to the lung during infection to promote improved influenza outcomes. Proc Natl Acad Sci U S A 2023; 120:e2300474120. [PMID: 38100417 PMCID: PMC10743374 DOI: 10.1073/pnas.2300474120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 10/30/2023] [Indexed: 12/17/2023] Open
Abstract
Seasonal influenza results in 3 to 5 million cases of severe disease and 250,000 to 500,000 deaths annually. Macrophages have been implicated in both the resolution and progression of the disease, but the drivers of these outcomes are poorly understood. We probed mouse lung transcriptomic datasets using the Digital Cell Quantifier algorithm to predict immune cell subsets that correlated with mild or severe influenza A virus (IAV) infection outcomes. We identified a unique lung macrophage population that transcriptionally resembled small serosal cavity macrophages and whose presence correlated with mild disease. Until now, the study of serosal macrophage translocation in the context of viral infections has been neglected. Here, we show that pleural macrophages (PMs) migrate from the pleural cavity to the lung after infection with IAV. We found that the depletion of PMs increased morbidity and pulmonary inflammation. There were increased proinflammatory cytokines in the pleural cavity and an influx of neutrophils within the lung. Our results show that PMs are recruited to the lung during IAV infection and contribute to recovery from influenza. This study expands our knowledge of PM plasticity and identifies a source of lung macrophages independent of monocyte recruitment and local proliferation.
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Affiliation(s)
- James P. Stumpff
- Department of Microbiology and Plant Pathology, University of California, Riverside, CA92521
| | - Sang Yong Kim
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, CA92521
| | - Matthew I. McFadden
- Department of Microbial Infection and Immunity, College of Medicine, The Ohio State University, Columbus, OH43210
- Infectious Diseases Institute, The Ohio State University, Columbus, OH43210
| | - Andrew Nishida
- Department of Microbiology, University of Washington, Seattle, WA98109
| | - Roksana Shirazi
- Department of Microbiology and Plant Pathology, University of California, Riverside, CA92521
| | - Yael Steuerman
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv6997801, Israel
| | - Irit Gat-Viks
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv6997801, Israel
| | - Adriana Forero
- Department of Microbial Infection and Immunity, College of Medicine, The Ohio State University, Columbus, OH43210
- Infectious Diseases Institute, The Ohio State University, Columbus, OH43210
| | - Meera G. Nair
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, CA92521
| | - Juliet Morrison
- Department of Microbiology and Plant Pathology, University of California, Riverside, CA92521
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10
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [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: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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11
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Morita K, Mizuno T, Azuma I, Suzuki Y, Kusuhara H. Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases. Toxicol Sci 2023; 197:kfad117. [PMID: 37941435 PMCID: PMC10823770 DOI: 10.1093/toxsci/kfad117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023] Open
Abstract
Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are three-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences on deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in Adverse Outcome Pathway.
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Affiliation(s)
- Katsuhisa Morita
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Tadahaya Mizuno
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Iori Azuma
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Yutaka Suzuki
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Hiroyuki Kusuhara
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
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12
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Liu XD, Zhang YT, McGrail DJ, Zhang X, Lam T, Hoang A, Hasanov E, Manyam G, Peterson CB, Zhu H, Kumar SV, Akbani R, Pilie PG, Tannir NM, Peng G, Jonasch E. SETD2 Loss and ATR Inhibition Synergize to Promote cGAS Signaling and Immunotherapy Response in Renal Cell Carcinoma. Clin Cancer Res 2023; 29:4002-4015. [PMID: 37527013 PMCID: PMC10592192 DOI: 10.1158/1078-0432.ccr-23-1003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 04/13/2023] [Accepted: 07/27/2023] [Indexed: 08/03/2023]
Abstract
PURPOSE Immune checkpoint blockade (ICB) demonstrates durable clinical benefits in a minority of patients with renal cell carcinoma (RCC). We aimed to identify the molecular features that determine the response and develop approaches to enhance it. EXPERIMENTAL DESIGN We investigated the effects of SET domain-containing protein 2 (SETD2) loss on the DNA damage response pathway, the cytosolic DNA-sensing pathway, the tumor immune microenvironment, and the response to ataxia telangiectasia and rad3-related (ATR) and checkpoint inhibition in RCC. RESULTS ATR inhibition activated the cyclic GMP-AMP synthase (cGAS)-interferon regulatory factor 3 (IRF3)-dependent cytosolic DNA-sensing pathway, resulting in the concurrent expression of inflammatory cytokines and immune checkpoints. Among the common RCC genotypes, SETD2 loss is associated with preferential ATR activation and sensitizes cells to ATR inhibition. SETD2 knockdown promoted the cytosolic DNA-sensing pathway in response to ATR inhibition. Treatment with the ATR inhibitor VE822 concurrently upregulated immune cell infiltration and immune checkpoint expression in Setd2 knockdown Renca tumors, providing a rationale for ATR inhibition plus ICB combination therapy. Setd2-deficient Renca tumors demonstrated greater vulnerability to ICB monotherapy or combination therapy with VE822 than Setd2-proficient tumors. Moreover, SETD2 mutations were associated with a higher response rate and prolonged overall survival in patients with ICB-treated RCC but not in patients with non-ICB-treated RCC. CONCLUSIONS SETD2 loss and ATR inhibition synergize to promote cGAS signaling and enhance immune cell infiltration, providing a mechanistic rationale for the combination of ATR and checkpoint inhibition in patients with RCC with SETD2 mutations.
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Affiliation(s)
- Xian-De Liu
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- These authors contributed equally
| | - Yan-Ting Zhang
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- These authors contributed equally
| | - Daniel J. McGrail
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH 44195, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Xuesong Zhang
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Truong Lam
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Anh Hoang
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Elshad Hasanov
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ganiraju Manyam
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Christine B. Peterson
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Haifeng Zhu
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shwetha V Kumar
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rehan Akbani
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Patrick G. Pilie
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Nizar M Tannir
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Guang Peng
- Department of Clinical Cancer Prevention at The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Eric Jonasch
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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13
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Cai M, Zhou J, McKennan C, Wang J. scMD: cell type deconvolution using single-cell DNA methylation references. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551733. [PMID: 37577715 PMCID: PMC10418231 DOI: 10.1101/2023.08.03.551733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
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14
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Speranza E. Understanding virus-host interactions in tissues. Nat Microbiol 2023; 8:1397-1407. [PMID: 37488255 DOI: 10.1038/s41564-023-01434-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 06/20/2023] [Indexed: 07/26/2023]
Abstract
Although virus-host interactions are usually studied in a single cell type using in vitro assays in immortalized cell lines or isolated cell populations, it is important to remember that what is happening inside one infected cell does not translate to understanding how an infected cell behaves in a tissue, organ or whole organism. Infections occur in complex tissue environments, which contain a host of factors that can alter the course of the infection, including immune cells, non-immune cells and extracellular-matrix components. These factors affect how the host responds to the virus and form the basis of the protective response. To understand virus infection, tools are needed that can profile the tissue environment. This Review highlights methods to study virus-host interactions in the infection microenvironment.
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Affiliation(s)
- Emily Speranza
- Cleveland Clinic Lerner Research Institute, Port Saint Lucie, FL, USA.
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15
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Cain A, Taga M, McCabe C, Green GS, Hekselman I, White CC, Lee DI, Gaur P, Rozenblatt-Rosen O, Zhang F, Yeger-Lotem E, Bennett DA, Yang HS, Regev A, Menon V, Habib N, De Jager PL. Multicellular communities are perturbed in the aging human brain and Alzheimer's disease. Nat Neurosci 2023; 26:1267-1280. [PMID: 37336975 PMCID: PMC10789499 DOI: 10.1038/s41593-023-01356-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/10/2023] [Indexed: 06/21/2023]
Abstract
The role of different cell types and their interactions in Alzheimer's disease (AD) is a complex and open question. Here, we pursued this question by assembling a high-resolution cellular map of the aging frontal cortex using single-nucleus RNA sequencing of 24 individuals with a range of clinicopathologic characteristics. We used this map to infer the neocortical cellular architecture of 638 individuals profiled by bulk RNA sequencing, providing the sample size necessary for identifying statistically robust associations. We uncovered diverse cell populations associated with AD, including a somatostatin inhibitory neuronal subtype and oligodendroglial states. We further identified a network of multicellular communities, each composed of coordinated subpopulations of neuronal, glial and endothelial cells, and we found that two of these communities are altered in AD. Finally, we used mediation analyses to prioritize cellular changes that might contribute to cognitive decline. Thus, our deconstruction of the aging neocortex provides a roadmap for evaluating the cellular microenvironments underlying AD and dementia.
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Affiliation(s)
- Anael Cain
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Mariko Taga
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Cristin McCabe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gilad S Green
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | | | - Dylan I Lee
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Pallavi Gaur
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Feng Zhang
- Broad Institute, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Hyun-Sik Yang
- Broad Institute, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Vilas Menon
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA.
| | - Naomi Habib
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Philip L De Jager
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA.
- Broad Institute, Cambridge, MA, USA.
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16
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Merotto L, Zopoglou M, Zackl C, Finotello F. Next-generation deconvolution of transcriptomic data to investigate the tumor microenvironment. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2023; 382:103-143. [PMID: 38225101 DOI: 10.1016/bs.ircmb.2023.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Methods for in silico deconvolution of bulk transcriptomics can characterize the cellular composition of the tumor microenvironment, quantifying the abundance of cell types associated with patients' prognosis and response to therapy. While first-generation deconvolution methods rely on precomputed, transcriptional signatures of a handful of cell types, second-generation methods can be trained with single-cell data to disentangle more fine-grained cell phenotypes and states. These novel approaches can also be applied to spatial transcriptomic data to reveal the spatial organization of tumors. In this review, we describe state-of-the-art deconvolution methods (first-generation, second-generation, and spatial) which can be used to investigate the tumor microenvironment, discussing their strengths and limitations. We conclude with an outlook on the challenges that need to be overcome to unlock the full potential of next-generation deconvolution for oncology and the life sciences.
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Affiliation(s)
- Lorenzo Merotto
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Maria Zopoglou
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Constantin Zackl
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Francesca Finotello
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria.
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17
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Menkhorst E, Santos LL, Zhou W, Yang G, Winship AL, Rainczuk KE, Nguyen P, Zhang JG, Moore P, Williams M, Lê Cao KA, Mansell A, Dimitriadis E. IL11 activates the placental inflammasome to drive preeclampsia. Front Immunol 2023; 14:1175926. [PMID: 37292200 PMCID: PMC10244672 DOI: 10.3389/fimmu.2023.1175926] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Preeclampsia is a life-threatening disorder of pregnancy unique to humans. Interleukin (IL)11 is elevated in serum from pregnancies that subsequently develop early-onset preeclampsia and pharmacological elevation of IL11 in pregnant mice causes the development of early-onset preeclampsia-like features (hypertension, proteinuria, and fetal growth restriction). However, the mechanism by which IL11 drives preeclampsia is unknown. Method Pregnant mice were administered PEGylated (PEG)IL11 or control (PEG) from embryonic day (E)10-16 and the effect on inflammasome activation, systolic blood pressure (during gestation and at 50/90 days post-natal), placental development, and fetal/post-natal pup growth measured. RNAseq analysis was performed on E13 placenta. Human 1st trimester placental villi were treated with IL11 and the effect on inflammasome activation and pyroptosis identified by immunohistochemistry and ELISA. Result PEGIL11 activated the placental inflammasome causing inflammation, fibrosis, and acute and chronic hypertension in wild-type mice. Global and placental-specific loss of the inflammasome adaptor protein Asc and global loss of the Nlrp3 sensor protein prevented PEGIL11-induced fibrosis and hypertension in mice but did not prevent PEGIL11-induced fetal growth restriction or stillbirths. RNA-sequencing and histology identified that PEGIL11 inhibited trophoblast differentiation towards spongiotrophoblast and syncytiotrophoblast lineages in mice and extravillous trophoblast lineages in human placental villi. Discussion Inhibition of ASC/NLRP3 inflammasome activity could prevent IL11-induced inflammation and fibrosis in various disease states including preeclampsia.
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Affiliation(s)
- Ellen Menkhorst
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, VIC, Australia
- Gynaecology Research Centre, Royal Women’s Hospital, Parkville, VIC, Australia
- Centre for Reproductive Health, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Leilani L. Santos
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, VIC, Australia
- Gynaecology Research Centre, Royal Women’s Hospital, Parkville, VIC, Australia
| | - Wei Zhou
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, VIC, Australia
- Gynaecology Research Centre, Royal Women’s Hospital, Parkville, VIC, Australia
| | - Guannan Yang
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, VIC, Australia
- Gynaecology Research Centre, Royal Women’s Hospital, Parkville, VIC, Australia
- Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, Australia
| | - Amy L. Winship
- Centre for Reproductive Health, Hudson Institute of Medical Research, Clayton, VIC, Australia
- Department of Anatomy and Developmental Biology, Development and Stem Cells Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Katarzyna E. Rainczuk
- Centre for Reproductive Health, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Philana Nguyen
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, VIC, Australia
- Gynaecology Research Centre, Royal Women’s Hospital, Parkville, VIC, Australia
| | - Jian-Guo Zhang
- Walter and Eliza Hall Institute, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Paddy Moore
- Abortion and Contraception, Royal Women’s Hospital, Parkville, VIC, Australia
| | - Michelle Williams
- Biomedical Animal Facility, The University of Melbourne, Parkville, VIC, Australia
| | - Kim-Anh Lê Cao
- Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, Australia
| | - Ashley Mansell
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Evdokia Dimitriadis
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, VIC, Australia
- Gynaecology Research Centre, Royal Women’s Hospital, Parkville, VIC, Australia
- Centre for Reproductive Health, Hudson Institute of Medical Research, Clayton, VIC, Australia
- Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, Australia
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18
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Luo J, Wu X, Cheng Y, Chen G, Wang J, Song X. Expression quantitative trait locus studies in the era of single-cell omics. Front Genet 2023; 14:1182579. [PMID: 37284065 PMCID: PMC10239882 DOI: 10.3389/fgene.2023.1182579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 04/26/2023] [Indexed: 06/08/2023] Open
Abstract
Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.
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Affiliation(s)
- Jie Luo
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xinyi Wu
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Yuan Cheng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Guang Chen
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Jian Wang
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xijiao Song
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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19
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Nava A, Alves da Quinta D, Prato L, Girotti R, Moron G, Llera AS, Fernández EA. Novel evaluation approach for molecular signature-based deconvolution methods. J Biomed Inform 2023; 142:104387. [PMID: 37172634 DOI: 10.1016/j.jbi.2023.104387] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/17/2023] [Accepted: 05/07/2023] [Indexed: 05/15/2023]
Abstract
The tumoral immune microenvironment (TIME) plays a key role in prognosis, therapeutic approach and pathophysiological understanding over oncological processes. Several computational immune cell-type deconvolution methods (DM), supported by diverse molecular signatures (MS), have been developed to uncover such TIME interplay from RNA-seq tumor biopsies. MS-DM pairs were benchmarked against each other by means of different metrics, such as Pearson's correlation, R2 and RMSE, but these only evaluate the linear association of the estimated proportion related to the expected one, missing the analysis of prediction-dependent bias trends and cell identification accuracy. We present a novel protocol composed of four tests allowing appropriate evaluation of the cell type identification performance and proportion prediction accuracy of molecular signature-deconvolution method pair by means of certainty and confidence cell-type identification scores (F1-score, distance to the optimal point and error rates) as well the Bland-Altman method for error-trend analysis. Our protocol was used to benchmark six state-of-the-art DMs (CIBERSORTx, DCQ, DeconRNASeq, EPIC, MIXTURE and quanTIseq) paired to five murine tissue-specific MSs, revealing a systematic overestimation of the number of different cell types across almost all methods.
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Affiliation(s)
- A Nava
- Fundación Instituto Leloir-CONICET, Buenos Aires, Argentina; Fundación Huésped, Buenos Aires, Argentina
| | - D Alves da Quinta
- Fundación Instituto Leloir-CONICET, Buenos Aires, Argentina; Universidad Argentina de la Empresa (UADE). Instituto de Tecnología (INTEC), Buenos Aires, Argentina
| | - L Prato
- Universidad de Villa María, Córdoba, Argentina
| | - R Girotti
- Universidad Argentina de la Empresa (UADE). Instituto de Tecnología (INTEC), Buenos Aires, Argentina
| | - G Moron
- Departamento de Bioquímica Clínica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba, Argentina; Centro de Investigaciones en Bioquímica Clínica e Inmunología, CONICET, Córdoba, Argentina
| | - A S Llera
- Fundación Instituto Leloir-CONICET, Buenos Aires, Argentina
| | - E A Fernández
- Facultad de Ingeniería, Carrera de Bioinformática, Universidad Católica de Córdoba (UCC), Córdoba, Argentina; Facultad de Ciencias Exactas Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina; Centro de Investigaciòn en Inmunología y Enfermedades Infecciosas, UCC, CONICET, Córdoba, Argentina.
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20
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Aubin RG, Montelongo J, Hu R, Camara PG. Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.06.527318. [PMID: 36798206 PMCID: PMC9934539 DOI: 10.1101/2023.02.06.527318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in functionality and accuracy with respect to current deconvolution methods. Using ConDecon, we discover the implication of neurodegenerative microglial inflammatory pathways in the mesenchymal transformation of ependymoma, recapitulate spatial patterns of cell differentiation during zebrafish embryogenesis, and make temporal inferences from bulk ATAC-seq data. Overall, ConDecon significantly enhances our understanding of dynamic cellular processes within bulk tissue samples.
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Affiliation(s)
- Rachael G Aubin
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Javier Montelongo
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Robert Hu
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Pablo G Camara
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
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21
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Distinct gene programs underpinning disease tolerance and resistance in influenza virus infection. Cell Syst 2022; 13:1002-1015.e9. [PMID: 36516834 DOI: 10.1016/j.cels.2022.11.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/30/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022]
Abstract
When challenged with an invading pathogen, the host-defense response is engaged to eliminate the pathogen (resistance) and to maintain health in the presence of the pathogen (disease tolerance). However, the identification of distinct molecular programs underpinning disease tolerance and resistance remained obscure. We exploited transcriptional and physiological monitoring across 33 mouse strains, during in vivo influenza virus infection, to identify two host-defense gene programs-one is associated with hallmarks of disease tolerance and the other with hallmarks of resistance. Both programs constitute generic responses in multiple mouse and human cell types. Our study describes the organizational principles of these programs and validates Arhgdia as a regulator of disease-tolerance states in epithelial cells. We further reveal that the baseline disease-tolerance state in peritoneal macrophages is associated with the pathophysiological response to injury and infection. Our framework provides a paradigm for the understanding of disease tolerance and resistance at the molecular level.
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22
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He D, Chen M, Wang W, Song C, Qin Y. Deconvolution of tumor composition using partially available DNA methylation data. BMC Bioinformatics 2022; 23:355. [PMID: 36002797 PMCID: PMC9400327 DOI: 10.1186/s12859-022-04893-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background Deciphering proportions of constitutional cell types in tumor tissues is a crucial step for the analysis of tumor heterogeneity and the prediction of response to immunotherapy. In the process of measuring cell population proportions, traditional experimental methods have been greatly hampered by the cost and extensive dropout events. At present, the public availability of large amounts of DNA methylation data makes it possible to use computational methods to predict proportions. Results In this paper, we proposed PRMeth, a method to deconvolve tumor mixtures using partially available DNA methylation data. By adopting an iteratively optimized non-negative matrix factorization framework, PRMeth took DNA methylation profiles of a portion of the cell types in the tissue mixtures (including blood and solid tumors) as input to estimate the proportions of all cell types as well as the methylation profiles of unknown cell types simultaneously. We compared PRMeth with five different methods through three benchmark datasets and the results show that PRMeth could infer the proportions of all cell types and recover the methylation profiles of unknown cell types effectively. Then, applying PRMeth to four types of tumors from The Cancer Genome Atlas (TCGA) database, we found that the immune cell proportions estimated by PRMeth were largely consistent with previous studies and met biological significance. Conclusions Our method can circumvent the difficulty of obtaining complete DNA methylation reference data and obtain satisfactory deconvolution accuracy, which will be conducive to exploring the new directions of cancer immunotherapy. PRMeth is implemented in R and is freely available from GitHub (https://github.com/hedingqin/PRMeth). Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04893-7.
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Affiliation(s)
- Dingqin He
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Ming Chen
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Wenjuan Wang
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Chunhui Song
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Yufang Qin
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China. .,Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China.
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23
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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: 11] [Impact Index Per Article: 5.5] [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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24
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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: 13] [Impact Index Per Article: 4.3] [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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25
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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: 2.7] [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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26
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Lamb D, De Sousa D, Quast K, Fundel-Clemens K, Erjefält JS, Sandén C, Hoffmann HJ, Kästle M, Schmid R, Menden K, Delic D. RORγt inhibitors block both IL-17 and IL-22 conferring a potential advantage over anti-IL-17 alone to treat severe asthma. Respir Res 2021; 22:158. [PMID: 34022896 PMCID: PMC8141258 DOI: 10.1186/s12931-021-01743-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/11/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND RORγt is a transcription factor that enables elaboration of Th17-associated cytokines (including IL-17 and IL-22) and is proposed as a pharmacological target for severe asthma. METHODS IL-17 immunohistochemistry was performed in severe asthma bronchial biopsies (specificity confirmed with in situ hybridization). Primary human small airway epithelial cells in air liquid interface and primary bronchial smooth muscle cells were stimulated with recombinant human IL-17 and/or IL-22 and pro-inflammatory cytokines measured. Balb/c mice were challenged intratracheally with IL-17 and/or IL-22 and airway hyperreactivity, pro-inflammatory cytokines and airway neutrophilia measured. Balb/c mice were sensitized intraperitoneally and challenged intratracheally with house dust mite extract and the effect of either a RORγt inhibitor (BIX119) or an anti-IL-11 antibody assessed on airway hyperreactivity, pro-inflammatory cytokines and airway neutrophilia measured. RESULTS We confirmed in severe asthma bronchial biopsies both the presence of IL-17-positive lymphocytes and that an IL-17 transcriptome profile in a severe asthma patient sub-population. Both IL-17 and IL-22 stimulated the release of pro-inflammatory cytokine and chemokine release from primary human lung cells and in mice. Furthermore, IL-22 in combination with IL-17, but neither alone, elicits airway hyperresponsiveness (AHR) in naïve mice. A RORγt inhibitor specifically blocked both IL-17 and IL-22, AHR and neutrophilia in a mouse house dust mite model unlike other registered or advanced pipeline modes of action. Full efficacy versus these parameters was associated with 90% inhibition of IL-17 and 50% inhibition of IL-22. In contrast, anti-IL-17 also blocked IL-17, but not IL-22, AHR or neutrophilia. Moreover, the deregulated genes in the lungs from these mice correlated well with deregulated genes from severe asthma biopsies suggesting that this model recapitulates significant severe asthma-relevant biology. Furthermore, these genes were reversed upon RORγt inhibition in the HDM model. Cell deconvolution suggested that the responsible cells were corticosteroid insensitive γδ-T-cells. CONCLUSION These data strongly suggest that both IL-17 and IL-22 are required for Th2-low endotype associated biology and that a RORγt inhibitor may provide improved clinical benefit in a severe asthma sub-population of patients by blocking both IL-17 and IL-22 biology compared with blocking IL-17 alone.
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MESH Headings
- Adolescent
- Adult
- Aged
- Animals
- Anti-Asthmatic Agents/pharmacology
- Asthma/drug therapy
- Asthma/immunology
- Asthma/metabolism
- Asthma/physiopathology
- Cells, Cultured
- Disease Models, Animal
- Epithelial Cells/drug effects
- Epithelial Cells/immunology
- Epithelial Cells/metabolism
- Female
- Humans
- Interleukin-17/metabolism
- Interleukins/antagonists & inhibitors
- Interleukins/metabolism
- Lung/drug effects
- Lung/immunology
- Lung/metabolism
- Lung/physiopathology
- Male
- Mice, Inbred BALB C
- Middle Aged
- Myocytes, Smooth Muscle/drug effects
- Myocytes, Smooth Muscle/immunology
- Myocytes, Smooth Muscle/metabolism
- Nuclear Receptor Subfamily 1, Group F, Member 3/antagonists & inhibitors
- Nuclear Receptor Subfamily 1, Group F, Member 3/metabolism
- Pyroglyphidae/immunology
- Signal Transduction
- Th17 Cells/drug effects
- Th17 Cells/immunology
- Th17 Cells/metabolism
- Young Adult
- Interleukin-22
- Mice
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Affiliation(s)
- David Lamb
- Immunology and Respiratory Disease Research, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach-an-der-Riss, Germany.
| | | | - Karsten Quast
- Immunology and Respiratory Disease Research, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach-an-der-Riss, Germany
| | - Katrin Fundel-Clemens
- Immunology and Respiratory Disease Research, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach-an-der-Riss, Germany
| | | | | | | | - Marc Kästle
- Immunology and Respiratory Disease Research, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach-an-der-Riss, Germany
| | - Ramona Schmid
- Immunology and Respiratory Disease Research, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach-an-der-Riss, Germany
| | - Kevin Menden
- Immunology and Respiratory Disease Research, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach-an-der-Riss, Germany
| | - Denis Delic
- Immunology and Respiratory Disease Research, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach-an-der-Riss, Germany
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27
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Bountress KE, Vladimirov V, McMichael G, Taylor ZN, Hardiman G, Chung D, Adams ZW, Danielson CK, Amstadter AB. Gene Expression Differences Between Young Adults Based on Trauma History and Post-traumatic Stress Disorder. Front Psychiatry 2021; 12:581093. [PMID: 33897478 PMCID: PMC8060466 DOI: 10.3389/fpsyt.2021.581093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 03/15/2021] [Indexed: 11/29/2022] Open
Abstract
Background: The purpose of this study was to identify gene expression differences associated with post-traumatic stress disorder (PTSD) and trauma exposure (TE) in a three-group study design comprised of those with and without trauma exposure and PTSD. Methods: We conducted gene expression and gene network analyses in a sample (n = 45) composed of female subjects of European Ancestry (EA) with PTSD, TE without PTSD, and controls. Results: We identified 283 genes differentially expressed between PTSD-TE groups. In an independent sample of Veterans (n = 78) a small minority of these genes were also differentially expressed. We identified 7 gene network modules significantly associated with PTSD and TE (Bonferroni corrected p ≤ 0.05), which at a false discovery rate (FDR) of q ≤ 0.2, were significantly enriched for biological pathways involved in focal adhesion, neuroactive ligand receptor interaction, and immune related processes among others. Conclusions: This study uses gene network analyses to identify significant gene modules associated with PTSD, TE, and controls. On an individual gene level, we identified a large number of differentially expressed genes between PTSD-TE groups, a minority of which were also differentially expressed in the independent sample. We also demonstrate a lack of network module preservation between PTSD and TE, suggesting that the molecular signature of PTSD and trauma are likely independent of each other. Our results provide a basis for the identification of likely disease pathways and biomarkers involved in the etiology of PTSD.
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Affiliation(s)
- Kaitlin E. Bountress
- Virginia Institute for Psychiatry and Behavioral Genetics, Virginia Commonwealth University (VCU), Richmond, VA, United States
| | - Vladimir Vladimirov
- Department of Psychiatry and Behavioral Sciences, College of Medicine Texas A&M University, Richmond, VA, United States
- Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD, United States
| | - Gowon McMichael
- Virginia Institute for Psychiatry and Behavioral Genetics, Virginia Commonwealth University (VCU), Richmond, VA, United States
| | - Z. Nathan Taylor
- Virginia Institute for Psychiatry and Behavioral Genetics, Virginia Commonwealth University (VCU), Richmond, VA, United States
| | - Gary Hardiman
- Institute for Global Food Security, Queens University Belfast, Belfast, United Kingdom
| | - Dongjun Chung
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Zachary W. Adams
- Department of Psychiatry, Indiana University of Medicine, Indianapolis, IN, United States
| | - Carla Kmett Danielson
- National Crime Victim Research and Treatment Center, Medical University of South Carolina, Charleston, SC, United States
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Ananda B. Amstadter
- Virginia Institute for Psychiatry and Behavioral Genetics, Virginia Commonwealth University (VCU), Richmond, VA, United States
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28
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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: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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29
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Sokolowski DJ, Faykoo-Martinez M, Erdman L, Hou H, Chan C, Zhu H, Holmes MM, Goldenberg A, Wilson MD. Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes. NAR Genom Bioinform 2021; 3:lqab011. [PMID: 33655208 PMCID: PMC7902236 DOI: 10.1093/nargab/lqab011] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 12/23/2020] [Accepted: 02/04/2021] [Indexed: 12/11/2022] Open
Abstract
RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.
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Affiliation(s)
- Dustin J Sokolowski
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | | | - Lauren Erdman
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, M5G 0A4, Canada
| | - Huayun Hou
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Cadia Chan
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Helen Zhu
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Melissa M Holmes
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Anna Goldenberg
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, M5G 0A4, Canada
| | - Michael D Wilson
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
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30
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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; 10:6137724. [PMID: 33590863 PMCID: PMC7931818 DOI: 10.1093/gigascience/giab002] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [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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31
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Bosworth A, Rickett NY, Dong X, Ng LFP, García-Dorival I, Matthews DA, Fletcher T, Jacobs M, Thomson EC, Carroll MW, Hiscox JA. Analysis of an Ebola virus disease survivor whose host and viral markers were predictive of death indicates the effectiveness of medical countermeasures and supportive care. Genome Med 2021; 13:5. [PMID: 33430949 PMCID: PMC7798020 DOI: 10.1186/s13073-020-00811-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 11/12/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Ebola virus disease (EVD) is an often-fatal infection where the effectiveness of medical countermeasures is uncertain. During the West African outbreak (2013-2016), several patients were treated with different types of anti-viral therapies including monoclonal antibody-based cocktails that had the potential to neutralise Ebola virus (EBOV). However, at the time, the efficacy of these therapies was uncertain. Given the scale of the outbreak, several clinical phenotypes came to the forefront including the ability of the same virus to cause recrudescence in the same patient-perhaps through persisting in immune privileged sites. Several key questions remained including establishing if monoclonal antibody therapy was effective in humans with severe EVD, whether virus escape mutants were selected during treatment, and what is the potential mechanism(s) of persistence. This was made possible through longitudinal samples taken from a UK patient with EVD. METHODS Several different sample types, plasma and cerebrospinal fluid, were collected and sequenced using Illumina-based RNAseq. Sequence reads were mapped both to EBOV and the human genome and differential gene expression analysis used to identify changes in the abundance of gene transcripts as infection progressed. Digital Cell Quantitation analysis was used to predict the immune phenotype in samples derived from blood. RESULTS The findings were compared to equivalent data from West African patients. The study found that both virus and host markers were predictive of a fatal outcome. This suggested that the extensive supportive care, and most likely the application of the medical countermeasure ZMab (a monoclonal antibody cocktail), contributed to survival of the UK patient. The switch from progression to a 'fatal' outcome to a 'survival' outcome could be seen in both the viral and host markers. The UK patient also suffered a recrudescence infection 10 months after the initial infection. Analysis of the sequencing data indicated that the virus entered a period of reduced or minimal replication, rather than other potential mechanisms of persistence-such as defective interfering genomes. CONCLUSIONS The data showed that comprehensive supportive care and the application of medical countermeasures are worth pursuing despite an initial unfavourable prognosis.
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Affiliation(s)
- Andrew Bosworth
- Public Health England, Manor Farm Road, Porton Down, Salisbury, UK
- Clinical Virology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
| | - Natasha Y Rickett
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Xiaofeng Dong
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Lisa F P Ng
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- Infectious Disease Horizontal Technology Centre (ID HTC), A*STAR, Singapore, Singapore
| | - Isabel García-Dorival
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - David A Matthews
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Tom Fletcher
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Michael Jacobs
- Department of Infection, Royal Free London NHS Foundation Trust, London, UK
| | - Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK.
| | - Miles W Carroll
- Public Health England, Manor Farm Road, Porton Down, Salisbury, UK.
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK.
- Nufield Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Julian A Hiscox
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK.
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
- Infectious Disease Horizontal Technology Centre (ID HTC), A*STAR, Singapore, Singapore.
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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: 5] [Impact Index Per Article: 1.7] [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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Qin Y, Zhang W, Sun X, Nan S, Wei N, Wu HJ, Zheng X. Deconvolution of heterogeneous tumor samples using partial reference signals. PLoS Comput Biol 2020; 16:e1008452. [PMID: 33253170 PMCID: PMC7728196 DOI: 10.1371/journal.pcbi.1008452] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/10/2020] [Accepted: 10/19/2020] [Indexed: 12/16/2022] Open
Abstract
Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE). Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods.
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Affiliation(s)
- Yufang Qin
- College of Information Technology, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Weiwei Zhang
- School of Science, East China University of Technology, Nanchang, Jiangxi, China
| | - Xiaoqiang Sun
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Siwei Nan
- Department of Mathematics, Shanghai Normal University, Shanghai, China
| | - Nana Wei
- Department of Mathematics, Shanghai Normal University, Shanghai, China
| | - Hua-Jun Wu
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai, China
- * E-mail:
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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: 185] [Impact Index Per Article: 46.3] [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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Danziger SA, McConnell M, Gockley J, Young MH, Rosenthal A, Schmitz F, Reiss DJ, Farmer P, Alapat DV, Singh A, Ashby C, Bauer M, Ren Y, Smith K, Couto SS, van Rhee F, Davies F, Zangari M, Petty N, Orlowski RZ, Dhodapkar MV, Copeland WB, Fox B, Hoering A, Fitch A, Newhall K, Barlogie B, Trotter MWB, Hershberg RM, Walker BA, Dervan AP, Ratushny AV, Morgan GJ. Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials. PLoS Med 2020; 17:e1003323. [PMID: 33147277 PMCID: PMC7641353 DOI: 10.1371/journal.pmed.1003323] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 09/18/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases. METHODS AND FINDINGS Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5-29, 49-69 versus 70-82 months, χ2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI -1 to 31, 92-120 versus 113-129 months, χ2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8+ T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6-36, 49-73 versus 74-90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies. CONCLUSIONS In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies.
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Affiliation(s)
- Samuel A. Danziger
- Bristol Myers Squibb, Seattle, Washington, United States of America
- * E-mail: (SAD); (AVR); (GJM)
| | - Mark McConnell
- Bristol Myers Squibb, Seattle, Washington, United States of America
| | - Jake Gockley
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Mary H. Young
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Adam Rosenthal
- Cancer Research and Biostatistics, Seattle, Washington, United States of America
| | - Frank Schmitz
- Sage Bionetworks, Seattle, Washington, United States of America
| | - David J. Reiss
- Bristol Myers Squibb, Seattle, Washington, United States of America
| | - Phil Farmer
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Daisy V. Alapat
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Amrit Singh
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Cody Ashby
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Michael Bauer
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Yan Ren
- Bristol Myers Squibb, Seattle, Washington, United States of America
| | - Kelsie Smith
- Bristol Myers Squibb, Seattle, Washington, United States of America
| | | | - Frits van Rhee
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Faith Davies
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Maurizio Zangari
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Nathan Petty
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Robert Z. Orlowski
- The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Madhav V. Dhodapkar
- Winship Cancer Institute, Emory University, Atlanta, Georgia, United States of America
| | | | - Brian Fox
- Bristol Myers Squibb, Seattle, Washington, United States of America
| | - Antje Hoering
- Cancer Research and Biostatistics, Seattle, Washington, United States of America
| | - Alison Fitch
- Bristol Myers Squibb, Seattle, Washington, United States of America
| | - Katie Newhall
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Bart Barlogie
- Department of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | | | | | - Brian A. Walker
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | | | - Alexander V. Ratushny
- Bristol Myers Squibb, Seattle, Washington, United States of America
- * E-mail: (SAD); (AVR); (GJM)
| | - Gareth J. Morgan
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- * E-mail: (SAD); (AVR); (GJM)
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Petitprez F, Levy S, Sun CM, Meylan M, Linhard C, Becht E, Elarouci N, Tavel D, Roumenina LT, Ayadi M, Sautès-Fridman C, Fridman WH, de Reyniès A. The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression. Genome Med 2020; 12:86. [PMID: 33023656 PMCID: PMC7541325 DOI: 10.1186/s13073-020-00783-w] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 09/14/2020] [Indexed: 02/08/2023] Open
Abstract
Quantifying tissue-infiltrating immune and stromal cells provides clinically relevant information for various diseases. While numerous methods can quantify immune or stromal cells in human tissue samples from transcriptomic data, few are available for mouse studies. We introduce murine Microenvironment Cell Population counter (mMCP-counter), a method based on highly specific transcriptomic markers that accurately quantify 16 immune and stromal murine cell populations. We validated mMCP-counter with flow cytometry data and showed that mMCP-counter outperforms existing methods. We showed that mMCP-counter scores are predictive of response to immune checkpoint blockade in cancer mouse models and identify early immune impacts of Alzheimer's disease.
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Affiliation(s)
- Florent Petitprez
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
- Programme Cartes d’Identité des Tumeurs, Ligue Nationale contre le Cancer, F-75013 Paris, France
- Present address: MRC Centre for Reproductive Health, The University of Edinburgh, The Queen’s Medical Research Institute, Edinburgh, UK
| | - Sacha Levy
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
| | - Cheng-Ming Sun
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
| | - Maxime Meylan
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
- Programme Cartes d’Identité des Tumeurs, Ligue Nationale contre le Cancer, F-75013 Paris, France
| | - Christophe Linhard
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
| | - Etienne Becht
- Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Nabila Elarouci
- Programme Cartes d’Identité des Tumeurs, Ligue Nationale contre le Cancer, F-75013 Paris, France
| | - David Tavel
- Programme Cartes d’Identité des Tumeurs, Ligue Nationale contre le Cancer, F-75013 Paris, France
| | - Lubka T. Roumenina
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
| | - Mira Ayadi
- Programme Cartes d’Identité des Tumeurs, Ligue Nationale contre le Cancer, F-75013 Paris, France
| | - Catherine Sautès-Fridman
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
| | - Wolf H. Fridman
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Team Inflammation, Complement and Cancer, F-75006 Paris, France
| | - Aurélien de Reyniès
- Programme Cartes d’Identité des Tumeurs, Ligue Nationale contre le Cancer, F-75013 Paris, France
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Chen Z, Ji C, Shen Q, Liu W, Qin FXF, Wu A. Tissue-specific deconvolution of immune cell composition by integrating bulk and single-cell transcriptomes. Bioinformatics 2020; 36:819-827. [PMID: 31504185 DOI: 10.1093/bioinformatics/btz672] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 08/13/2019] [Accepted: 08/22/2019] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Many methods have been developed to estimate immune cell composition from tissue transcriptomes. One common characteristic of these methods is that they are trained using a set of general immune cell transcriptomes that ignores tissue specificities. However, as immune cells are localized in different tissues, they may have distinct expression profiles. Hence, calculations that use general signature matrices may hinder the deconvolution accuracy. RESULTS This study used single cell RNA-sequencing (scRNA-Seq) data from different mouse tissues instead of general signature expression values to generate tissue-specific signature gene matrices that are used as the input of the deconvolution model. First, the transcriptome of immune cells in each tissue was extracted from scRNA-Seq data and used to construct the entire expression matrix of tissue immune cells. Then, after comparing different gene selection strategies, the expressions of 162 seq-ImmuCC derived signature genes in tissue immune cell scRNA-Seq data were regarded as the tissue specific signature matrices. Finally, a modest improvement in performance was observed in multiple tissues that refer to a traditional general signature matrix in the deconvolution model. With the fast accumulation of scRNA-Seq data, the introduction of these data into an estimation of immune cell compositions for different tissues will open a new window for avoiding tissue bias for immune cell expression. AVAILABILITY AND IMPLEMENTATION The signature matrices were available at https://github.com/wuaipinglab/ImmuCC/tree/master/tissue_immucc/SignatureMatrix). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziyi Chen
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China.,Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Chengyang Ji
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China.,Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Qin Shen
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China.,Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Wei Liu
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China.,Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - F Xiao-Feng Qin
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China.,Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Aiping Wu
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China.,Suzhou Institute of Systems Medicine, Suzhou 215123, China
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Robinson EK, Covarrubias S, Carpenter S. The how and why of lncRNA function: An innate immune perspective. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2020; 1863:194419. [PMID: 31487549 PMCID: PMC7185634 DOI: 10.1016/j.bbagrm.2019.194419] [Citation(s) in RCA: 181] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 08/21/2019] [Indexed: 02/06/2023]
Abstract
Next-generation sequencing has provided a more complete picture of the composition of the human transcriptome indicating that much of the "blueprint" is a vastness of poorly understood non-protein-coding transcripts. This includes a newly identified class of genes called long noncoding RNAs (lncRNAs). The lack of sequence conservation for lncRNAs across species meant that their biological importance was initially met with some skepticism. LncRNAs mediate their functions through interactions with proteins, RNA, DNA, or a combination of these. Their functions can often be dictated by their localization, sequence, and/or secondary structure. Here we provide a review of the approaches typically adopted to study the complexity of these genes with an emphasis on recent discoveries within the innate immune field. Finally, we discuss the challenges, as well as the emergence of new technologies that will continue to move this field forward and provide greater insight into the biological importance of this class of genes. This article is part of a Special Issue entitled: ncRNA in control of gene expression edited by Kotb Abdelmohsen.
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Affiliation(s)
- Elektra K Robinson
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, United States of America
| | - Sergio Covarrubias
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, United States of America
| | - Susan Carpenter
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, United States of America.
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Görtler F, Schön M, Simeth J, Solbrig S, Wettig T, Oefner PJ, Spang R, Altenbuchinger M. Loss-Function Learning for Digital Tissue Deconvolution. J Comput Biol 2020; 27:342-355. [DOI: 10.1089/cmb.2019.0462] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
- Franziska Görtler
- Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Marian Schön
- Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Jakob Simeth
- Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Stefan Solbrig
- Department of Physics, University of Regensburg, Regensburg, Germany
| | - Tilo Wettig
- Department of Physics, University of Regensburg, Regensburg, Germany
| | - Peter J. Oefner
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Rainer Spang
- Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Michael Altenbuchinger
- Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
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Price A, Okumura A, Haddock E, Feldmann F, Meade-White K, Sharma P, Artami M, Lipkin WI, Threadgill DW, Feldmann H, Rasmussen AL. Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes. Cell Rep 2020; 30:1702-1713.e6. [PMID: 32049004 PMCID: PMC11062563 DOI: 10.1016/j.celrep.2020.01.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 11/07/2019] [Accepted: 01/07/2020] [Indexed: 01/26/2023] Open
Abstract
Host response to infection is a major determinant of disease severity in Ebola virus disease (EVD), but gene expression programs associated with outcome are poorly characterized. Collaborative Cross (CC) mice develop strain-dependent EVD phenotypes of differential severity, ranging from tolerance to lethality. We screen 10 CC lines and identify clinical, virologic, and transcriptomic features that distinguish tolerant from lethal outcomes. Tolerance is associated with tightly regulated induction of immune and inflammatory responses shortly following infection, as well as reduced inflammatory macrophages and increased antigen-presenting cells, B-1 cells, and γδ T cells. Lethal disease is characterized by suppressed early gene expression and reduced lymphocytes, followed by uncontrolled inflammatory signaling, leading to death. We apply machine learning to predict outcomes with 99% accuracy in mice using transcriptomic profiles. This signature predicts outcomes in a cohort of EVD patients from western Africa with 75% accuracy, demonstrating potential clinical utility.
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Affiliation(s)
- Adam Price
- Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA
| | - Atsushi Okumura
- Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA; Laboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA
| | - Elaine Haddock
- Laboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA
| | - Friederike Feldmann
- Rocky Mountain Veterinary Branch, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA
| | - Kimberly Meade-White
- Laboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA; Rocky Mountain Veterinary Branch, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA
| | - Pryanka Sharma
- Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA
| | - Methinee Artami
- Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA
| | - W Ian Lipkin
- Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA
| | - David W Threadgill
- Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, USA; Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
| | - Heinz Feldmann
- Laboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA
| | - Angela L Rasmussen
- Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA.
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41
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Macchietto MG, Langlois RA, Shen SS. Virus-induced transposable element expression up-regulation in human and mouse host cells. Life Sci Alliance 2020; 3:3/2/e201900536. [PMID: 31964680 PMCID: PMC6977392 DOI: 10.26508/lsa.201900536] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 01/09/2020] [Accepted: 01/10/2020] [Indexed: 12/18/2022] Open
Abstract
Genome-wide transposon expression up-regulation in host cells regardless of virus, species, and host cell tissue types occurs early during viral infection and likely contributes to promoting the host innate immune response. Virus–host cell interactions initiate a host cell–defensive response during virus infection. How transposable elements in the host cell respond to viral stress at the molecular level remains largely unclear. By reanalyzing next generation sequencing data sets from dozens of virus infection studies from the Gene Expression Omnibus database, we found that genome-wide transposon expression up-regulation in host cells occurs near antiviral response genes and exists in all studies regardless of virus, species, and host cell tissue types. Some transposons were found to be up-regulated almost immediately upon infection and before increases in virus replication and significant increases in interferon β expression. These findings indicate that transposon up-regulation is a common phenomenon during virus infection in human and mouse and that early up-regulated transposons are part of the first wave response during virus infection.
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Affiliation(s)
| | - Ryan A Langlois
- Biochemistry, Molecular Biology and Biophysics Graduate Program, University of Minnesota, Minneapolis, MN, USA
| | - Steven S Shen
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA .,Clinical Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
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42
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Danziger SA, Gibbs DL, Shmulevich I, McConnell M, Trotter MWB, Schmitz F, Reiss DJ, Ratushny AV. ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells. PLoS One 2019; 14:e0224693. [PMID: 31743345 PMCID: PMC6863530 DOI: 10.1371/journal.pone.0224693] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 10/18/2019] [Indexed: 12/19/2022] Open
Abstract
Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
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Affiliation(s)
- Samuel A. Danziger
- Celgene Corporation, Seattle, Washington, United States of America
- * E-mail: (SAD); (AVR)
| | - David L. Gibbs
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Ilya Shmulevich
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Mark McConnell
- Celgene Corporation, Seattle, Washington, United States of America
| | - Matthew W. B. Trotter
- Celgene Corporation, Seattle, Washington, United States of America
- Celgene Institute for Translational Research Europe, Seville, Sevilla, Spain
| | - Frank Schmitz
- Celgene Corporation, Seattle, Washington, United States of America
| | - David J. Reiss
- Celgene Corporation, Seattle, Washington, United States of America
| | - Alexander V. Ratushny
- Celgene Corporation, Seattle, Washington, United States of America
- * E-mail: (SAD); (AVR)
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43
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Zhang X, Pan A, Jia S, Ideozu JE, Woods K, Murkowski K, Hessner MJ, Simpson PM, Levy H. Cystic Fibrosis Plasma Blunts the Immune Response to Bacterial Infection. Am J Respir Cell Mol Biol 2019; 61:301-311. [PMID: 30848661 PMCID: PMC6839930 DOI: 10.1165/rcmb.2018-0114oc] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 03/07/2019] [Indexed: 12/18/2022] Open
Abstract
Cystic fibrosis (CF) is caused by mutations of the gene encoding the CF transmembrane conductance regulator. It remains unclear whether the abnormal immune response in CF involves extrinsic signals released from the external or internal environment. We sought to characterize the peripheral immune signatures in CF and its association with clinical phenotypes. Healthy peripheral blood mononuclear cells (PBMCs) were cultured with plasma from CF probands (CFPs) or healthy control subjects (HCs) followed by nCounter gene and microRNA (miRNA) profiling. A discovery cohort of 12 CFPs and 12 HCs and a validation cohort of 103 CFPs and 31 HCs (our previous microarray data [GSE71799]) were analyzed to characterize the composition of cultured immune cells and establish a miRNA‒mRNA network. Cell compositions and miRNA profiles were associated with clinical characteristics of the cohorts. Significantly differentially expressed genes and abundance of myeloid cells were downregulated in PMBCs after culture with CF plasma (P < 0.05). Top-ranked miRNAs that increased in response to CF plasma (adjusted P < 0.05) included miR-155 and miR-146a, which target many immune-related genes, such as IL-8. Pseudomonas aeruginosa infection was negatively associated with abundance of monocytes and the presence of those regulatory miRNAs. Extrinsic signals in plasma from patients with CF led to monocyte inactivation and miRNA upregulation in PBMCs. An improved understanding of the immune effects of extrinsic factors in CF holds great promise for integrating immunomodulatory cell therapies into current treatment strategies in CF.
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Affiliation(s)
- Xi Zhang
- Division of Pulmonary Medicine, Department of Pediatrics, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Human Molecular Genetics Program, Stanley Manne Children’s Research Institute of Chicago, Chicago, Illinois
| | - Amy Pan
- Children’s Research Institute of the Children’s Hospital of Wisconsin, Wauwatosa, Wisconsin; and
- Division of Quantitative Health Sciences, Department of Pediatrics
| | - Shuang Jia
- Division of Endocrinology, Department of Pediatrics
- Department of Pediatrics, Max McGee National Research Center for Juvenile Diabetes, and
| | - Justin E. Ideozu
- Division of Pulmonary Medicine, Department of Pediatrics, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Human Molecular Genetics Program, Stanley Manne Children’s Research Institute of Chicago, Chicago, Illinois
| | - Katherine Woods
- Division of Pediatric Critical Care Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kathleen Murkowski
- Division of Pediatric Critical Care Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Martin J. Hessner
- Children’s Research Institute of the Children’s Hospital of Wisconsin, Wauwatosa, Wisconsin; and
- Division of Endocrinology, Department of Pediatrics
- Department of Pediatrics, Max McGee National Research Center for Juvenile Diabetes, and
| | - Pippa M. Simpson
- Children’s Research Institute of the Children’s Hospital of Wisconsin, Wauwatosa, Wisconsin; and
- Division of Quantitative Health Sciences, Department of Pediatrics
| | - Hara Levy
- Division of Pulmonary Medicine, Department of Pediatrics, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Human Molecular Genetics Program, Stanley Manne Children’s Research Institute of Chicago, Chicago, Illinois
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44
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Avila Cobos F, Vandesompele J, Mestdagh P, De Preter K. Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 2019; 34:1969-1979. [PMID: 29351586 DOI: 10.1093/bioinformatics/bty019] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/10/2018] [Indexed: 12/22/2022] Open
Abstract
Summary Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results. Contact katleen.depreter@ugent.be. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francisco Avila Cobos
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Jo Vandesompele
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Pieter Mestdagh
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
| | - Katleen De Preter
- Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.,Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N), 9000 Ghent, Belgium
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45
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Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles. Processes (Basel) 2019. [DOI: 10.3390/pr7050301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.
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46
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Hao Y, Yan M, Heath BR, Lei YL, Xie Y. Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares. PLoS Comput Biol 2019; 15:e1006976. [PMID: 31059559 PMCID: PMC6522071 DOI: 10.1371/journal.pcbi.1006976] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 05/16/2019] [Accepted: 03/25/2019] [Indexed: 02/08/2023] Open
Abstract
Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides an estimate related to the absolute quantity of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissue-infiltrating immune cell landscape. The source code for FARDEEP is implemented in R and available for download at https://github.com/YuningHao/FARDEEP.git. Rapidly emerging evidence suggests that the tumor immune microenvironment not only predisposes cancer patients to diverse treatment outcomes but also represents a promising source of biomarkers for better patient stratification. Different from the immunohistochemistry-based scoring practice, which focuses on a few selected marker proteins, immune deconvolution pipelines inform a previously untapped method to comprehensively reveal the tumor-infiltrating immune landscape. Recognizing the numerous strengths of existing immune deconvolution algorithms, here we show data outliers, which are inevitable in whole tissue sequencing data sets, substantially skew estimation results. Moreover, an estimate related to the absolute amount of each immune subset offers valuable insight into the nature of the host response in addition to percentage information alone. Thus, we engineered a new immune deconvolution pipeline, coined as Fast and Robust Deconvolution of Expression Profiles (FARDEEP), to automatically detect and remove outliers prior feeding data into the deconvolution algorithm and to provide estimates related to the absolute quantity of each immune subset. Utilizing both synthetic and real data sets, we found that FARDEEP returns superior coefficients and offers a robust tool to reveal the immune landscape of human cancers.
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Affiliation(s)
- Yuning Hao
- Department of Statistics and Probability, Michigan State University, East Lansing, United States of America
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, United States of America
| | - Ming Yan
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, United States of America
- Department of Mathematics, Michigan State University, East Lansing, United States of America
| | - Blake R. Heath
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry Ann Arbor, United States of America
| | - Yu L. Lei
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry Ann Arbor, United States of America
- University of Michigan Rogel Cancer Center, Ann Arbor, United States of America
- * E-mail: (YLL); (YX)
| | - Yuying Xie
- Department of Statistics and Probability, Michigan State University, East Lansing, United States of America
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, United States of America
- * E-mail: (YLL); (YX)
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47
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Pedragosa M, Riera G, Casella V, Esteve-Codina A, Steuerman Y, Seth C, Bocharov G, Heath S, Gat-Viks I, Argilaguet J, Meyerhans A. Linking Cell Dynamics With Gene Coexpression Networks to Characterize Key Events in Chronic Virus Infections. Front Immunol 2019; 10:1002. [PMID: 31130969 PMCID: PMC6509617 DOI: 10.3389/fimmu.2019.01002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 04/18/2019] [Indexed: 01/29/2023] Open
Abstract
The host immune response against infection requires the coordinated action of many diverse cell subsets that dynamically adapt to a pathogen threat. Due to the complexity of such a response, most immunological studies have focused on a few genes, proteins, or cell types. With the development of “omic”-technologies and computational analysis methods, attempts to analyze and understand complex system dynamics are now feasible. However, the decomposition of transcriptomic data sets generated from complete organs remains a major challenge. Here, we combined Weighted Gene Coexpression Network Analysis (WGCNA) and Digital Cell Quantifier (DCQ) to analyze time-resolved mouse splenic transcriptomes in acute and chronic Lymphocytic Choriomeningitis Virus (LCMV) infections. This enabled us to generate hypotheses about complex immune functioning after a virus-induced perturbation. This strategy was validated by successfully predicting several known immune phenomena, such as effector cytotoxic T lymphocyte (CTL) expansion and exhaustion. Furthermore, we predicted and subsequently verified experimentally macrophage-CD8 T cell cooperativity and the participation of virus-specific CD8+ T cells with an early effector transcriptome profile in the host adaptation to chronic infection. Thus, the linking of gene expression changes with immune cell kinetics provides novel insights into the complex immune processes within infected tissues.
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Affiliation(s)
- Mireia Pedragosa
- Infection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Graciela Riera
- Infection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Valentina Casella
- Infection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Anna Esteve-Codina
- CNAG-CRG, Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Yael Steuerman
- Cell Research and Immunology Department, Tel Aviv University, Tel Aviv, Israel
| | - Celina Seth
- Infection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Gennady Bocharov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia.,Institute for Personalized Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Simon Heath
- CNAG-CRG, Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Irit Gat-Viks
- Cell Research and Immunology Department, Tel Aviv University, Tel Aviv, Israel
| | - Jordi Argilaguet
- Infection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Andreas Meyerhans
- Infection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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48
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Frishberg A, Peshes-Yaloz N, Cohn O, Rosentul D, Steuerman Y, Valadarsky L, Yankovitz G, Mandelboim M, Iraqi FA, Amit I, Mayo L, Bacharach E, Gat-Viks I. Cell composition analysis of bulk genomics using single-cell data. Nat Methods 2019; 16:327-332. [PMID: 30886410 PMCID: PMC6443043 DOI: 10.1038/s41592-019-0355-5] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Accepted: 02/12/2019] [Indexed: 12/21/2022]
Abstract
Single-cell expression profiling (scRNA-seq) is a rich resource of cellular heterogeneity. While profiling every sample under study would be advantageous, it is time-consuming and costly. Here we introduce Cell Population Mapping (CPM), a deconvolution algorithm in which the composition of cell types and states is inferred from the bulk transcriptome using reference scRNA-seq profiles ('scBio' CRAN R-package). Analysis of individual variations in lungs of influenza virus-infected mice, using CPM, revealed that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change was confirmed in subsequent experiments and was further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.
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Affiliation(s)
- Amit Frishberg
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Naama Peshes-Yaloz
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ofir Cohn
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Diana Rosentul
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yael Steuerman
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Liran Valadarsky
- Department of Immunology, The Weizmann Institute of Science, Rehovot, Israel
| | - Gal Yankovitz
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Michal Mandelboim
- National Center for Influenza and Respiratory Viruses, Central Virology Laboratory, Sheba Medical Center at Tel HaShomer, Ramat-Gan, Israel.,Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ido Amit
- Department of Immunology, The Weizmann Institute of Science, Rehovot, Israel
| | - Lior Mayo
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Eran Bacharach
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
| | - Irit Gat-Viks
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
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49
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Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 2019; 20:163-172. [PMID: 30643263 PMCID: PMC6340744 DOI: 10.1038/s41590-018-0276-y] [Citation(s) in RCA: 2029] [Impact Index Per Article: 405.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 11/09/2018] [Indexed: 02/07/2023]
Abstract
Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologues of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.
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50
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Cross RW, Speranza E, Borisevich V, Widen SG, Wood TG, Shim RS, Adams RD, Gerhardt DM, Bennett RS, Honko AN, Johnson JC, Hensley LE, Geisbert TW, Connor JH. Comparative Transcriptomics in Ebola Makona-Infected Ferrets, Nonhuman Primates, and Humans. J Infect Dis 2018; 218:S486-S495. [PMID: 30476250 PMCID: PMC6249602 DOI: 10.1093/infdis/jiy455] [Citation(s) in RCA: 12] [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] [Indexed: 01/29/2023] Open
Abstract
The domestic ferret is a uniformly lethal model of infection for 3 species of Ebolavirus known to be pathogenic in humans. Reagents to systematically analyze the ferret host response to infection are lacking; however, the recent publication of a draft ferret genome has opened the potential for transcriptional analysis of ferret models of disease. In this work, we present comparative analysis of longitudinally sampled blood taken from ferrets and nonhuman primates infected with lethal doses of the Makona variant of Zaire ebolavirus. Strong induction of proinflammatory and prothrombotic signaling programs were present in both ferrets and nonhuman primates, and both transcriptomes were similar to previously published datasets of fatal cases of human Ebola virus infection.
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Affiliation(s)
- Robert W Cross
- Galveston National Laboratory, University of Texas Medical Branch, Galveston
- Departments of Microbiology and Immunology, University of Texas Medical Branch, Galveston
| | - Emily Speranza
- Department of Microbiology, Bioinformatics Program, National Emerging Infectious Disease Laboratories, Boston University, Massachusetts
| | - Viktoriya Borisevich
- Galveston National Laboratory, University of Texas Medical Branch, Galveston
- Departments of Microbiology and Immunology, University of Texas Medical Branch, Galveston
| | - Steven G Widen
- Departments of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston
| | - Thomas G Wood
- Departments of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston
| | - Rebecca S Shim
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland
| | - Ricky D Adams
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland
| | - Dawn M Gerhardt
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland
| | - Richard S Bennett
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland
| | - Anna N Honko
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland
| | - Joshua C Johnson
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland
| | - Lisa E Hensley
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland
| | - Thomas W Geisbert
- Galveston National Laboratory, University of Texas Medical Branch, Galveston
- Departments of Microbiology and Immunology, University of Texas Medical Branch, Galveston
| | - John H Connor
- Departments of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston
- Department of Microbiology, Bioinformatics Program, National Emerging Infectious Disease Laboratories, Boston University, Massachusetts
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