1
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Curion F, Theis FJ. Machine learning integrative approaches to advance computational immunology. Genome Med 2024; 16:80. [PMID: 38862979 PMCID: PMC11165829 DOI: 10.1186/s13073-024-01350-3] [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: 06/29/2023] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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2
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Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [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: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
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Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
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3
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Boldrini M, Xiao Y, Sing T, Zhu C, Jabbi M, Pantazopoulos H, Gürsoy G, Martinowich K, Punzi G, Vallender EJ, Zody M, Berretta S, Hyde TM, Kleinman JE, Marenco S, Roussos P, Lewis DA, Turecki G, Lehner T, Mann JJ. Omics approaches to investigate the pathogenesis of suicide. Biol Psychiatry 2024:S0006-3223(24)01352-0. [PMID: 38821194 DOI: 10.1016/j.biopsych.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/02/2024]
Abstract
Suicide is the second leading cause of death in U.S. adolescents and young adults, and generally associated with a psychiatric disorder. Suicidal behavior has a complex etiology and pathogenesis. Moderate heritability suggests genetic causes. Associations between childhood and recent life adversity indicate contributions from epigenetic factors. Genomic contributions to suicide pathogenesis remain largely unknown. This paper is based on a workshop held to design strategies to identify molecular drivers of suicide neurobiology that would be putative new treatment targets. The panel determined that, while bulk tissue studies provide comprehensive information, single-nucleus approaches identifying cell-type specific changes are needed. While single nuclei techniques lack information on cytoplasm, processes, spines, and synapses, spatial multiomic technologies on intact tissue detect cell alterations specific to brain tissue layers and subregions. Because suicide has genetic and environmental drivers, multiomic approaches combining cell-type specific epigenome, transcriptome, and proteome provide a more complete picture of pathogenesis. To determine the direction of effect of suicide risk gene variants on RNA and protein expression, and how these interact with epigenetic marks, single nuclei and spatial multiomics quantitative trait loci maps should be integrated with whole genome sequencing and genome-wide association databases. The workshop concluded with the recommendation for the formation of an international suicide biology consortium that will bring together brain banks and investigators with expertise in cutting-edge omics technologies to delineate the biology of suicide and identify novel potential treatment targets to be tested in cellular and animal models for drug and biomarkers discovery, to guide suicide prevention.
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Affiliation(s)
- Maura Boldrini
- Department of Psychiatry, Columbia University, New York, NY; Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY.
| | - Yang Xiao
- Department of Biomedical Engineering, Columbia University, New York, NY
| | - Tarjinder Sing
- Department of Psychiatry, Columbia University, New York, NY; Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY; New York Genome Center, New York, NY
| | - Chenxu Zhu
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY; New York Genome Center, New York, NY
| | - Mbemba Jabbi
- Department of Psychiatry and Behavioral Sciences, Mulva Clinics for the Neurosciences, Dell Medical School, The University of Texas at Austin, Austin, TX
| | - Harry Pantazopoulos
- Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS
| | - Gamze Gürsoy
- New York Genome Center, New York, NY; Departments of Biomedical Informatics and Computer Science, Columbia University, New York, NY
| | - Keri Martinowich
- Lieber Institute for Brain Development, Department of Psychiatry and Behavioral Sciences, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Giovanna Punzi
- Lieber Institute for Brain Development, Department of Psychiatry and Behavioral Sciences, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Eric J Vallender
- Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS
| | | | - Sabina Berretta
- Department of Psychiatry, Harvard Brain Tissue Resource Center, Harvard Medical School, McLean Hospital, Belmont, MA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Department of Psychiatry and Behavioral Sciences, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Department of Psychiatry and Behavioral Sciences, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Stefano Marenco
- Human Brain Collection Core (HBCC), National Institute of Mental Health's (NIMH) Division of Intramural Research Programs (DIRP), Bethesda, MD
| | - Panagiotis Roussos
- Center for Precision Medicine and Translational Therapeutics; Mental Illness Research Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - David A Lewis
- Department of Psychiatry, Douglas Institute, McGill University, Montréal, QC, Canada
| | - Gustavo Turecki
- Departments of Psychiatry and Neuroscience, University of Pittsburgh, Pittsburgh, PA
| | | | - J John Mann
- Department of Psychiatry, Columbia University, New York, NY; Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY
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4
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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5
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Shi W, Zhang J, Huang S, Fan Q, Cao J, Zeng J, Wu L, Yang C. Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry. JACS AU 2024; 4:1723-1743. [PMID: 38818076 PMCID: PMC11134576 DOI: 10.1021/jacsau.4c00118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 06/01/2024]
Abstract
Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.
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Affiliation(s)
- Weixiong Shi
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jing Zhang
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
| | - Shanqing Huang
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qian Fan
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jiao Cao
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jun Zeng
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lingling Wu
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Chaoyong Yang
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
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Tian T, Zhang J, Lin X, Wei Z, Hakonarson H. Dependency-aware deep generative models for multitasking analysis of spatial omics data. Nat Methods 2024:10.1038/s41592-024-02257-y. [PMID: 38783067 DOI: 10.1038/s41592-024-02257-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 03/25/2024] [Indexed: 05/25/2024]
Abstract
Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.
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Affiliation(s)
- Tian Tian
- School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, Hubei, China
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jie Zhang
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
| | - Xiang Lin
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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7
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Johnson AL, Lopez-Bertoni H. Cellular diversity through space and time: adding new dimensions to GBM therapeutic development. Front Genet 2024; 15:1356611. [PMID: 38774283 PMCID: PMC11106394 DOI: 10.3389/fgene.2024.1356611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/15/2024] [Indexed: 05/24/2024] Open
Abstract
The current median survival for glioblastoma (GBM) patients is only about 16 months, with many patients succumbing to the disease in just a matter of months, making it the most common and aggressive primary brain cancer in adults. This poor outcome is, in part, due to the lack of new treatment options with only one FDA-approved treatment in the last decade. Advances in sequencing techniques and transcriptomic analyses have revealed a vast degree of heterogeneity in GBM, from inter-patient diversity to intra-tumoral cellular variability. These cutting-edge approaches are providing new molecular insights highlighting a critical role for the tumor microenvironment (TME) as a driver of cellular plasticity and phenotypic heterogeneity. With this expanded molecular toolbox, the influence of TME factors, including endogenous (e.g., oxygen and nutrient availability and interactions with non-malignant cells) and iatrogenically induced (e.g., post-therapeutic intervention) stimuli, on tumor cell states can be explored to a greater depth. There exists a critical need for interrogating the temporal and spatial aspects of patient tumors at a high, cell-level resolution to identify therapeutically targetable states, interactions and mechanisms. In this review, we discuss advancements in our understanding of spatiotemporal diversity in GBM with an emphasis on the influence of hypoxia and immune cell interactions on tumor cell heterogeneity. Additionally, we describe specific high-resolution spatially resolved methodologies and their potential to expand the impact of pre-clinical GBM studies. Finally, we highlight clinical attempts at targeting hypoxia- and immune-related mechanisms of malignancy and the potential therapeutic opportunities afforded by single-cell and spatial exploration of GBM patient specimens.
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Affiliation(s)
- Amanda L. Johnson
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Baltimore, MD, United States
| | - Hernando Lopez-Bertoni
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Baltimore, MD, United States
- Oncology, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD, United States
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8
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Xu J, Huang D, Zhang X. scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307835. [PMID: 38483032 PMCID: PMC11109621 DOI: 10.1002/advs.202307835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/24/2024] [Indexed: 05/23/2024]
Abstract
Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- University of Chinese Academy of SciencesBeijing100049China
| | - De‐Shuang Huang
- Eastern Institute for Advanced StudyEastern Institute of TechnologyNingbo315200China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- Center of Economic BotanyCore Botanical GardensChinese Academy of SciencesWuhan430074China
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9
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Liu Y, Chen Y, Lu H, Zhong W, Yuan GC, Ma P. Orthogonal multimodality integration and clustering in single-cell data. BMC Bioinformatics 2024; 25:164. [PMID: 38664601 PMCID: PMC11045458 DOI: 10.1186/s12859-024-05773-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.
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Affiliation(s)
- Yufang Liu
- Department of Statistics, University of Georgia, Athens, GA, 30602, USA
| | - Yongkai Chen
- Department of Statistics, University of Georgia, Athens, GA, 30602, USA
| | - Haoran Lu
- Department of Statistics, University of Georgia, Athens, GA, 30602, USA
| | - Wenxuan Zhong
- Department of Statistics, University of Georgia, Athens, GA, 30602, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Ping Ma
- Department of Statistics, University of Georgia, Athens, GA, 30602, USA.
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10
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Matchett KP, Paris J, Teichmann SA, Henderson NC. Spatial genomics: mapping human steatotic liver disease. Nat Rev Gastroenterol Hepatol 2024:10.1038/s41575-024-00915-2. [PMID: 38654090 DOI: 10.1038/s41575-024-00915-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/28/2024] [Indexed: 04/25/2024]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.
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Affiliation(s)
- Kylie P Matchett
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Jasmin Paris
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Neil C Henderson
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK.
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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11
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Liu W, Zhong Q. High-dimensional covariate-augmented overdispersed poisson factor model. Biometrics 2024; 80:ujae031. [PMID: 38682464 DOI: 10.1093/biomtc/ujae031] [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: 05/17/2023] [Revised: 03/22/2024] [Accepted: 04/05/2024] [Indexed: 05/01/2024]
Abstract
The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.
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Affiliation(s)
- Wei Liu
- School of Mathematics, Sichuan University, Chengdu 610041, China
| | - Qingzhi Zhong
- School of Economics, Jinan University, Guangzhou 510632, China
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12
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Babu E, Sen S. Explore & actuate: the future of personalized medicine in oncology through emerging technologies. Curr Opin Oncol 2024; 36:93-101. [PMID: 38441149 DOI: 10.1097/cco.0000000000001016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings. RECENT FINDINGS To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation. SUMMARY The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.
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Affiliation(s)
- Erald Babu
- UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai, Kalina Campus, Mumbai, Maharashtra, India
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13
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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14
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Schäfer PSL, Dimitrov D, Villablanca EJ, Saez-Rodriguez J. Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system. Nat Immunol 2024; 25:405-417. [PMID: 38413722 DOI: 10.1038/s41590-024-01768-2] [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: 05/31/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.
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Affiliation(s)
- Philipp Sven Lars Schäfer
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Daniel Dimitrov
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Eduardo J Villablanca
- Division of Immunology and Allergy, Department of Medicine Solna, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Julio Saez-Rodriguez
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.
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15
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Kwok SJJ, Forward S, Fahlberg MD, Assita ER, Cosgriff S, Lee SH, Abbott GR, Zhu H, Minasian NH, Vote AS, Martino N, Yun SH. High-dimensional multi-pass flow cytometry via spectrally encoded cellular barcoding. Nat Biomed Eng 2024; 8:310-324. [PMID: 38036616 DOI: 10.1038/s41551-023-01144-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/21/2023] [Indexed: 12/02/2023]
Abstract
Advances in immunology, immuno-oncology, drug discovery and vaccine development demand improvements in the capabilities of flow cytometry to allow it to measure more protein markers per cell at multiple timepoints. However, the size of panels of fluorophore markers is limited by overlaps in fluorescence-emission spectra, and flow cytometers typically perform cell measurements at one timepoint. Here we describe multi-pass high-dimensional flow cytometry, a method leveraging cellular barcoding via microparticles emitting near-infrared laser light to track and repeatedly measure each cell using more markers and fewer colours. By using live human peripheral blood mononuclear cells, we show that the method enables the time-resolved characterization of the same cells before and after stimulation, their analysis via a 10-marker panel with minimal compensation for spectral spillover and their deep immunophenotyping via a 32-marker panel, where the same cells are analysed in 3 back-to-back cycles with 10-13 markers per cycle, reducing overall spillover and simplifying marker-panel design. Cellular barcoding in flow cytometry extends the utility of the technique for high-dimensional multi-pass single-cell analyses.
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Affiliation(s)
| | | | | | | | | | | | | | - Han Zhu
- LASE Innovation Inc., Woburn, MA, USA
| | | | | | - Nicola Martino
- Harvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Cambridge, MA, USA
| | - Seok-Hyun Yun
- Harvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Cambridge, MA, USA.
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16
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Hu W, Foord C, Hsu J, Fan L, Corley MJ, Bhatia TN, Xu S, Belchikov N, He Y, Pang AP, Lanjewar SN, Jarroux J, Joglekar A, Milner TA, Ndhlovu LC, Zhang J, Butelman E, Sloan SA, Lee VM, Gan L, Tilgner HU. ScISOr-ATAC reveals convergent and divergent splicing and chromatin specificities between matched cell types across cortical regions, evolution, and in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.24.581897. [PMID: 38464236 PMCID: PMC10925193 DOI: 10.1101/2024.02.24.581897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Multimodal measurements have become widespread in genomics, however measuring open chromatin accessibility and splicing simultaneously in frozen brain tissues remains unconquered. Hence, we devised Single-Cell-ISOform-RNA sequencing coupled with the Assay-for-Transposase-Accessible-Chromatin (ScISOr-ATAC). We utilized ScISOr-ATAC to assess whether chromatin and splicing alterations in the brain convergently affect the same cell types or divergently different ones. We applied ScISOr-ATAC to three major conditions: comparing (i) the Rhesus macaque (Macaca mulatta) prefrontal cortex (PFC) and visual cortex (VIS), (ii) cross species divergence of Rhesus macaque versus human PFC, as well as (iii) dysregulation in Alzheimer's disease in human PFC. We found that among cortical-layer biased excitatory neuron subtypes, splicing is highly brain-region specific for L3-5/L6 IT_RORB neurons, moderately specific in L2-3 IT_CUX2.RORB neurons and unspecific in L2-3 IT_CUX2 neurons. In contrast, at the chromatin level, L2-3 IT_CUX2.RORB neurons show the highest brain-region specificity compared to other subtypes. Likewise, when comparing human and macaque PFC, strong evolutionary divergence on one molecular modality does not necessarily imply strong such divergence on another molecular level in the same cell type. Finally, in Alzheimer's disease, oligodendrocytes show convergently high dysregulation in both chromatin and splicing. However, chromatin and splicing dysregulation most strongly affect distinct oligodendrocyte subtypes. Overall, these results indicate that chromatin and splicing can show convergent or divergent results depending on the performed comparison, justifying the need for their concurrent measurement to investigate complex systems. Taken together, ScISOr-ATAC allows for the characterization of single-cell splicing and chromatin patterns and the comparison of sample groups in frozen brain samples.
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Affiliation(s)
- Wen Hu
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Careen Foord
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Justine Hsu
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Li Fan
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Helen and Robert Appel Alzheimer's Disease Research Institute
| | - Michael J Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Tarun N Bhatia
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Natan Belchikov
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
- Physiology, Biophysics & Systems Biology Program, Weill Cornell Medicine, New York, NY, USA
| | - Yi He
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Alina Ps Pang
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Samantha N Lanjewar
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Julien Jarroux
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Anoushka Joglekar
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Teresa A Milner
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Lishomwa C Ndhlovu
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Eduardo Butelman
- Neuropsychoimaging of Addiction and Related Conditions Research Program, Dept. of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven A Sloan
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Virginia My Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Li Gan
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Helen and Robert Appel Alzheimer's Disease Research Institute
| | - Hagen U Tilgner
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
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17
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Chen E, Ling AL, Reardon DA, Chiocca EA. Lessons learned from phase 3 trials of immunotherapy for glioblastoma: Time for longitudinal sampling? Neuro Oncol 2024; 26:211-225. [PMID: 37995317 PMCID: PMC10836778 DOI: 10.1093/neuonc/noad211] [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: 11/25/2023] Open
Abstract
Glioblastoma (GBM)'s median overall survival is almost 21 months. Six phase 3 immunotherapy clinical trials have recently been published, yet 5/6 did not meet approval by regulatory bodies. For the sixth, approval is uncertain. Trial failures result from multiple factors, ranging from intrinsic tumor biology to clinical trial design. Understanding the clinical and basic science of these 6 trials is compelled by other immunotherapies reaching the point of advanced phase 3 clinical trial testing. We need to understand more of the science in human GBMs in early trials: the "window of opportunity" design may not be best to understand complex changes brought about by immunotherapeutic perturbations of the GBM microenvironment. The convergence of increased safety of image-guided biopsies with "multi-omics" of small cell numbers now permits longitudinal sampling of tumor and biofluids to dissect the complex temporal changes in the GBM microenvironment as a function of the immunotherapy.
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Affiliation(s)
- Ethan Chen
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Alexander L Ling
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - David A Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - E Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
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18
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Wang X, Wu X, Hong N, Jin W. Progress in single-cell multimodal sequencing and multi-omics data integration. Biophys Rev 2024; 16:13-28. [PMID: 38495443 PMCID: PMC10937857 DOI: 10.1007/s12551-023-01092-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.
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Affiliation(s)
- Xuefei Wang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Xinchao Wu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
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19
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Ikeuchi T, Akhi R, Cardona Rodriguez B, Fraser D, Williams D, Kim TS, Greenwell-Wild T, Overmiller A, Morasso M, Moutsopoulos N. Dissociation of murine oral mucosal tissues for single cell applications. J Immunol Methods 2024; 525:113605. [PMID: 38142927 PMCID: PMC10842481 DOI: 10.1016/j.jim.2023.113605] [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] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 12/26/2023]
Abstract
Single-cell RNA sequencing and flow cytometry approaches have been instrumental in understanding cellular states within various tissues and organs. However, tissue dissociation methods can potentially alter results and create bias due to preferential recovery of particular cell types. Here we present efforts to optimize methods for dissociation of murine oral mucosal tissues and provide three different protocols that can be utilized to isolate major cell populations in the oral mucosa. These methods can be used both in health and in states of inflammation, such as periodontitis. The optimized protocols use different enzymatic approaches (collagenase II, collagenase IV and the Miltenyi whole skin dissociation kit) and yield preferential recovery of immune, stromal and epithelial cells, respectively. We suggest choosing the dissociation method based on the cell population of interest to study, while understanding the limitations of each approach.
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Affiliation(s)
- Tomoko Ikeuchi
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Ramin Akhi
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA
| | - Belmaliz Cardona Rodriguez
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Fraser
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA
| | - Drake Williams
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA
| | - Tae Sung Kim
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA
| | - Teresa Greenwell-Wild
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew Overmiller
- Laboratory of Skin Biology, National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH, Bethesda, MD 20892, USA
| | - Maria Morasso
- Laboratory of Skin Biology, National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH, Bethesda, MD 20892, USA
| | - Niki Moutsopoulos
- Oral Immunity and Infection Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA.
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20
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Guo ZH, Wang YB, Wang S, Zhang Q, Huang DS. scCorrector: a robust method for integrating multi-study single-cell data. Brief Bioinform 2024; 25:bbad525. [PMID: 38271483 PMCID: PMC10810333 DOI: 10.1093/bib/bbad525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/12/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.
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Affiliation(s)
- Zhen-Hao Guo
- College of Electronics and Information Engineering, Tongji University, Shanghai 200000, China
| | - Yan-Bin Wang
- College of Computer Science and Technology, Zhejiang University 310027, China
| | - Siguo Wang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
| | - Qinhu Zhang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
| | - De-Shuang Huang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
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21
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Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [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: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
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Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
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22
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Cao J, Zheng Z, Sun D, Chen X, Cheng R, Lv T, An Y, Zheng J, Song J, Wu L, Yang C. Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing. Nat Biotechnol 2024:10.1038/s41587-023-02086-y. [PMID: 38228777 DOI: 10.1038/s41587-023-02086-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/04/2023] [Indexed: 01/18/2024]
Abstract
Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.
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Affiliation(s)
- Jiao Cao
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhong Zheng
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Sun
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Chen
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui Cheng
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianpeng Lv
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu An
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junhua Zheng
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Jia Song
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Lingling Wu
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chaoyong Yang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, State Key Laboratory of Physical Chemical of Solid Surfaces, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
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23
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Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
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Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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24
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Walsh LA, Quail DF. Decoding the tumor microenvironment with spatial technologies. Nat Immunol 2023; 24:1982-1993. [PMID: 38012408 DOI: 10.1038/s41590-023-01678-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
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Affiliation(s)
- Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
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25
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Toninelli M, Rossetti G, Pagani M. Charting the tumor microenvironment with spatial profiling technologies. Trends Cancer 2023; 9:1085-1096. [PMID: 37673713 DOI: 10.1016/j.trecan.2023.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/08/2023]
Abstract
In recent years technologies that can achieve readouts at cellular resolution such as single-cell RNA sequencing (scRNA-seq) have provided a comprehensive characterization of the cellular proportions and phenotypes that populate the tumor microenvironment (TME). However, because of the sample dissociation steps required by these protocols, they fail to capture information related to the intricate spatial context in which cells operate as well as their dense networks of interactions. Spatial profiling technologies have recently emerged as a valuable way to investigate the physical organization of cells crowding the TME in intact tissues. In this review we first discuss how spatial profiling technologies have propelled TME characterization, and then explore their potential to improve both diagnosis and prognosis for cancer patients in the clinic.
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Affiliation(s)
- Mattia Toninelli
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Grazisa Rossetti
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Massimiliano Pagani
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine (BIOMETRA), Università degli Studi, Milan, Italy.
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26
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Saarenpää S, Shalev O, Ashkenazy H, Carlos V, Lundberg DS, Weigel D, Giacomello S. Spatial metatranscriptomics resolves host-bacteria-fungi interactomes. Nat Biotechnol 2023:10.1038/s41587-023-01979-2. [PMID: 37985875 DOI: 10.1038/s41587-023-01979-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2023] [Indexed: 11/22/2023]
Abstract
The interactions of microorganisms among themselves and with their multicellular host take place at the microscale, forming complex networks and spatial patterns. Existing technology does not allow the simultaneous investigation of spatial interactions between a host and the multitude of its colonizing microorganisms, which limits our understanding of host-microorganism interactions within a plant or animal tissue. Here we present spatial metatranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and find tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intrakingdom spatial interactions among microorganisms, as well as the host response to microbial hotspots. SmT provides an approach for answering fundamental questions on host-microbiome interplay.
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Affiliation(s)
- Sami Saarenpää
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Or Shalev
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Systems Biology of Microbial Communities, University of Tübingen, Tübingen, Germany
| | - Haim Ashkenazy
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
| | - Vanessa Carlos
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany
| | - Derek Severi Lundberg
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Detlef Weigel
- Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Stefania Giacomello
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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27
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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28
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Li Z, Gu H, Xu X, Tian Y, Huang X, Du Y. Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing. Front Immunol 2023; 14:1288027. [PMID: 38022625 PMCID: PMC10654630 DOI: 10.3389/fimmu.2023.1288027] [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: 09/03/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.
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Affiliation(s)
- Zhongkang Li
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Haihan Gu
- Department of Pharmacy, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xiaotong Xu
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yanpeng Tian
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xianghua Huang
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yanfang Du
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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29
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Greenstreet L, Afanassiev A, Kijima Y, Heitz M, Ishiguro S, King S, Yachie N, Schiebinger G. DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods. Cell Syst 2023; 14:844-859.e4. [PMID: 37751737 DOI: 10.1016/j.cels.2023.08.005] [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: 04/27/2022] [Revised: 04/19/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023]
Abstract
While single-cell sequencing technologies provide unprecedented insights into genomic profiles at the cellular level, they lose the spatial context of cells. Over the past decade, diverse spatial transcriptomics and multi-omics technologies have been developed to analyze molecular profiles of tissues. In this article, we categorize current spatial genomics technologies into three classes: optical imaging, positional indexing, and mathematical cartography. We discuss trade-offs in resolution and scale, identify limitations, and highlight synergies between existing single-cell and spatial genomics methods. Further, we propose DNA-GPS (global positioning system), a theoretical framework for large-scale optics-free spatial genomics that combines ideas from mathematical cartography and positional indexing. DNA-GPS has the potential to achieve scalable spatial genomics for multiple measurement modalities, and by eliminating the need for optical measurement, it has the potential to position cells in three-dimensions (3D).
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Affiliation(s)
- Laura Greenstreet
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Anton Afanassiev
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Yusuke Kijima
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada; Department of Aquatic Bioscience, The University of Tokyo, Tokyo, Japan
| | - Matthieu Heitz
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Soh Ishiguro
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Samuel King
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Nozomu Yachie
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan; Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Osaka, Japan; Graduate School of Media and Governance, Keio University, Fujisawa, Japan.
| | - Geoffrey Schiebinger
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada; School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada.
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30
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Kasmani MY, Topchyan P, Brown AK, Brown RJ, Wu X, Chen Y, Khatun A, Alson D, Wu Y, Burns R, Lin CW, Kudek MR, Sun J, Cui W. A spatial sequencing atlas of age-induced changes in the lung during influenza infection. Nat Commun 2023; 14:6597. [PMID: 37852965 PMCID: PMC10584893 DOI: 10.1038/s41467-023-42021-y] [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: 06/06/2022] [Accepted: 09/26/2023] [Indexed: 10/20/2023] Open
Abstract
Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.
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Affiliation(s)
- Moujtaba Y Kasmani
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Paytsar Topchyan
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Ashley K Brown
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Ryan J Brown
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Xiaopeng Wu
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Yao Chen
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Achia Khatun
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Donia Alson
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
- Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Yue Wu
- Carter Immunology Center, University of Virginia, Charlottesville, VA, 22908, USA
- Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - Robert Burns
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
| | - Chien-Wei Lin
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Matthew R Kudek
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA
- Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Jie Sun
- Carter Immunology Center, University of Virginia, Charlottesville, VA, 22908, USA
- Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - Weiguo Cui
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
- Blood Research Institute, Versiti Wisconsin, Milwaukee, WI, 53226, USA.
- Department of Pathology, Northwestern University, Chicago, IL, 60611, USA.
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31
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Zhou R, Yang G, Zhang Y, Wang Y. Spatial transcriptomics in development and disease. MOLECULAR BIOMEDICINE 2023; 4:32. [PMID: 37806992 PMCID: PMC10560656 DOI: 10.1186/s43556-023-00144-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.
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Affiliation(s)
- Ran Zhou
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gaoxia Yang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yan Zhang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Yuan Wang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
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32
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [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: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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33
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Amaral P, Carbonell-Sala S, De La Vega FM, Faial T, Frankish A, Gingeras T, Guigo R, Harrow JL, Hatzigeorgiou AG, Johnson R, Murphy TD, Pertea M, Pruitt KD, Pujar S, Takahashi H, Ulitsky I, Varabyou A, Wells CA, Yandell M, Carninci P, Salzberg SL. The status of the human gene catalogue. Nature 2023; 622:41-47. [PMID: 37794265 PMCID: PMC10575709 DOI: 10.1038/s41586-023-06490-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/27/2023] [Indexed: 10/06/2023]
Abstract
Scientists have been trying to identify every gene in the human genome since the initial draft was published in 2001. In the years since, much progress has been made in identifying protein-coding genes, currently estimated to number fewer than 20,000, with an ever-expanding number of distinct protein-coding isoforms. Here we review the status of the human gene catalogue and the efforts to complete it in recent years. Beside the ongoing annotation of protein-coding genes, their isoforms and pseudogenes, the invention of high-throughput RNA sequencing and other technological breakthroughs have led to a rapid growth in the number of reported non-coding RNA genes. For most of these non-coding RNAs, the functional relevance is currently unclear; we look at recent advances that offer paths forward to identifying their functions and towards eventually completing the human gene catalogue. Finally, we examine the need for a universal annotation standard that includes all medically significant genes and maintains their relationships with different reference genomes for the use of the human gene catalogue in clinical settings.
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Affiliation(s)
- Paulo Amaral
- INSPER Institute of Education and Research, Sao Paulo, Brazil
| | | | - Francisco M De La Vega
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Tempus Labs, Chicago, IL, USA
| | | | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Thomas Gingeras
- Department of Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Roderic Guigo
- Centre for Genomic Regulation (CRG), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Jennifer L Harrow
- Centre for Genomics Research, Discovery Sciences, AstraZeneca, Royston, UK
| | - Artemis G Hatzigeorgiou
- Department of Computer Science and Biomedical Informatics, Universithy of Thessaly, Lamia, Greece
- Hellenic Pasteur Institute, Athens, Greece
| | - Rory Johnson
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
- Conway Institute of Biomedical and Biomolecular Research, University College Dublin, Dublin, Ireland
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Terence D Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Mihaela Pertea
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Kim D Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Shashikant Pujar
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Hazuki Takahashi
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Igor Ulitsky
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Ales Varabyou
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Christine A Wells
- Stem Cell Systems, Department of Anatomy and Physiology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark Yandell
- Departent of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA
| | - Piero Carninci
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Human Technopole, Milan, Italy.
| | - Steven L Salzberg
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
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Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 2023; 24:695-713. [PMID: 37280296 PMCID: PMC10242609 DOI: 10.1038/s41580-023-00615-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/08/2023]
Abstract
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
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Affiliation(s)
- Alev Baysoy
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
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35
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Shi J, Pan Y, Liu X, Cao W, Mu Y, Zhu Q. Spatial Omics Sequencing Based on Microfluidic Array Chips. BIOSENSORS 2023; 13:712. [PMID: 37504111 PMCID: PMC10377411 DOI: 10.3390/bios13070712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Spatial profiling technologies fill the gap left by the loss of spatial information in traditional single-cell sequencing, showing great application prospects. After just a few years of quick development, spatial profiling technologies have made great progress in resolution and simplicity. This review introduces the development of spatial omics sequencing based on microfluidic array chips and describes barcoding strategies using various microfluidic designs with simplicity and efficiency. At the same time, the pros and cons of each strategy are compared. Moreover, commercialized solutions for spatial profiling are also introduced. In the end, the future perspective of spatial omics sequencing and research directions are discussed.
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Affiliation(s)
- Jianyu Shi
- State Key Laboratory of Industrial Control Technology, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Yating Pan
- State Key Laboratory of Industrial Control Technology, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Xudong Liu
- State Key Laboratory of Industrial Control Technology, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Wenjian Cao
- State Key Laboratory of Industrial Control Technology, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Ying Mu
- State Key Laboratory of Industrial Control Technology, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Qiangyuan Zhu
- State Key Laboratory of Industrial Control Technology, Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
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36
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Gurkar AU, Gerencser AA, Mora AL, Nelson AC, Zhang AR, Lagnado AB, Enninful A, Benz C, Furman D, Beaulieu D, Jurk D, Thompson EL, Wu F, Rodriguez F, Barthel G, Chen H, Phatnani H, Heckenbach I, Chuang JH, Horrell J, Petrescu J, Alder JK, Lee JH, Niedernhofer LJ, Kumar M, Königshoff M, Bueno M, Sokka M, Scheibye-Knudsen M, Neretti N, Eickelberg O, Adams PD, Hu Q, Zhu Q, Porritt RA, Dong R, Peters S, Victorelli S, Pengo T, Khaliullin T, Suryadevara V, Fu X, Bar-Joseph Z, Ji Z, Passos JF. Spatial mapping of cellular senescence: emerging challenges and opportunities. NATURE AGING 2023; 3:776-790. [PMID: 37400722 PMCID: PMC10505496 DOI: 10.1038/s43587-023-00446-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.
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Affiliation(s)
- Aditi U Gurkar
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Ana L Mora
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Andrew C Nelson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Anru R Zhang
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Anthony B Lagnado
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | | | - David Furman
- Buck Institute for Research on Aging, Novato, CA, USA
- Stanford 1000 Immunomes Project, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral, Pilar, Argentina
| | - Delphine Beaulieu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diana Jurk
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth L Thompson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Fei Wu
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Fernanda Rodriguez
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Grant Barthel
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Hao Chen
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Hemali Phatnani
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jeremy Horrell
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Joana Petrescu
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | - Jonathan K Alder
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jun Hee Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Laura J Niedernhofer
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Manoj Kumar
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Melanie Königshoff
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Bueno
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miiko Sokka
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | | | - Nicola Neretti
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Oliver Eickelberg
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Qianjiang Hu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Quan Zhu
- University of California, San Diego, CA, USA
| | - Rebecca A Porritt
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Runze Dong
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Samuel Peters
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Stella Victorelli
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Thomas Pengo
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Timur Khaliullin
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Vidyani Suryadevara
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Xiaonan Fu
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Zhicheng Ji
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - João F Passos
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
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37
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Kang HM, Lee JH. Spatial Single-Cell Technologies for Exploring Gastrointestinal Tissue Transcriptome. Compr Physiol 2023; 13:4709-4718. [PMID: 37358516 PMCID: PMC10386894 DOI: 10.1002/cphy.c210053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
In the gastrointestinal (GI) system, like in other organ systems, the histological structure is a key determinant of physiological function. Tissues form multiple layers in the GI tract to perform their specialized functions in secretion, absorption, and motility. Even at the single layer, the heterogeneous cell population performs a diverse range of digestive or regulatory functions. Although many details of such functions at the histological and cell biological levels were revealed by traditional methods such as cell sorting, isolation, and culture, as well as histological methods such as immunostaining and RNA in situ hybridization, recent advances in spatial single-cell technologies could further contribute to our understanding of the molecular makeup of GI histological structures by providing a genome-wide overview of how different genes are expressed across individual cells and tissue layers. The current minireview summarizes recent advances in the spatial transcriptomics field and discusses how such technologies can promote our understanding of GI physiology. © 2023 American Physiological Society. Compr Physiol 13:4709-4718, 2023.
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Affiliation(s)
- Hyun Min Kang
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Jun Hee Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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38
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He C, Zhou P, Nie Q. exFINDER: identify external communication signals using single-cell transcriptomics data. Nucleic Acids Res 2023; 51:e58. [PMID: 37026478 PMCID: PMC10250247 DOI: 10.1093/nar/gkad262] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 04/08/2023] Open
Abstract
Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.
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Affiliation(s)
- Changhan He
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
- Department of Cell and Developmental Biology, University of California, Irvine, Irvine, CA 92697, USA
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39
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Xu Z, Zhang T, Chen H, Zhu Y, Lv Y, Zhang S, Chen J, Chen H, Yang L, Jiang W, Ni S, Lu F, Wang Z, Yang H, Dong L, Chen F, Zhang H, Chen Y, Liu J, Zhang D, Fan L, Guo G, Wang Y. High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq. Nat Commun 2023; 14:2734. [PMID: 37173341 PMCID: PMC10182092 DOI: 10.1038/s41467-023-38409-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research.
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Affiliation(s)
- Ziye Xu
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
| | | | - Hongyu Chen
- School of Medicine, Hangzhou City University, Hangzhou, China
- Institute of Bioinformatics, Zhejiang University, Hangzhou, China
- James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China
| | - Yuyi Zhu
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
| | - Yuexiao Lv
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
| | - Shunji Zhang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiaye Chen
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Haide Chen
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
| | - Lili Yang
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weiqin Jiang
- Department of Colorectal Surgery, the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | | | | | | | | | | | - Feng Chen
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hong Zhang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Department of Nuclear Medicine and PET/CT Center, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Chen
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | | | - Dandan Zhang
- Department of Pathology, and Department of Medical Oncology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Pathology, Key Laboratory of Disease Proteomics of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, China
| | - Longjiang Fan
- School of Medicine, Hangzhou City University, Hangzhou, China.
- Institute of Bioinformatics, Zhejiang University, Hangzhou, China.
- James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China.
| | - Guoji Guo
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China.
| | - Yongcheng Wang
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China.
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
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40
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He C, Zhou P, Nie Q. exFINDER: identify external communication signals using single-cell transcriptomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.24.533888. [PMID: 37034624 PMCID: PMC10081188 DOI: 10.1101/2023.03.24.533888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.
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Affiliation(s)
- Changhan He
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
- Department of Cell and Developmental Biology, University of California, Irvine, Irvine, CA 92697, USA
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41
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Amaral P, Carbonell-Sala S, De La Vega FM, Faial T, Frankish A, Gingeras T, Guigo R, Harrow JL, Hatzigeorgiou AG, Johnson R, Murphy TD, Pertea M, Pruitt KD, Pujar S, Takahashi H, Ulitsky I, Varabyou A, Wells CA, Yandell M, Carninci P, Salzberg SL. The status of the human gene catalogue. ARXIV 2023:arXiv:2303.13996v1. [PMID: 36994150 PMCID: PMC10055485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants.
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Affiliation(s)
- Paulo Amaral
- INSPER Institute of Education and Research, São Paulo, SP, Brasil
| | - Silvia Carbonell-Sala
- Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Catalonia, Spain
| | - Francisco M. De La Vega
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA; Tempus Labs, Inc., Chicago, IL
| | | | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Thomas Gingeras
- Department of Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
| | - Roderic Guigo
- Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003, Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | - Jennifer L Harrow
- Centre for Genomics Research, Discovery Sciences, AstraZeneca, Da Vinci Building. Melbourn Science Park, Royston UK SG8 6HB
| | - Artemis G. Hatzigeorgiou
- Universithy of Thessaly, Department of Computer Science and Biomedical Informatics, Lamia, Greece; Hellenic Pasteur Institute, Athens, Greece
| | - Rory Johnson
- School of Biology and Environmental Science, University College Dublin, D04 V1W8 Dublin, Ireland; Conway Institute of Biomedical and Biomolecular Research, University College Dublin, D04 V1W8 Dublin, Ireland; Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland; Department for BioMedical Research, University of Bern, 3008 Bern, Switzerland
| | - Terence D. Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Mihaela Pertea
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kim D. Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Shashikant Pujar
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Hazuki Takahashi
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama Kanagawa 230-0045 Japan
| | - Igor Ulitsky
- Department of Immunology and Regenerative Biology; Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ales Varabyou
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Christine A. Wells
- Stem Cell Systems, Department of Anatomy and Physiology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville 3010 Vic Australia
| | - Mark Yandell
- Departent of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA
| | - Piero Carninci
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Human Technopole, via Rita Levi Montalcini 1, Milan 20157 Italy
| | - Steven L. Salzberg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Department of Immunology and Regenerative Biology; Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot 76100, Israel
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
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Gene expression and epigenetic regulation co-mapped in brain tissues. Nature 2023:10.1038/d41586-023-00436-z. [PMID: 36922662 DOI: 10.1038/d41586-023-00436-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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