1
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Cuevas-Diaz Duran R, Wei H, Wu J. Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets. BMC Genomics 2024; 25:444. [PMID: 38711017 PMCID: PMC11073985 DOI: 10.1186/s12864-024-10364-5] [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: 09/02/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.
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Affiliation(s)
- Raquel Cuevas-Diaz Duran
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, 64710, Mexico.
| | - Haichao Wei
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, 77030, USA
| | - Jiaqian Wu
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, 77030, USA.
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
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2
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Jiang Q, Chen S, Chen X, Jiang R. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism. Bioinformatics 2024; 40:btae265. [PMID: 38625746 PMCID: PMC11076148 DOI: 10.1093/bioinformatics/btae265] [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: 12/07/2023] [Revised: 04/06/2024] [Accepted: 04/13/2024] [Indexed: 04/17/2024] Open
Abstract
MOTIVATION With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution. RESULTS Here, we present scPRAM, a method for predicting perturbation responses in single-cell gene expression based on attention mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations. AVAILABILITY AND IMPLEMENTATION https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.
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Affiliation(s)
- Qun Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Xiaoyang Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rui Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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3
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Ding R, Wang Q, Gong L, Zhang T, Zou X, Xiong K, Liao Q, Plass M, Li L. scQTLbase: an integrated human single-cell eQTL database. Nucleic Acids Res 2024; 52:D1010-D1017. [PMID: 37791879 PMCID: PMC10767909 DOI: 10.1093/nar/gkad781] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/24/2023] [Accepted: 09/15/2023] [Indexed: 10/05/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified numerous genetic variants associated with diseases and traits. However, the functional interpretation of these variants remains challenging. Expression quantitative trait loci (eQTLs) have been widely used to identify mutations linked to disease, yet they explain only 20-50% of disease-related variants. Single-cell eQTLs (sc-eQTLs) studies provide an immense opportunity to identify new disease risk genes with expanded eQTL scales and transcriptional regulation at a much finer resolution. However, there is no comprehensive database dedicated to single-cell eQTLs that users can use to search, analyse and visualize them. Therefore, we developed the scQTLbase (http://bioinfo.szbl.ac.cn/scQTLbase), the first integrated human sc-eQTLs portal, featuring 304 datasets spanning 57 cell types and 95 cell states. It contains ∼16 million SNPs significantly associated with cell-type/state gene expression and ∼0.69 million disease-associated sc-eQTLs from 3 333 traits/diseases. In addition, scQTLbase offers sc-eQTL search, gene expression visualization in UMAP plots, a genome browser, and colocalization visualization based on the GWAS dataset of interest. scQTLbase provides a one-stop portal for sc-eQTLs that will significantly advance the discovery of disease susceptibility genes.
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Affiliation(s)
- Ruofan Ding
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Qixuan Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Lihai Gong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Ting Zhang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Xudong Zou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Kewei Xiong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Qi Liao
- School of Public Health, Health Science Center, Ningbo University, Ningbo 315211, China
| | - Mireya Plass
- Gene Regulation of Cell Identity Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), and Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L′Hospitalet de Llobregat, Barcelona, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
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4
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Grima R, Esmenjaud PM. Quantifying and correcting bias in transcriptional parameter inference from single-cell data. Biophys J 2024; 123:4-30. [PMID: 37885177 PMCID: PMC10808030 DOI: 10.1016/j.bpj.2023.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/12/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.
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Affiliation(s)
- Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
| | - Pierre-Marie Esmenjaud
- Biology Department, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
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5
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Tyebally R, Xue A, Powell JE. The potential clinical impact of cell type-specific genetic regulation: Crohn's disease. Clin Transl Med 2023; 13:e1474. [PMID: 37983917 PMCID: PMC10659767 DOI: 10.1002/ctm2.1474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
Complex diseases are heterogenous due to variation in their genetic and environmental underpinnings, leading to varied treatment responses. Genome-wide association studies (GWAS) integrated with single-cell expression quantitative trait loci analyses (eQTL) can pinpoint cell-type specific candidate disease-relevant genes and pathways. This knowledge can be applied to patient stratification and novel therapeutic target identification. Here, we describe the translational potential of cell-type specific genetic regulation, using Crohn's disease as an example.
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Affiliation(s)
- Rika Tyebally
- Translational GenomicsGarvan Institute of Medical Research, DarlinghurstSydneyNew South WalesAustralia
- UNSW Cellular Genomics Futures InstituteUniversity of New South Wales, KingstonSydneyNew South WalesAustralia
| | - Angli Xue
- Translational GenomicsGarvan Institute of Medical Research, DarlinghurstSydneyNew South WalesAustralia
- School of Biomedical SciencesUniversity of New South WalesSydneyNew South WalesAustralia
| | - Joseph E. Powell
- Translational GenomicsGarvan Institute of Medical Research, DarlinghurstSydneyNew South WalesAustralia
- UNSW Cellular Genomics Futures InstituteUniversity of New South Wales, KingstonSydneyNew South WalesAustralia
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6
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Ren H, Zhou X, Yang J, Kou K, Chen T, Pu Z, Ye K, Fan X, Zhang D, Kang X, Fan Z, Lei M, Sun T, Tan X, Ou X. Single-cell RNA sequencing of murine hearts for studying the development of the cardiac conduction system. Sci Data 2023; 10:577. [PMID: 37666871 PMCID: PMC10477280 DOI: 10.1038/s41597-023-02333-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 06/26/2023] [Indexed: 09/06/2023] Open
Abstract
The development of the cardiac conduction system (CCS) is essential for correct heart function. However, critical details on the cell types populating the CCS in the mammalian heart during the development remain to be resolved. Using single-cell RNA sequencing, we generated a large dataset of transcriptomes of ~0.5 million individual cells isolated from murine hearts at six successive developmental corresponding to the early, middle and late stages of heart development. The dataset provides a powerful library for studying the development of the heart's CCS and other cardiac components. Our initial analysis identified distinct cell types between 20 to 26 cell types across different stages, of which ten are involved in forming the CCS. Our dataset allows researchers to reuse the datasets for data mining and a wide range of analyses. Collectively, our data add valuable transcriptomic resources for further study of cardiac development, such as gene expression, transcriptional regulation and functional gene activity in developing hearts, particularly the CCS.
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Affiliation(s)
- Huiying Ren
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
- Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Xiaolin Zhou
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Jun Yang
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Kun Kou
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
- Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Tangting Chen
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Zhaoli Pu
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Kejun Ye
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Xuehui Fan
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Dan Zhang
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Xinjiang Kang
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Zhongcai Fan
- Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Ming Lei
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
- Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, United Kingdom.
| | - Tianyi Sun
- Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, United Kingdom.
| | - Xiaoqiu Tan
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
- Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
- Department of Physiology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
| | - Xianhong Ou
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
- Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
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7
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Yin Y, Wu S, Niu L, Huang S. Atonal homolog 7 (ATOH7) confers neuroprotection for photoreceptor cells in glaucoma via inhibition of the notch pathway. J Neurochem 2023; 166:847-861. [PMID: 37526008 DOI: 10.1111/jnc.15905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/13/2023] [Accepted: 06/20/2023] [Indexed: 08/02/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies enable the profiling and analysis of the transcriptomes of single cells and hold promise for clarifying gene mechanisms at single-cell resolution. We based this study on scRNA-seq data to reveal glaucoma-related genes and downstream pathways with neuroprotection effects. The scRNA-seq datasets related to glaucoma of retinal tissue samples of human beings and Atonal Homolog 7 (ATOH7)-null mice were obtained from the GEO database. The 74 top marker genes and 20 cell clusters were obtained in human retinal tissue samples. The key gene ATOH7 was found after the intersection with genes from GeneCards data. In the ATOH7-null mouse retinal tissue samples, pseudotime inference demonstrated significant changes in cell differentiation. Moreover, mouse retinal photoreceptor cells (PRCs) were cultured and treated with lentivirus carrying oe-ATOH7 alone or in combination with Notch signaling pathway activator Jagged-1/FC, after which cell biological functions were determined. The involvement of ATOH7 in glaucoma was identified through regulating PRCs. Furthermore, ATOH7 conferred neuroprotection in PRCs in glaucoma by mediating the Notch signaling pathway. In vitro data confirmed that ATOH7 overexpression promoted the differentiation of PRCs and inhibited their apoptosis by suppressing the Notch signaling pathway. The evidence provided by our study highlighted the involvement of ATOH7 in the blockade of the Notch signaling pathway, resulting in the neuroprotection for PRCs in glaucoma.
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Affiliation(s)
- Yuan Yin
- Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, People's Republic of China
| | - Shuai Wu
- Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, People's Republic of China
| | - Lingzhi Niu
- Department of Ophthalmology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, People's Republic of China
| | - Shiwei Huang
- Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, People's Republic of China
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8
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SoRelle ED, Reinoso-Vizcaino NM, Dai J, Barry AP, Chan C, Luftig MA. Epstein-Barr virus evades restrictive host chromatin closure by subverting B cell activation and germinal center regulatory loci. Cell Rep 2023; 42:112958. [PMID: 37561629 PMCID: PMC10559315 DOI: 10.1016/j.celrep.2023.112958] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/02/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023] Open
Abstract
Chromatin accessibility fundamentally governs gene expression and biological response programs that can be manipulated by pathogens. Here we capture dynamic chromatin landscapes of individual B cells during Epstein-Barr virus (EBV) infection. EBV+ cells that exhibit arrest via antiviral sensing and proliferation-linked DNA damage experience global accessibility reduction. Proliferative EBV+ cells develop expression-linked architectures and motif accessibility profiles resembling in vivo germinal center (GC) phenotypes. Remarkably, EBV elicits dark zone (DZ), light zone (LZ), and post-GC B cell chromatin features despite BCL6 downregulation. Integration of single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data enables genome-wide cis-regulatory predictions implicating EBV nuclear antigens (EBNAs) in phenotype-specific control of GC B cell activation, survival, and immune evasion. Knockouts validate bioinformatically identified regulators (MEF2C and NFE2L2) of EBV-induced GC phenotypes and EBNA-associated loci that regulate gene expression (CD274/PD-L1). These data and methods can inform high-resolution investigations of EBV-host interactions, B cell fates, and virus-mediated lymphomagenesis.
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Affiliation(s)
- Elliott D SoRelle
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA.
| | - Nicolás M Reinoso-Vizcaino
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Joanne Dai
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Ashley P Barry
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Cliburn Chan
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA
| | - Micah A Luftig
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710, USA.
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9
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Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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10
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Flynn E, Almonte-Loya A, Fragiadakis GK. Single-Cell Multiomics. Annu Rev Biomed Data Sci 2023; 6:313-337. [PMID: 37159875 PMCID: PMC11146013 DOI: 10.1146/annurev-biodatasci-020422-050645] [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] [Indexed: 05/11/2023]
Abstract
Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.
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Affiliation(s)
- Emily Flynn
- CoLabs, University of California, San Francisco, California, USA;
| | - Ana Almonte-Loya
- CoLabs, University of California, San Francisco, California, USA;
- Biomedical Informatics Program, University of California, San Francisco, California, USA
| | - Gabriela K Fragiadakis
- CoLabs, University of California, San Francisco, California, USA;
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
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11
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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12
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Jiao C, Reckstadt C, König F, Homberger C, Yu J, Vogel J, Westermann AJ, Sharma CM, Beisel CL. RNA recording in single bacterial cells using reprogrammed tracrRNAs. Nat Biotechnol 2023; 41:1107-1116. [PMID: 36604543 PMCID: PMC7614944 DOI: 10.1038/s41587-022-01604-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 11/07/2022] [Indexed: 01/07/2023]
Abstract
Capturing an individual cell's transcriptional history is a challenge exacerbated by the functional heterogeneity of cellular communities. Here, we leverage reprogrammed tracrRNAs (Rptrs) to record selected cellular transcripts as stored DNA edits in single living bacterial cells. Rptrs are designed to base pair with sensed transcripts, converting them into guide RNAs. The guide RNAs then direct a Cas9 base editor to target an introduced DNA target. The extent of base editing can then be read in the future by sequencing. We use this approach, called TIGER (transcribed RNAs inferred by genetically encoded records), to record heterologous and endogenous transcripts in individual bacterial cells. TIGER can quantify relative expression, distinguish single-nucleotide differences, record multiple transcripts simultaneously and read out single-cell phenomena. We further apply TIGER to record metabolic bet hedging and antibiotic resistance mobilization in Escherichia coli as well as host cell invasion by Salmonella. Through RNA recording, TIGER connects current cellular states with past transcriptional states to decipher complex cellular responses in single cells.
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Affiliation(s)
- Chunlei Jiao
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany
| | - Claas Reckstadt
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany
| | - Fabian König
- Department of Molecular Infection Biology II, Institute of Molecular Infection Biology, University of Würzburg, Würzburg, Germany
| | - Christina Homberger
- Institute of Molecular Infection Biology, University of Würzburg, Würzburg, Germany
| | - Jiaqi Yu
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany
| | - Jörg Vogel
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany
- Institute of Molecular Infection Biology, University of Würzburg, Würzburg, Germany
- Medical Faculty, University of Würzburg, Würzburg, Germany
| | - Alexander J Westermann
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany
- Institute of Molecular Infection Biology, University of Würzburg, Würzburg, Germany
| | - Cynthia M Sharma
- Department of Molecular Infection Biology II, Institute of Molecular Infection Biology, University of Würzburg, Würzburg, Germany
| | - Chase L Beisel
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany.
- Medical Faculty, University of Würzburg, Würzburg, Germany.
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13
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Cheng Y, Ma X, Yuan L, Sun Z, Wang P. Evaluating imputation methods for single-cell RNA-seq data. BMC Bioinformatics 2023; 24:302. [PMID: 37507764 PMCID: PMC10386301 DOI: 10.1186/s12859-023-05417-7] [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: 06/29/2020] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms. RESULTS In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain. CONCLUSIONS In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.
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Affiliation(s)
- Yi Cheng
- School of Intelligence Science and Technology, Key Laboratory of Machine Perception (MOE), Peking University, Beijing, 100871, China
| | - Xiuli Ma
- School of Intelligence Science and Technology, Key Laboratory of Machine Perception (MOE), Peking University, Beijing, 100871, China.
| | - Lang Yuan
- School of Intelligence Science and Technology, Key Laboratory of Machine Perception (MOE), Peking University, Beijing, 100871, China
| | - Zhaoguo Sun
- School of Intelligence Science and Technology, Key Laboratory of Machine Perception (MOE), Peking University, Beijing, 100871, China
| | - Pingzhang Wang
- Department of Immunology, NHC Key Laboratory of Medical Immunology (Peking University), School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.
- Peking University Center for Human Disease Genomics, Beijing, 100191, China.
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14
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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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15
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The Application of Single-Cell RNA Sequencing in the Inflammatory Tumor Microenvironment. Biomolecules 2023; 13:biom13020344. [PMID: 36830713 PMCID: PMC9953711 DOI: 10.3390/biom13020344] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
The initiation and progression of tumors are complex. The cancer evolution-development hypothesis holds that the dysregulation of immune balance is caused by the synergistic effect of immune genetic factors and environmental factors that stimulate and maintain non-resolving inflammation. Throughout the cancer development process, this inflammation creates a microenvironment for the evolution and development of cancer. Research on the inflammatory tumor microenvironment (TME) explains the initiation and progression of cancer and guides anti-cancer immunotherapy. Single-cell RNA sequencing (scRNA-seq) can detect the transcription levels of cells at the single-cell resolution level, reveal the heterogeneity and evolutionary trajectory of infiltrated immune cells and cancer cells, and provide insight into the composition and function of each cell group in the inflammatory TME. This paper summarizes the application of scRNA-seq in inflammatory TME.
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16
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Pudjihartono M, Perry JK, Print C, O'Sullivan JM, Schierding W. Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis. Clin Epigenetics 2022; 14:120. [PMID: 36171609 PMCID: PMC9520844 DOI: 10.1186/s13148-022-01342-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression. MAIN BODY We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data. CONCLUSION We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.
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Affiliation(s)
| | - Jo K Perry
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Cris Print
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, School of Medical Sciences, University of Auckland, Auckland, 1142, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Australian Parkinson's Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand.
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand.
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17
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Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution. Int J Mol Sci 2022; 23:ijms231810214. [PMID: 36142130 PMCID: PMC9499411 DOI: 10.3390/ijms231810214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/12/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022] Open
Abstract
Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.
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18
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SoRelle ED, Dai J, Reinoso-Vizcaino NM, Barry AP, Chan C, Luftig MA. Time-resolved transcriptomes reveal diverse B cell fate trajectories in the early response to Epstein-Barr virus infection. Cell Rep 2022; 40:111286. [PMID: 36044865 PMCID: PMC9879279 DOI: 10.1016/j.celrep.2022.111286] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/07/2022] [Accepted: 08/08/2022] [Indexed: 01/28/2023] Open
Abstract
Epstein-Barr virus infection of B lymphocytes elicits diverse host responses via well-adapted transcriptional control dynamics. Consequently, this host-pathogen interaction provides a powerful system to explore fundamental processes leading to consensus fate decisions. Here, we use single-cell transcriptomics to construct a genome-wide multistate model of B cell fates upon EBV infection. Additional single-cell data from human tonsils reveal correspondence of model states to analogous in vivo phenotypes within secondary lymphoid tissue, including an EBV+ analog of multipotent activated precursors that can yield early memory B cells. These resources yield exquisitely detailed perspectives of the transforming cellular landscape during an oncogenic viral infection that simulates antigen-induced B cell activation and differentiation. Thus, they support investigations of state-specific EBV-host dynamics, effector B cell fates, and lymphomagenesis. To demonstrate this potential, we identify EBV infection dynamics in FCRL4+/TBX21+ atypical memory B cells that are pathogenically associated with numerous immune disorders.
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Affiliation(s)
- Elliott D. SoRelle
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710,Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710,Corresponding Authors: Elliott D. SoRelle () & Micah A. Luftig ()
| | - Joanne Dai
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710,Current address: Amgen Inc., 1120 Veterans Blvd, South San Francisco, CA 94080
| | - Nicolás M. Reinoso-Vizcaino
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710
| | - Ashley P. Barry
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710
| | - Cliburn Chan
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710
| | - Micah A. Luftig
- Department of Molecular Genetics and Microbiology, Duke Center for Virology, Duke University School of Medicine, Durham, NC 27710,Corresponding Authors: Elliott D. SoRelle () & Micah A. Luftig ()
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19
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Senabouth A, Daniszewski M, Lidgerwood GE, Liang HH, Hernández D, Mirzaei M, Keenan SN, Zhang R, Han X, Neavin D, Rooney L, Lopez Sanchez MIG, Gulluyan L, Paulo JA, Clarke L, Kearns LS, Gnanasambandapillai V, Chan CL, Nguyen U, Steinmann AM, McCloy RA, Farbehi N, Gupta VK, Mackey DA, Bylsma G, Verma N, MacGregor S, Watt MJ, Guymer RH, Powell JE, Hewitt AW, Pébay A. Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration. Nat Commun 2022; 13:4233. [PMID: 35882847 PMCID: PMC9325891 DOI: 10.1038/s41467-022-31707-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
There are currently no treatments for geographic atrophy, the advanced form of age-related macular degeneration. Hence, innovative studies are needed to model this condition and prevent or delay its progression. Induced pluripotent stem cells generated from patients with geographic atrophy and healthy individuals were differentiated to retinal pigment epithelium. Integrating transcriptional profiles of 127,659 retinal pigment epithelium cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identify 445 expression quantitative trait loci in cis that are asssociated with disease status and specific to retinal pigment epithelium subpopulations. Transcriptomics and proteomics approaches identify molecular pathways significantly upregulated in geographic atrophy, including in mitochondrial functions, metabolic pathways and extracellular cellular matrix reorganization. Five significant protein quantitative trait loci that regulate protein expression in the retinal pigment epithelium and in geographic atrophy are identified - two of which share variants with cis- expression quantitative trait loci, including proteins involved in mitochondrial biology and neurodegeneration. Investigation of mitochondrial metabolism confirms mitochondrial dysfunction as a core constitutive difference of the retinal pigment epithelium from patients with geographic atrophy. This study uncovers important differences in retinal pigment epithelium homeostasis associated with geographic atrophy.
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Affiliation(s)
- Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Maciej Daniszewski
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Grace E Lidgerwood
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Helena H Liang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Damián Hernández
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Mehdi Mirzaei
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Stacey N Keenan
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ran Zhang
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Xikun Han
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Louise Rooney
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | | | - Lerna Gulluyan
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Linda Clarke
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Lisa S Kearns
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | | | - Chia-Ling Chan
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Uyen Nguyen
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Angela M Steinmann
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Rachael A McCloy
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Nona Farbehi
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Vivek K Gupta
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - David A Mackey
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Perth, WA, 6009, Australia
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia
| | - Guy Bylsma
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Perth, WA, 6009, Australia
| | - Nitin Verma
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Matthew J Watt
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Robyn H Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
- Department of Surgery, Ophthalmology, Royal Victorian Eye and Ear Hospital, The University of Melbourne, East Melbourne, VIC, 3002, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, 2052, Australia.
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia.
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia.
- Department of Surgery, Ophthalmology, Royal Victorian Eye and Ear Hospital, The University of Melbourne, East Melbourne, VIC, 3002, Australia.
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia.
| | - Alice Pébay
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia.
- Department of Surgery, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, 3010, Australia.
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20
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Cheong A, Nagel ZD. Human Variation in DNA Repair, Immune Function, and Cancer Risk. Front Immunol 2022; 13:899574. [PMID: 35935942 PMCID: PMC9354717 DOI: 10.3389/fimmu.2022.899574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
DNA damage constantly threatens genome integrity, and DNA repair deficiency is associated with increased cancer risk. An intuitive and widely accepted explanation for this relationship is that unrepaired DNA damage leads to carcinogenesis due to the accumulation of mutations in somatic cells. But DNA repair also plays key roles in the function of immune cells, and immunodeficiency is an important risk factor for many cancers. Thus, it is possible that emerging links between inter-individual variation in DNA repair capacity and cancer risk are driven, at least in part, by variation in immune function, but this idea is underexplored. In this review we present an overview of the current understanding of the links between cancer risk and both inter-individual variation in DNA repair capacity and inter-individual variation in immune function. We discuss factors that play a role in both types of variability, including age, lifestyle, and environmental exposures. In conclusion, we propose a research paradigm that incorporates functional studies of both genome integrity and the immune system to predict cancer risk and lay the groundwork for personalized prevention.
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21
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DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data. Comput Biol Med 2022; 146:105578. [DOI: 10.1016/j.compbiomed.2022.105578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 11/18/2022]
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22
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Daniszewski M, Senabouth A, Liang HH, Han X, Lidgerwood GE, Hernández D, Sivakumaran P, Clarke JE, Lim SY, Lees JG, Rooney L, Gulluyan L, Souzeau E, Graham SL, Chan CL, Nguyen U, Farbehi N, Gnanasambandapillai V, McCloy RA, Clarke L, Kearns LS, Mackey DA, Craig JE, MacGregor S, Powell JE, Pébay A, Hewitt AW. Retinal ganglion cell-specific genetic regulation in primary open-angle glaucoma. CELL GENOMICS 2022; 2:100142. [PMID: 36778138 PMCID: PMC9903700 DOI: 10.1016/j.xgen.2022.100142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 03/08/2021] [Accepted: 05/11/2022] [Indexed: 10/18/2022]
Abstract
To assess the transcriptomic profile of disease-specific cell populations, fibroblasts from patients with primary open-angle glaucoma (POAG) were reprogrammed into induced pluripotent stem cells (iPSCs) before being differentiated into retinal organoids and compared with those from healthy individuals. We performed single-cell RNA sequencing of a total of 247,520 cells and identified cluster-specific molecular signatures. Comparing the gene expression profile between cases and controls, we identified novel genetic associations for this blinding disease. Expression quantitative trait mapping identified a total of 4,443 significant loci across all cell types, 312 of which are specific to the retinal ganglion cell subpopulations, which ultimately degenerate in POAG. Transcriptome-wide association analysis identified genes at loci previously associated with POAG, and analysis, conditional on disease status, implicated 97 statistically significant retinal ganglion cell-specific expression quantitative trait loci. This work highlights the power of large-scale iPSC studies to uncover context-specific profiles for a genetically complex disease.
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Affiliation(s)
- Maciej Daniszewski
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC 3010, Australia,Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Anne Senabouth
- Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia
| | - Helena H. Liang
- Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Xikun Han
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Grace E. Lidgerwood
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC 3010, Australia,Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Damián Hernández
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC 3010, Australia,Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Priyadharshini Sivakumaran
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Jordan E. Clarke
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Shiang Y. Lim
- Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,O’Brien Institute Department of St Vincent’s Institute of Medical Research, Melbourne, Fitzroy, VIC 3065, Australia
| | - Jarmon G. Lees
- O’Brien Institute Department of St Vincent’s Institute of Medical Research, Melbourne, Fitzroy, VIC 3065, Australia,Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Louise Rooney
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC 3010, Australia,Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Lerna Gulluyan
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC 3010, Australia,Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Emmanuelle Souzeau
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, SA 5042, Australia
| | - Stuart L. Graham
- Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Chia-Ling Chan
- Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia
| | - Uyen Nguyen
- Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia
| | - Nona Farbehi
- Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia
| | - Vikkitharan Gnanasambandapillai
- Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia
| | - Rachael A. McCloy
- Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia
| | - Linda Clarke
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - Lisa S. Kearns
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia
| | - David A. Mackey
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Crawley, WA 6009, Australia,School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS 7005, Australia
| | - Jamie E. Craig
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, SA 5042, Australia
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Joseph E. Powell
- Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia,UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia,Corresponding author
| | - Alice Pébay
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC 3010, Australia,Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia,Corresponding author
| | - Alex W. Hewitt
- Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia,School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS 7005, Australia,Corresponding author
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23
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Mu W, Sarkar H, Srivastava A, Choi K, Patro R, Love MI. Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets. Bioinformatics 2022; 38:2773-2780. [PMID: 35561168 PMCID: PMC9113279 DOI: 10.1093/bioinformatics/btac212] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 03/05/2022] [Accepted: 04/05/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected. RESULTS We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes. AVAILABILITY AND IMPLEMENTATION The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wancen Mu
- To whom correspondence should be addressed. or
| | - Hirak Sarkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA
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24
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Maria M, Pouyanfar N, Örd T, Kaikkonen MU. The Power of Single-Cell RNA Sequencing in eQTL Discovery. Genes (Basel) 2022; 13:genes13030502. [PMID: 35328055 PMCID: PMC8949403 DOI: 10.3390/genes13030502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 02/05/2023] Open
Abstract
Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing (scRNA-Seq) technologies have created new exciting opportunities for spatiotemporal assessment of changes in gene expression at the single-cell level in complex and inherited conditions. A growing number of studies have demonstrated the power of scRNA-Seq in eQTL mapping across different cell types, developmental stages and stimuli that could be obscured when using bulk RNA-Seq methods. In this review, we outline the methodological principles, advantages, limitations and the future experimental and analytical considerations of single-cell eQTL studies. We look forward to the explosion of single-cell eQTL studies applied to large-scale population genetics to take us one step closer to understanding the molecular mechanisms of disease.
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25
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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26
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Slenders L, Tessels DE, van der Laan SW, Pasterkamp G, Mokry M. The Applications of Single-Cell RNA Sequencing in Atherosclerotic Disease. Front Cardiovasc Med 2022; 9:826103. [PMID: 35211529 PMCID: PMC8860895 DOI: 10.3389/fcvm.2022.826103] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/03/2022] [Indexed: 02/05/2023] Open
Abstract
Atherosclerosis still is the primary cause of death worldwide. Our characterization of the atherosclerotic lesion is mainly rooted in definitions based on pathological descriptions. We often speak in absolutes regarding plaque phenotypes: vulnerable vs. stable plaques or plaque rupture vs. plaque erosion. By focusing on these concepts, we may have oversimplified the atherosclerotic disease and its mechanisms. The widely used definitions of pathology-based plaque phenotypes can be fine-tuned with observations made with various -omics techniques. Recent advancements in single-cell transcriptomics provide the opportunity to characterize the cellular composition of the atherosclerotic plaque. This additional layer of information facilitates the in-depth characterization of the atherosclerotic plaque. In this review, we discuss the impact that single-cell transcriptomics may exert on our current understanding of atherosclerosis.
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Affiliation(s)
- Lotte Slenders
- Central Diagnostics Laboratory, University Medical Center Utrecht, University Utrecht, Utrecht, Netherlands
| | - Daniëlle E Tessels
- Central Diagnostics Laboratory, University Medical Center Utrecht, University Utrecht, Utrecht, Netherlands
| | - Sander W van der Laan
- Central Diagnostics Laboratory, University Medical Center Utrecht, University Utrecht, Utrecht, Netherlands
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, University Medical Center Utrecht, University Utrecht, Utrecht, Netherlands
| | - Michal Mokry
- Central Diagnostics Laboratory, University Medical Center Utrecht, University Utrecht, Utrecht, Netherlands.,Laboratory of Experimental Cardiology, Department of Cardiology, University Medical Center Utrecht, University Utrecht, Utrecht, Netherlands
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27
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Ghodke-Puranik Y, Jin Z, Zimmerman KD, Ainsworth HC, Fan W, Jensen MA, Dorschner JM, Vsetecka DM, Amin S, Makol A, Ernste F, Osborn T, Moder K, Chowdhary V, Langefeld CD, Niewold TB. Single-cell expression quantitative trait loci (eQTL) analysis of SLE-risk loci in lupus patient monocytes. Arthritis Res Ther 2021; 23:290. [PMID: 34847931 PMCID: PMC8630910 DOI: 10.1186/s13075-021-02660-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 10/17/2021] [Indexed: 02/07/2023] Open
Abstract
Background We performed expression quantitative trait locus (eQTL) analysis in single classical (CL) and non-classical (NCL) monocytes from patients with systemic lupus erythematosus (SLE) to quantify the impact of well-established genetic risk alleles on transcription at single-cell resolution. Methods Single-cell gene expression was quantified using qPCR in purified monocyte subpopulations (CD14++CD16− CL and CD14dimCD16+ NCL) from SLE patients. Novel analysis methods were used to control for the within-person correlations observed, and eQTLs were compared between cell types and risk alleles. Results The SLE-risk alleles demonstrated significantly more eQTLs in NCLs as compared to CLs (p = 0.0004). There were 18 eQTLs exclusive to NCL cells, 5 eQTLs exclusive to CL cells, and only one shared eQTL, supporting large differences in the impact of the risk alleles between these monocyte subsets. The SPP1 and TNFAIP3 loci were associated with the greatest number of transcripts. Patterns of shared influence in which different SNPs impacted the same transcript also differed between monocyte subsets, with greater evidence for synergy in NCL cells. IRF1 expression demonstrated an on/off pattern, in which expression was zero in all of the monocytes studied from some individuals, and this pattern was associated with a number of SLE risk alleles. We observed corroborating evidence of this IRF1 expression pattern in public data sets. Conclusions We document multiple SLE-risk allele eQTLs in single monocytes which differ greatly between CL and NCL subsets. These data support the importance of the SPP1 and TNFAIP3 risk variants and the IRF1 transcript in SLE patient monocyte function. Supplementary Information The online version contains supplementary material available at 10.1186/s13075-021-02660-2.
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Affiliation(s)
- Yogita Ghodke-Puranik
- Colton Center for Autoimmunity, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Zhongbo Jin
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Kip D Zimmerman
- Department of Biostatistics and Data Science and Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Hannah C Ainsworth
- Department of Biostatistics and Data Science and Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Wei Fan
- Department of Rheumatology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mark A Jensen
- Colton Center for Autoimmunity, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Jessica M Dorschner
- Department of Immunology and Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | - Danielle M Vsetecka
- Department of Immunology and Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | - Shreyasee Amin
- Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | - Ashima Makol
- Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | | | - Thomas Osborn
- Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | - Kevin Moder
- Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | - Vaidehi Chowdhary
- Division of Rheumatology, Allergy and Immunology, Yale University School of Medicine, New Haven, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science and Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Timothy B Niewold
- Colton Center for Autoimmunity, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA.
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28
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Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease. Biochem Soc Trans 2021; 49:2091-2100. [PMID: 34581766 PMCID: PMC8589426 DOI: 10.1042/bst20210128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/25/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022]
Abstract
Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.
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29
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Auerbach BJ, Hu J, Reilly MP, Li M. Applications of single-cell genomics and computational strategies to study common disease and population-level variation. Genome Res 2021; 31:1728-1741. [PMID: 34599006 PMCID: PMC8494214 DOI: 10.1101/gr.275430.121] [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] [Indexed: 12/13/2022]
Abstract
The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.
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Affiliation(s)
- Benjamin J Auerbach
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
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30
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Wang P, Yao L, Luo M, Zhou W, Jin X, Xu Z, Yan S, Li Y, Xu C, Cheng R, Huang Y, Lin X, Ma K, Cao H, Liu H, Xue G, Han F, Nie H, Jiang Q. Single-cell transcriptome and TCR profiling reveal activated and expanded T cell populations in Parkinson's disease. Cell Discov 2021; 7:52. [PMID: 34282123 PMCID: PMC8289849 DOI: 10.1038/s41421-021-00280-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023] Open
Abstract
Given the chronic inflammatory nature of Parkinson's disease (PD), T cell immunity may be important for disease onset. Here, we performed single-cell transcriptome and TCR sequencing, and conducted integrative analyses to decode composition, function and lineage relationship of T cells in the blood and cerebrospinal fluid of PD. Combined expression and TCR-based lineage tracking, we discovered a large population of CD8+ T cells showing continuous progression from central memory to terminal effector T cells in PD patients. Additionally, we identified a group of cytotoxic CD4+ T cells (CD4 CTLs) remarkably expanded in PD patients, which derived from Th1 cells by TCR-based fate decision. Finally, we screened putative TCR-antigen pairs that existed in both blood and cerebrospinal fluid of PD patients to provide potential evidence for peripheral T cells to participate in neuronal degeneration. Our study provides valuable insights and rich resources for understanding the adaptive immune response in PD.
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Affiliation(s)
- Pingping Wang
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Lifen Yao
- grid.412596.d0000 0004 1797 9737Department of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang China
| | - Meng Luo
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Wenyang Zhou
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Xiyun Jin
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Zhaochun Xu
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Shi Yan
- grid.412596.d0000 0004 1797 9737Department of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang China
| | - Yiqun Li
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Chang Xu
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Rui Cheng
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Yan Huang
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Xiaoyu Lin
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Kexin Ma
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Huimin Cao
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Hongxin Liu
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Guangfu Xue
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Fang Han
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Huan Nie
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China
| | - Qinghua Jiang
- grid.19373.3f0000 0001 0193 3564School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Big Data (Harbin Institute of Technology), Ministry of Education, Harbin, Heilongjiang China
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31
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O'Donoghue SI. Grand Challenges in Bioinformatics Data Visualization. FRONTIERS IN BIOINFORMATICS 2021; 1:669186. [PMID: 36303723 PMCID: PMC9581027 DOI: 10.3389/fbinf.2021.669186] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/30/2021] [Indexed: 01/17/2023] Open
Affiliation(s)
- Seán I. O'Donoghue
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
- CSIRO Data61, Eveleigh, NSW, Australia
- *Correspondence: Seán I. O'Donoghue,
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Wang BG, Ding HX, Lv Z, Xu Q, Yuan Y. Interaction of HULC polymorphisms with Helicobacter pylori infection plays a strong role for the prediction of gastric cancer risk. Future Oncol 2021; 16:1997-2006. [PMID: 32941073 DOI: 10.2217/fon-2020-0228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Aim: Gene-environment interactions have better efficacy in predicting cancer susceptibility than a single gene. Materials & methods: Eight tag single nucleotide polymorphisms encompassing the whole HULC gene were detected by KASP platform (LGC Genomics, Hoddesdon, UK) in 631 gastric cancer (GC) cases and 953 controls. Results: The HULC gene rs7770772 polymorphism could increase GC risk (recessive model: odds ratio = 1.95). The multifactor dimensionality reduction (MDR) analysis suggested that the 2D model HULC rs7770772-Helicobacter pylori had better effect on GC risk prediction (maximum testing accuracy = 0.7005). No significant result was observed in our experimental expression quantitative trait loci analysis. Conclusion: 2D model HULC rs7770772-H. pylori might have superior efficacy for GC risk than a single factor.
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Affiliation(s)
- Ben-Gang Wang
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, & Key Laboratory of Cancer Etiology & Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, PR China.,Hepatobiliary Surgery Department of General Surgery Institute, The First Affiliated Hospital of China Medical University, Shenyang 110001, PR China
| | - Han-Xi Ding
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, & Key Laboratory of Cancer Etiology & Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, PR China
| | - Zhi Lv
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, & Key Laboratory of Cancer Etiology & Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, PR China
| | - Qian Xu
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, & Key Laboratory of Cancer Etiology & Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, PR China
| | - Yuan Yuan
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, & Key Laboratory of Cancer Etiology & Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, PR China
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33
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Computational principles and challenges in single-cell data integration. Nat Biotechnol 2021; 39:1202-1215. [PMID: 33941931 DOI: 10.1038/s41587-021-00895-7] [Citation(s) in RCA: 158] [Impact Index Per Article: 52.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 03/16/2021] [Indexed: 02/07/2023]
Abstract
The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.
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34
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Adil A, Kumar V, Jan AT, Asger M. Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis. Front Neurosci 2021; 15:591122. [PMID: 33967674 PMCID: PMC8100238 DOI: 10.3389/fnins.2021.591122] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 03/19/2021] [Indexed: 11/17/2022] Open
Abstract
Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.
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Affiliation(s)
- Asif Adil
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Vijay Kumar
- Department of Biotechnology, Yeungnam University, Gyeongsan, South Korea
| | - Arif Tasleem Jan
- School of Biosciences and Biotechnology, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Mohammed Asger
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
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35
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Neavin D, Nguyen Q, Daniszewski MS, Liang HH, Chiu HS, Wee YK, Senabouth A, Lukowski SW, Crombie DE, Lidgerwood GE, Hernández D, Vickers JC, Cook AL, Palpant NJ, Pébay A, Hewitt AW, Powell JE. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biol 2021; 22:76. [PMID: 33673841 PMCID: PMC7934233 DOI: 10.1186/s13059-021-02293-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 02/10/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression. RESULTS Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming. CONCLUSIONS This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.
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Affiliation(s)
- Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Maciej S Daniszewski
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - Helena H Liang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Han Sheng Chiu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Yong Kiat Wee
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia
| | - Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia
| | - Samuel W Lukowski
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Duncan E Crombie
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Grace E Lidgerwood
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - Damián Hernández
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - James C Vickers
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Anthony L Cook
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Alice Pébay
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- Department of Anatomy and Physiology, The University of Melbourne, Melbourne, Australia
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia.
- UNSW Cellular Genomics Futures Institute, School of Medical Sciences, University of New South Wales, Sydney, Australia.
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36
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Buren EV, Hu M, Weng C, Jin F, Li Y, Wu D, Li Y. TWO-SIGMA: A novel two-component single cell model-based association method for single-cell RNA-seq data. Genet Epidemiol 2021; 45:142-153. [PMID: 32989764 PMCID: PMC8570615 DOI: 10.1002/gepi.22361] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 08/31/2020] [Accepted: 09/03/2020] [Indexed: 01/06/2023]
Abstract
In this paper, we develop TWO-SIGMA, a TWO-component SInGle cell Model-based Association method for differential expression (DE) analyses in single-cell RNA-seq (scRNA-seq) data. The first component models the probability of "drop-out" with a mixed-effects logistic regression model and the second component models the (conditional) mean expression with a mixed-effects negative binomial regression model. TWO-SIGMA is extremely flexible in that it: (i) does not require a log-transformation of the outcome, (ii) allows for overdispersed and zero-inflated counts, (iii) accommodates a correlation structure between cells from the same individual via random effect terms, (iv) can analyze unbalanced designs (in which the number of cells does not need to be identical for all samples), (v) can control for additional sample-level and cell-level covariates including batch effects, (vi) provides interpretable effect size estimates, and (vii) enables general tests of DE beyond two-group comparisons. To our knowledge, TWO-SIGMA is the only method for analyzing scRNA-seq data that can simultaneously accomplish each of these features. Simulations studies show that TWO-SIGMA outperforms alternative regression-based approaches in both type-I error control and power enhancement when the data contains even moderate within-sample correlation. A real data analysis using pancreas islet single-cells exhibits the flexibility of TWO-SIGMA and demonstrates that incorrectly failing to include random effect terms can have dramatic impacts on scientific conclusions. TWO-SIGMA is implemented in the R package twosigma available at https://github.com/edvanburen/twosigma.
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Affiliation(s)
- Eric Van Buren
- Department of Biostatistics, The University of North Carolina at Chapel Hill
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation
| | - Chen Weng
- Department of Genetics, School of Medicine, Case Western Reserve University
| | - Fulai Jin
- Department of Genetics, School of Medicine, Case Western Reserve University
| | - Yan Li
- Department of Genetics, School of Medicine, Case Western Reserve University
| | - Di Wu
- Department of Biostatistics, The University of North Carolina at Chapel Hill
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, The University of North Carolina at Chapel Hill
| | - Yun Li
- Department of Biostatistics, The University of North Carolina at Chapel Hill
- Department of Genetics, The University of North Carolina at Chapel Hill
- Department of Computer Science, The University of North Carolina at Chapel Hill
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37
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Abdelgawad ME, Desterke C, Uzan G, Naserian S. Single-cell transcriptomic profiling and characterization of endothelial progenitor cells: new approach for finding novel markers. Stem Cell Res Ther 2021; 12:145. [PMID: 33627177 PMCID: PMC7905656 DOI: 10.1186/s13287-021-02185-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/24/2021] [Indexed: 12/14/2022] Open
Abstract
Background Endothelial progenitor cells (EPCs) are promising candidates for the cellular therapy of peripheral arterial and cardiovascular diseases. However, hitherto there is no specific marker(s) defining precisely EPCs. Herein, we are proposing a new in silico approach for finding novel EPC markers. Methods We assembled five groups of chosen EPC-related genes/factors using PubMed literature and Gene Ontology databases. This shortened database of EPC factors was fed into publically published transcriptome matrix to compare their expression between endothelial colony-forming cells (ECFCs), HUVECs, and two adult endothelial cell types (ECs) from the skin and adipose tissue. Further, the database was used for functional enrichment on Mouse Phenotype database and protein-protein interaction network analyses. Moreover, we built a digital matrix of healthy donors’ PBMCs (33 thousand single-cell transcriptomes) and analyzed the expression of these EPC factors. Results Transcriptome analyses showed that BMP2, 4, and ephrinB2 were exclusively highly expressed in EPCs; the expression of neuropilin-1 and VEGF-C were significantly higher in EPCs and HUVECs compared with other ECs; Notch 1 was highly expressed in EPCs and skin-ECs; MIR21 was highly expressed in skin-ECs; PECAM-1 was significantly higher in EPCs and adipose ECs. Moreover, functional enrichment of EPC-related genes on Mouse Phenotype and STRING protein database has revealed significant relations between chosen EPC factors and endothelial and vascular functions, development, and morphogenesis, where ephrinB2, BMP2, and BMP4 were highly expressed in EPCs and were connected to abnormal vascular functions. Single-cell RNA-sequencing analyses have revealed that among the EPC-regulated markers in transcriptome analyses, (i) ICAM1 and Endoglin were weekly expressed in the monocyte compartment of the peripheral blood; (ii) CD163 and CD36 were highly expressed in the CD14+ monocyte compartment whereas CSF1R was highly expressed in the CD16+ monocyte compartment, (iii) L-selectin and IL6R were globally expressed in the lymphoid/myeloid compartments, and (iv) interestingly, PLAUR/UPAR and NOTCH2 were highly expressed in both CD14+ and CD16+ monocytic compartments. Conclusions The current study has identified novel EPC markers that could be used for better characterization of EPC subpopulation in adult peripheral blood and subsequent usage of EPCs for various cell therapy and regenerative medicine applications.
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Affiliation(s)
- Mohamed Essameldin Abdelgawad
- Biochemistry & Molecular Biotechnology Division, Chemistry Department, Faculty of Science; Innovative Cellular Microenvironment Optimization Platform (ICMOP), Helwan University, Cairo, Egypt. .,Inserm UMR-S-MD 1197, Hôpital Paul Brousse - Bâtiment Lavoisier, 12-14 avenue Paul Vaillant Couturier, 94800, Villejuif, France. .,Paris-Saclay University, Villejuif, France.
| | - Christophe Desterke
- Paris-Saclay University, Villejuif, France.,Inserm UMR-S-MD A9, Hôpital Paul Brousse, Villejuif, France
| | - Georges Uzan
- Inserm UMR-S-MD 1197, Hôpital Paul Brousse - Bâtiment Lavoisier, 12-14 avenue Paul Vaillant Couturier, 94800, Villejuif, France.,Paris-Saclay University, Villejuif, France
| | - Sina Naserian
- Inserm UMR-S-MD 1197, Hôpital Paul Brousse - Bâtiment Lavoisier, 12-14 avenue Paul Vaillant Couturier, 94800, Villejuif, France. .,Paris-Saclay University, Villejuif, France. .,CellMedEx, Saint Maur des Fossés, France.
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38
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Abstract
Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed computational methods offer a solution to the challenges of scRNAseq data analysis, providing previously inaccessible biological insight at unprecedented levels of detail. Here, we go over the inherent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.
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Affiliation(s)
- Guy Shapira
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Noam Shomron
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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39
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Transcriptomic Changes of Murine Visceral Fat Exposed to Intermittent Hypoxia at Single Cell Resolution. Int J Mol Sci 2020; 22:ijms22010261. [PMID: 33383883 PMCID: PMC7795619 DOI: 10.3390/ijms22010261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 11/22/2020] [Accepted: 12/24/2020] [Indexed: 12/12/2022] Open
Abstract
Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA) and induces metabolic dysfunction manifesting as inflammation, increased lipolysis and insulin resistance in visceral white adipose tissues (vWAT). However, the cell types and their corresponding transcriptional pathways underlying these functional perturbations are unknown. Here, we applied single nucleus RNA sequencing (snRNA-seq) coupled with aggregate RNA-seq methods to evaluate the cellular heterogeneity in vWAT following IH exposures mimicking OSA. C57BL/6 male mice were exposed to IH and room air (RA) for 6 weeks, and nuclei from vWAT were isolated and processed for snRNA-seq followed by differential expressed gene (DEGs) analyses by cell type, along with gene ontology and canonical pathways enrichment tests of significance. IH induced significant transcriptional changes compared to RA across 14 different cell types identified in vWAT. We identified cell-specific signature markers, transcriptional networks, metabolic signaling pathways, and cellular subpopulation enrichment in vWAT. Globally, we also identify 298 common regulated genes across multiple cellular types that are associated with metabolic pathways. Deconvolution of cell types in vWAT using global RNA-seq revealed that distinct adipocytes appear to be differentially implicated in key aspects of metabolic dysfunction. Thus, the heterogeneity of vWAT and its response to IH at the cellular level provides important insights into the metabolic morbidity of OSA and may possibly translate into therapeutic targets.
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40
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Maney DL, Merritt JR, Prichard MR, Horton BM, Yi SV. Inside the supergene of the bird with four sexes. Horm Behav 2020; 126:104850. [PMID: 32937166 PMCID: PMC7725849 DOI: 10.1016/j.yhbeh.2020.104850] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/04/2020] [Accepted: 09/06/2020] [Indexed: 02/07/2023]
Abstract
The white-throated sparrow (Zonotrichia albicollis) offers unique opportunities to understand the adaptive value of supergenes, particularly their role in alternative phenotypes. In this species, alternative plumage morphs segregate with a nonrecombining segment of chromosome 2, which has been called a 'supergene'. The species mates disassortatively with respect to the supergene; that is, each breeding pair consists of one individual with it and one without it. This species has therefore been called the "bird with four sexes". The supergene segregates with a behavioral phenotype; birds with it are more aggressive and less parental than birds without it. Here, we review our efforts to identify the genes inside the supergene that are responsible for the behavioral polymorphism. The gene ESR1, which encodes estrogen receptor α, differs between the morphs and predicts both territorial and parental behavior. Variation in the regulatory regions of ESR1 causes an imbalance in expression of the two alleles, and the degree to which this imbalance favors the supergene allele predicts territorial singing. In heterozygotes, knockdown of ESR1 causes a phenotypic switch, from more aggressive to less aggressive. We recently showed that another gene important for social behavior, vasoactive intestinal peptide (VIP), is differentially expressed between the morphs and predicts territorial singing. We hypothesize that ESR1 and VIP contribute to behavior in a coordinated way and could represent co-adapted alleles. Because the supergene contains more than 1000 individual genes, this species provides rich possibilities for discovering alleles that work together to mediate life-history trade-offs and maximize the fitness of alternative complex phenotypes.
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Affiliation(s)
- Donna L Maney
- Department of Psychology, Emory University, Atlanta, GA, USA.
| | | | | | - Brent M Horton
- Department of Biology, Millersville University, Millersville, PA, USA
| | - Soojin V Yi
- School of Biological Sciences, Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
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41
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Tong O, Fairfax BP. Dissecting genetic determinants of variation in human immune responses. Curr Opin Immunol 2020; 65:74-78. [PMID: 32634755 DOI: 10.1016/j.coi.2020.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 05/18/2020] [Indexed: 10/23/2022]
Abstract
The immune system is paradigmatic for a complex arrangement of heterogenous cells performing distinct, frequently temporally and anatomically dissociated, functions. Immune dysfunction is a common characteristic across most diseases and human genetic approaches have revealed that many disease risk loci are associated with expression profiles and counts of specific immune subsets. Furthermore, genetic regulators of immune function may only demonstrate activity in specific disease-linked contexts. Here we explore steps taken to dissect the genetic determinants of variation in immune response across cell counts and function, and the insights these have provided into human immunity.
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Affiliation(s)
- Orion Tong
- Department of Oncology, Weatherall Institute of Molecular Medicine, Oxford, United Kingdom
| | - Benjamin P Fairfax
- Department of Oncology, Weatherall Institute of Molecular Medicine, Oxford, United Kingdom.
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42
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Triqueneaux G, Burny C, Symmons O, Janczarski S, Gruffat H, Yvert G. Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans. Commun Biol 2020; 3:346. [PMID: 32620900 PMCID: PMC7335051 DOI: 10.1038/s42003-020-1075-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Variability in gene expression across a population of homogeneous cells is known to influence various biological processes. In model organisms, natural genetic variants were found that modify expression dispersion (variability at a fixed mean) but very few studies have detected such effects in humans. Here, we analyzed single-cell expression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derived from individuals of the Yoruba population. Using data from over 30 million cells, we found substantial inter-individual variation of dispersion. We demonstrate, via de novo cell line generation and subcloning experiments, that this variation exceeds the variation associated with cellular immortalization. We detected a genetic association between the expression dispersion of CD63 and the rs971 SNP. Our results show that human DNA variants can have inherently-probabilistic effects on gene expression. Such subtle genetic effects may participate to phenotypic variation and disease outcome. Triqueneaux, Burny, Symmons et al. show association between gene expression noise and genotypes, using single-cell expression of four proteins across human-derived lymphoblastoid cell lines. This study suggests that very subtle regulatory effects of human DNA variants may contribute to phenotypic variation and disease outcome.
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Affiliation(s)
- Gérard Triqueneaux
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France
| | - Claire Burny
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France.,Institut für Populationsgenetik, Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria
| | - Orsolya Symmons
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France.,Max Planck Institute for Biology of Ageing, Cologne, 50931, Germany
| | - Stéphane Janczarski
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France
| | - Henri Gruffat
- CIRI-Centre International de Recherche en Infectiologie, Universite Claude Bernard Lyon 1, Univ Lyon, Inserm U1111, CNRS UMR5308, Ecole Normale Superieure de Lyon, 69007, Lyon, France
| | - Gaël Yvert
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France.
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43
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Calvani J, Terada M, Lesaffre C, Eloudzeri M, Lamarthée B, Burger C, Tinel C, Anglicheau D, Vermorel A, Couzi L, Loupy A, Duong Van Huyen JP, Bruneval P, Rabant M. In situ multiplex immunofluorescence analysis of the inflammatory burden in kidney allograft rejection: A new tool to characterize the alloimmune response. Am J Transplant 2020; 20:942-953. [PMID: 31715060 DOI: 10.1111/ajt.15699] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 10/10/2019] [Accepted: 11/04/2019] [Indexed: 01/25/2023]
Abstract
The exact composition of leukocyte infiltration during kidney allograft rejection is difficult to comprehend and visualize on the same biopsy slide. Using an innovative technology of multiplex immunofluorescence (mIF), we were able to detect simultaneously NK cells, macrophages, and T cells and to determine their intra- or extravascular localization using an endothelial marker. Twenty antibody-mediated rejection (ABMR), 20 T cell-mediated rejection (TCMR), and five normal biopsies were labeled, with automatic leukocyte quantification and localization. This method was compared to a classic NKp46 immunohistochemistry (IHC) with manual quantification and to mRNA quantification. mIF automatic quantification was strongly correlated to IHC (r = .91, P < .001) and to mRNA expression levels (r > .46, P < .021). T cells and macrophages were the 2 predominant populations involved in rejection (48.0 ± 4.4% and 49.3 ± 4.4%, respectively, in ABMR; 51.8 ± 6.0% and 45.3 ± 5.8% in TCMR). NK cells constituted a rare population in both ABMR (2.7 ± 0.7%) and TCMR (2.9 ± 0.6%). The intravascular compartment was mainly composed of T cells, including during ABMR, in peritubular and glomerular capillaries. However, NK cell and macrophage densities were significantly higher during ABMR in glomerular and peritubular capillaries. To conclude, this study demonstrates the feasibility and utility of mIF imaging to study and better understand the kidney allograft rejection process.
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Affiliation(s)
- Julien Calvani
- INSERM U970, Paris, France.,Department of Pathology, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
| | - Megumi Terada
- INSERM U970, Paris, France.,Department of Pathology, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
| | | | - Maëva Eloudzeri
- Department of Pathology, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France.,INSERM U1151, Paris, France
| | | | - Carole Burger
- INSERM U1151, Paris, France.,Department of Nephrology and Kidney Transplantation, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
| | - Claire Tinel
- INSERM U1151, Paris, France.,Department of Nephrology and Kidney Transplantation, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
| | - Dany Anglicheau
- INSERM U1151, Paris, France.,Department of Nephrology and Kidney Transplantation, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France.,Paris Descartes, Sorbonne Paris Cité University, Paris, France
| | - Agathe Vermorel
- Department of Nephrology, Transplantation, Dialysis and Apheresis, Bordeaux, France.,INSERM U5164, Bordeaux, France
| | - Lionel Couzi
- Department of Nephrology, Transplantation, Dialysis and Apheresis, Bordeaux, France.,INSERM U5164, Bordeaux, France
| | - Alexandre Loupy
- INSERM U970, Paris, France.,Department of Nephrology and Kidney Transplantation, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France.,Paris Descartes, Sorbonne Paris Cité University, Paris, France
| | - Jean-Paul Duong Van Huyen
- INSERM U970, Paris, France.,Department of Pathology, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France.,Paris Descartes, Sorbonne Paris Cité University, Paris, France
| | - Patrick Bruneval
- INSERM U970, Paris, France.,Department of Pathology, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France.,Paris Descartes, Sorbonne Paris Cité University, Paris, France
| | - Marion Rabant
- Department of Pathology, Necker Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France.,INSERM U1151, Paris, France.,Paris Descartes, Sorbonne Paris Cité University, Paris, France
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44
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Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity. Cells 2020; 9:cells9030759. [PMID: 32204559 PMCID: PMC7140709 DOI: 10.3390/cells9030759] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/12/2020] [Accepted: 03/17/2020] [Indexed: 12/27/2022] Open
Abstract
Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.
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45
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Gu W, Liu S, Chen L, Liu Y, Gu C, Ren HQ, Wu B. Single-Cell RNA Sequencing Reveals Size-Dependent Effects of Polystyrene Microplastics on Immune and Secretory Cell Populations from Zebrafish Intestines. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:3417-3427. [PMID: 32092251 DOI: 10.1021/acs.est.9b06386] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Microplastics (MPs) as widespread contamination pose a high risk for aquatic organisms. However, the current understanding of MP toxicity is based on cell population-averaged measurements. Our aim was to gain a comprehensive understanding of the size-dependent effects of polystyrene MPs (PS-MPs) on intestinal cell populations in zebrafish and characterize the interplay of MPs, intestinal cells, and intestinal microbiota. Here, we used single-cell RNA sequencing to determine the transcriptome heterogeneity of 12 000 intestinal cells obtained from zebrafish exposed to 100 nm, 5 μm, and 200 μm PS-MPs for 21 days. Eight intestinal cell populations were identified. Combined with changes in intestinal microbiota, our findings highlight a previously unrecognized end point that all three sizes of PS-MPs induced dysfunction of intestinal immune cells (including effects on phagosomes and the regulation of immune system processes) and increased the abundance of pathogenic bacteria. However, only 100 nm PS-MPs altered the expression of genes related to phagocyte-produced reactive oxygen species (ROS) generation and increased mucus secretion by secretory cells. Microsize PS-MPs specifically changed the lysosome (5 μm) and cell surface receptor signaling (200 μm) processes of the macrophages. Our findings pinpoint to cell-specific and size-dependent responses to PS-MPs in fish intestine, which can provide a reference for future study directions.
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Affiliation(s)
- Weiqing Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, 163 Xianlin Avenue, Nanjing, Jiangsu 210023, P.R. China
| | - Su Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, 163 Xianlin Avenue, Nanjing, Jiangsu 210023, P.R. China
- Department of Environmental Science, School of Engineering, China Pharmaceutical University, Nanjing, Jiangsu 211198, P.R. China
| | - Ling Chen
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, 163 Xianlin Avenue, Nanjing, Jiangsu 210023, P.R. China
| | - Yuxuan Liu
- College of Environment, Hohai University, Nanjing, Jiangsu 210098, P.R. China
| | - Cheng Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, 163 Xianlin Avenue, Nanjing, Jiangsu 210023, P.R. China
| | - Hong-Qiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, 163 Xianlin Avenue, Nanjing, Jiangsu 210023, P.R. China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, 163 Xianlin Avenue, Nanjing, Jiangsu 210023, P.R. China
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46
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Morgan MD, Patin E, Jagla B, Hasan M, Quintana-Murci L, Marioni JC. Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels. PLoS Genet 2020; 16:e1008686. [PMID: 32168362 PMCID: PMC7094872 DOI: 10.1371/journal.pgen.1008686] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/25/2020] [Accepted: 02/19/2020] [Indexed: 11/19/2022] Open
Abstract
Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.
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Affiliation(s)
- Michael D. Morgan
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Cancer Research UK–Cambridge Institute, Robinson Way, Cambridge, United Kingdom
| | - Etienne Patin
- Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, Paris, France
| | - Bernd Jagla
- Cytometry and Biomarkers UTechS, Institut Pasteur, Paris, France
- Hub Bioinformatique et Biostatisque, Départment de Biologie Computationalle—USR 3756 CNRS, Institut Pasteur, Paris, France
| | - Milena Hasan
- Cytometry and Biomarkers UTechS, Institut Pasteur, Paris, France
| | - Lluís Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, Paris, France
- Human Genomics and Evolution, Collège de France, Paris, France
| | - John C. Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Cancer Research UK–Cambridge Institute, Robinson Way, Cambridge, United Kingdom
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
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47
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Hu Y, Ranganathan M, Shu C, Liang X, Ganesh S, Osafo-Addo A, Yan C, Zhang X, Aouizerat BE, Krystal JH, D'Souza DC, Xu K. Single-cell Transcriptome Mapping Identifies Common and Cell-type Specific Genes Affected by Acute Delta9-tetrahydrocannabinol in Humans. Sci Rep 2020; 10:3450. [PMID: 32103029 PMCID: PMC7044203 DOI: 10.1038/s41598-020-59827-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 02/03/2020] [Indexed: 01/02/2023] Open
Abstract
Delta-9-tetrahydrocannabinol (THC) is known to modulate immune response in peripheral blood cells. The mechanisms of THC's effects on gene expression in human immune cells remains poorly understood. Combining a within-subject design with single cell transcriptome mapping, we report that THC acutely alters gene expression in 15,973 blood cells. We identified 294 transcriptome-wide significant genes among eight cell types including 69 common genes and 225 cell-type-specific genes affected by THC administration, including those genes involving in immune response, cytokine production, cell proliferation and apoptosis. We revealed distinct transcriptomic sub-clusters affected by THC in major immune cell types where THC perturbed cell-type-specific intracellular gene expression correlations. Gene set enrichment analysis further supports the findings of THC's common and cell-type-specific effects on immune response and cell toxicity. This comprehensive single-cell transcriptomic profiling provides important insights into THC's acute effects on immune function that may have important medical implications.
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Affiliation(s)
- Ying Hu
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA
| | - Mohini Ranganathan
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Chang Shu
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Xiaoyu Liang
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Suhas Ganesh
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Awo Osafo-Addo
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Chunhua Yan
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA
| | - Xinyu Zhang
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Bradley E Aouizerat
- Bluestone Center for Clinical Research, College of Dentistry, New York University, New York, NY, 10010, USA
- Department of Oral and Maxillofacial Surgery, College of Dentistry, New York University, New York, NY, 10010, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Deepak C D'Souza
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, 300 George street, Suite 901, New Haven, CT, 06511, USA.
- Connecticut Veteran Healthcare System, West Haven, CT, 06516, USA.
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48
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Li F, Huang Q, Luster TA, Hu H, Zhang H, Ng WL, Khodadadi-Jamayran A, Wang W, Chen T, Deng J, Ranieri M, Fang Z, Pyon V, Dowling CM, Bagdatlioglu E, Almonte C, Labbe K, Silver H, Rabin AR, Jani K, Tsirigos A, Papagiannakopoulos T, Hammerman PS, Velcheti V, Freeman GJ, Qi J, Miller G, Wong KK. In Vivo Epigenetic CRISPR Screen Identifies Asf1a as an Immunotherapeutic Target in Kras-Mutant Lung Adenocarcinoma. Cancer Discov 2020; 10:270-287. [PMID: 31744829 PMCID: PMC7007372 DOI: 10.1158/2159-8290.cd-19-0780] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 10/11/2019] [Accepted: 11/13/2019] [Indexed: 11/16/2022]
Abstract
Despite substantial progress in lung cancer immunotherapy, the overall response rate in patients with KRAS-mutant lung adenocarcinoma (LUAD) remains low. Combining standard immunotherapy with adjuvant approaches that enhance adaptive immune responses-such as epigenetic modulation of antitumor immunity-is therefore an attractive strategy. To identify epigenetic regulators of tumor immunity, we constructed an epigenetic-focused single guide RNA library and performed an in vivo CRISPR screen in a Kras G12D/Trp53 -/- LUAD model. Our data showed that loss of the histone chaperone Asf1a in tumor cells sensitizes tumors to anti-PD-1 treatment. Mechanistic studies revealed that tumor cell-intrinsic Asf1a deficiency induced immunogenic macrophage differentiation in the tumor microenvironment by upregulating GM-CSF expression and potentiated T-cell activation in combination with anti-PD-1. Our results provide a rationale for a novel combination therapy consisting of ASF1A inhibition and anti-PD-1 immunotherapy. SIGNIFICANCE: Using an in vivo epigenetic CRISPR screen, we identified Asf1a as a critical regulator of LUAD sensitivity to anti-PD-1 therapy. Asf1a deficiency synergized with anti-PD-1 immunotherapy by promoting M1-like macrophage polarization and T-cell activation. Thus, we provide a new immunotherapeutic strategy for this subtype of patients with LUAD.See related commentary by Menzel and Black, p. 179.This article is highlighted in the In This Issue feature, p. 161.
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Affiliation(s)
- Fei Li
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Qingyuan Huang
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Troy A Luster
- Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Hai Hu
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Hua Zhang
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Wai-Lung Ng
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR
| | - Alireza Khodadadi-Jamayran
- Applied Bioinformatics Laboratories and Genome Technology Center, Division of Advanced Research Technologies, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Wei Wang
- S. Arthur Localio Laboratory, Department of Surgery, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Ting Chen
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Jiehui Deng
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Michela Ranieri
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Zhaoyuan Fang
- State Key Laboratory of Cell Biology, Innovation Center for Cell Signaling Network, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Val Pyon
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Catríona M Dowling
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Ece Bagdatlioglu
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Christina Almonte
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Kristen Labbe
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Heather Silver
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Alexandra R Rabin
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Kandarp Jani
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Aristotelis Tsirigos
- Applied Bioinformatics Laboratories and Genome Technology Center, Division of Advanced Research Technologies, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
- Department of Pathology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Thales Papagiannakopoulos
- Department of Pathology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Peter S Hammerman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Vamsidhar Velcheti
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Gordon J Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jun Qi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - George Miller
- S. Arthur Localio Laboratory, Department of Surgery, New York University Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Kwok-Kin Wong
- Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, NYU Langone Health, New York, New York.
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49
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Broekema RV, Bakker OB, Jonkers IH. A practical view of fine-mapping and gene prioritization in the post-genome-wide association era. Open Biol 2020; 10:190221. [PMID: 31937202 PMCID: PMC7014684 DOI: 10.1098/rsob.190221] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 12/05/2019] [Indexed: 12/17/2022] Open
Abstract
Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.
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Affiliation(s)
| | | | - I. H. Jonkers
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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50
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Jia Z, Wang S, Liu Q. Identification of differentially expressed genes by single-cell transcriptional profiling of umbilical cord and synovial fluid mesenchymal stem cells. J Cell Mol Med 2019; 24:1945-1957. [PMID: 31845522 PMCID: PMC6991657 DOI: 10.1111/jcmm.14891] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 11/22/2019] [Accepted: 11/27/2019] [Indexed: 12/23/2022] Open
Abstract
The purpose of this study was to measure the heterogeneity in human umbilical cord–derived mesenchymal stem cells (hUC‐MSCs) and human synovial fluid–derived mesenchymal stem cells (hSF‐MSCs) by single‐cell RNA‐sequencing (scRNA‐seq). Using Chromium™ technology, scRNA‐seq was performed on hUC‐MSCs and hSF‐MSCs from samples that passed our quality control checks. In order to identify subgroups and activated pathways, several bioinformatics tools were used to analyse the transcriptomic profiles, including clustering, principle components analysis (PCA), t‐Distributed Stochastic Neighbor Embedding (t‐SNE), gene set enrichment analysis, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. scRNA‐seq was performed on the two sample sets. In total, there were 104 761 163 reads for the hUC‐MSCs and 6 577 715 for the hSF‐MSCs, with >60% mapping rate. Based on PCA and t‐SNE analyses, we identified 11 subsets within hUC‐MSCs and seven subsets within hSF‐MSCs. Gene set enrichment analysis determined that there were 533, 57, 32, 44, 10, 319, 731, 1037, 90, 25 and 230 differentially expressed genes (DEGs) in the 11 subsets of hUC‐MSCs and 204, 577, 30, 577, 16, 57 and 35 DEGs in the seven subsets of hSF‐MSCs. scRNA‐seq was not only able to identify subpopulations of hUC‐MSCs and hSF‐MSCs within the sample sets, but also provided a digital transcript count of hUC‐MSCs and hSF‐MSCs within a single patient. scRNA‐seq analysis may elucidate some of the biological characteristics of MSCs and allow for a better understanding of the multi‐directional differentiation, immunomodulatory properties and tissue repair capabilities of MSCs.
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Affiliation(s)
- Zhaofeng Jia
- Department of Osteoarthropathy and Institute of Orthopedic Research, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University and the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Shijin Wang
- Department of Orthopaedics, Taian City Central Hospital, Taian, China
| | - Qisong Liu
- Institute for Regenerative Medicine, Texas A&M Health Science Center College of Medicine, Temple, TX, USA
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