1
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LeRoy N, Smith J, Zheng G, Rymuza J, Gharavi E, Brown D, Zhang A, Sheffield N. Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings. NAR Genom Bioinform 2024; 6:lqae073. [PMID: 38974799 PMCID: PMC11224678 DOI: 10.1093/nargab/lqae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/29/2024] [Accepted: 06/20/2024] [Indexed: 07/09/2024] Open
Abstract
Data from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) are now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. We implemented our approach in scEmbed, an unsupervised machine-learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, models pre-trained on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. scEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.
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Affiliation(s)
- Nathan J LeRoy
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Jason P Smith
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Guangtao Zheng
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Julia Rymuza
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Erfaneh Gharavi
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Donald E Brown
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Aidong Zhang
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan C Sheffield
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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2
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Loers JU, Vermeirssen V. A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data. Brief Bioinform 2024; 25:bbae382. [PMID: 39207727 PMCID: PMC11359808 DOI: 10.1093/bib/bbae382] [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: 04/05/2024] [Revised: 06/27/2024] [Accepted: 07/23/2024] [Indexed: 09/04/2024] Open
Abstract
Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.
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Affiliation(s)
- Jens Uwe Loers
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Corneel Heymanslaan 10, 9000 Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Zwijnaarde-Technologiepark 71, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Vanessa Vermeirssen
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Corneel Heymanslaan 10, 9000 Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Zwijnaarde-Technologiepark 71, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
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3
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Wang X, Lian Q, Dong H, Xu S, Su Y, Wu X. Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae014. [PMID: 39049508 PMCID: PMC11423854 DOI: 10.1093/gpbjnl/qzae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 07/27/2024]
Abstract
Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.
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Affiliation(s)
- Xi Wang
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Qiwei Lian
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Haoyu Dong
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
| | - Shuo Xu
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Yaru Su
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
| | - Xiaohui Wu
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
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4
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Zeng Y, Ma Q, Chen J, Kong X, Chen Z, Liu H, Liu L, Qian Y, Wang X, Lu S. Single-cell sequencing: Current applications in various tuberculosis specimen types. Cell Prolif 2024:e13698. [PMID: 38956399 DOI: 10.1111/cpr.13698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/21/2024] [Accepted: 06/07/2024] [Indexed: 07/04/2024] Open
Abstract
Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.
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Affiliation(s)
- Yuqin Zeng
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Quan Ma
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Jinyun Chen
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Xingxing Kong
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Zhanpeng Chen
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Huazhen Liu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Lanlan Liu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Yan Qian
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Xiaomin Wang
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Shuihua Lu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
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5
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Rueda-Alaña E, Grillo M, Vázquez E, Salas SM, Senovilla-Ganzo R, Escobar L, Quintas A, Benguría A, Aransay AM, Bengoa-Vergniory N, Dopazo A, Encinas JM, Nilsson M, García-Moreno F. BirthSeq, a new method to isolate and analyze dated cells in different vertebrates. Development 2024; 151:dev202429. [PMID: 38856078 DOI: 10.1242/dev.202429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 05/27/2024] [Indexed: 06/11/2024]
Abstract
Embryonic development is a complex and dynamic process that unfolds over time and involves the production and diversification of increasing numbers of cells. The impact of developmental time on the formation of the central nervous system is well documented, with evidence showing that time plays a crucial role in establishing the identity of neuronal subtypes. However, the study of how time translates into genetic instructions driving cell fate is limited by the scarcity of suitable experimental tools. We introduce BirthSeq, a new method for isolating and analyzing cells based on their birth date. This innovative technique allows for in vivo labeling of cells, isolation via fluorescence-activated cell sorting, and analysis using high-throughput techniques. We calibrated the BirthSeq method for developmental organs across three vertebrate species (mouse, chick and gecko), and utilized it for single-cell RNA sequencing and novel spatially resolved transcriptomic approaches in mouse and chick, respectively. Overall, BirthSeq provides a versatile tool for studying virtually any tissue in different vertebrate organisms, aiding developmental biology research by targeting cells and their temporal cues.
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Affiliation(s)
- Eneritz Rueda-Alaña
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - Marco Grillo
- Science for Life Laboratory, Department of Biophysics and Biochemistry, Stockholm University, 17165, Solna, Sweden
| | - Enrique Vázquez
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Sergio Marco Salas
- Science for Life Laboratory, Department of Biophysics and Biochemistry, Stockholm University, 17165, Solna, Sweden
| | - Rodrigo Senovilla-Ganzo
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - Laura Escobar
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - Ana Quintas
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Alberto Benguría
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Ana María Aransay
- Genome Analysis Platform, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, 48160 Derio, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
| | - Nora Bengoa-Vergniory
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
- Oxford Parkinson's Disease Centre and Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- IKERBASQUE Foundation, María Díaz de Haro 3, 6th Floor, 48013 BilbaoSpain
| | - Ana Dopazo
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain
| | - Juan Manuel Encinas
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
- IKERBASQUE Foundation, María Díaz de Haro 3, 6th Floor, 48013 BilbaoSpain
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biophysics and Biochemistry, Stockholm University, 17165, Solna, Sweden
| | - Fernando García-Moreno
- Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), 48940, Leioa, Spain
- Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
- IKERBASQUE Foundation, María Díaz de Haro 3, 6th Floor, 48013 BilbaoSpain
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6
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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7
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Regner MJ, Garcia-Recio S, Thennavan A, Wisniewska K, Mendez-Giraldez R, Felsheim B, Spanheimer PM, Parker JS, Perou CM, Franco HL. Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598858. [PMID: 38948758 PMCID: PMC11212881 DOI: 10.1101/2024.06.13.598858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.
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Affiliation(s)
- Matthew J. Regner
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Aatish Thennavan
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX, USA, 77030
| | - Kamila Wisniewska
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Raul Mendez-Giraldez
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Brooke Felsheim
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Philip M. Spanheimer
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Joel S. Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hector L. Franco
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Division of Clinical and Translational Cancer Research, University of Puerto Rico Comprehensive Cancer Center, San Juan, PR 00935
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8
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Shu C, Street K, Breton CV, Bastain TM, Wilson ML. A review of single-cell transcriptomics and epigenomics studies in maternal and child health. Epigenomics 2024; 16:775-793. [PMID: 38709139 PMCID: PMC11318716 DOI: 10.1080/17501911.2024.2343276] [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: 12/18/2023] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.
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Affiliation(s)
- Chang Shu
- Center for Genetic Epidemiology, Division of Epidemiology & Genetics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Kelly Street
- Division of Biostatistics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Carrie V Breton
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Theresa M Bastain
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Melissa L Wilson
- Division of Disease Prevention, Policy, & Global Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles,CA USA
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9
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Wei W, Xia X, Li T, Chen Q, Feng X. Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data. BMC Genomics 2024; 25:402. [PMID: 38658838 PMCID: PMC11040744 DOI: 10.1186/s12864-024-10322-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND In recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis. RESULTS We proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise. CONCLUSION Shaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud , and its source code is publicly available at https://github.com/WiedenWei/shaoxia .
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Affiliation(s)
- Weideng Wei
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China
| | - Xiaoqiang Xia
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China
| | - Taiwen Li
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China
| | - Qianming Chen
- Key Laboratory of Oral Biomedical Research of Zhejiang Province, Affiliated Stomatology Hospital, Zhejiang University School of Stomatology, Hangzhou, Zhejiang, 310006, China
| | - Xiaodong Feng
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China.
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10
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Khandibharad S, Singh S. Single-cell ATAC sequencing identifies sleepy macrophages during reciprocity of cytokines in L. major infection. Microbiol Spectr 2024; 12:e0347823. [PMID: 38299832 PMCID: PMC10913457 DOI: 10.1128/spectrum.03478-23] [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: 09/25/2023] [Accepted: 12/31/2023] [Indexed: 02/02/2024] Open
Abstract
The hallmark characteristic of macrophages lies in their inherent plasticity, allowing them to adapt to dynamic microenvironments. Leishmania strategically modulates the phenotypic plasticity of macrophages, creating a favorable environment for intracellular survival and persistent infection through regulatory cytokine such as interleukin (IL)-10. Nevertheless, these effector cells can counteract infection by modulating crucial cytokines like IL-12 and key components involved in its production. Using sophisticated tool of single-cell assay for transposase accessible chromatin (ATAC) sequencing, we systematically examined the regulatory axis of IL-10 and IL-12 in a time-dependent manner during Leishmania major infection in macrophages Our analysis revealed the cellular heterogeneity post-infection with the regulators of IL-10 and IL-12, unveiling a reciprocal relationship between these cytokines. Notably, our significant findings highlighted the presence of sleepy macrophages and their pivotal role in mediating reciprocity between IL-10 and IL-12. To summarize, the roles of cytokine expression, transcription factors, cell cycle, and epigenetics of host cell machinery were vital in identification of sleepy macrophages, which is a transient state where transcription factors controlled the epigenetic remodeling and expression of genes involved in pro-inflammatory cytokine expression and recruitment of immune cells.IMPORTANCELeishmaniasis is an endemic affecting 99 countries and territories globally, as outlined in the 2022 World Health Organization report. The disease's severity is compounded by compromised host immune systems, emphasizing the pivotal role of the interplay between parasite and host immune factors in disease regulation. In instances of cutaneous leishmaniasis induced by L. major, macrophages function as sentinel cells. Our findings indicate that the plasticity and phenotype of macrophages can be modulated to express a cytokine profile involving IL-10 and IL-12, mediated by the regulation of transcription factors and their target genes post-L. major infection in macrophages. Employing sophisticated methodologies such as single-cell ATAC sequencing and computational genomics, we have identified a distinctive subset of macrophages termed "sleepy macrophages." These macrophages exhibit downregulated housekeeping genes while expressing a unique set of variable features. This data set constitutes a valuable resource for comprehending the intricate host-parasite interplay during L. major infection.
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Affiliation(s)
- Shweta Khandibharad
- Systems Medicine Lab, National Centre for Cell Science, SP Pune University Campus, Pune, India
| | - Shailza Singh
- Systems Medicine Lab, National Centre for Cell Science, SP Pune University Campus, Pune, India
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11
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Tang S, Cui X, Wang R, Li S, Li S, Huang X, Chen S. scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data. Nat Commun 2024; 15:1629. [PMID: 38388573 PMCID: PMC10884038 DOI: 10.1038/s41467-024-46045-w] [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: 09/21/2023] [Accepted: 02/12/2024] [Indexed: 02/24/2024] Open
Abstract
Single-cell chromatin accessibility sequencing (scCAS) has emerged as a valuable tool for interrogating and elucidating epigenomic heterogeneity and gene regulation. However, scCAS data inherently suffers from limitations such as high sparsity and dimensionality, which pose significant challenges for downstream analyses. Although several methods are proposed to enhance scCAS data, there are still challenges and limitations that hinder the effectiveness of these methods. Here, we propose scCASE, a scCAS data enhancement method based on non-negative matrix factorization which incorporates an iteratively updating cell-to-cell similarity matrix. Through comprehensive experiments on multiple datasets, we demonstrate the advantages of scCASE over existing methods for scCAS data enhancement. The interpretable cell type-specific peaks identified by scCASE can provide valuable biological insights into cell subpopulations. Moreover, to leverage the large compendia of available omics data as a reference, we further expand scCASE to scCASER, which enables the incorporation of external reference data to improve enhancement performance.
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Affiliation(s)
- Songming Tang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Xuejian Cui
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Rongxiang Wang
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22903, USA
| | - Sijie Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Siyu Li
- School of Statistics and Data Science, Nankai University, Tianjin, 300071, China
| | - Xin Huang
- Beijing Key Laboratory for Radiobiology, Department of Radiation Biology, Beijing Institute of Radiation Medicine, 100850, Beijing, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.
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12
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Martini L, Bardini R, Savino A, Di Carlo S. Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation. Genes (Basel) 2024; 15:268. [PMID: 38540327 PMCID: PMC10970009 DOI: 10.3390/genes15030268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/13/2024] [Accepted: 02/17/2024] [Indexed: 06/15/2024] Open
Abstract
It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.
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Affiliation(s)
| | | | | | - Stefano Di Carlo
- Control and Computer Engineering Department, Politecnico di Torino, 10129 Torino, Italy; (L.M.); (R.B.); (A.S.)
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13
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Kang H, Lee J. Adipose tissue macrophage heterogeneity in the single-cell genomics era. Mol Cells 2024; 47:100031. [PMID: 38354858 PMCID: PMC10960114 DOI: 10.1016/j.mocell.2024.100031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/16/2024] Open
Abstract
It is now well-accepted that obesity-induced inflammation plays an important role in the development of insulin resistance and type 2 diabetes. A key source of the inflammation is the murine epididymal and human visceral adipose tissue. The current paradigm is that obesity activates multiple proinflammatory immune cell types in adipose tissue, including adipose-tissue macrophages (ATMs), T Helper 1 (Th1) T cells, and natural killer (NK) cells, while concomitantly suppressing anti-inflammatory immune cells such as T Helper 2 (Th2) T cells and regulatory T cells (Tregs). A key feature of the current paradigm is that obesity induces the anti-inflammatory M2 ATMs in lean adipose tissue to polarize into proinflammatory M1 ATMs. However, recent single-cell transcriptomics studies suggest that the story is much more complex. Here we describe the single-cell genomics technologies that have been developed recently and the emerging results from studies using these technologies. While further studies are needed, it is clear that ATMs are highly heterogeneous. Moreover, while a variety of ATM clusters with quite distinct features have been found to be expanded by obesity, none truly resemble classical M1 ATMs. It is likely that single-cell transcriptomics technology will further revolutionize the field, thereby promoting our understanding of ATMs, adipose-tissue inflammation, and insulin resistance and accelerating the development of therapies for type 2 diabetes.
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Affiliation(s)
- Haneul Kang
- Soonchunhyang Institute of Medi-Bio Science (SIMS) and Department of Integrated Biomedical Science, Soonchunhyang University, Cheonan-si, South Korea
| | - Jongsoon Lee
- Soonchunhyang Institute of Medi-Bio Science (SIMS) and Department of Integrated Biomedical Science, Soonchunhyang University, Cheonan-si, South Korea.
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14
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Zhang K, Zemke NR, Armand EJ, Ren B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat Methods 2024; 21:217-227. [PMID: 38191932 PMCID: PMC10864184 DOI: 10.1038/s41592-023-02139-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/23/2023] [Indexed: 01/10/2024]
Abstract
Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.
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Affiliation(s)
- Kai Zhang
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, China
| | - Nathan R Zemke
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Ethan J Armand
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
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15
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Wang Q, Feng D, Jia S, Lu Q, Zhao M. B-Cell Receptor Repertoire: Recent Advances in Autoimmune Diseases. Clin Rev Allergy Immunol 2024; 66:76-98. [PMID: 38459209 DOI: 10.1007/s12016-024-08984-6] [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] [Accepted: 02/27/2024] [Indexed: 03/10/2024]
Abstract
In the field of contemporary medicine, autoimmune diseases (AIDs) are a prevalent and debilitating group of illnesses. However, they present extensive and profound challenges in terms of etiology, pathogenesis, and treatment. A major reason for this is the elusive pathophysiological mechanisms driving disease onset. Increasing evidence suggests the indispensable role of B cells in the pathogenesis of autoimmune diseases. Interestingly, B-cell receptor (BCR) repertoires in autoimmune diseases display a distinct skewing that can provide insights into disease pathogenesis. Over the past few years, advances in high-throughput sequencing have provided powerful tools for analyzing B-cell repertoire to understand the mechanisms during the period of B-cell immune response. In this paper, we have provided an overview of the mechanisms and analytical methods for generating BCR repertoire diversity and summarize the latest research progress on BCR repertoire in autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), primary Sjögren's syndrome (pSS), multiple sclerosis (MS), and type 1 diabetes (T1D). Overall, B-cell repertoire analysis is a potent tool to understand the involvement of B cells in autoimmune diseases, facilitating the creation of innovative therapeutic strategies targeting specific B-cell clones or subsets.
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Affiliation(s)
- Qian Wang
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Delong Feng
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Sujie Jia
- Department of Pharmacy, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
| | - Ming Zhao
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China.
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
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16
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Yan F, Suzuki A, Iwaya C, Pei G, Chen X, Yoshioka H, Yu M, Simon LM, Iwata J, Zhao Z. Single-cell multiomics decodes regulatory programs for mouse secondary palate development. Nat Commun 2024; 15:821. [PMID: 38280850 PMCID: PMC10821874 DOI: 10.1038/s41467-024-45199-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 01/17/2024] [Indexed: 01/29/2024] Open
Abstract
Perturbations in gene regulation during palatogenesis can lead to cleft palate, which is among the most common congenital birth defects. Here, we perform single-cell multiome sequencing and profile chromatin accessibility and gene expression simultaneously within the same cells (n = 36,154) isolated from mouse secondary palate across embryonic days (E) 12.5, E13.5, E14.0, and E14.5. We construct five trajectories representing continuous differentiation of cranial neural crest-derived multipotent cells into distinct lineages. By linking open chromatin signals to gene expression changes, we characterize the underlying lineage-determining transcription factors. In silico perturbation analysis identifies transcription factors SHOX2 and MEOX2 as important regulators of the development of the anterior and posterior palate, respectively. In conclusion, our study charts epigenetic and transcriptional dynamics in palatogenesis, serving as a valuable resource for further cleft palate research.
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Affiliation(s)
- Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Akiko Suzuki
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri - Kansas City, Kansas City, Missouri, 64108, USA
| | - Chihiro Iwaya
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Xian Chen
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Hiroki Yoshioka
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Meifang Yu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Junichi Iwata
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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17
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Zahedi R, Ghamsari R, Argha A, Macphillamy C, Beheshti A, Alizadehsani R, Lovell NH, Lotfollahi M, Alinejad-Rokny H. Deep learning in spatially resolved transcriptfomics: a comprehensive technical view. Brief Bioinform 2024; 25:bbae082. [PMID: 38483255 PMCID: PMC10939360 DOI: 10.1093/bib/bbae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/22/2024] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.
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Affiliation(s)
- Roxana Zahedi
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Reza Ghamsari
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Ahmadreza Argha
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Callum Macphillamy
- School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, 5371, Australia
| | - Amin Beheshti
- School of Computing, Macquarie University, Sydney, 2109, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Melbourne, VIC, 3216, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Mohammad Lotfollahi
- Computational Health Center, Helmholtz Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Hamid Alinejad-Rokny
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
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18
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Zeitler L, André K, Alberti A, Denby Wilkes C, Soutourina J, Goldar A. A genome-wide comprehensive analysis of nucleosome positioning in yeast. PLoS Comput Biol 2024; 20:e1011799. [PMID: 38266035 PMCID: PMC10843174 DOI: 10.1371/journal.pcbi.1011799] [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: 06/27/2023] [Revised: 02/05/2024] [Accepted: 01/03/2024] [Indexed: 01/26/2024] Open
Abstract
In eukaryotic cells, the one-dimensional DNA molecules need to be tightly packaged into the spatially constraining nucleus. Folding is achieved on its lowest level by wrapping the DNA around nucleosomes. Their arrangement regulates other nuclear processes, such as transcription and DNA repair. Despite strong efforts to study nucleosome positioning using Next Generation Sequencing (NGS) data, the mechanism of their collective arrangement along the gene body remains poorly understood. Here, we classify nucleosome distributions of protein-coding genes in Saccharomyces cerevisiae according to their profile similarity and analyse their differences using functional Principal Component Analysis. By decomposing the NGS signals into their main descriptive functions, we compared wild type and chromatin remodeler-deficient strains, keeping position-specific details preserved whilst considering the nucleosome arrangement as a whole. A correlation analysis with other genomic properties, such as gene size and length of the upstream Nucleosome Depleted Region (NDR), identified key factors that influence the nucleosome distribution. We reveal that the RSC chromatin remodeler-which is responsible for NDR maintenance-is indispensable for decoupling nucleosome arrangement within the gene from positioning outside, which interfere in rsc8-depleted conditions. Moreover, nucleosome profiles in chd1Δ strains displayed a clear correlation with RNA polymerase II presence, whereas wild type cells did not indicate a noticeable interdependence. We propose that RSC is pivotal for global nucleosome organisation, whilst Chd1 plays a key role for maintaining local arrangement.
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Affiliation(s)
- Leo Zeitler
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC),Gif-sur-Yvette, France
| | - Kévin André
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC),Gif-sur-Yvette, France
| | - Adriana Alberti
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC),Gif-sur-Yvette, France
| | - Cyril Denby Wilkes
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC),Gif-sur-Yvette, France
| | - Julie Soutourina
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC),Gif-sur-Yvette, France
| | - Arach Goldar
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC),Gif-sur-Yvette, France
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19
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Wyler E. Single-Cell RNA-Sequencing of RVFV Infection. Methods Mol Biol 2024; 2824:361-372. [PMID: 39039423 DOI: 10.1007/978-1-0716-3926-9_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
On the RNA level, viral infections are characterized by perturbations in the host cell transcriptome as well as the development of viral genetic information. Investigating the abundance and dynamic of RNA molecules can provide ample information to understand many aspects of the infection, from viral replication to pathogenesis. A key aspect therein is the resolution of the data, as infections are generally highly heterogeneous. Even in simple model systems such as cell lines, viral infections happen in a very asynchronous way. Quantifying RNAs at single-cell resolution can therefore substantially increase our understanding of these processes.Whereas measuring the RNA in bulk, that is, in samples containing thousands to hundreds of thousands of cells, is established and widely used since many years, methods for studying not only just a few different RNAs in individual cells became widely available only recently. Here, I outline and compare current concepts and methodologies for using single-cell RNA-sequencing to study virus infections. This covers sample preparation, cell preservation, biosafety considerations, and various experimental methods, with a special focus on the aspects that are important for studying virus infections. Since there is not "the one" method for doing single-cell RNA-sequencing, I will not provide a detailed protocol. Rather, this chapter should serve as a primer for getting started with single-cell RNA-sequencing experiments of virus infections and discusses the criteria that allow readers to choose the best procedures for their specific research question.
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Affiliation(s)
- Emanuel Wyler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Helmholtz Association, Berlin, Germany.
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20
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Amarasinghe SL, Yang P, Voogd O, Yang H, Du MM, Su S, Brown D, Jabbari J, Bowden R, Ritchie M. scPipe: an extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor. NAR Genom Bioinform 2023; 5:lqad105. [PMID: 38046273 PMCID: PMC10689045 DOI: 10.1093/nargab/lqad105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/23/2023] [Indexed: 12/05/2023] Open
Abstract
scPipe is a flexible R/Bioconductor package originally developed to analyse platform-independent single-cell RNA-Seq data. To expand its preprocessing capability to accommodate new single-cell technologies, we further developed scPipe to handle single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data. After executing multiple data cleaning steps to remove duplicated reads, low abundance features and cells of poor quality, a SingleCellExperiment object is created that contains a sparse count matrix with features of interest in the rows and cells in the columns. Quality control information (e.g. counts per cell, features per cell, total number of fragments, fraction of fragments per peak) and any relevant feature annotations are stored as metadata. We demonstrate that scPipe can efficiently identify 'true' cells and provides flexibility for the user to fine-tune the quality control thresholds using various feature and cell-based metrics collected during data preprocessing. Researchers can then take advantage of various downstream single-cell tools available in Bioconductor for further analysis of scATAC-Seq data such as dimensionality reduction, clustering, motif enrichment, differential accessibility and cis-regulatory network analysis. The scPipe package enables a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.
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Affiliation(s)
- Shanika L Amarasinghe
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
- The Australian Regenerative Medicine Institute, Monash University, Clayton, Victoria, 3800, Australia
| | - Phil Yang
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
| | - Oliver Voogd
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
| | - Haoyu Yang
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
| | - Mei R M Du
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
| | - Shian Su
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
| | - Daniel V Brown
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Jafar S Jabbari
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Rory Bowden
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Matthew E Ritchie
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, 3010, Australia
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21
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Minow MAA, Marand AP, Schmitz RJ. Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits. Annu Rev Genet 2023; 57:297-319. [PMID: 37562412 PMCID: PMC10775913 DOI: 10.1146/annurev-genet-022123-110824] [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: 08/12/2023]
Abstract
The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.
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Affiliation(s)
- Mark A A Minow
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
| | | | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
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22
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Meng R, Yin S, Sun J, Hu H, Zhao Q. scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention. Comput Biol Med 2023; 165:107414. [PMID: 37660567 DOI: 10.1016/j.compbiomed.2023.107414] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/05/2023]
Abstract
In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.
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Affiliation(s)
- Rui Meng
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Shuaidong Yin
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou, 350108, China.
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
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23
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Zhang B, Upadhyay R, Hao Y, Samanovic MI, Herati RS, Blair JD, Axelrad J, Mulligan MJ, Littman DR, Satija R. Multimodal single-cell datasets characterize antigen-specific CD8 + T cells across SARS-CoV-2 vaccination and infection. Nat Immunol 2023; 24:1725-1734. [PMID: 37735591 PMCID: PMC10522491 DOI: 10.1038/s41590-023-01608-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/31/2023] [Indexed: 09/23/2023]
Abstract
The immune response to SARS-CoV-2 antigen after infection or vaccination is defined by the durable production of antibodies and T cells. Population-based monitoring typically focuses on antibody titer, but there is a need for improved characterization and quantification of T cell responses. Here, we used multimodal sequencing technologies to perform a longitudinal analysis of circulating human leukocytes collected before and after immunization with the mRNA vaccine BNT162b2. Our data indicated distinct subpopulations of CD8+ T cells, which reliably appeared 28 days after prime vaccination. Using a suite of cross-modality integration tools, we defined their transcriptome, accessible chromatin landscape and immunophenotype, and we identified unique biomarkers within each modality. We further showed that this vaccine-induced population was SARS-CoV-2 antigen-specific and capable of rapid clonal expansion. Moreover, we identified these CD8+ T cell populations in scRNA-seq datasets from COVID-19 patients and found that their relative frequency and differentiation outcomes were predictive of subsequent clinical outcomes.
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Affiliation(s)
- Bingjie Zhang
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Cell Biology, New York University Grossman School of Medicine, New York, NY, USA
| | - Rabi Upadhyay
- Department of Cell Biology, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Yuhan Hao
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Marie I Samanovic
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Ramin S Herati
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - John D Blair
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Jordan Axelrad
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Mark J Mulligan
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Dan R Littman
- Department of Cell Biology, New York University Grossman School of Medicine, New York, NY, USA.
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
| | - Rahul Satija
- New York Genome Center, New York, NY, USA.
- Center for Genomics and Systems Biology, New York University, New York, NY, USA.
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24
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Tian L, Xie Y, Xie Z, Tian J, Tian W. AtacAnnoR: a reference-based annotation tool for single cell ATAC-seq data. Brief Bioinform 2023; 24:bbad268. [PMID: 37497729 DOI: 10.1093/bib/bbad268] [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: 03/14/2023] [Revised: 06/14/2023] [Accepted: 07/04/2023] [Indexed: 07/28/2023] Open
Abstract
Here, we present AtacAnnoR, a two-round annotation method for scATAC-seq data using well-annotated scRNA-seq data as reference. We evaluate AtacAnnoR's performance against six competing methods on 11 benchmark datasets. Our results show that AtacAnnoR achieves the highest mean accuracy and the highest mean balanced accuracy and performs particularly well when unpaired scRNA-seq data are used as the reference. Furthermore, AtacAnnoR implements a 'Combine and Discard' strategy to further improve annotation accuracy when annotations of multiple references are available. AtacAnnoR has been implemented in an R package and can be directly integrated into currently popular scATAC-seq analysis pipelines.
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Affiliation(s)
- Lejin Tian
- State Key Laboratory of Genetic Engineering, Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Yunxiao Xie
- State Key Laboratory of Genetic Engineering, Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhaobin Xie
- State Key Laboratory of Genetic Engineering, Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
| | | | - Weidong Tian
- State Key Laboratory of Genetic Engineering, Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
- Children's Hospital of Fudan University, Shanghai, China
- Children's Hospital of Shandong University, Jinan, China
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25
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Zhang K, Zemke NR, Armand EJ, Ren B. SnapATAC2: a fast, scalable and versatile tool for analysis of single-cell omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557221. [PMID: 37745443 PMCID: PMC10515871 DOI: 10.1101/2023.09.11.557221] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Single-cell omics technologies have ushered in a new era for the study of dynamic gene regulation in complex tissues during development and disease pathogenesis. A major computational challenge in analyzing these datasets is to project the large-scale and high dimensional data into low-dimensional space while retaining the relative relationships between cells in order to decompose the cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Conventional dimensionality reduction methods suffer from computational inefficiency, difficulty to capture the full spectrum of cellular heterogeneity, or inability to apply across diverse molecular modalities. Here, we report a fast and nonlinear dimensionality reduction algorithm that not only more accurately captures the heterogeneities of single-cell omics data, but also features runtime and memory usage that is computational efficient and linearly proportional to cell numbers. We implement this algorithm in a Python package named SnapATAC2, and demonstrate its superior performance, remarkable scalability and general adaptability using an array of single-cell omics data types, including single-cell ATAC-seq, single-cell RNA-seq, single-cell Hi-C, and single-cell multiomics datasets.
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26
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Erfanian N, Heydari AA, Feriz AM, Iañez P, Derakhshani A, Ghasemigol M, Farahpour M, Razavi SM, Nasseri S, Safarpour H, Sahebkar A. Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother 2023; 165:115077. [PMID: 37393865 DOI: 10.1016/j.biopha.2023.115077] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/22/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023] Open
Abstract
Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.
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Affiliation(s)
- Nafiseh Erfanian
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - A Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA, USA; Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Adib Miraki Feriz
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Pablo Iañez
- Cellular Systems Genomics Group, Josep Carreras Research Institute, Barcelona, Spain
| | - Afshin Derakhshani
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | | | - Mohsen Farahpour
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Seyyed Mohammad Razavi
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Saeed Nasseri
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Hossein Safarpour
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran.
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
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27
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Berson E, Sreenivas A, Phongpreecha T, Perna A, Grandi FC, Xue L, Ravindra NG, Payrovnaziri N, Mataraso S, Kim Y, Espinosa C, Chang AL, Becker M, Montine KS, Fox EJ, Chang HY, Corces MR, Aghaeepour N, Montine TJ. Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature. Nat Commun 2023; 14:4947. [PMID: 37587197 PMCID: PMC10432546 DOI: 10.1038/s41467-023-40611-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.
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Affiliation(s)
- Eloise Berson
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| | - Anjali Sreenivas
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Amalia Perna
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Fiorella C Grandi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Lei Xue
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Neal G Ravindra
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Neelufar Payrovnaziri
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Samson Mataraso
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Yeasul Kim
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Edward J Fox
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
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28
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Singh PP, Benayoun BA. Considerations for reproducible omics in aging research. NATURE AGING 2023; 3:921-930. [PMID: 37386258 PMCID: PMC10527412 DOI: 10.1038/s43587-023-00448-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 06/01/2023] [Indexed: 07/01/2023]
Abstract
Technical advancements over the past two decades have enabled the measurement of the panoply of molecules of cells and tissues including transcriptomes, epigenomes, metabolomes and proteomes at unprecedented resolution. Unbiased profiling of these molecular landscapes in the context of aging can reveal important details about mechanisms underlying age-related functional decline and age-related diseases. However, the high-throughput nature of these experiments creates unique analytical and design demands for robustness and reproducibility. In addition, 'omic' experiments are generally onerous, making it crucial to effectively design them to eliminate as many spurious sources of variation as possible as well as account for any biological or technical parameter that may influence such measures. In this Perspective, we provide general guidelines on best practices in the design and analysis of omic experiments in aging research from experimental design to data analysis and considerations for long-term reproducibility and validation of such studies.
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Affiliation(s)
- Param Priya Singh
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Aging Research Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Bérénice A Benayoun
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
- Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA, USA.
- Biochemistry and Molecular Medicine Department, USC Keck School of Medicine, Los Angeles, CA, USA.
- Epigenetics and Gene Regulation, USC Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
- USC Stem Cell Initiative, Los Angeles, CA, USA.
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29
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 179] [Impact Index Per Article: 179.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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30
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Bocci F, Jia D, Nie Q, Jolly MK, Onuchic J. Theoretical and computational tools to model multistable gene regulatory networks. ARXIV 2023:arXiv:2302.07401v2. [PMID: 36824430 PMCID: PMC9949162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.
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31
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Kwoji ID, Aiyegoro OA, Okpeku M, Adeleke MA. 'Multi-omics' data integration: applications in probiotics studies. NPJ Sci Food 2023; 7:25. [PMID: 37277356 DOI: 10.1038/s41538-023-00199-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 05/22/2023] [Indexed: 06/07/2023] Open
Abstract
The concept of probiotics is witnessing increasing attention due to its benefits in influencing the host microbiome and the modulation of host immunity through the strengthening of the gut barrier and stimulation of antibodies. These benefits, combined with the need for improved nutraceuticals, have resulted in the extensive characterization of probiotics leading to an outburst of data generated using several 'omics' technologies. The recent development in system biology approaches to microbial science is paving the way for integrating data generated from different omics techniques for understanding the flow of molecular information from one 'omics' level to the other with clear information on regulatory features and phenotypes. The limitations and tendencies of a 'single omics' application to ignore the influence of other molecular processes justify the need for 'multi-omics' application in probiotics selections and understanding its action on the host. Different omics techniques, including genomics, transcriptomics, proteomics, metabolomics and lipidomics, used for studying probiotics and their influence on the host and the microbiome are discussed in this review. Furthermore, the rationale for 'multi-omics' and multi-omics data integration platforms supporting probiotics and microbiome analyses was also elucidated. This review showed that multi-omics application is useful in selecting probiotics and understanding their functions on the host microbiome. Hence, recommend a multi-omics approach for holistically understanding probiotics and the microbiome.
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Affiliation(s)
- Iliya Dauda Kwoji
- Discipline of Genetics, School of Life Sciences, College of Agriculture, Engineering and Sciences, University of KwaZulu-Natal, 4090, Durban, South Africa
| | - Olayinka Ayobami Aiyegoro
- Unit for Environmental Sciences and Management, North-West University, Potchefstroom, Northwest, South Africa
| | - Moses Okpeku
- Discipline of Genetics, School of Life Sciences, College of Agriculture, Engineering and Sciences, University of KwaZulu-Natal, 4090, Durban, South Africa
| | - Matthew Adekunle Adeleke
- Discipline of Genetics, School of Life Sciences, College of Agriculture, Engineering and Sciences, University of KwaZulu-Natal, 4090, Durban, South Africa.
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32
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Cai C, Yue Y, Yue B. Single-cell RNA sequencing in skeletal muscle developmental biology. Biomed Pharmacother 2023; 162:114631. [PMID: 37003036 DOI: 10.1016/j.biopha.2023.114631] [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: 02/12/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/01/2023] Open
Abstract
Skeletal muscle is the most extensive tissue in mammals, and they perform several functions; it is derived from paraxial mesodermal somites and undergoes hyperplasia and hypertrophy to form multinucleated, contractile, and functional muscle fibers. Skeletal muscle is a complex heterogeneous tissue composed of various cell types that establish communication strategies to exchange biological information; therefore, characterizing the cellular heterogeneity and transcriptional signatures of skeletal muscle is central to understanding its ontogeny's details. Studies of skeletal myogenesis have focused primarily on myogenic cells' proliferation, differentiation, migration, and fusion and ignored the intricate network of cells with specific biological functions. The rapid development of single-cell sequencing technology has recently enabled the exploration of skeletal muscle cell types and molecular events during development. This review summarizes the progress in single-cell RNA sequencing and its applications in skeletal myogenesis, which will provide insights into skeletal muscle pathophysiology.
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Affiliation(s)
- Cuicui Cai
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu 610225, China; Guyuan Branch, Ningxia Academy of Agriculture and Forestry Sciences, Guyuan 7560000, China
| | - Yuan Yue
- Department of Pathobiology and Immunology, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Binglin Yue
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu 610225, China.
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33
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Loewa A, Feng JJ, Hedtrich S. Human disease models in drug development. NATURE REVIEWS BIOENGINEERING 2023; 1:1-15. [PMID: 37359774 PMCID: PMC10173243 DOI: 10.1038/s44222-023-00063-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 06/20/2023]
Abstract
Biomedical research is undergoing a paradigm shift towards approaches centred on human disease models owing to the notoriously high failure rates of the current drug development process. Major drivers for this transition are the limitations of animal models, which, despite remaining the gold standard in basic and preclinical research, suffer from interspecies differences and poor prediction of human physiological and pathological conditions. To bridge this translational gap, bioengineered human disease models with high clinical mimicry are being developed. In this Review, we discuss preclinical and clinical studies that benefited from these models, focusing on organoids, bioengineered tissue models and organs-on-chips. Furthermore, we provide a high-level design framework to facilitate clinical translation and accelerate drug development using bioengineered human disease models.
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Affiliation(s)
- Anna Loewa
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - James J. Feng
- Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC Canada
- Department of Mathematics, University of British Columbia, Vancouver, BC Canada
| | - Sarah Hedtrich
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Center of Biological Design, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC Canada
- Max-Delbrück Center for Molecular Medicine (MCD), Helmholtz Association, Berlin, Germany
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Ma W, Lu J, Wu H. Cellcano: supervised cell type identification for single cell ATAC-seq data. Nat Commun 2023; 14:1864. [PMID: 37012226 PMCID: PMC10070275 DOI: 10.1038/s41467-023-37439-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 03/15/2023] [Indexed: 04/05/2023] Open
Abstract
Computational cell type identification is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/ .
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Affiliation(s)
- Wenjing Ma
- Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, GA, 30322, USA
| | - Jiaying Lu
- Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, GA, 30322, USA
| | - Hao Wu
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, P. R. China.
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, USA.
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Feng L, Si J, Yue J, Zhao M, Qi W, Zhu S, Mo J, Wang L, Lan G, Liang J. The Landscape of Accessible Chromatin and Developmental Transcriptome Maps Reveal a Genetic Mechanism of Skeletal Muscle Development in Pigs. Int J Mol Sci 2023; 24:ijms24076413. [PMID: 37047386 PMCID: PMC10094211 DOI: 10.3390/ijms24076413] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023] Open
Abstract
The epigenetic regulation mechanism of porcine skeletal muscle development relies on the openness of chromatin and is also precisely regulated by transcriptional machinery. However, fewer studies have exploited the temporal changes in gene expression and the landscape of accessible chromatin to reveal the underlying molecular mechanisms controlling muscle development. To address this, skeletal muscle biopsy samples were taken from Landrace pigs at days 0 (D0), 60 (D60), 120 (D120), and 180 (D180) after birth and were then analyzed using RNA-seq and ATAC-seq. The RNA-seq analysis identified 8554 effective differential genes, among which ACBD7, TMEM220, and ATP1A2 were identified as key genes related to the development of porcine skeletal muscle. Some potential cis-regulatory elements identified by ATAC-seq analysis contain binding sites for many transcription factors, including SP1 and EGR1, which are also the predicted transcription factors regulating the expression of ACBD7 genes. Moreover, the omics analyses revealed regulatory regions that become ectopically active after birth during porcine skeletal muscle development after birth and identified 151,245, 53,435, 30,494, and 40,911 peaks. The enriched functional elements are related to the cell cycle, muscle development, and lipid metabolism. In summary, comprehensive high-resolution gene expression maps were developed for the transcriptome and accessible chromatin during postnatal skeletal muscle development in pigs.
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Affiliation(s)
- Lingli Feng
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
| | - Jinglei Si
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
| | - Jingwei Yue
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100097, China
| | - Mingwei Zhao
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
| | - Wenjing Qi
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
| | - Siran Zhu
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
| | - Jiayuan Mo
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
| | - Lixian Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100097, China
| | - Ganqiu Lan
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
| | - Jing Liang
- Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, Guangxi University, Nanning 530004, China (G.L.)
- Correspondence:
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Smits JG, Arts JA, Frölich S, Snabel RR, Heuts BM, Martens JH, van Heeringen SJ, Zhou H. scANANSE gene regulatory network and motif analysis of single-cell clusters. F1000Res 2023; 12:243. [PMID: 38116584 PMCID: PMC10728588 DOI: 10.12688/f1000research.130530.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/03/2023] [Indexed: 12/21/2023] Open
Abstract
The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.
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Affiliation(s)
- Jos G.A. Smits
- Molecular Developmental Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
| | - Julian A. Arts
- Molecular Developmental Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
| | - Siebren Frölich
- Molecular Developmental Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
| | - Rebecca R. Snabel
- Molecular Developmental Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
| | - Branco M.H. Heuts
- Molecular Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
| | - Joost H.A. Martens
- Molecular Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
| | - Simon J. van Heeringen
- Molecular Developmental Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
| | - Huiqing Zhou
- Molecular Developmental Biology, Radboud University, Nijmegen, Gelderland, The Netherlands
- Human Genetics, Radboud University Medical Centre, Nijmegen, Gelderland, The Netherlands
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37
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Wang Z, Zhang Y, Yu Y, Zhang J, Liu Y, Zou Q. A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder. Int J Mol Sci 2023; 24:ijms24054784. [PMID: 36902216 PMCID: PMC10003007 DOI: 10.3390/ijms24054784] [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: 11/11/2022] [Revised: 01/02/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular states and dynamics. However, few research efforts have been dedicated to modeling the relationship between regulatory grammars and single-cell chromatin accessibility and incorporating different analysis scenarios of scATAC-seq data into the general framework. To this end, we propose a unified deep learning framework based on the ProdDep Transformer Encoder, dubbed PROTRAIT, for scATAC-seq data analysis. Specifically motivated by the deep language model, PROTRAIT leverages the ProdDep Transformer Encoder to capture the syntax of transcription factor (TF)-DNA binding motifs from scATAC-seq peaks for predicting single-cell chromatin accessibility and learning single-cell embedding. Based on cell embedding, PROTRAIT annotates cell types using the Louvain algorithm. Furthermore, according to the identified likely noises of raw scATAC-seq data, PROTRAIT denoises these values based on predated chromatin accessibility. In addition, PROTRAIT employs differential accessibility analysis to infer TF activity at single-cell and single-nucleotide resolution. Extensive experiments based on the Buenrostro2018 dataset validate the effeteness of PROTRAIT for chromatin accessibility prediction, cell type annotation, and scATAC-seq data denoising, therein outperforming current approaches in terms of different evaluation metrics. Besides, we confirm the consistency between the inferred TF activity and the literature review. We also demonstrate the scalability of PROTRAIT to analyze datasets containing over one million cells.
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Affiliation(s)
- Zixuan Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yun Yu
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Junming Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yuhang Liu
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence:
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38
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Iqbal W, Zhou W. Computational Methods for Single-cell DNA Methylome Analysis. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:48-66. [PMID: 35718270 PMCID: PMC10372927 DOI: 10.1016/j.gpb.2022.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/28/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022]
Abstract
Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.
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Affiliation(s)
- Waleed Iqbal
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Zhang B, Upadhyay R, Hao Y, Samanovic MI, Herati RS, Blair J, Axelrad J, Mulligan MJ, Littman DR, Satija R. Multimodal characterization of antigen-specific CD8 + T cells across SARS-CoV-2 vaccination and infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.24.525203. [PMID: 36747786 PMCID: PMC9900816 DOI: 10.1101/2023.01.24.525203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The human immune response to SARS-CoV-2 antigen after infection or vaccination is defined by the durable production of antibodies and T cells. Population-based monitoring typically focuses on antibody titer, but there is a need for improved characterization and quantification of T cell responses. Here, we utilize multimodal sequencing technologies to perform a longitudinal analysis of circulating human leukocytes collected before and after BNT162b2 immunization. Our data reveal distinct subpopulations of CD8 + T cells which reliably appear 28 days after prime vaccination (7 days post boost). Using a suite of cross-modality integration tools, we define their transcriptome, accessible chromatin landscape, and immunophenotype, and identify unique biomarkers within each modality. By leveraging DNA-oligo-tagged peptide-MHC multimers and T cell receptor sequencing, we demonstrate that this vaccine-induced population is SARS-CoV-2 antigen-specific and capable of rapid clonal expansion. Moreover, we also identify these CD8 + populations in scRNA-seq datasets from COVID-19 patients and find that their relative frequency and differentiation outcomes are predictive of subsequent clinical outcomes. Our work contributes to our understanding of T cell immunity, and highlights the potential for integrative and multimodal analysis to characterize rare cell populations.
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Affiliation(s)
- Bingjie Zhang
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Cell Biology and Regenerative Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Rabi Upadhyay
- Department of Cell Biology and Regenerative Medicine, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Yuhan Hao
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Marie I. Samanovic
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Ramin S. Herati
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - John Blair
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Jordan Axelrad
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Mark J. Mulligan
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Dan R. Littman
- Department of Cell Biology and Regenerative Medicine, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
- Howard Hughes Medical Institute, New York, NY, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
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40
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Tien FM, Lu HH, Lin SY, Tsai HC. Epigenetic remodeling of the immune landscape in cancer: therapeutic hurdles and opportunities. J Biomed Sci 2023; 30:3. [PMID: 36627707 PMCID: PMC9832644 DOI: 10.1186/s12929-022-00893-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
The tumor immune microenvironment represents a sophisticated ecosystem where various immune cell subtypes communicate with cancer cells and stromal cells. The dynamic cellular composition and functional characteristics of the immune landscape along the trajectory of cancer development greatly impact the therapeutic efficacy and clinical outcome in patients receiving systemic antitumor therapy. Mounting evidence has suggested that epigenetic mechanisms are the underpinning of many aspects of antitumor immunity and facilitate immune state transitions during differentiation, activation, inhibition, or dysfunction. Thus, targeting epigenetic modifiers to remodel the immune microenvironment holds great potential as an integral part of anticancer regimens. In this review, we summarize the epigenetic profiles and key epigenetic modifiers in individual immune cell types that define the functional coordinates of tumor permissive and non-permissive immune landscapes. We discuss the immunomodulatory roles of current and prospective epigenetic therapeutic agents, which may open new opportunities in enhancing cancer immunotherapy or overcoming existing therapeutic challenges in the management of cancer.
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Affiliation(s)
- Feng-Ming Tien
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, 100233, Taiwan
| | - Hsuan-Hsuan Lu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan
- Center for Frontier Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan
| | - Shu-Yung Lin
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, 100233, Taiwan
| | - Hsing-Chen Tsai
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan.
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, 100233, Taiwan.
- Center for Frontier Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan.
- Graduate Institute of Toxicology, College of Medicine, National Taiwan University, No. 1 Jen Ai Road Section 1, Rm542, Taipei, 100233, Taiwan.
- Department of Medical Research, National Taiwan University Hospital, Taipei, 100225, Taiwan.
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41
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Huang K, Gong H, Guan J, Zhang L, Hu C, Zhao W, Huang L, Zhang W, Kim P, Zhou X. AgeAnno: a knowledgebase of single-cell annotation of aging in human. Nucleic Acids Res 2023; 51:D805-D815. [PMID: 36200838 PMCID: PMC9825500 DOI: 10.1093/nar/gkac847] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 01/30/2023] Open
Abstract
Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research.
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Affiliation(s)
- Kexin Huang
- West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
- Med-X Center for Informatics, Sichuan University,Chengdu,Sichuan 610041, P.R. China
| | - Hoaran Gong
- West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
- Med-X Center for Informatics, Sichuan University,Chengdu,Sichuan 610041, P.R. China
| | - Jingjing Guan
- School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710071, P.R. China
| | - Lingxiao Zhang
- School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710071, P.R. China
| | - Changbao Hu
- School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710071, P.R. China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710071, P.R. China
| | - Wei Zhang
- West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
- Med-X Center for Informatics, Sichuan University,Chengdu,Sichuan 610041, P.R. China
| | - Pora Kim
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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42
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Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers. Semin Immunopathol 2023; 45:61-69. [PMID: 36625902 DOI: 10.1007/s00281-022-00981-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/11/2022] [Indexed: 01/11/2023]
Abstract
Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution. This facilitates unraveling the heterogeneity of childhood cancers to identify cell subtypes that are prone to treatment resistance. The rapid accumulation of single-cell data from different modalities necessitates the development of novel computational approaches for processing, visualizing, and analyzing single-cell data. Here, we review single-cell approaches utilized or under development in the context of childhood cancers. We review computational methods for analyzing single-cell data and discuss best practices for their application. Finally, we review the impact of several studies of childhood tumors analyzed with these approaches and future directions to implement single-cell studies into translational cancer research in pediatric oncology.
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43
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Samara A, Spildrejorde M, Sharma A, Falck M, Leithaug M, Modafferi S, Bjørnstad PM, Acharya G, Gervin K, Lyle R, Eskeland R. A multi-omics approach to visualize early neuronal differentiation from hESCs in 4D. iScience 2022; 25:105279. [PMID: 36304110 PMCID: PMC9593815 DOI: 10.1016/j.isci.2022.105279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/22/2022] [Accepted: 09/28/2022] [Indexed: 11/19/2022] Open
Abstract
Neuronal differentiation of pluripotent stem cells is an established method to study physiology, disease, and medication safety. However, the sequence of events in human neuronal differentiation and the ability of in vitro models to recapitulate early brain development are poorly understood. We developed a protocol optimized for the study of early human brain development and neuropharmacological applications. We comprehensively characterized gene expression and epigenetic profiles at four timepoints, because the cells differentiate from embryonic stem cells towards a heterogeneous population of progenitors, immature and mature neurons bearing telencephalic signatures. A multi-omics roadmap of neuronal differentiation, combined with searchable interactive gene analysis tools, allows for extensive exploration of early neuronal development and the effect of medications.
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Affiliation(s)
- Athina Samara
- Division of Clinical Paediatrics, Department of Women’s and Children’s Health, Karolinska Institutet, Solna, Sweden
- Astrid Lindgren Children′s Hospital Karolinska University Hospital, Stockholm, Sweden
| | - Mari Spildrejorde
- PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ankush Sharma
- Department of Informatics, University of Oslo, Oslo, Norway
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Martin Falck
- PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Magnus Leithaug
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Stefania Modafferi
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Pål Marius Bjørnstad
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Ganesh Acharya
- Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Alfred Nobels Allé 8, SE-14152 Stockholm, Sweden
- Center for Fetal Medicine, Karolinska University Hospital Huddinge, SE-14186 Stockholm, Sweden
| | - Kristina Gervin
- PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, School of Pharmacy, University of Oslo, Oslo, Norway
- Division of Clinical Neuroscience, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Robert Lyle
- PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ragnhild Eskeland
- PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
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44
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Cao Y, Lin Y, Patrick E, Yang P, Yang JYH. scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction. Bioinformatics 2022; 38:4745-4753. [PMID: 36040148 PMCID: PMC9563679 DOI: 10.1093/bioinformatics/btac590] [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: 05/12/2022] [Revised: 07/21/2022] [Accepted: 08/28/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into samples from individuals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each sample (e.g. individuals). RESULTS Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. We demonstrate that summarizing a broad collection of features at the sample level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of individuals. AVAILABILITY AND IMPLEMENTATION scFeatures is publicly available as an R package at https://github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Section 2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yue Cao
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
| | - Yingxin Lin
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
| | - Ellis Patrick
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
- Computational Systems Biology Group, Children’s Medical Research Institute, Westmead, NSW 2145, Australia
| | - Pengyi Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
- Computational Systems Biology Group, Children’s Medical Research Institute, Westmead, NSW 2145, Australia
| | - Jean Yee Hwa Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
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45
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Hu K, Liu H, Lawson ND, Zhu LJ. scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data. Front Cell Dev Biol 2022; 10:981859. [PMID: 36238687 PMCID: PMC9551270 DOI: 10.3389/fcell.2022.981859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.
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Affiliation(s)
- Kai Hu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Haibo Liu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Nathan D. Lawson
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Lihua Julie Zhu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Program in Molecular Medicine, Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, United States
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46
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Shi P, Nie Y, Yang J, Zhang W, Tang Z, Xu J. Fundamental and practical approaches for single-cell ATAC-seq analysis. ABIOTECH 2022; 3:212-223. [PMID: 36313930 PMCID: PMC9590475 DOI: 10.1007/s42994-022-00082-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/07/2022] [Indexed: 11/28/2022]
Abstract
Assays for transposase-accessible chromatin through high-throughput sequencing (ATAC-seq) are effective tools in the study of genome-wide chromatin accessibility landscapes. With the rapid development of single-cell technology, open chromatin regions that play essential roles in epigenetic regulation have been measured at the single-cell level using single-cell ATAC-seq approaches. The application of scATAC-seq has become as popular as that of scRNA-seq. However, owing to the nature of scATAC-seq data, which are sparse and noisy, processing the data requires different methodologies and empirical experience. This review presents a practical guide for processing scATAC-seq data, from quality evaluation to downstream analysis, for various applications. In addition to the epigenomic profiling from scATAC-seq, we also discuss recent studies in which the function of non-coding variants has been investigated based on cell type-specific cis-regulatory elements and how to use the by-product genetic information obtained from scATAC-seq to infer single-cell copy number variants and trace cell lineage. We anticipate that this review will assist researchers in designing and implementing scATAC-seq assays to facilitate research in diverse fields.
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Affiliation(s)
- Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Yage Nie
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jiawen Yang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Weixing Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Zhongjie Tang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
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Amodio M, Youlten SE, Venkat A, San Juan BP, Chaffer CL, Krishnaswamy S. Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer. PATTERNS 2022; 3:100577. [PMID: 36124302 PMCID: PMC9481959 DOI: 10.1016/j.patter.2022.100577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/14/2022] [Accepted: 08/04/2022] [Indexed: 11/03/2022]
Abstract
Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information together, rather than siloing each technology into separate analysis pipelines. To advance this goal, we introduce a framework called the single-cell multi-modal generative adversarial network (scMMGAN) that integrates data from multiple modalities into a unified representation in the ambient data space for downstream analysis using a combination of adversarial learning and data geometry techniques. The framework’s key improvement is an additional diffusion geometry loss with a new kernel that constrains the otherwise over-parameterized GAN. We demonstrate scMMGAN’s ability to produce more meaningful alignments than alternative methods on a wide variety of data modalities and that its output can be used to draw conclusions from real-world biological experimental data. Integrating data from multiple modalities into one analysis Using data geometry to regularize cycle-consistent GANs Quantifying uncertainty through noise augmentation
Biological experimental data are increasingly being generated along multiple different axes, with new and more complex technologies specializing in particular measurements being developed every year. Measuring a single subject or system with multiple specialized data-collecting tools creates a natural interest in integrating the results of these individual instruments to form a single unified view. The model introduced here presents a computational technique designed for this purpose. With the single-cell multi-modal GAN (scMMGAN), there is an opportunity to measure along many different omic directions and synthesize the information from each into one larger understanding of the system under study.
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48
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Park HJ, Jung H. Neuro-immune interactions at single-cell resolution in neurodevelopmental, infectious, and neurodegenerative diseases. Anim Cells Syst (Seoul) 2022; 26:137-147. [PMID: 36046030 PMCID: PMC9423835 DOI: 10.1080/19768354.2022.2110937] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Recent technological advance in single-cell and single-nucleus transcriptomics has made it possible to generate an unprecedentedly detailed landscape of neuro-immune interactions in healthy and diseased brains. In this review, we overview the recent literature that catalogs single-cell-level gene expression in brains with signs of inflammation, focusing on maternal immune activation, viral infection, and auto-immune diseases. The literature also includes a series of papers that provide strong evidence for immunological contributions to neurodegenerative diseases, which, in a strict sense, are not considered neuroinflammatory. To help with the discussion, we present a diagram of experimental and analytical flows in the single-cell analysis of the brain. We also discuss the recurring themes of neuro-immune interactions and suggest future research directions.
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Affiliation(s)
- Hyun Jung Park
- Samsung Medical Center, Samsung Genome Institute, Seoul, Republic of Korea
| | - Hosung Jung
- Department of Anatomy, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
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49
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Benevento M, Hökfelt T, Harkany T. Ontogenetic rules for the molecular diversification of hypothalamic neurons. Nat Rev Neurosci 2022; 23:611-627. [PMID: 35906427 DOI: 10.1038/s41583-022-00615-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2022] [Indexed: 11/09/2022]
Abstract
The hypothalamus is an evolutionarily conserved endocrine interface that, among other roles, links central homeostatic control to adaptive bodily responses by releasing hormones and neuropeptides from its many neuronal subtypes. In its preoptic, anterior, tuberal and mammillary subdivisions, a kaleidoscope of magnocellular and parvocellular neuroendocrine command neurons, local-circuit neurons, and neurons that project to extrahypothalamic areas are intermingled in partially overlapping patches of nuclei. Molecular fingerprinting has produced data of unprecedented mass and depth to distinguish and even to predict the synaptic and endocrine competences, connectivity and stimulus selectivity of many neuronal modalities. These new insights support eminent studies from the past century but challenge others on the molecular rules that shape the developmental segregation of hypothalamic neuronal subtypes and their use of morphogenic cues for terminal differentiation. Here, we integrate single-cell RNA sequencing studies with those of mouse genetics and endocrinology to describe key stages of hypothalamus development, including local neurogenesis, the direct terminal differentiation of glutamatergic neurons, transition cascades for GABAergic and GABAergic cell-derived dopamine cells, waves of local neuronal migration, and sequential enrichment in neuropeptides and hormones. We particularly emphasize how transcription factors determine neuronal identity and, consequently, circuit architecture, and whether their deviations triggered by environmental factors and hormones provoke neuroendocrine illnesses.
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Affiliation(s)
- Marco Benevento
- Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Tomas Hökfelt
- Department of Neuroscience, Biomedicum 7D, Karolinska Institutet, Solna, Sweden
| | - Tibor Harkany
- Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, Austria. .,Department of Neuroscience, Biomedicum 7D, Karolinska Institutet, Solna, Sweden.
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50
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Hwang CD, Pagani CA, Nunez JH, Cherief M, Qin Q, Gomez-Salazar M, Kadaikal B, Kang H, Chowdary AR, Patel N, James AW, Levi B. Contemporary perspectives on heterotopic ossification. JCI Insight 2022; 7:158996. [PMID: 35866484 PMCID: PMC9431693 DOI: 10.1172/jci.insight.158996] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Heterotopic ossification (HO) is the formation of ectopic bone that is primarily genetically driven (fibrodysplasia ossificans progressiva [FOP]) or acquired in the setting of trauma (tHO). HO has undergone intense investigation, especially over the last 50 years, as awareness has increased around improving clinical technologies and incidence, such as with ongoing wartime conflicts. Current treatments for tHO and FOP remain prophylactic and include NSAIDs and glucocorticoids, respectively, whereas other proposed therapeutic modalities exhibit prohibitive risk profiles. Contemporary studies have elucidated mechanisms behind tHO and FOP and have described new distinct niches independent of inflammation that regulate ectopic bone formation. These investigations have propagated a paradigm shift in the approach to treatment and management of a historically difficult surgical problem, with ongoing clinical trials and promising new targets.
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Affiliation(s)
- Charles D Hwang
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Massachusetts General Hospital, Harvard University, Boston, Massachusetts, USA
| | - Chase A Pagani
- Department of Surgery, Center for Organogenesis Research and Trauma, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Johanna H Nunez
- Department of Surgery, Center for Organogenesis Research and Trauma, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Masnsen Cherief
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Qizhi Qin
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Balram Kadaikal
- Department of Surgery, Center for Organogenesis Research and Trauma, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Heeseog Kang
- Department of Surgery, Center for Organogenesis Research and Trauma, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ashish R Chowdary
- Department of Surgery, Center for Organogenesis Research and Trauma, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Nicole Patel
- Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Aaron W James
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Benjamin Levi
- Department of Surgery, Center for Organogenesis Research and Trauma, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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