151
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Vahid MR, Brown EL, Steen CB, Zhang W, Jeon HS, Kang M, Gentles AJ, Newman AM. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat Biotechnol 2023; 41:1543-1548. [PMID: 36879008 PMCID: PMC10635828 DOI: 10.1038/s41587-023-01697-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 01/25/2023] [Indexed: 03/08/2023]
Abstract
Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.
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Affiliation(s)
- Milad R Vahid
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Erin L Brown
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Chloé B Steen
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Wubing Zhang
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Hyun Soo Jeon
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Minji Kang
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Andrew J Gentles
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
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152
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Saqib J, Park B, Jin Y, Seo J, Mo J, Kim J. Identification of Niche-Specific Gene Signatures between Malignant Tumor Microenvironments by Integrating Single Cell and Spatial Transcriptomics Data. Genes (Basel) 2023; 14:2033. [PMID: 38002976 PMCID: PMC10671538 DOI: 10.3390/genes14112033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
The tumor microenvironment significantly affects the transcriptomic states of tumor cells. Single-cell RNA sequencing (scRNA-seq) helps elucidate the transcriptomes of individual cancer cells and their neighboring cells. However, cell dissociation results in the loss of information on neighboring cells. To address this challenge and comprehensively assess the gene activity in tissue samples, it is imperative to integrate scRNA-seq with spatial transcriptomics. In our previous study on physically interacting cell sequencing (PIC-seq), we demonstrated that gene expression in single cells is affected by neighboring cell information. In the present study, we proposed a strategy to identify niche-specific gene signatures by harmonizing scRNA-seq and spatial transcriptomic data. This approach was applied to the paired or matched scRNA-seq and Visium platform data of five cancer types: breast cancer, gastrointestinal stromal tumor, liver hepatocellular carcinoma, uterine corpus endometrial carcinoma, and ovarian cancer. We observed distinct gene signatures specific to cellular niches and their neighboring counterparts. Intriguingly, these niche-specific genes display considerable dissimilarity to cell type markers and exhibit unique functional attributes independent of the cancer types. Collectively, these results demonstrate the potential of this integrative approach for identifying novel marker genes and their spatial relationships.
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Affiliation(s)
| | | | | | | | | | - Junil Kim
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 06978, Republic of Korea; (J.S.); (Y.J.); (J.M.)
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153
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Yayon N, Kedlian VR, Boehme L, Suo C, Wachter B, Beuschel RT, Amsalem O, Polanski K, Koplev S, Tuck E, Dann E, Van Hulle J, Perera S, Putteman T, Predeus AV, Dabrowska M, Richardson L, Tudor C, Kreins AY, Engelbert J, Stephenson E, Kleshchevnikov V, De Rita F, Crossland D, Bosticardo M, Pala F, Prigmore E, Chipampe NJ, Prete M, Fei L, To K, Barker RA, He X, Van Nieuwerburgh F, Bayraktar O, Patel M, Davies GE, Haniffa MA, Uhlmann V, Notarangelo LD, Germain RN, Radtke AJ, Marioni JC, Taghon T, Teichmann SA. A spatial human thymus cell atlas mapped to a continuous tissue axis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.562925. [PMID: 37986877 PMCID: PMC10659407 DOI: 10.1101/2023.10.25.562925] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
T cells develop from circulating precursors, which enter the thymus and migrate throughout specialised sub-compartments to support maturation and selection. This process starts already in early fetal development and is highly active until the involution of the thymus in adolescence. To map the micro-anatomical underpinnings of this process in pre- vs. post-natal states, we undertook a spatially resolved analysis and established a new quantitative morphological framework for the thymus, the Cortico-Medullary Axis. Using this axis in conjunction with the curation of a multimodal single-cell, spatial transcriptomics and high-resolution multiplex imaging atlas, we show that canonical thymocyte trajectories and thymic epithelial cells are highly organised and fully established by post-conception week 12, pinpoint TEC progenitor states, find that TEC subsets and peripheral tissue genes are associated with Hassall's Corpuscles and uncover divergence in the pace and drivers of medullary entry between CD4 vs. CD8 T cell lineages. These findings are complemented with a holistic toolkit for spatial analysis and annotation, providing a basis for a detailed understanding of T lymphocyte development.
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Affiliation(s)
- Nadav Yayon
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
| | | | - Lena Boehme
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
| | - Chenqu Suo
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Brianna Wachter
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Rebecca T Beuschel
- National Institute of Allergy and Infectious Diseases, NIH, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Bethesda, MD, United States
| | - Oren Amsalem
- Beth Israel Deaconess Medical Center, Harvard Medical School, Division of Endocrinology, Diabetes and Metabolism, Boston, MA, United States
| | | | - Simon Koplev
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Elizabeth Tuck
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Emma Dann
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Jolien Van Hulle
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
| | - Shani Perera
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Tom Putteman
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
| | | | - Monika Dabrowska
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Laura Richardson
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Catherine Tudor
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Alexandra Y Kreins
- Great Ormond Street Hospital for Children NHS Foundation Trust, Department of Immunology and Gene Therapy, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, Infection, Immunity and Inflammation Research & Teaching Department, London, United Kingdom
| | - Justin Engelbert
- Newcastle University, Biosciences Institute, Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom
| | - Emily Stephenson
- Newcastle University, Biosciences Institute, Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom
| | | | - Fabrizio De Rita
- Freeman Hospital, Department of Adult Congenital Heart Disease and Paediatric Cardiology/Cardiothoracic Surgery, Newcastle upon Tyne, United Kingdom
| | - David Crossland
- Freeman Hospital, Department of Adult Congenital Heart Disease and Paediatric Cardiology/Cardiothoracic Surgery, Newcastle upon Tyne, United Kingdom
| | - Marita Bosticardo
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Francesca Pala
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Elena Prigmore
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | | | - Martin Prete
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Lijiang Fei
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Ken To
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Roger A Barker
- University of Cambridge, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, United Kingdom
| | - Xiaoling He
- University of Cambridge, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, United Kingdom
| | - Filip Van Nieuwerburgh
- Ghent University, Laboratory of Pharmaceutical Biotechnology, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Omer Bayraktar
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Minal Patel
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Graham E Davies
- Great Ormond Street Hospital for Children NHS Foundation Trust, Department of Immunology and Gene Therapy, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, Infection, Immunity and Inflammation Research & Teaching Department, London, United Kingdom
| | - Muzlifah A Haniffa
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
- Newcastle University, Biosciences Institute, Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom
| | - Virginie Uhlmann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
| | - Luigi D Notarangelo
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Ronald N Germain
- National Institute of Allergy and Infectious Diseases, NIH, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Bethesda, MD, United States
| | - Andrea J Radtke
- National Institute of Allergy and Infectious Diseases, NIH, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Bethesda, MD, United States
| | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK, Cambridge, United Kingdom
| | - Tom Taghon
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
- University of Cambridge, Cavendish Laboratory, Cambridge, United Kingdom
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154
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Yang Y, McCullough CG, Seninge L, Guo L, Kwon WJ, Zhang Y, Li NY, Gaddam S, Pan C, Zhen H, Torkelson J, Glass IA, Charville G, Que J, Stuart J, Ding H, Oro A. A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563664. [PMID: 37961271 PMCID: PMC10634774 DOI: 10.1101/2023.10.24.563664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.
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155
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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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156
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Shireman JM, Cheng L, Goel A, Garcia DM, Partha S, Quiñones-Hinojosa A, Kendziorski C, Dey M. Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough? Front Oncol 2023; 13:1266397. [PMID: 37916170 PMCID: PMC10618006 DOI: 10.3389/fonc.2023.1266397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023] Open
Abstract
Spatial transcriptomics, the technology of visualizing cellular gene expression landscape in a cells native tissue location, has emerged as a powerful tool that allows us to address scientific questions that were elusive just a few years ago. This technological advance is a decisive jump in the technological evolution that is revolutionizing studies of tissue structure and function in health and disease through the introduction of an entirely new dimension of data, spatial context. Perhaps the organ within the body that relies most on spatial organization is the brain. The central nervous system's complex microenvironmental and spatial architecture is tightly regulated during development, is maintained in health, and is detrimental when disturbed by pathologies. This inherent spatial complexity of the central nervous system makes it an exciting organ to study using spatial transcriptomics for pathologies primarily affecting the brain, of which Glioblastoma is one of the worst. Glioblastoma is a hyper-aggressive, incurable, neoplasm and has been hypothesized to not only integrate into the spatial architecture of the surrounding brain, but also possess an architecture of its own that might be actively remodeling the surrounding brain. In this review we will examine the current landscape of spatial transcriptomics in glioblastoma, outline novel findings emerging from the rising use of spatial transcriptomics, and discuss future directions and ultimate clinical/translational avenues.
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Affiliation(s)
- Jack M. Shireman
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Lingxin Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Amiti Goel
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Diogo Moniz Garcia
- Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, United States
| | - Sanil Partha
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Mahua Dey
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
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157
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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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158
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Larsson L, Franzén L, Ståhl PL, Lundeberg J. Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization. Bioinformatics 2023; 39:btad626. [PMID: 37846051 PMCID: PMC10597621 DOI: 10.1093/bioinformatics/btad626] [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: 05/26/2023] [Revised: 09/20/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023] Open
Abstract
SUMMARY Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation. AVAILABILITY AND IMPLEMENTATION The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.
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Affiliation(s)
- Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Lovisa Franzén
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
- Respiratory & Immunology, Neuroscience, Vaccines & Immune Therapies Safety, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, 431 83 Mölndal, Gothenburg, Sweden
| | - Patrik L Ståhl
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
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159
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Luo J, Deng M, Zhang X, Sun X. ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods. Genome Res 2023; 33:1788-1805. [PMID: 37827697 PMCID: PMC10691505 DOI: 10.1101/gr.278001.123] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023]
Abstract
Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.
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Affiliation(s)
- Jiaxin Luo
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, 100871, China
| | - Xuegong Zhang
- Bioinformatics Division of BNRIST and Department of Automation, MOE Key Lab of Bioinformatics, Tsinghua University, Beijing, 100084, China
| | - Xiaoqiang Sun
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China;
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160
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Velten B, Stegle O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 2023; 20:1462-1474. [PMID: 37710019 DOI: 10.1038/s41592-023-01992-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
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Affiliation(s)
- Britta Velten
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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161
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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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162
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Charitakis N, Salim A, Piers AT, Watt KI, Porrello ER, Elliott DA, Ramialison M. Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods. Genome Biol 2023; 24:209. [PMID: 37723583 PMCID: PMC10506280 DOI: 10.1186/s13059-023-03045-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 08/21/2023] [Indexed: 09/20/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
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Affiliation(s)
- Natalie Charitakis
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
| | - Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, Bouverie St, Carlton, VIC, 3053, Australia
- School of Mathematics and Statistics, University of Melbourne, Swanston Street, Parkville, VIC, 3010, Australia
| | - Adam T Piers
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, VIC, 3052, Australia
| | - Kevin I Watt
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Department of Diabetes, Monash University, Alfred Centre, Commercial Road, Melbourne, VIC, 3004, Australia
| | - Enzo R Porrello
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
| | - David A Elliott
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
| | - Mirana Ramialison
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
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163
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Zhang T, Wan L, Xiao H, Wang L, Hu J, Lu H. Single-cell RNA sequencing reveals cellular and molecular heterogeneity in fibrocartilaginous enthesis formation. eLife 2023; 12:e85873. [PMID: 37698466 PMCID: PMC10513478 DOI: 10.7554/elife.85873] [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: 12/30/2022] [Accepted: 09/10/2023] [Indexed: 09/13/2023] Open
Abstract
The attachment site of the rotator cuff (RC) is a classic fibrocartilaginous enthesis, which is the junction between bone and tendon with typical characteristics of a fibrocartilage transition zone. Enthesis development has historically been studied with lineage tracing of individual genes selected a priori, which does not allow for the determination of single-cell landscapes yielding mature cell types and tissues. Here, in together with open-source GSE182997 datasets (three samples) provided by Fang et al., we applied Single-cell RNA sequencing (scRNA-seq) to delineate the comprehensive postnatal RC enthesis growth and the temporal atlas from as early as postnatal day 1 up to postnatal week 8. And, we furtherly performed single-cell spatial transcriptomic sequencing on postnatal day 1 mouse enthesis, in order to deconvolute bone-tendon junction (BTJ) chondrocytes onto spatial spots. In summary, we deciphered the cellular heterogeneity and the molecular dynamics during fibrocartilage differentiation. Combined with current spatial transcriptomic data, our results provide a transcriptional resource that will support future investigations of enthesis development at the mechanistic level and may shed light on the strategies for enhanced RC healing outcomes.
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Affiliation(s)
- Tao Zhang
- Department of Sports Medicine, Xiangya Hospital Central South UniversityChangshaChina
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan ProvinceChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangshaChina
| | - Liyang Wan
- Department of Sports Medicine, Xiangya Hospital Central South UniversityChangshaChina
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan ProvinceChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangshaChina
| | - Han Xiao
- Department of Sports Medicine, Xiangya Hospital Central South UniversityChangshaChina
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan ProvinceChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangshaChina
| | - Linfeng Wang
- Department of Sports Medicine, Xiangya Hospital Central South UniversityChangshaChina
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan ProvinceChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangshaChina
| | - Jianzhong Hu
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan ProvinceChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangshaChina
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South UniversityChangshaChina
| | - Hongbin Lu
- Department of Sports Medicine, Xiangya Hospital Central South UniversityChangshaChina
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan ProvinceChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangshaChina
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164
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Zilbauer M, James KR, Kaur M, Pott S, Li Z, Burger A, Thiagarajah JR, Burclaff J, Jahnsen FL, Perrone F, Ross AD, Matteoli G, Stakenborg N, Sujino T, Moor A, Bartolome-Casado R, Bækkevold ES, Zhou R, Xie B, Lau KS, Din S, Magness ST, Yao Q, Beyaz S, Arends M, Denadai-Souza A, Coburn LA, Gaublomme JT, Baldock R, Papatheodorou I, Ordovas-Montanes J, Boeckxstaens G, Hupalowska A, Teichmann SA, Regev A, Xavier RJ, Simmons A, Snyder MP, Wilson KT. A Roadmap for the Human Gut Cell Atlas. Nat Rev Gastroenterol Hepatol 2023; 20:597-614. [PMID: 37258747 PMCID: PMC10527367 DOI: 10.1038/s41575-023-00784-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/14/2023] [Indexed: 06/02/2023]
Abstract
The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.
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Affiliation(s)
- Matthias Zilbauer
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- University Department of Paediatrics, University of Cambridge, Cambridge, UK.
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Cambridge University Hospitals, Cambridge, UK.
| | - Kylie R James
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Mandeep Kaur
- School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa
| | - Sebastian Pott
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Zhixin Li
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Albert Burger
- Department of Computer Science, Heriot-watt University, Edinburgh, UK
| | - Jay R Thiagarajah
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph Burclaff
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Frode L Jahnsen
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Francesca Perrone
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Alexander D Ross
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
- University Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Gianluca Matteoli
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Nathalie Stakenborg
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Tomohisa Sujino
- Center for the Diagnostic and Therapeutic Endoscopy, School of Medicine, Keio University, Tokyo, Japan
| | - Andreas Moor
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Raquel Bartolome-Casado
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Wellcome Sanger Institute, Hinxton, UK
| | - Espen S Bækkevold
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Ran Zhou
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Bingqing Xie
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shahida Din
- Edinburgh IBD Unit, Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Scott T Magness
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qiuming Yao
- Department of Computer Science and Engineering, University of Nebraska Lincoln, Lincoln, NE, USA
| | - Semir Beyaz
- Cold Spring Harbour Laboratory, Cold Spring Harbour, New York, NY, USA
| | - Mark Arends
- Division of Pathology, Centre for Comparative Pathology, Cancer Research UK Edinburgh Centre, Institute of Cancer and Genetics, University of Edinburgh, Edinburgh, UK
| | - Alexandre Denadai-Souza
- Laboratory of Mucosal Biology, Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Lori A Coburn
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
| | | | | | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Jose Ordovas-Montanes
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Guy Boeckxstaens
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | | | - Sarah A Teichmann
- Wellcome Sanger Institute, Hinxton, UK
- Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK
| | - Aviv Regev
- Genentech, San Francisco, CA, USA
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ramnik J Xavier
- Broad Institute and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alison Simmons
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | | | - Keith T Wilson
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
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165
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Ravikumar V, Xu T, Al-Holou WN, Fattahi S, Rao A. Efficient Inference of Spatially-Varying Gaussian Markov Random Fields With Applications in Gene Regulatory Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2920-2932. [PMID: 37276119 PMCID: PMC10623339 DOI: 10.1109/tcbb.2023.3282028] [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] [Indexed: 06/07/2023]
Abstract
In this paper, we study the problem of inferring spatially-varying Gaussian Markov random fields (SV-GMRF) where the goal is to learn a network of sparse, context-specific GMRFs representing network relationships between genes. An important application of SV-GMRFs is in inference of gene regulatory networks from spatially-resolved transcriptomics datasets. The current work on inference of SV-GMRFs are based on the regularized maximum likelihood estimation (MLE) and suffer from overwhelmingly high computational cost due to their highly nonlinear nature. To alleviate this challenge, we propose a simple and efficient optimization problem in lieu of MLE that comes equipped with strong statistical and computational guarantees. Our proposed optimization problem is extremely efficient in practice: we can solve instances of SV-GMRFs with more than 2 million variables in less than 2 minutes. We apply the developed framework to study how gene regulatory networks in Glioblastoma are spatially rewired within tissue, and identify prominent activity of the transcription factor HES4 and ribosomal proteins as characterizing the gene expression network in the tumor peri-vascular niche that is known to harbor treatment resistant stem cells.
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166
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Playoust E, Remark R, Vivier E, Milpied P. Germinal center-dependent and -independent immune responses of tumor-infiltrating B cells in human cancers. Cell Mol Immunol 2023; 20:1040-1050. [PMID: 37419983 PMCID: PMC10468534 DOI: 10.1038/s41423-023-01060-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 06/14/2023] [Indexed: 07/09/2023] Open
Abstract
B cells play essential roles in immunity, mainly through the production of high affinity plasma cells (PCs) and memory B (Bmem) cells. The affinity maturation and differentiation of B cells rely on the integration of B-cell receptor (BCR) intrinsic and extrinsic signals provided by antigen binding and the microenvironment, respectively. In recent years, tumor infiltrating B (TIL-B) cells and PCs (TIL-PCs) have been revealed as important players in antitumor responses in human cancers, but their interplay and dynamics remain largely unknown. In lymphoid organs, B-cell responses involve both germinal center (GC)-dependent and GC-independent pathways for Bmem cell and PC production. Affinity maturation of BCR repertoires occurs in GC reactions with specific spatiotemporal dynamics of signal integration by B cells. In general, the reactivation of high-affinity Bmem cells by antigens triggers GC-independent production of large numbers of PC without BCR rediversification. Understanding B-cell dynamics in immune responses requires the integration of multiple tools and readouts such as single-cell phenotyping and RNA-seq, in situ analyses, BCR repertoire analysis, BCR specificity and affinity assays, and functional tests. Here, we review how those tools have recently been applied to study TIL-B cells and TIL-PC in different types of solid tumors. We assessed the published evidence for different models of TIL-B-cell dynamics involving GC-dependent or GC-independent local responses and the resulting production of antigen-specific PCs. Altogether, we highlight the need for more integrative B-cell immunology studies to rationally investigate TIL-B cells as a leverage for antitumor therapies.
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Affiliation(s)
- Eve Playoust
- Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
| | | | - Eric Vivier
- Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
- Innate Pharma, Marseille, France
| | - Pierre Milpied
- Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France.
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167
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Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Justet A, Adams TS, Homer R, Amei A, Rosas IO, Kaminski N, Wang Z, Yan X. A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554722. [PMID: 37662370 PMCID: PMC10473707 DOI: 10.1101/2023.08.24.554722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER uses a machine learning approach to remove the systematic difference between ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimated cell type proportions, SDePER imputes cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution. Applications to coarse-grained simulated data and four real datasets showed that SDePER achieved more accurate and robust results than existing methods, suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.
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168
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Mantri M, Zhang HH, Spanos E, Ren YA, Vlaminck ID. A Spatiotemporal Molecular Atlas of the Ovulating Mouse Ovary. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554210. [PMID: 37662215 PMCID: PMC10473623 DOI: 10.1101/2023.08.21.554210] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Ovulation is essential for reproductive success, yet the underlying cellular and molecular mechanisms are far from clear. Here, we applied high-resolution spatiotemporal transcriptomics to map out cell-type- and ovulation-stage-specific molecular programs as function of time during follicle maturation and ovulation in mice. Our analysis revealed dynamic molecular transitions within granulosa cell types that occur in tight coordination with mesenchymal cell proliferation. We identified new molecular markers for the emerging cumulus cell fate during the preantral-to-antral transition. We describe transcriptional programs that respond rapidly to ovulation stimulation and those associated with follicle rupture, highlighting the prominent roles of apoptotic and metabolic pathways during the final stages of follicle maturation. We further report stage-specific oocyte-cumulus cell interactions and diverging molecular differentiation in follicles approaching ovulation. Collectively, this study provides insights into the cellular and molecular processes that regulate mouse ovarian follicle maturation and ovulation with important implications for advancing therapeutic strategies in reproductive medicine.
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Affiliation(s)
- Madhav Mantri
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York
| | | | - Emmanuel Spanos
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Yi A Ren
- Department of Animal Science, Cornell University, Ithaca, New York
| | - Iwijn De Vlaminck
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York
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169
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Chen TY, You L, Hardillo JAU, Chien MP. Spatial Transcriptomic Technologies. Cells 2023; 12:2042. [PMID: 37626852 PMCID: PMC10453065 DOI: 10.3390/cells12162042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/02/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.
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Affiliation(s)
- Tsai-Ying Chen
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (T.-Y.C.); (L.Y.)
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Oncode Institute, 3521 AL Utrecht, The Netherlands
| | - Li You
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (T.-Y.C.); (L.Y.)
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Oncode Institute, 3521 AL Utrecht, The Netherlands
| | - Jose Angelito U. Hardillo
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Miao-Ping Chien
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (T.-Y.C.); (L.Y.)
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Oncode Institute, 3521 AL Utrecht, The Netherlands
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
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170
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Liu Z, Wu D, Zhai W, Ma L. SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics. Nat Commun 2023; 14:4727. [PMID: 37550279 PMCID: PMC10406862 DOI: 10.1038/s41467-023-40458-9] [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: 01/10/2023] [Accepted: 07/25/2023] [Indexed: 08/09/2023] Open
Abstract
Recent advancements in spatial transcriptomic technologies have enabled the measurement of whole transcriptome profiles with preserved spatial context. However, limited by spatial resolution, the measured expressions at each spot are often from a mixture of multiple cells. Computational deconvolution methods designed for spatial transcriptomic data rarely make use of the valuable spatial information as well as the neighboring similarity information. Here, we propose SONAR, a Spatially weighted pOissoN-gAmma Regression model for cell-type deconvolution with spatial transcriptomic data. SONAR directly models the raw counts of spatial transcriptomic data and applies a geographically weighted regression framework that incorporates neighboring information to enhance local estimation of regional cell type composition. In addition, SONAR applies an additional elastic weighting step to adaptively filter dissimilar neighbors, which effectively prevents the introduction of local estimation bias in transition regions with sharp boundaries. We demonstrate the performance of SONAR over other state-of-the-art methods on synthetic data with various spatial patterns. We find that SONAR can accurately map region-specific cell types in real spatial transcriptomic data including mouse brain, human heart and human pancreatic ductal adenocarcinoma. We further show that SONAR can reveal the detailed distributions and fine-grained co-localization of immune cells within the microenvironment at the tumor-normal tissue margin in human liver cancer.
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Affiliation(s)
- Zhiyuan Liu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
- University of the Chinese Academy of Sciences, 100049, Beijing, China
| | - Dafei Wu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
| | - Weiwei Zhai
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China.
- University of the Chinese Academy of Sciences, 100049, Beijing, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223, Kunming, China.
| | - Liang Ma
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China.
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171
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Drexler R, Khatri R, Sauvigny T, Mohme M, Maire CL, Ryba A, Zghaibeh Y, Dührsen L, Salviano-Silva A, Lamszus K, Westphal M, Gempt J, Wefers AK, Neumann J, Bode H, Hausmann F, Huber TB, Bonn S, Jütten K, Delev D, Weber KJ, Harter PN, Onken J, Vajkoczy P, Capper D, Wiestler B, Weller M, Snijder B, Buck A, Weiss T, Keough MB, Ni L, Monje M, Silverbush D, Hovestadt V, Suvà ML, Krishna S, Hervey-Jumper SL, Schüller U, Heiland DH, Hänzelmann S, Ricklefs FL. Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552017. [PMID: 37609137 PMCID: PMC10441357 DOI: 10.1101/2023.08.04.552017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.
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Affiliation(s)
- Richard Drexler
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurology, Stanford University, Stanford, CA, 94305, USA
| | - Robin Khatri
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Sauvigny
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Malte Mohme
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cecile L. Maire
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alice Ryba
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yahya Zghaibeh
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lasse Dührsen
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Amanda Salviano-Silva
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katrin Lamszus
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Manfred Westphal
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Gempt
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Annika K. Wefers
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Julia Neumann
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, Research Institute Children’s Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany
| | - Helena Bode
- Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany
| | - Fabian Hausmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias B. Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kerstin Jütten
- Department of Neurosurgery, University Hospital Aachen, Aachen, Germany
| | - Daniel Delev
- Department of Neurosurgery, University Hospital Aachen, Aachen, Germany
| | - Katharina J. Weber
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
- University Cancer Center (UCT) Frankfurt, Frankfurt am Main, Germany
| | - Patrick N. Harter
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Frankfurt am Main, Germany
- Institute of Neuropathology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Julia Onken
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - David Capper
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University Munich, Munich
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Switzerland. Department of Neurology, University of Zürich, Switzerland
| | - Berend Snijder
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Alicia Buck
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Switzerland. Department of Neurology, University of Zürich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Switzerland. Department of Neurology, University of Zürich, Switzerland
| | - Michael B. Keough
- Department of Neurology, Stanford University, Stanford, CA, 94305, USA
| | - Lijun Ni
- Department of Neurology, Stanford University, Stanford, CA, 94305, USA
| | - Michelle Monje
- Department of Neurology, Stanford University, Stanford, CA, 94305, USA
| | | | | | - Mario L. Suvà
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Saritha Krishna
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Shawn L. Hervey-Jumper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Ulrich Schüller
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, Research Institute Children’s Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany
| | - Dieter H. Heiland
- Department of Neurosurgery, Medical Center University of Freiburg, Freiburg, Germany
| | - Sonja Hänzelmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Franz L. Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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172
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Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 2023; 24:494-515. [PMID: 36864178 PMCID: PMC9979144 DOI: 10.1038/s41576-023-00580-2] [Citation(s) in RCA: 211] [Impact Index Per Article: 211.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 03/04/2023]
Abstract
The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.
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Affiliation(s)
- Katy Vandereyken
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Alejandro Sifrim
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Bernard Thienpont
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium.
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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173
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 161] [Impact Index Per Article: 161.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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174
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Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [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: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
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Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
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175
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Hegarty BE, Gruenhagen GW, Johnson ZV, Baker CM, Streelman JT. Spatially resolved cell atlas of the teleost telencephalon and deep homology of the vertebrate forebrain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549873. [PMID: 37503039 PMCID: PMC10370212 DOI: 10.1101/2023.07.20.549873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The telencephalon has undergone remarkable diversification and expansion throughout vertebrate evolution, exhibiting striking differences in structural and functional complexity. Nevertheless, fundamental features are shared across vertebrate taxa, such as the presence of distinct regions including the pallium, subpallium, and olfactory structures. Teleost fishes have a uniquely 'everted' telencephalon, which has made it challenging to compare brain regions in fish to those in other vertebrates. Here we combine spatial transcriptomics and single-nucleus RNA-sequencing to generate a spatially-resolved transcriptional atlas of the cichlid fish telencephalon. We then compare cell-types and anatomical regions in the cichlid telencephalon with those in amphibians, reptiles, birds, and mammals. We uncover striking transcriptional similarities between cell populations in the fish telencephalon and subpallial, hippocampal, and cortical cell populations in tetrapods. Ultimately, our work lends new insights into the organization and evolution of conserved cell-types and regions in the vertebrate forebrain.
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Affiliation(s)
- Brianna E Hegarty
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332
| | - George W Gruenhagen
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332
| | - Zachary V Johnson
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332
- Emory National Primate Research Center, Emory University, Atlanta, GA 30329
- Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA 30329
| | - Cristina M Baker
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jeffrey T Streelman
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332
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176
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Zhang H, Hunter MV, Chou J, Quinn JF, Zhou M, White RM, Tansey W. BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment. Cell Syst 2023; 14:605-619.e7. [PMID: 37473731 PMCID: PMC10368078 DOI: 10.1016/j.cels.2023.06.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/09/2023] [Accepted: 06/09/2023] [Indexed: 07/22/2023]
Abstract
Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics holds the potential to quantify such variation, but existing analysis methods are limited by their focus on individual tasks such as spot deconvolution. We present BayesTME, an end-to-end Bayesian method for analyzing spatial transcriptomics data. BayesTME unifies several previously distinct analysis goals under a single, holistic generative model. This unified approach enables BayesTME to deconvolve spots into cell phenotypes without any need for paired single-cell RNA-seq. BayesTME then goes beyond spot deconvolution to uncover spatial expression patterns among coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. BayesTME achieves state-of-the-art performance across myriad benchmarks. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena such as bilateral symmetry and tumor-associated fibroblast and macrophage reprogramming. BayesTME is open source.
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Affiliation(s)
- Haoran Zhang
- Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA
| | - Miranda V Hunter
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jacqueline Chou
- Department of Physiology, Biophysics, & Systems Biology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Jeffrey F Quinn
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Mingyuan Zhou
- McCombs School of Business, University of Texas at Austin, Austin, TX 78712, USA
| | - Richard M White
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, UK
| | - Wesley Tansey
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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177
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Kanemaru K, Cranley J, Muraro D, Miranda AMA, Ho SY, Wilbrey-Clark A, Patrick Pett J, Polanski K, Richardson L, Litvinukova M, Kumasaka N, Qin Y, Jablonska Z, Semprich CI, Mach L, Dabrowska M, Richoz N, Bolt L, Mamanova L, Kapuge R, Barnett SN, Perera S, Talavera-López C, Mulas I, Mahbubani KT, Tuck L, Wang L, Huang MM, Prete M, Pritchard S, Dark J, Saeb-Parsy K, Patel M, Clatworthy MR, Hübner N, Chowdhury RA, Noseda M, Teichmann SA. Spatially resolved multiomics of human cardiac niches. Nature 2023; 619:801-810. [PMID: 37438528 PMCID: PMC10371870 DOI: 10.1038/s41586-023-06311-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 06/12/2023] [Indexed: 07/14/2023]
Abstract
The function of a cell is defined by its intrinsic characteristics and its niche: the tissue microenvironment in which it dwells. Here we combine single-cell and spatial transcriptomics data to discover cellular niches within eight regions of the human heart. We map cells to microanatomical locations and integrate knowledge-based and unsupervised structural annotations. We also profile the cells of the human cardiac conduction system1. The results revealed their distinctive repertoire of ion channels, G-protein-coupled receptors (GPCRs) and regulatory networks, and implicated FOXP2 in the pacemaker phenotype. We show that the sinoatrial node is compartmentalized, with a core of pacemaker cells, fibroblasts and glial cells supporting glutamatergic signalling. Using a custom CellPhoneDB.org module, we identify trans-synaptic pacemaker cell interactions with glia. We introduce a druggable target prediction tool, drug2cell, which leverages single-cell profiles and drug-target interactions to provide mechanistic insights into the chronotropic effects of drugs, including GLP-1 analogues. In the epicardium, we show enrichment of both IgG+ and IgA+ plasma cells forming immune niches that may contribute to infection defence. Overall, we provide new clarity to cardiac electro-anatomy and immunology, and our suite of computational approaches can be applied to other tissues and organs.
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Affiliation(s)
- Kazumasa Kanemaru
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - James Cranley
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniele Muraro
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Siew Yen Ho
- Cardiac Morphology Unit, Royal Brompton Hospital and Imperial College London, London, UK
| | - Anna Wilbrey-Clark
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Jan Patrick Pett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Krzysztof Polanski
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Laura Richardson
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Monika Litvinukova
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Natsuhiko Kumasaka
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Yue Qin
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Zuzanna Jablonska
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Claudia I Semprich
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Lukas Mach
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton Hospital, London, UK
| | - Monika Dabrowska
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Nathan Richoz
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Liam Bolt
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Lira Mamanova
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Rakeshlal Kapuge
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Sam N Barnett
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Shani Perera
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Carlos Talavera-López
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Würzburg Institute for Systems Immunology, Max Planck Research Group, Julius-Maximilian-Universität, Würzburg, Germany
| | - Ilaria Mulas
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Krishnaa T Mahbubani
- Department of Surgery, University of Cambridge, and Cambridge Biorepository for Translational Medicine, NIHR Cambridge Biomedical Centre, Cambridge, UK
| | - Liz Tuck
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Lu Wang
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Margaret M Huang
- Department of Surgery, University of Cambridge, and Cambridge Biorepository for Translational Medicine, NIHR Cambridge Biomedical Centre, Cambridge, UK
| | - Martin Prete
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Sophie Pritchard
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - John Dark
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Kourosh Saeb-Parsy
- Department of Surgery, University of Cambridge, and Cambridge Biorepository for Translational Medicine, NIHR Cambridge Biomedical Centre, Cambridge, UK
| | - Minal Patel
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Menna R Clatworthy
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Norbert Hübner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité-Universitätsmedizin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | | | - Michela Noseda
- National Heart and Lung Institute, Imperial College London, London, UK.
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK.
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178
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Fangma Y, Liu M, Liao J, Chen Z, Zheng Y. Dissecting the brain with spatially resolved multi-omics. J Pharm Anal 2023; 13:694-710. [PMID: 37577383 PMCID: PMC10422112 DOI: 10.1016/j.jpha.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 08/15/2023] Open
Abstract
Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.
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Affiliation(s)
- Yijia Fangma
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Mengting Liu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yanrong Zheng
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
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179
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Merotto L, Zopoglou M, Zackl C, Finotello F. Next-generation deconvolution of transcriptomic data to investigate the tumor microenvironment. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2023; 382:103-143. [PMID: 38225101 DOI: 10.1016/bs.ircmb.2023.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Methods for in silico deconvolution of bulk transcriptomics can characterize the cellular composition of the tumor microenvironment, quantifying the abundance of cell types associated with patients' prognosis and response to therapy. While first-generation deconvolution methods rely on precomputed, transcriptional signatures of a handful of cell types, second-generation methods can be trained with single-cell data to disentangle more fine-grained cell phenotypes and states. These novel approaches can also be applied to spatial transcriptomic data to reveal the spatial organization of tumors. In this review, we describe state-of-the-art deconvolution methods (first-generation, second-generation, and spatial) which can be used to investigate the tumor microenvironment, discussing their strengths and limitations. We conclude with an outlook on the challenges that need to be overcome to unlock the full potential of next-generation deconvolution for oncology and the life sciences.
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Affiliation(s)
- Lorenzo Merotto
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Maria Zopoglou
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Constantin Zackl
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Francesca Finotello
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria.
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180
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Li Y, Luo Y. Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.532112. [PMID: 37333198 PMCID: PMC10274700 DOI: 10.1101/2023.03.10.532112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Spatially resolved transcriptomics performs high-throughput measurement of transcriptomes while preserving spatial information about the cellular organizations. However, many spatially resolved transcriptomic technologies can only distinguish spots consisting of a mixture of cells instead of working at single-cell resolution. Here, we present STdGCN, a graph neural network model designed for cell type deconvolution of spatial transcriptomic (ST) data that can leverage abundant single-cell RNA sequencing (scRNA-seq) data as reference. STdGCN is the first model incorporating the expression profiles from single cell data as well as the spatial localization information from the ST data for cell type deconvolution. Extensive benchmarking experiments on multiple ST datasets showed that STdGCN outperformed 14 published state-of-the-art models. Applied to a human breast cancer Visium dataset, STdGCN discerned spatial distributions between stroma, lymphocytes and cancer cells for tumor microenvironment dissection. In a human heart ST dataset, STdGCN detected the changes of potential endothelial-cardiomyocyte communications during tissue development.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
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181
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Rájová J, Davidsson M, Avallone M, Hartnor M, Aldrin-Kirk P, Cardoso T, Nolbrant S, Mollbrink A, Storm P, Heuer A, Parmar M, Björklund T. Deconvolution of spatial sequencing provides accurate characterization of hESC-derived DA transplants in vivo. Mol Ther Methods Clin Dev 2023; 29:381-394. [PMID: 37251982 PMCID: PMC10209706 DOI: 10.1016/j.omtm.2023.04.008] [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: 01/09/2023] [Accepted: 04/28/2023] [Indexed: 05/31/2023]
Abstract
Cell therapy for Parkinson's disease has experienced substantial growth in the past decades with several ongoing clinical trials. Despite increasing refinement of differentiation protocols and standardization of the transplanted neural precursors, the transcriptomic analysis of cells in the transplant after its full maturation in vivo has not been thoroughly investigated. Here, we present spatial transcriptomics analysis of fully differentiated grafts in their host tissue. Unlike earlier transcriptomics analyses using single-cell technologies, we observe that cells derived from human embryonic stem cells (hESCs) in the grafts adopt mature dopaminergic signatures. We show that the presence of phenotypic dopaminergic genes, which were found to be differentially expressed in the transplants, is concentrated toward the edges of the grafts, in agreement with the immunohistochemical analyses. Deconvolution shows dopamine neurons being the dominating cell type in many features beneath the graft area. These findings further support the preferred environmental niche of TH-positive cells and confirm their dopaminergic phenotype through the presence of multiple dopaminergic markers.
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Affiliation(s)
- Jana Rájová
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Marcus Davidsson
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Martino Avallone
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Morgan Hartnor
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Patrick Aldrin-Kirk
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Tiago Cardoso
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Sara Nolbrant
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Annelie Mollbrink
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, 106 91 Stockholm, Sweden
| | - Petter Storm
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Andreas Heuer
- Behavioural Neuroscience Laboratory, Department of Experimental Medical Sciences, Lund University, 221 84 Lund, Sweden
| | - Malin Parmar
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Tomas Björklund
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
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182
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Cui G, Dong K, Zhou JY, Li S, Wu Y, Han Q, Yao B, Shen Q, Zhao YL, Yang Y, Cai J, Zhang S, Yang YG. Spatiotemporal transcriptomic atlas reveals the dynamic characteristics and key regulators of planarian regeneration. Nat Commun 2023; 14:3205. [PMID: 37268637 DOI: 10.1038/s41467-023-39016-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 05/25/2023] [Indexed: 06/04/2023] Open
Abstract
Whole-body regeneration of planarians is a natural wonder but how it occurs remains elusive. It requires coordinated responses from each cell in the remaining tissue with spatial awareness to regenerate new cells and missing body parts. While previous studies identified new genes essential to regeneration, a more efficient screening approach that can identify regeneration-associated genes in the spatial context is needed. Here, we present a comprehensive three-dimensional spatiotemporal transcriptomic landscape of planarian regeneration. We describe a pluripotent neoblast subtype, and show that depletion of its marker gene makes planarians more susceptible to sub-lethal radiation. Furthermore, we identified spatial gene expression modules essential for tissue development. Functional analysis of hub genes in spatial modules, such as plk1, shows their important roles in regeneration. Our three-dimensional transcriptomic atlas provides a powerful tool for deciphering regeneration and identifying homeostasis-related genes, and provides a publicly available online spatiotemporal analysis resource for planarian regeneration research.
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Affiliation(s)
- Guanshen Cui
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
| | - Kangning Dong
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jia-Yi Zhou
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- China National Center for Bioinformation, Beijing, 100101, China.
| | - Shang Li
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ying Wu
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
| | - Qinghua Han
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bofei Yao
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
| | - Qunlun Shen
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yong-Liang Zhao
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ying Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- China National Center for Bioinformation, Beijing, 100101, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China
- Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jun Cai
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Shihua Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
| | - Yun-Gui Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- China National Center for Bioinformation, Beijing, 100101, China.
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 101408, China.
- Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
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183
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Tian L, Chen F, Macosko EZ. The expanding vistas of spatial transcriptomics. Nat Biotechnol 2023; 41:773-782. [PMID: 36192637 PMCID: PMC10091579 DOI: 10.1038/s41587-022-01448-2] [Citation(s) in RCA: 93] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/26/2022] [Indexed: 11/09/2022]
Abstract
The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell-cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics.
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Affiliation(s)
- Luyi Tian
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Stem Cell and Regenerative Biology, Cambridge, MA, USA.
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
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184
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Park H, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han‐Eol Park
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
- School of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Rosalind H. Lee
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Christian P. Macks
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Chung Whan Lee
- Department of ChemistryGachon UniversitySeongnamGyeonggi‐do13120Republic of Korea
| | - Junho K. Hur
- Department of GeneticsCollege of MedicineHanyang UniversitySeoul04763Republic of Korea
| | - Chang Ho Sohn
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
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185
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Li X, Xiao C, Qi J, Xue W, Xu X, Mu Z, Zhang J, Li CY, Ding W. STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data. Nucleic Acids Res 2023:7177883. [PMID: 37224539 DOI: 10.1093/nar/gkad419] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 05/26/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.
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Affiliation(s)
- Xiangshang Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chunfu Xiao
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Juntian Qi
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | | | - Xinwei Xu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- College of Life Science, Peking University, Beijing 100871, China
| | - Zelin Mu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Jie Zhang
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chuan-Yun Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Wanqiu Ding
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Bioinformatics Core, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
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186
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Hahn N, Bens M, Kempfer M, Reißig C, Schmidl L, Geis C. Protecting RNA quality for spatial transcriptomics while improving immunofluorescent staining quality. Front Neurosci 2023; 17:1198154. [PMID: 37274189 PMCID: PMC10234422 DOI: 10.3389/fnins.2023.1198154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/03/2023] [Indexed: 06/06/2023] Open
Abstract
In comparison to bulk sequencing or single cell sequencing, spatial transcriptomics preserves the spatial information in tissue slices and can even be mapped to immunofluorescent stainings, allowing translation of gene expression information into their spatial context. This enables to unravel complex interactions of neighboring cells or to link cell morphology to transcriptome data. The 10× Genomics Visium platform offers to combine spatial transcriptomics with immunofluorescent staining of cryo-sectioned tissue slices. We applied this technique to fresh frozen mouse brain slices and developed a protocol that still protects RNA quality while improving buffers for immunofluorescent staining. We investigated the impact of various parameters, including fixation time and buffer composition, on RNA quality and antibody binding. Here, we propose an improved version of the manufacturer protocol, which does not alter RNA quality and facilitates the use of multiple additional antibodies that were not compatible with the manufacturer protocol before. Finally, we discuss the influence of various staining parameters, which contribute to the development of application specific staining protocols.
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Affiliation(s)
- Nina Hahn
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - Martin Bens
- Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
| | - Marin Kempfer
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, Jena, Germany
| | - Christin Reißig
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, Jena, Germany
| | - Lars Schmidl
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, Jena, Germany
| | - Christian Geis
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
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187
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Du J, Yang YC, An ZJ, Zhang MH, Fu XH, Huang ZF, Yuan Y, Hou J. Advances in spatial transcriptomics and related data analysis strategies. J Transl Med 2023; 21:330. [PMID: 37202762 PMCID: PMC10193345 DOI: 10.1186/s12967-023-04150-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.
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Affiliation(s)
- Jun Du
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Yu-Chen Yang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Zhi-Jie An
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Ming-Hui Zhang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Xue-Hang Fu
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Zou-Fang Huang
- Ganzhou Key Laboratory of Hematology, Department of Hematology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000 Jiangxi China
| | - Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240 China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240 China
| | - Jian Hou
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
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188
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Piwecka M, Rajewsky N, Rybak-Wolf A. Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat Rev Neurol 2023:10.1038/s41582-023-00809-y. [PMID: 37198436 DOI: 10.1038/s41582-023-00809-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2023] [Indexed: 05/19/2023]
Abstract
In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencing technologies.
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Affiliation(s)
- Monika Piwecka
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Nikolaus Rajewsky
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Agnieszka Rybak-Wolf
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrueck Center for Molecular Medicine, Berlin, Germany.
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189
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Geras A, Darvish Shafighi S, Domżał K, Filipiuk I, Rączkowska A, Szymczak P, Toosi H, Kaczmarek L, Koperski Ł, Lagergren J, Nowis D, Szczurek E. Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data. Genome Biol 2023; 24:120. [PMID: 37198601 PMCID: PMC10190053 DOI: 10.1186/s13059-023-02951-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.
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Affiliation(s)
- Agnieszka Geras
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Shadi Darvish Shafighi
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR, Paris, France
| | - Kacper Domżał
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Igor Filipiuk
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alicja Rączkowska
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Paulina Szymczak
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Hosein Toosi
- KTH Royal Institute of Technology, Stockholm, Sweden
| | - Leszek Kaczmarek
- BRAINCITY, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Łukasz Koperski
- Department of Pathology, Medical University of Warsaw, Warsaw, Poland
| | | | - Dominika Nowis
- Laboratory of Experimental Medicine, Medical University of Warsaw, Warsaw, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland.
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190
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Ozulumba T, Montalbine AN, Ortiz-Cárdenas JE, Pompano RR. New tools for immunologists: models of lymph node function from cells to tissues. Front Immunol 2023; 14:1183286. [PMID: 37234163 PMCID: PMC10206051 DOI: 10.3389/fimmu.2023.1183286] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
The lymph node is a highly structured organ that mediates the body's adaptive immune response to antigens and other foreign particles. Central to its function is the distinct spatial assortment of lymphocytes and stromal cells, as well as chemokines that drive the signaling cascades which underpin immune responses. Investigations of lymph node biology were historically explored in vivo in animal models, using technologies that were breakthroughs in their time such as immunofluorescence with monoclonal antibodies, genetic reporters, in vivo two-photon imaging, and, more recently spatial biology techniques. However, new approaches are needed to enable tests of cell behavior and spatiotemporal dynamics under well controlled experimental perturbation, particularly for human immunity. This review presents a suite of technologies, comprising in vitro, ex vivo and in silico models, developed to study the lymph node or its components. We discuss the use of these tools to model cell behaviors in increasing order of complexity, from cell motility, to cell-cell interactions, to organ-level functions such as vaccination. Next, we identify current challenges regarding cell sourcing and culture, real time measurements of lymph node behavior in vivo and tool development for analysis and control of engineered cultures. Finally, we propose new research directions and offer our perspective on the future of this rapidly growing field. We anticipate that this review will be especially beneficial to immunologists looking to expand their toolkit for probing lymph node structure and function.
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Affiliation(s)
- Tochukwu Ozulumba
- Department of Chemistry, University of Virginia, Charlottesville, VA, United States
| | - Alyssa N. Montalbine
- Department of Chemistry, University of Virginia, Charlottesville, VA, United States
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA, United States
| | - Jennifer E. Ortiz-Cárdenas
- Department of Chemistry, University of Virginia, Charlottesville, VA, United States
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Rebecca R. Pompano
- Department of Chemistry, University of Virginia, Charlottesville, VA, United States
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
- Carter Immunology Center and University of Virginia (UVA) Cancer Center, University of Virginia School of Medicine, Charlottesville, VA, United States
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191
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Li K, Sun YH, Ouyang Z, Negi S, Gao Z, Zhu J, Wang W, Chen Y, Piya S, Hu W, Zavodszky MI, Yalamanchili H, Cao S, Gehrke A, Sheehan M, Huh D, Casey F, Zhang X, Zhang B. scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing. BMC Genomics 2023; 24:228. [PMID: 37131143 PMCID: PMC10155351 DOI: 10.1186/s12864-023-09332-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/25/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.
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Affiliation(s)
- Kejie Li
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yu H Sun
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | | | - Soumya Negi
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Zhen Gao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Jing Zhu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wanli Wang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yirui Chen
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Sarbottam Piya
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wenxing Hu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Maria I Zavodszky
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Hima Yalamanchili
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Shaolong Cao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Andrew Gehrke
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Mark Sheehan
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Dann Huh
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Fergal Casey
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Xinmin Zhang
- Data Science, BioInfoRx Inc., Madison, WI, 53719, USA
| | - Baohong Zhang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA.
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192
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Miranda AMA, Janbandhu V, Maatz H, Kanemaru K, Cranley J, Teichmann SA, Hübner N, Schneider MD, Harvey RP, Noseda M. Single-cell transcriptomics for the assessment of cardiac disease. Nat Rev Cardiol 2023; 20:289-308. [PMID: 36539452 DOI: 10.1038/s41569-022-00805-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/03/2022] [Indexed: 12/24/2022]
Abstract
Cardiovascular disease is the leading cause of death globally. An advanced understanding of cardiovascular disease mechanisms is required to improve therapeutic strategies and patient risk stratification. State-of-the-art, large-scale, single-cell and single-nucleus transcriptomics facilitate the exploration of the cardiac cellular landscape at an unprecedented level, beyond its descriptive features, and can further our understanding of the mechanisms of disease and guide functional studies. In this Review, we provide an overview of the technical challenges in the experimental design of single-cell and single-nucleus transcriptomics studies, as well as a discussion of the type of inferences that can be made from the data derived from these studies. Furthermore, we describe novel findings derived from transcriptomics studies for each major cardiac cell type in both health and disease, and from development to adulthood. This Review also provides a guide to interpreting the exhaustive list of newly identified cardiac cell types and states, and highlights the consensus and discordances in annotation, indicating an urgent need for standardization. We describe advanced applications such as integration of single-cell data with spatial transcriptomics to map genes and cells on tissue and define cellular microenvironments that regulate homeostasis and disease progression. Finally, we discuss current and future translational and clinical implications of novel transcriptomics approaches, and provide an outlook of how these technologies will change the way we diagnose and treat heart disease.
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Affiliation(s)
| | - Vaibhao Janbandhu
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Henrike Maatz
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kazumasa Kanemaru
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - James Cranley
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sarah A Teichmann
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Deptartment of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Norbert Hübner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | | | - Richard P Harvey
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Michela Noseda
- National Heart and Lung Institute, Imperial College London, London, UK.
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193
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Qian J, Liao J, Liu Z, Chi Y, Fang Y, Zheng Y, Shao X, Liu B, Cui Y, Guo W, Hu Y, Bao H, Yang P, Chen Q, Li M, Zhang B, Fan X. Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace. Nat Commun 2023; 14:2484. [PMID: 37120608 PMCID: PMC10148590 DOI: 10.1038/s41467-023-38121-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 04/17/2023] [Indexed: 05/01/2023] Open
Abstract
Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.
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Affiliation(s)
- Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China.
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China.
| | - Ziqi Liu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Ying Chi
- DAMO Academy, Alibaba group, 310052, Hangzhou, China
| | - Yin Fang
- College of Computer Science and Technology, Zhejiang University, 310013, Hangzhou, China
| | - Yanrong Zheng
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 310053, Hangzhou, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, 310006, Hangzhou, China
| | - Bingqi Liu
- School of Mathematical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Yongjin Cui
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Wenbo Guo
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Yining Hu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Hudong Bao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Penghui Yang
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Qian Chen
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Mingxiao Li
- Institute of Microelectronics of the Chinese Academy of Sciences, 100029, Beijing, China
| | - Bing Zhang
- DAMO Academy, Alibaba group, 310052, Hangzhou, China.
- iMedicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Healthcare, 310058, Hangzhou, China.
- Alibaba Cloud, Alibaba Group, 310052, Hangzhou, China.
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China.
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China.
- iMedicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Healthcare, 310058, Hangzhou, China.
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194
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Coleman K, Hu J, Schroeder A, Lee EB, Li M. SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning. Commun Biol 2023; 6:378. [PMID: 37029267 PMCID: PMC10082183 DOI: 10.1038/s42003-023-04761-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Abstract
Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type deconvolution methods. Given the accuracy and computational speed of SpaDecon, we anticipate it will be valuable for SRT data analysis and will facilitate the integration of genomics and digital pathology.
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Affiliation(s)
- Kyle Coleman
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Amelia Schroeder
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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195
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Gong L, Luo J, Zhang Y, Yang Y, Li S, Fang X, Zhang B, Huang J, Chow LKY, Chung D, Huang J, Huang C, Liu Q, Bai L, Tiu YC, Wu P, Wang Y, Tsao GSW, Kwong DLW, Lee AWM, Dai W, Guan XY. Nasopharyngeal carcinoma cells promote regulatory T cell development and suppressive activity via CD70-CD27 interaction. Nat Commun 2023; 14:1912. [PMID: 37024479 PMCID: PMC10079957 DOI: 10.1038/s41467-023-37614-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Despite the intense CD8+ T-cell infiltration in the tumor microenvironment of nasopharyngeal carcinoma, anti-PD-1 immunotherapy shows an unsatisfactory response rate in clinical trials, hindered by immunosuppressive signals. To understand how microenvironmental characteristics alter immune homeostasis and limit immunotherapy efficacy in nasopharyngeal carcinoma, here we establish a multi-center single-cell cohort based on public data, containing 357,206 cells from 50 patient samples. We reveal that nasopharyngeal carcinoma cells enhance development and suppressive activity of regulatory T cells via CD70-CD27 interaction. CD70 blocking reverts Treg-mediated suppression and thus reinvigorate CD8+ T-cell immunity. Anti-CD70+ anti-PD-1 therapy is evaluated in xenograft-derived organoids and humanized mice, exhibiting an improved tumor-killing efficacy. Mechanistically, CD70 knockout inhibits a collective lipid signaling network in CD4+ naïve and regulatory T cells involving mitochondrial integrity, cholesterol homeostasis, and fatty acid metabolism. Furthermore, ATAC-Seq delineates that CD70 is transcriptionally upregulated by NFKB2 via an Epstein-Barr virus-dependent epigenetic modification. Our findings identify CD70+ nasopharyngeal carcinoma cells as a metabolic switch that enforces the lipid-driven development, functional specialization and homeostasis of Tregs, leading to immune evasion. This study also demonstrates that CD70 blockade can act synergistically with anti-PD-1 treatment to reinvigorate T-cell immunity against nasopharyngeal carcinoma.
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Affiliation(s)
- Lanqi Gong
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Jie Luo
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yu Zhang
- Department of Pediatric Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yuma Yang
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Shanshan Li
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Xiaona Fang
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Baifeng Zhang
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jiao Huang
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Larry Ka-Yue Chow
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Dittman Chung
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jinlin Huang
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Cuicui Huang
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Qin Liu
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Lu Bai
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Yuen Chak Tiu
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pingan Wu
- Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Yan Wang
- Department of Pathology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - George Sai-Wah Tsao
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Dora Lai-Wan Kwong
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Anne Wing-Mui Lee
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Advanced Energy Science and Technology Guangdong Laboratory, Huizhou, China
| | - Wei Dai
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Xin-Yuan Guan
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Advanced Energy Science and Technology Guangdong Laboratory, Huizhou, China.
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196
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Russell AJC, Weir JA, Nadaf NM, Shabet M, Kumar V, Kambhampati S, Raichur R, Marrero GJ, Liu S, Balderrama KS, Vanderburg CR, Shanmugam V, Tian L, Wu CJ, Yoon CH, Macosko EZ, Chen F. Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.01.535228. [PMID: 37066158 PMCID: PMC10103946 DOI: 10.1101/2023.04.01.535228] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are 'tagged' with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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197
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Arutyunyan A, Roberts K, Troulé K, Wong FCK, Sheridan MA, Kats I, Garcia-Alonso L, Velten B, Hoo R, Ruiz-Morales ER, Sancho-Serra C, Shilts J, Handfield LF, Marconato L, Tuck E, Gardner L, Mazzeo CI, Li Q, Kelava I, Wright GJ, Prigmore E, Teichmann SA, Bayraktar OA, Moffett A, Stegle O, Turco MY, Vento-Tormo R. Spatial multiomics map of trophoblast development in early pregnancy. Nature 2023; 616:143-151. [PMID: 36991123 PMCID: PMC10076224 DOI: 10.1038/s41586-023-05869-0] [Citation(s) in RCA: 63] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 02/21/2023] [Indexed: 03/31/2023]
Abstract
The relationship between the human placenta-the extraembryonic organ made by the fetus, and the decidua-the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) derived from placental villi infiltrate the decidua, transforming the maternal arteries into high-conductance vessels1. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia2. Here we have generated a spatially resolved multiomics single-cell atlas of the entire human maternal-fetal interface including the myometrium, which enables us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to infer the possible transcription factors mediating EVT invasion and show that they are preserved in in vitro models of EVT differentiation from primary trophoblast organoids3,4 and trophoblast stem cells5. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We predict the cell-cell communication events contributing to trophoblast invasion and placental bed giant cell formation, and model the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provide a comprehensive analysis of postimplantation trophoblast differentiation that can be used to inform the design of experimental models of the human placenta in early pregnancy.
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Affiliation(s)
- Anna Arutyunyan
- Wellcome Sanger Institute, Cambridge, UK
- Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
| | | | | | | | - Megan A Sheridan
- Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Ilia Kats
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Britta Velten
- Wellcome Sanger Institute, Cambridge, UK
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Regina Hoo
- Wellcome Sanger Institute, Cambridge, UK
| | | | | | | | | | - Luca Marconato
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Lucy Gardner
- Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | - Qian Li
- Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Iva Kelava
- Wellcome Sanger Institute, Cambridge, UK
| | - Gavin J Wright
- Department of Biology, Hull York Medical School, York Biomedical Research Institute, University of York, York, UK
| | | | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Theory of Condensed Matter, Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK
| | | | - Ashley Moffett
- Centre for Trophoblast Research, University of Cambridge, Cambridge, UK.
- Department of Pathology, University of Cambridge, Cambridge, UK.
| | - Oliver Stegle
- Wellcome Sanger Institute, Cambridge, UK.
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
| | - Margherita Y Turco
- Centre for Trophoblast Research, University of Cambridge, Cambridge, UK.
- Department of Pathology, University of Cambridge, Cambridge, UK.
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
| | - Roser Vento-Tormo
- Wellcome Sanger Institute, Cambridge, UK.
- Centre for Trophoblast Research, University of Cambridge, Cambridge, UK.
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198
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Regal JA, Guerra García ME, Jain V, Chandramohan V, Ashley DM, Gregory SG, Thompson EM, López GY, Reitman ZJ. Ganglioglioma deep transcriptomics reveals primitive neuroectoderm neural precursor-like population. Acta Neuropathol Commun 2023; 11:50. [PMID: 36966348 PMCID: PMC10039537 DOI: 10.1186/s40478-023-01548-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 03/06/2023] [Indexed: 03/27/2023] Open
Abstract
Gangliogliomas are brain tumors composed of neuron-like and macroglia-like components that occur in children and young adults. Gangliogliomas are often characterized by a rare population of immature astrocyte-appearing cells expressing CD34, a marker expressed in the neuroectoderm (neural precursor cells) during embryogenesis. New insights are needed to refine tumor classification and to identify therapeutic approaches. We evaluated five gangliogliomas with single nucleus RNA-seq, cellular indexing of transcriptomes and epitopes by sequencing, and/or spatially-resolved RNA-seq. We uncovered a population of CD34+ neoplastic cells with mixed neuroectodermal, immature astrocyte, and neuronal markers. Gene regulatory network interrogation in these neuroectoderm-like cells revealed control of transcriptional programming by TCF7L2/MEIS1-PAX6 and SOX2, similar to that found during neuroectodermal/neural development. Developmental trajectory analyses place neuroectoderm-like tumor cells as precursor cells that give rise to neuron-like and macroglia-like neoplastic cells. Spatially-resolved transcriptomics revealed a neuroectoderm-like tumor cell niche with relative lack of vascular and immune cells. We used these high resolution results to deconvolute clinically-annotated transcriptomic data, confirming that CD34+ cell-associated gene programs associate with gangliogliomas compared to other glial brain tumors. Together, these deep transcriptomic approaches characterized a ganglioglioma cellular hierarchy-confirming CD34+ neuroectoderm-like tumor precursor cells, controlling transcription programs, cell signaling, and associated immune cell states. These findings may guide tumor classification, diagnosis, prognostication, and therapeutic investigations.
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Affiliation(s)
- Joshua A Regal
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA
| | | | - Vaibhav Jain
- Duke Molecular Physiology Institute, Duke University, Durham, NC, 27710, USA
| | | | - David M Ashley
- Department of Neurosurgery, Duke University, Durham, NC, 27710, USA
| | - Simon G Gregory
- Duke Molecular Physiology Institute, Duke University, Durham, NC, 27710, USA
| | - Eric M Thompson
- Department of Neurosurgery, Duke University, Durham, NC, 27710, USA
| | - Giselle Y López
- Department of Neurosurgery, Duke University, Durham, NC, 27710, USA
- Department of Pathology, Duke University, Durham, NC, 27710, USA
| | - Zachary J Reitman
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA.
- Department of Neurosurgery, Duke University, Durham, NC, 27710, USA.
- Department of Pathology, Duke University, Durham, NC, 27710, USA.
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199
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Wang F, Han R, Chen S. An Overlooked and Underrated Endemic Mycosis-Talaromycosis and the Pathogenic Fungus Talaromyces marneffei. Clin Microbiol Rev 2023; 36:e0005122. [PMID: 36648228 PMCID: PMC10035316 DOI: 10.1128/cmr.00051-22] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Talaromycosis is an invasive mycosis endemic in tropical and subtropical Asia and is caused by the pathogenic fungus Talaromyces marneffei. Approximately 17,300 cases of T. marneffei infection are diagnosed annually, and the reported mortality rate is extremely high (~1/3). Despite the devastating impact of talaromycosis on immunocompromised individuals, particularly HIV-positive persons, and the increase in reported occurrences in HIV-uninfected persons, diagnostic and therapeutic approaches for talaromycosis have received far too little attention worldwide. In 2021, scientists living in countries where talaromycosis is endemic raised a global demand for it to be recognized as a neglected tropical disease. Therefore, T. marneffei and the infectious disease induced by this fungus must be treated with concern. T. marneffei is a thermally dimorphic saprophytic fungus with a complicated mycological growth process that may produce various cell types in its life cycle, including conidia, hyphae, and yeast, all of which are associated with its pathogenicity. However, understanding of the pathogenic mechanism of T. marneffei has been limited until recently. To achieve a holistic view of T. marneffei and talaromycosis, the current knowledge about talaromycosis and research breakthroughs regarding T. marneffei growth biology are discussed in this review, along with the interaction of the fungus with environmental stimuli and the host immune response to fungal infection. Importantly, the future research directions required for understanding this serious infection and its causative pathogenic fungus are also emphasized to identify solutions that will alleviate the suffering of susceptible individuals worldwide.
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Affiliation(s)
- Fang Wang
- Intensive Care Unit, Biomedical Research Center, Shenzhen Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - RunHua Han
- Department of Chemistry, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shi Chen
- Intensive Care Unit, Biomedical Research Center, Shenzhen Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- Department of Burn and Plastic Surgery, Biomedical Research Center, Shenzhen Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
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200
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Li H, Zhou J, Li Z, Chen S, Liao X, Zhang B, Zhang R, Wang Y, Sun S, Gao X. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nat Commun 2023; 14:1548. [PMID: 36941264 PMCID: PMC10027878 DOI: 10.1038/s41467-023-37168-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 03/03/2023] [Indexed: 03/23/2023] Open
Abstract
Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.
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Affiliation(s)
- Haoyang Li
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Juexiao Zhou
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhongxiao Li
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Siyuan Chen
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Xingyu Liao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Bin Zhang
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | | | - Yu Wang
- Syneron Technology, Guangzhou, 510000, China
| | - Shiwei Sun
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Xin Gao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
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