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Tu AB, Krishna G, Smith KR, Lewis JS. Harnessing Immunomodulatory Polymers for Treatment of Autoimmunity, Allergy, and Transplant Rejection. Annu Rev Biomed Eng 2024; 26:415-440. [PMID: 38959388 DOI: 10.1146/annurev-bioeng-110122-014306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Autoimmunity, allergy, and transplant rejection are a collection of chronic diseases that are currently incurable, drastically decrease patient quality of life, and consume considerable health care resources. Underlying each of these diseases is a dysregulated immune system that results in the mounting of an inflammatory response against self or an innocuous antigen. As a consequence, afflicted patients are required to adhere to lifelong regimens of multiple immunomodulatory drugs to control disease and reclaim agency. Unfortunately, current immunomodulatory drugs are associated with a myriad of side effects and adverse events, such as increased risk of cancer and increased risk of serious infection, which negatively impacts patient adherence rates and quality of life. The field of immunoengineering is a new discipline that aims to harness endogenous biological pathways to thwart disease and minimize side effects using novel biomaterial-based strategies. We highlight and discuss polymeric micro/nanoparticles with inherent immunomodulatory properties that are currently under investigation in biomaterial-based therapies for treatment of autoimmunity, allergy, and transplant rejection.
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Affiliation(s)
- Allen B Tu
- Department of Biomedical Engineering, University of California, Davis, California, USA
| | - Gaddam Krishna
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA;
| | - Kevin R Smith
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA;
| | - Jamal S Lewis
- Department of Biomedical Engineering, University of California, Davis, California, USA
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA;
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Velez-Arce A, Huang K, Li MM, Lin X, Gao W, Fu T, Kellis M, Pentelute BL, Zitnik M. TDC-2: Multimodal Foundation for Therapeutic Science. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598655. [PMID: 38948789 PMCID: PMC11212894 DOI: 10.1101/2024.06.12.598655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Therapeutics Data Commons (tdcommons.ai) is an open science initiative with unified datasets, AI models, and benchmarks to support research across therapeutic modalities and drug discovery and development stages. The Commons 2.0 (TDC-2) is a comprehensive overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new multimodal tasks and model frameworks, and comprehensive benchmarks. TDC-2 introduces over 1,000 multimodal datasets spanning approximately 85 million cells, pre-calculated embeddings from 5 state-of-the-art single-cell models, and a biomedical knowledge graph. TDC-2 drastically expands the coverage of ML tasks across therapeutic pipelines and 10+ new modalities, spanning but not limited to single-cell gene expression data, clinical trial data, peptide sequence data, peptidomimetics protein-peptide interaction data regarding newly discovered ligands derived from AS-MS spectroscopy, novel 3D structural data for proteins, and cell-type-specific protein-protein interaction networks at single-cell resolution. TDC-2 introduces multimodal data access under an API-first design using the model-view-controller paradigm. TDC-2 introduces 7 novel ML tasks with fine-grained biological contexts: contextualized drug-target identification, single-cell chemical/genetic perturbation response prediction, protein-peptide binding affinity prediction task, and clinical trial outcome prediction task, which introduce antigen-processing-pathway-specific, cell-type-specific, peptide-specific, and patient-specific biological contexts. TDC-2 also releases benchmarks evaluating 15+ state-of-the-art models across 5+ new learning tasks evaluating models on diverse biological contexts and sampling approaches. Among these, TDC-2 provides the first benchmark for context-specific learning. TDC-2, to our knowledge, is also the first to introduce a protein-peptide binding interaction benchmark.
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Herren R, Geva-Zatorsky N. Spatial features of skip lesions in Crohn's disease. Trends Immunol 2024; 45:470-481. [PMID: 38782626 DOI: 10.1016/j.it.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Skip lesions are an enigmatic spatial feature characterizing Crohn's disease (CD). They comprise inflamed and adjacent non-inflamed tissue sections with a clear demarcation. Currently, spatial features of the human gastrointestinal (GI) system lack clarity regarding the organization of microbes, mucus, tissue, and host cells during inflammation. New technologies with multiplexing abilities and innovative approaches provide ways of examining the spatial organization of inflamed and non-inflamed tissues in CD, which may open new avenues for diagnosis, prognosis, and treatment. In this review, we present evidence of the relevance of spatial context in patients with CD and the methods and ideas recently published in studies of spatiality during inflammation. With this review, we aim to provide inspiration for further research to address existing gaps.
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Affiliation(s)
- Rachel Herren
- Department of Cell Biology and Cancer Science, Rappaport Technion Integrated Cancer Center (RTICC), Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, 3525422 Haifa, Israel
| | - Naama Geva-Zatorsky
- Department of Cell Biology and Cancer Science, Rappaport Technion Integrated Cancer Center (RTICC), Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, 3525422 Haifa, Israel; CIFAR, MaRS Centre, West Tower 661 University Avenue, Suite 505, Toronto, ON M5G 1M1, Canada.
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4
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Rauber S, Mohammadian H, Schmidkonz C, Atzinger A, Soare A, Treutlein C, Kemble S, Mahony CB, Geisthoff M, Angeli MR, Raimondo MG, Xu C, Yang KT, Lu L, Labinsky H, Saad MSA, Gwellem CA, Chang J, Huang K, Kampylafka E, Knitza J, Bilyy R, Distler JHW, Hanlon MM, Fearon U, Veale DJ, Roemer FW, Bäuerle T, Maric HM, Maschauer S, Ekici AB, Buckley CD, Croft AP, Kuwert T, Prante O, Cañete JD, Schett G, Ramming A. CD200 + fibroblasts form a pro-resolving mesenchymal network in arthritis. Nat Immunol 2024; 25:682-692. [PMID: 38396288 DOI: 10.1038/s41590-024-01774-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 01/30/2024] [Indexed: 02/25/2024]
Abstract
Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.
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Affiliation(s)
- Simon Rauber
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hashem Mohammadian
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christian Schmidkonz
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Industrial Engineering and Health, Technical University Amberg-Weiden, Institute of Medical Engineering, Weiden, Germany
| | - Armin Atzinger
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Alina Soare
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christoph Treutlein
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Samuel Kemble
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Christopher B Mahony
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Manuel Geisthoff
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Mario R Angeli
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Maria G Raimondo
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Cong Xu
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Kai-Ting Yang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Le Lu
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hannah Labinsky
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Mina S A Saad
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Charles A Gwellem
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jiyang Chang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Kaiyue Huang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Eleni Kampylafka
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Johannes Knitza
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Rostyslav Bilyy
- Department of Histology, Cytology, Embryology, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
- Institute of Cellular Biology and Pathology 'Nicolae Simionescu', Bucharest, Romania
| | - Jörg H W Distler
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Clinic for Rheumatology, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Hiller Research Center, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Megan M Hanlon
- Molecular Rheumatology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Ursula Fearon
- Molecular Rheumatology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Douglas J Veale
- EULAR Centre for Arthritis & Rheumatic Diseases, St. Vincent's University Hospital, University College Dublin, Dublin, Ireland
| | - Frank W Roemer
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Tobias Bäuerle
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hans M Maric
- Rudolf-Virchow-Center for Integrative and Translational Imaging, University of Würzburg, Würzburg, Germany
| | - Simone Maschauer
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | | | - Adam P Croft
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Torsten Kuwert
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Olaf Prante
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | | | - Georg Schett
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Andreas Ramming
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
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Ermann J, Lefton M, Wei K, Gutierrez-Arcelus M. Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling. Curr Rheumatol Rep 2024; 26:144-154. [PMID: 38227172 DOI: 10.1007/s11926-023-01132-7] [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] [Accepted: 12/28/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis. RECENT FINDINGS The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.
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Affiliation(s)
- Joerg Ermann
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Micah Lefton
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Harvard Medical School, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Fan X, Sun AR, Young RSE, Afara IO, Hamilton BR, Ong LJY, Crawford R, Prasadam I. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Res 2024; 12:7. [PMID: 38311627 PMCID: PMC10838951 DOI: 10.1038/s41413-023-00304-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 02/06/2024] Open
Abstract
Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.
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Affiliation(s)
- Xiwei Fan
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Antonia Rujia Sun
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Reuben S E Young
- Central Analytical Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
- Molecular Horizons, University of Wollongong, Wollongong, NSW, Australia
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia
| | - Brett R Hamilton
- Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, QLD, Australia
| | - Louis Jun Ye Ong
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ross Crawford
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Indira Prasadam
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
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Liu D, He C, Liu Z, Xu L, Li J, Zhao Z, Hu X, Chen H, Sun B, Wang Y. The Prognostic and Immune Significance of CILP2 in Pan-Cancer and Its Relationship with the Progression of Pancreatic Cancer. Cancers (Basel) 2023; 15:5842. [PMID: 38136386 PMCID: PMC10741840 DOI: 10.3390/cancers15245842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/18/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Cartilage intermediate layer protein 2 (CILP2) facilitates interactions between matrix components in cartilage and has emerged as a potential prognostic biomarker for cancer. This study aimed to investigate the function and mechanisms of CILP2 in pan-cancer. We evaluated the pan-cancer expression, methylation, and mutation data of CILP2 for its clinical prognostic value. Additionally, we explored the immunological characteristics of CILP2 in pan-cancer and then focused specifically on pancreatic ductal adenocarcinoma (PAAD). The subtype analysis of PAAD identified subtype-specific expression and immunological characteristics. Finally, in vitro and in vivo experiments assessed the impact of CILP2 on pancreatic cancer progression. CILP2 exhibited high expression in most malignancies, with significant heterogeneity in epigenetic modifications across multiple cancer types. The abnormal methylation and copy number variations in CILP2 were correlated with poor prognoses. Upregulated CILP2 was associated with TGFB/TGFBR1 and more malignant subtypes. CILP2 exhibited a negative correlation with immune checkpoints in PAAD, suggesting potential for immunotherapy. CILP2 activated the AKT pathway, and it increased proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) in pancreatic cancer. We demonstrated that CILP2 significantly contributes to pancreatic cancer progression. It serves as a prognostic biomarker and a potential target for immunotherapy.
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Affiliation(s)
- Danxi Liu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Cong He
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Zonglin Liu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Licheng Xu
- Department of Otorhinolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China
- The Key Laboratory of Myocardial Ischemia, Ministry of Education, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Jiacheng Li
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Zhongjie Zhao
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Xuewei Hu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
| | - Hua Chen
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Bei Sun
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Yongwei Wang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
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8
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Fiocchi C. Omics and Multi-Omics in IBD: No Integration, No Breakthroughs. Int J Mol Sci 2023; 24:14912. [PMID: 37834360 PMCID: PMC10573814 DOI: 10.3390/ijms241914912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/27/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.
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Affiliation(s)
- Claudio Fiocchi
- Department of Inflammation & Immunity, Lerner Research Institute, Cleveland, OH 44195, USA;
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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9
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Allard-Chamard H, Li Q, Rahman P. Emerging Concepts in Precision Medicine in Axial Spondyloarthritis. Curr Rheumatol Rep 2023; 25:204-212. [PMID: 37505349 DOI: 10.1007/s11926-023-01113-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2023] [Indexed: 07/29/2023]
Abstract
PURPOSE OF REVIEW Axial spondyloarthritis (AxSpA) is among the rheumatology's most heritable complex diseases, yet precision medicine at clinics still needs to be explored. We reviewed the emerging concepts and recent developments in polygenic risk scores, Mendelian randomization, pharmacogenomics, single-cell sequencing, and spatial transcriptomics. RECENT FINDINGS Polygenic risk score has resulted in encouraging results with potential diagnostic utility as it appears to outperform current diagnostic tools. Its performance and generalizability vary with ethnicity. Mendelian randomization has elucidated multiple causal associations, particularly between inflammatory bowel disease and AxSpA. Single-cell transcriptomics (particularly scRNA-seq and scATAC-seq) has identified numerous cell types, including synovial and blood immunological cells, to understand the contribution of both innate and adaptative immunity in AxSpA. Current molecular tools provide an exciting opportunity to advance precision medicine for AxSpA patients. However, extensive research and implementation strategies are still required before they can have an impact in the clinic.
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Affiliation(s)
- Hugues Allard-Chamard
- Division of Rheumatology, Faculté de Médecine Et Des Sciences de La Santé de L'Université de Sherbrooke Et Centre de Recherche du CHUS, Sherbrooke, QC, J1K 2R1, Canada
| | - Quan Li
- Department of Medicine, Memorial University, St. John's, NL, Canada
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Proton Rahman
- Department of Medicine, Division of Rheumatology, Memorial University of Newfoundland, 154 LeMarchant Rd, St. John's, Newfoundland, A1C-5B8, Canada.
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10
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Hegarty C, Neto N, Cahill P, Floudas A. Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions. Comput Struct Biotechnol J 2023; 21:4009-4020. [PMID: 37649712 PMCID: PMC10462794 DOI: 10.1016/j.csbj.2023.08.005] [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: 04/04/2023] [Revised: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.
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Affiliation(s)
- Ciara Hegarty
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Nuno Neto
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Ireland
| | - Paul Cahill
- Vascular Biology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Achilleas Floudas
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
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11
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Fenton KA, Pedersen HL. Advanced methods and novel biomarkers in autoimmune diseases ‑ a review of the recent years progress in systemic lupus erythematosus. Front Med (Lausanne) 2023; 10:1183535. [PMID: 37425332 PMCID: PMC10326284 DOI: 10.3389/fmed.2023.1183535] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023] Open
Abstract
There are several autoimmune and rheumatic diseases affecting different organs of the human body. Multiple sclerosis (MS) mainly affects brain, rheumatoid arthritis (RA) mainly affects joints, Type 1 diabetes (T1D) mainly affects pancreas, Sjogren's syndrome (SS) mainly affects salivary glands, while systemic lupus erythematosus (SLE) affects almost every organ of the body. Autoimmune diseases are characterized by production of autoantibodies, activation of immune cells, increased expression of pro-inflammatory cytokines, and activation of type I interferons. Despite improvements in treatments and diagnostic tools, the time it takes for the patients to be diagnosed is too long, and the main treatment for these diseases is still non-specific anti-inflammatory drugs. Thus, there is an urgent need for better biomarkers, as well as tailored, personalized treatment. This review focus on SLE and the organs affected in this disease. We have used the results from various rheumatic and autoimmune diseases and the organs involved with an aim to identify advanced methods and possible biomarkers to be utilized in the diagnosis of SLE, disease monitoring, and response to treatment.
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Affiliation(s)
- Kristin Andreassen Fenton
- UiT The Arctic University of Norway, Tromsø, Norway
- Centre of Clinical Research and Education, University Hospital of North Norway, Tromsø, Norway
| | - Hege Lynum Pedersen
- UiT The Arctic University of Norway, Tromsø, Norway
- Centre of Clinical Research and Education, University Hospital of North Norway, Tromsø, Norway
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12
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Tekkela S, Theocharidis G, McGrath JA, Onoufriadis A. Spatial transcriptomics in human skin research. Exp Dermatol 2023. [PMID: 37150587 DOI: 10.1111/exd.14827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/17/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023]
Abstract
Spatial transcriptomics is a revolutionary technique that enables researchers to characterise tissue architecture and localisation of gene expression. A plethora of technologies that map gene expression are currently being developed, aiming to facilitate spatially resolved, high-dimensional assessment of gene transcription in the context of human skin research. Knowing which gene is expressed by which cell and in which location within skin, facilitates understanding of skin function and dysfunction in both health and disease. In this review, we summarise the available spatial transcriptomic methods and we describe their application to a broad spectrum of dermatological diseases.
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Affiliation(s)
- Stavroula Tekkela
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Georgios Theocharidis
- Joslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John A McGrath
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Alexandros Onoufriadis
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
- Laboratory of Medical Biology and Genetics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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13
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Heydari AA, Sindi SS. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing. BIOPHYSICS REVIEWS 2023; 4:011306. [PMID: 38505815 PMCID: PMC10903438 DOI: 10.1063/5.0091135] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 12/19/2022] [Indexed: 03/21/2024]
Abstract
Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.
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14
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Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research. Int J Mol Sci 2023; 24:ijms24032271. [PMID: 36768592 PMCID: PMC9917071 DOI: 10.3390/ijms24032271] [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: 11/30/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Musculoskeletal disorders, including fractures, scoliosis, heterotopic ossification, osteoporosis, osteoarthritis, disc degeneration, and muscular injury, etc., can occur at any stage of human life. Understanding the occurrence and development mechanism of musculoskeletal disorders, as well as the changes in tissues and cells during therapy, might help us find targeted treatment methods. Single-cell techniques provide excellent tools for studying alterations at the cellular level of disorders. However, the application of these techniques in research on musculoskeletal disorders is still limited. This review summarizes the current single-cell and spatial omics used in musculoskeletal disorders. Cell isolation, experimental methods, and feasible experimental designs for single-cell studies of musculoskeletal system diseases have been reviewed based on tissue characteristics. Then, the paper summarizes the latest findings of single-cell studies in musculoskeletal disorders from three aspects: bone and ossification, joint, and muscle and tendon disorders. Recent discoveries about the cell populations involved in these diseases are highlighted. Furthermore, the therapeutic responses of musculoskeletal disorders, especially single-cell changes after the treatments of implants, stem cell therapies, and drugs are described. Finally, the application potential and future development directions of single-cell and spatial omics in research on musculoskeletal diseases are discussed.
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15
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Raghubar AM, Crawford J, Jones K, Lam PY, Andersen SB, Matigian NA, Ng MSY, Healy H, Kassianos AJ, Mallett AJ. Spatial Transcriptomics in Kidney Tissue. Methods Mol Biol 2023; 2664:233-282. [PMID: 37423994 DOI: 10.1007/978-1-0716-3179-9_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Unlike bulk and single-cell/single-nuclei RNA sequencing methods, spatial transcriptome sequencing (ST-seq) resolves transcriptome expression within the spatial context of intact tissue. This is achieved by integrating histology with RNA sequencing. These methodologies are completed sequentially on the same tissue section placed on a glass slide with printed oligo-dT spots, termed ST-spots. Transcriptomes within the tissue section are captured by the underlying ST-spots and receive a spatial barcode in the process. The sequenced ST-spot transcriptomes are subsequently aligned with the hematoxylin and eosin (H&E) image, giving morphological context to the gene expression signatures within intact tissue. We have successfully employed ST-seq to characterize mouse and human kidney tissue. Here, we describe in detail the application of Visium Spatial Tissue Optimization (TO) and Visium Spatial Gene Expression (GEx) protocols for ST-seq in fresh frozen kidney tissue.
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Affiliation(s)
- Arti M Raghubar
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Anatomical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Joanna Crawford
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Kahli Jones
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Pui Y Lam
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Stacey B Andersen
- Genome Innovation Hub, University of Queensland, Brisbane, QLD, Australia
- UQ Sequencing Facility, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Nicholas A Matigian
- QCIF Facility for Advanced Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Monica S Y Ng
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Nephrology Department, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Helen Healy
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Andrew J Kassianos
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Andrew J Mallett
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
- College of Medicine & Dentistry, James Cook University, Townsville, QLD, Australia.
- Department of Renal Medicine, Townsville University Hospital, Townsville, QLD, Australia.
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16
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Huang M, Xu H. Genetic susceptibility to autoimmunity-Current status and challenges. Adv Immunol 2022; 156:25-54. [PMID: 36410874 DOI: 10.1016/bs.ai.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Autoimmune diseases (ADs) often arise from a combination of genetic and environmental triggers that disrupt the immune system's capability to properly tolerate body self-antigens. Familial studies provided the earliest insights into the risk loci of such diseases, while genome-wide association studies (GWAS) significantly broadened the horizons. A drug targeting a prominent pathological pathway can be applied to multiple indications sharing overlapping mechanisms. Advances in genomic technologies used in genetic studies provide critical insights into future research on gene-environment interactions in autoimmunity. This Review summarizes the history and recent advances in the understanding of genetic susceptibility to ADs and related immune disorders, including coronavirus disease 2019 (COVID-19), and their indications for the development of diagnostic or prognostic markers for translational applications.
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Affiliation(s)
| | - Huji Xu
- School of Medicine, Tsinghua University, Beijing, China; Department of Rheumatology and Immunology, Shanghai Changzheng Hospital, The Navel Medical University, Shanghai, China; Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, China.
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17
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Tans R, Dey S, Dey NS, Cao JH, Paul PS, Calder G, O’Toole P, Kaye PM, Heeren RMA. Mass spectrometry imaging identifies altered hepatic lipid signatures during experimental Leishmania donovani infection. Front Immunol 2022; 13:862104. [PMID: 36003389 PMCID: PMC9394181 DOI: 10.3389/fimmu.2022.862104] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Spatial analysis of lipids in inflammatory microenvironments is key to understand the pathogenesis of infectious disease. Granulomatous inflammation is a hallmark of leishmaniasis and changes in host and parasite lipid metabolism have been observed at the bulk tissue level in various infection models. Here, mass spectrometry imaging (MSI) is applied to spatially map hepatic lipid composition following infection with Leishmania donovani, an experimental mouse model of visceral leishmaniasis. Methods Livers from naïve and L. donovani-infected C57BL/6 mice were harvested at 14- and 20-days post-infection (n=5 per time point). 12 µm transverse sections were cut and covered with norhamane, prior to lipid analysis using MALDI-MSI. MALDI-MSI was performed in negative mode on a Rapiflex (Bruker Daltonics) at 5 and 50 µm spatial resolution and data-dependent analysis (DDA) on an Orbitrap-Elite (Thermo-Scientific) at 50 µm spatial resolution for structural identification analysis of lipids. Results Aberrant lipid abundances were observed in a heterogeneous distribution across infected mouse livers compared to naïve mouse liver. Distinctive localized correlated lipid masses were found in granulomas and surrounding parenchymal tissue. Structural identification revealed 40 different lipids common to naïve and d14/d20 infected mouse livers, whereas 15 identified lipids were only detected in infected mouse livers. For pathology-guided MSI imaging, we deduced lipids from manually annotated granulomatous and parenchyma regions of interests (ROIs), identifying 34 lipids that showed significantly different intensities between parenchyma and granulomas across all infected livers. Discussion Our results identify specific lipids that spatially correlate to the major histopathological feature of Leishmania donovani infection in the liver, viz. hepatic granulomas. In addition, we identified a three-fold increase in the number of unique phosphatidylglycerols (PGs) in infected liver tissue and provide direct evidence that arachidonic acid-containing phospholipids are localized with hepatic granulomas. These phospholipids may serve as important precursors for downstream oxylipin generation with consequences for the regulation of the inflammatory cascade. This study provides the first description of the use of MSI to define spatial-temporal lipid changes at local sites of infection induced by Leishmania donovani in mice.
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Affiliation(s)
- Roel Tans
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, Netherlands
| | - Shoumit Dey
- York Biomedical Research Institute, Hull York Medical School, University of York, York, United Kingdom
| | - Nidhi Sharma Dey
- York Biomedical Research Institute, Hull York Medical School, University of York, York, United Kingdom
| | - Jian-Hua Cao
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, Netherlands
| | - Prasanjit S. Paul
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, Netherlands
| | - Grant Calder
- Department of Biology, University of York, York, United Kingdom
| | - Peter O’Toole
- Department of Biology, University of York, York, United Kingdom
| | - Paul M. Kaye
- York Biomedical Research Institute, Hull York Medical School, University of York, York, United Kingdom
- *Correspondence: Paul M. Kaye, ; Ron M. A. Heeren,
| | - Ron M. A. Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, Netherlands
- *Correspondence: Paul M. Kaye, ; Ron M. A. Heeren,
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18
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Raghubar AM, Pham DT, Tan X, Grice LF, Crawford J, Lam PY, Andersen SB, Yoon S, Teoh SM, Matigian NA, Stewart A, Francis L, Ng MSY, Healy HG, Combes AN, Kassianos AJ, Nguyen Q, Mallett AJ. Spatially Resolved Transcriptomes of Mammalian Kidneys Illustrate the Molecular Complexity and Interactions of Functional Nephron Segments. Front Med (Lausanne) 2022; 9:873923. [PMID: 35872784 PMCID: PMC9300864 DOI: 10.3389/fmed.2022.873923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/23/2022] [Indexed: 11/30/2022] Open
Abstract
Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules–peritubular capillaries by screening for co-expression of ligand–receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (padj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand–receptor interactions were identified within glomeruli and regions of proximal tubules–peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.
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Affiliation(s)
- Arti M. Raghubar
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Anatomical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Duy T. Pham
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Xiao Tan
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Laura F. Grice
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Joanna Crawford
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Pui Yeng Lam
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Stacey B. Andersen
- Genome Innovation Hub, University of Queensland, Brisbane, QLD, Australia
- UQ Sequencing Facility, Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Sohye Yoon
- Genome Innovation Hub, University of Queensland, Brisbane, QLD, Australia
| | - Siok Min Teoh
- UQ Diamantina Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, QLD, Australia
| | - Nicholas A. Matigian
- QCIF Facility for Advanced Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Anne Stewart
- Anatomical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
| | - Leo Francis
- Anatomical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
| | - Monica S. Y. Ng
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Nephrology Department, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Helen G. Healy
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Alexander N. Combes
- Department of Anatomy and Developmental Biology, Stem Cells and Development Program, Monash Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia
| | - Andrew J. Kassianos
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Andrew J. Mallett
| | - Andrew J. Mallett
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- College of Medicine & Dentistry, James Cook University, Townsville, Queensland, QLD, Australia
- Department of Renal Medicine, Townsville University Hospital, Townsville, Queensland, QLD, Australia
- Quan Nguyen
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Munemura R, Maehara T, Murakami Y, Koga R, Aoyagi R, Kaneko N, Doi A, Perugino CA, Della-Torre E, Saeki T, Sato Y, Yamamoto H, Kiyoshima T, Stone JH, Pillai S, Nakamura S. Distinct disease-specific Tfh cell populations in two different fibrotic diseases: IgG4-related disease and Kimura's disease. J Allergy Clin Immunol 2022; 150:440-455.e17. [PMID: 35568079 PMCID: PMC10369367 DOI: 10.1016/j.jaci.2022.03.034] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 03/01/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND How T follicular (Tfh) cells contribute to many different B-cell class-switching events during T cell-dependent immune responses has been unclear. Diseases with polarized isotype switching offer a unique opportunity for the exploration of Tfh subsets. Secondary and tertiary lymphoid organs (SLOs and TLOs) in patients with elevated tissue expression levels of IgE (Kimura's disease, KD) and those of IgG4 (IgG4-related disease, IgG4-RD) can provide important insights regarding cytokine expression by Tfh cells. OBJECTIVE To identify disease-specific Tfh cell subsets in SLOs and TLOs expressing IL-10 or IL-13 and thus identify different cellular drivers of class switching in two distinct types of fibrotic disorders: allergic fibrosis (driven by type 2 immune cells) and inflammatory fibrosis (driven by cytotoxic T lymphocytes). METHODS Single-cell RNA-sequencing, in situ sequencing, and multi-color immunofluorescence analysis was used to investigate B cells, Tfh cells and infiltrating type 2 cells in lesion tissues from patients with KD or IgG4-RD. RESULTS Infiltrating Tfh cells in TLOs from IgG4-RD were divided into six main clusters. We encountered abundant infiltrating IL-10-expressing LAG3+ Tfh cells in patients with IgG4-RD. Furthermore, we found that infiltrating AID+CD19+B cells expressing IL-4, IL-10, and IL-21 receptors correlated with IgG4 expression. In contrast, we found that infiltrating IL-13-expressing Tfh cells were abundant in affected tissues from patients with KD. Moreover, we observed few infiltrating IL-13-expressing Tfh cells in tissues from patients with IgG4-RD, despite high serum levels of IgE (but low IgE in the disease lesions). Cytotoxic T cells were abundant in IgG4-RD, and in contrast Type 2 immune cells were abundant in KD. CONCLUSIONS This single-cell dataset revealed a novel subset of IL10+LAG3+Tfh cells infiltrating the affected organs of IgG4-RD patients. In contrast, IL13+Tfh cells and type 2 immune cells infiltrated those of KD patients.
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Affiliation(s)
- Ryusuke Munemura
- Section of Oral and Maxillofacial Oncology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - Takashi Maehara
- Section of Oral and Maxillofacial Oncology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan; Dento-craniofacial Development and Regeneration Research Center, Faculty of Dental Science, Kyushu University, Fukuoka, Japan.
| | - Yuka Murakami
- Section of Oral and Maxillofacial Oncology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - Risako Koga
- Section of Oral and Maxillofacial Oncology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - Ryuichi Aoyagi
- Section of Oral and Maxillofacial Oncology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - Naoki Kaneko
- Section of Oral and Maxillofacial Oncology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | | | - Cory A Perugino
- Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Ragon Institute of MGH, MIT, and Harvard, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
| | - Emanuel Della-Torre
- Unit of Immunology, Rheumatology, Allergy, and Rare Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Takako Saeki
- Department of Internal Medicine, Nagaoka Red Cross Hospital, Nagaoka, Japan
| | - Yasuharu Sato
- Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
| | - Hidetaka Yamamoto
- Division of Diagnostic Pathology, Kyushu University Hospital, Fukuoka, Japan
| | - Tamotsu Kiyoshima
- Laboratory of Oral Pathology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - John H Stone
- Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
| | - Shiv Pillai
- Ragon Institute of MGH, MIT, and Harvard, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
| | - Seiji Nakamura
- Section of Oral and Maxillofacial Oncology, Division of Maxillofacial Diagnostic and Surgical Sciences, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
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20
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Zheng Z, Chang L, Li J, Wu Y, Chen G, Zou L. Insights Gained and Future Outlook From scRNAseq Studies in Autoimmune Rheumatic Diseases. Front Immunol 2022; 13:849050. [PMID: 35251048 PMCID: PMC8891165 DOI: 10.3389/fimmu.2022.849050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/27/2022] [Indexed: 11/13/2022] Open
Abstract
Autoimmune rheumatic diseases have a major impact on public health as one of the most common morbidities, and many of these disorders involve both local and systemic manifestations with severe consequences for patient health and quality of life. However, treatment options for many of these diseases remain inadequate for a substantial portion of patients, and progress in developing novel therapeutics has been slow. This lack of progress can be largely attributed to an insufficient understanding of the complex mechanisms driving pathogenesis. Recently, the emergence of single-cell RNA sequencing (scRNAseq) has offered a powerful new tool for interrogating rheumatic diseases, with the potential to assess biological heterogeneity and individual cell function in rheumatic diseases. In this review, we discuss the major insights gained from current scRNAseq interrogations of human rheumatic diseases. We highlight novel cell populations and key molecular signatures uncovered, and also raise a number of hypotheses for follow-up study that may be of interest to the field. We also provide an outlook into two emerging single-cell technologies (repertoire sequencing and spatial transcriptomics) that have yet to be utilized in the field of rheumatic diseases, but which offer immense potential in expanding our understanding of immune and stromal cell behavior. We hope that scRNAseq may serve as a wellspring for the generation and interrogation of novel hypotheses regarding autoreactive lymphocytes and tissue infiltration patterns, and help uncover novel avenues for therapeutic development.
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Affiliation(s)
- Zihan Zheng
- Institute of Immunology, Army Medical University, Chongqing, China.,Department of Autoimmune Diseases, Chongqing International Institute for Immunology, Chongqing, China
| | - Ling Chang
- Institute of Immunology, Army Medical University, Chongqing, China
| | - Jingyi Li
- Department of Rheumatology and Immunology, First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, China
| | - Yuzhang Wu
- Institute of Immunology, Army Medical University, Chongqing, China
| | - Guangxing Chen
- Center for Joint Surgery, First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, China
| | - Liyun Zou
- Institute of Immunology, Army Medical University, Chongqing, China
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21
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Marshall JL, Noel T, Wang QS, Chen H, Murray E, Subramanian A, Vernon KA, Bazua-Valenti S, Liguori K, Keller K, Stickels RR, McBean B, Heneghan RM, Weins A, Macosko EZ, Chen F, Greka A. High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. iScience 2022; 25:104097. [PMID: 35372810 PMCID: PMC8971939 DOI: 10.1016/j.isci.2022.104097] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022] Open
Abstract
High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods. A cell neighborhood around LYVE1+ macrophages was discovered in human kidneys The blood pressure regulating apparatus was re-organized in a diabetic mouse model Cell neighborhoods around Trem2+ macrophages were found in a model of proteinopathy A 77 gene signature associated with the UPR was defined in a model of proteinopathy
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22
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Clinical and translational values of spatial transcriptomics. Signal Transduct Target Ther 2022; 7:111. [PMID: 35365599 PMCID: PMC8972902 DOI: 10.1038/s41392-022-00960-w] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 02/06/2023] Open
Abstract
The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.
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23
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Schonfeldova B, Zec K, Udalova IA. Synovial single-cell heterogeneity, zonation and interactions: a patchwork of effectors in arthritis. Rheumatology (Oxford) 2022; 61:913-925. [PMID: 34559213 PMCID: PMC8889290 DOI: 10.1093/rheumatology/keab721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/27/2021] [Accepted: 09/13/2021] [Indexed: 02/07/2023] Open
Abstract
Despite extensive research, there is still no treatment that would lead to remission in all patients with rheumatoid arthritis as our understanding of the affected site, the synovium, is still incomplete. Recently, single-cell technologies helped to decipher the cellular heterogeneity of the synovium; however, certain synovial cell populations, such as endothelial cells or peripheral neurons, remain to be profiled on a single-cell level. Furthermore, associations between certain cellular states and inflammation were found; whether these cells cause the inflammation remains to be answered. Similarly, cellular zonation and interactions between individual effectors in the synovium are yet to be fully determined. A deeper understanding of cell signalling and interactions in the synovium is crucial for a better design of therapeutics with the goal of complete remission in all patients.
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Affiliation(s)
- Barbora Schonfeldova
- The Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Science, University of Oxford, Oxford, UK
| | - Kristina Zec
- The Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Science, University of Oxford, Oxford, UK
| | - Irina A Udalova
- The Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Science, University of Oxford, Oxford, UK
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24
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Abstract
Rheumatoid arthritis is an autoimmune disease that causes significant morbidity. Application of cellular profiling techniques such as single-cell transcriptomics and spatial transcriptomics has uncovered novel pathogenic cell types in RA joint tissues and revealed marked heterogeneity in the cellular composition among RA patients. Together, these insights provide exciting opportunities to translate discoveries into precision medicine in RA. The present review aims to highlight novel insights into RA pathology and discuss key steps needed to translate these discoveries into actionable changes in clinical practice. We review the efforts to identify surrogate biomarkers that could be used to predict RA synovial tissue phenotypes and the corresponding responses to therapy. Finally, we discuss the opportunity to develop novel patient-derived organoid systems as a platform for therapeutic target validation.
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Affiliation(s)
- Kartik Bhamidipati
- Department of Medicine, Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, USA.
| | - Kevin Wei
- Center for Cellular Profiling - Single Cell Multiomics Core, Brigham and Women's Hospital, Harvard Medical School, USA.
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25
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Bae S, Na KJ, Koh J, Lee DS, Choi H, Kim YT. CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data. Nucleic Acids Res 2022; 50:e57. [PMID: 35191503 PMCID: PMC9177989 DOI: 10.1093/nar/gkac084] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 01/06/2022] [Accepted: 01/26/2022] [Indexed: 02/07/2023] Open
Abstract
Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.
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Affiliation(s)
- Sungwoo Bae
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kwon Joong Na
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.,Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jaemoon Koh
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dong Soo Lee
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Republic of Korea
| | - Young Tae Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.,Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
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26
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Vickovic S, Schapiro D, Carlberg K, Lötstedt B, Larsson L, Hildebrandt F, Korotkova M, Hensvold AH, Catrina AI, Sorger PK, Malmström V, Regev A, Ståhl PL. Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium. Commun Biol 2022; 5:129. [PMID: 35149753 PMCID: PMC8837632 DOI: 10.1038/s42003-022-03050-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/10/2022] [Indexed: 12/14/2022] Open
Abstract
The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis.
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Affiliation(s)
- Sanja Vickovic
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden. .,New York Genome Center, New York, NY, USA.
| | - Denis Schapiro
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.,Institute for Computational Biomedicine and Institute of Pathology, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
| | - Konstantin Carlberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Britta Lötstedt
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Franziska Hildebrandt
- Department of Molecular Biosciences, the Wenner Gren Institute, Stockholm University, Stockholm, Sweden
| | - Marina Korotkova
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Aase H Hensvold
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Anca I Catrina
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Vivianne Malmström
- Karolinska Institutet, Division of Rheumatology, Department of Medicine, Center for Molecular Medicine, Stockholm, Sweden.,Unit of Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.,Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Patrik L Ståhl
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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27
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Unraveling B cell trajectories at single cell resolution. Trends Immunol 2022; 43:210-229. [DOI: 10.1016/j.it.2022.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/03/2022] [Accepted: 01/04/2022] [Indexed: 12/31/2022]
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28
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Liu X, Jiang Y, Song D, Zhang L, Xu G, Hou R, Zhang Y, Chen J, Cheng Y, Liu L, Xu X, Chen G, Wu D, Chen T, Chen A, Wang X. Clinical challenges of tissue preparation for spatial transcriptome. Clin Transl Med 2022; 12:e669. [PMID: 35083877 PMCID: PMC8792118 DOI: 10.1002/ctm2.669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 02/06/2023] Open
Abstract
Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para-cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in 'dry' status. The direct cryo-preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi-orientations.
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Affiliation(s)
- Xiaoxia Liu
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
| | - Yujia Jiang
- BGIShenzhenChina
- BGI College & Henan Institute of Medical and Pharmaceutical SciencesZhengzhou UniversityZhengzhouChina
| | - Dongli Song
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
| | - Linlin Zhang
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
| | - Guang Xu
- Institute of Computer ScienceFudan UniversityShanghaiChina
| | - Rui Hou
- Shanghai Biotechnology CorporationShanghaiChina
| | - Yong Zhang
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
| | - Jian Chen
- Shanghai Lung Cancer CenterShanghai Chest HospitalShanghai Jiao Tong UniversityShanghaiChina
| | - Yunfeng Cheng
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
| | | | | | - Gang Chen
- Department of PathologyZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Duojiao Wu
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
| | - Tianxiang Chen
- Shanghai Lung Cancer CenterShanghai Chest HospitalShanghai Jiao Tong UniversityShanghaiChina
| | | | - Xiangdong Wang
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
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29
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Kasper M, Heming M, Schafflick D, Li X, Lautwein T, Meyer zu Horste M, Bauer D, Walscheid K, Wiendl H, Loser K, Heiligenhaus A, Meyer zu Hörste G. Intraocular dendritic cells characterize HLA-B27-associated acute anterior uveitis. eLife 2021; 10:e67396. [PMID: 34783307 PMCID: PMC8594918 DOI: 10.7554/elife.67396] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 09/21/2021] [Indexed: 12/30/2022] Open
Abstract
Uveitis describes a heterogeneous group of inflammatory eye diseases characterized by infiltration of leukocytes into the uveal tissues. Uveitis associated with the HLA haplotype B27 (HLA-B27) is a common subtype of uveitis and a prototypical ocular immune-mediated disease. Local immune mechanisms driving human uveitis are poorly characterized mainly due to the limited available biomaterial and subsequent technical limitations. Here, we provide the first high-resolution characterization of intraocular leukocytes in HLA-B27-positive (n = 4) and -negative (n = 2) anterior uveitis and an infectious endophthalmitis control (n = 1) by combining single-cell RNA-sequencing with flow cytometry and protein analysis. Ocular cell infiltrates consisted primarily of lymphocytes in both subtypes of uveitis and of myeloid cells in infectious endophthalmitis. HLA-B27-positive uveitis exclusively featured a plasmacytoid and classical dendritic cell (cDC) infiltrate. Moreover, cDCs were central in predicted local cell-cell communication. This suggests a unique pattern of ocular leukocyte infiltration in HLA-B27-positive uveitis with relevance to DCs.
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Affiliation(s)
- Maren Kasper
- Ophtha-Lab, Department of Ophthalmology, and Uveitis Centre at St. Franziskus HospitalMünsterGermany
| | - Michael Heming
- Department of Neurology with Institute of Translational Neurology, University Hospital MuensterMuensterGermany
| | - David Schafflick
- Department of Neurology with Institute of Translational Neurology, University Hospital MuensterMuensterGermany
| | - Xiaolin Li
- Department of Neurology with Institute of Translational Neurology, University Hospital MuensterMuensterGermany
| | - Tobias Lautwein
- Department of Neurology with Institute of Translational Neurology, University Hospital MuensterMuensterGermany
| | | | - Dirk Bauer
- Ophtha-Lab, Department of Ophthalmology, and Uveitis Centre at St. Franziskus HospitalMünsterGermany
| | - Karoline Walscheid
- Ophtha-Lab, Department of Ophthalmology, and Uveitis Centre at St. Franziskus HospitalMünsterGermany
- Department of Ophthalmology, University of Duisburg-EssenEssenGermany
| | - Heinz Wiendl
- Department of Neurology with Institute of Translational Neurology, University Hospital MuensterMuensterGermany
| | - Karin Loser
- Department of Human Medicine, University of OldenburgOldenburgGermany
| | - Arnd Heiligenhaus
- Ophtha-Lab, Department of Ophthalmology, and Uveitis Centre at St. Franziskus HospitalMünsterGermany
- University of Duisburg-EssenEssenGermany
| | - Gerd Meyer zu Hörste
- Department of Neurology with Institute of Translational Neurology, University Hospital MuensterMuensterGermany
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30
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Reiff DD, Stoll ML, Cron RQ. Precision medicine in juvenile idiopathic arthritis-has the time arrived? THE LANCET. RHEUMATOLOGY 2021; 3:e808-e817. [PMID: 38297525 DOI: 10.1016/s2665-9913(21)00252-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/07/2021] [Accepted: 07/28/2021] [Indexed: 12/14/2022]
Abstract
The introduction of disease-modifying anti-rheumatic drug therapies for treating children and adolescents with chronic arthritis (ie, juvenile idiopathic arthritis [JIA]) has revolutionised care and outcomes. The biologic revolution continues to expand, with ever-changing immunological targets coming to market after basic research and clinical trials. The first class of biologics that was beneficial for children with JIA was tumour necrosis factor (TNF) inhibitors. If used early and aggressively, TNF inhibitors are capable of inducing disease remission for most of the seven subtypes of JIA, with the exception of systemic JIA (which more frequently responds to interleukin [IL]-1 or IL-6 inhibition). Nevertheless, there are still subsets of patients with JIA with disease that is difficult to treat or who develop extra-articular features that require a different therapeutic approach. Although finding an effective biological therapy for individual children with JIA can be trial and error, ongoing research and clinical trials are providing insight into a more personalised approach to care. In addition, redefining the JIA classification, in part based on shared similarities with various adult arthritides, could allow for extrapolation of knowledge from studies in adults with chronic arthritis.
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Affiliation(s)
- Daniel D Reiff
- Department of Pediatrics, Division of Rheumatology, University of Alabama, Birmingham, AL, USA
| | - Matthew L Stoll
- Department of Pediatrics, Division of Rheumatology, University of Alabama, Birmingham, AL, USA
| | - Randy Q Cron
- Department of Pediatrics, Division of Rheumatology, University of Alabama, Birmingham, AL, USA.
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31
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Andersson A, Larsson L, Stenbeck L, Salmén F, Ehinger A, Wu SZ, Al-Eryani G, Roden D, Swarbrick A, Borg Å, Frisén J, Engblom C, Lundeberg J. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat Commun 2021; 12:6012. [PMID: 34650042 PMCID: PMC8516894 DOI: 10.1038/s41467-021-26271-2] [Citation(s) in RCA: 110] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 09/27/2021] [Indexed: 12/14/2022] Open
Abstract
In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.
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Affiliation(s)
- Alma Andersson
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Linnea Stenbeck
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Salmén
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, the Netherlands
| | - Anna Ehinger
- Department of Genetics and Pathology, Laboratory Medicine Region Skåne, Lund, Sweden
- Department of Clinical Sciences Lund, Division of Oncology, Lund University, Lund, Sweden
| | - Sunny Z Wu
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia
- St Vincent's Clinical School, Faculty of Medicine, Sydney, Australia
| | - Ghamdan Al-Eryani
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia
- St Vincent's Clinical School, Faculty of Medicine, Sydney, Australia
| | - Daniel Roden
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia
- St Vincent's Clinical School, Faculty of Medicine, Sydney, Australia
| | - Alex Swarbrick
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia
- St Vincent's Clinical School, Faculty of Medicine, Sydney, Australia
| | - Åke Borg
- Department of Clinical Sciences Lund, Division of Oncology, Lund University, Lund, Sweden
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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32
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Foster DS, Januszyk M, Yost KE, Chinta MS, Gulati GS, Nguyen AT, Burcham AR, Salhotra A, Ransom RC, Henn D, Chen K, Mascharak S, Tolentino K, Titan AL, Jones RE, da Silva O, Leavitt WT, Marshall CD, des Jardins-Park HE, Hu MS, Wan DC, Wernig G, Wagh D, Coller J, Norton JA, Gurtner GC, Newman AM, Chang HY, Longaker MT. Integrated spatial multiomics reveals fibroblast fate during tissue repair. Proc Natl Acad Sci U S A 2021; 118:e2110025118. [PMID: 34620713 PMCID: PMC8521719 DOI: 10.1073/pnas.2110025118] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 11/18/2022] Open
Abstract
In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.
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Affiliation(s)
- Deshka S Foster
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
| | - Michael Januszyk
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
| | - Kathryn E Yost
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305
| | - Malini S Chinta
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Gunsagar S Gulati
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - Alan T Nguyen
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Austin R Burcham
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Ankit Salhotra
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - R Chase Ransom
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - Dominic Henn
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Kellen Chen
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Shamik Mascharak
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - Karen Tolentino
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305
| | - Ashley L Titan
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
| | - R Ellen Jones
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Oscar da Silva
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - W Tripp Leavitt
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Clement D Marshall
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
| | - Heather E des Jardins-Park
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - Michael S Hu
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
| | - Derrick C Wan
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
| | - Gerlinde Wernig
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - Dhananjay Wagh
- Stanford Functional Genomics Facility, Stanford University, Stanford, CA 94305
| | - John Coller
- Stanford Functional Genomics Facility, Stanford University, Stanford, CA 94305
| | - Jeffrey A Norton
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
| | - Geoffrey C Gurtner
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305;
- HHMI, Stanford University, Stanford, CA 94305
| | - Michael T Longaker
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305;
- Department of Surgery, Stanford University School of Medicine, Stanford CA 94305
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
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Hanna SJ, Tatovic D, Thayer TC, Dayan CM. Insights From Single Cell RNA Sequencing Into the Immunology of Type 1 Diabetes- Cell Phenotypes and Antigen Specificity. Front Immunol 2021; 12:751701. [PMID: 34659258 PMCID: PMC8519581 DOI: 10.3389/fimmu.2021.751701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/14/2021] [Indexed: 01/10/2023] Open
Abstract
In the past few years, huge advances have been made in techniques to analyse cells at an individual level using RNA sequencing, and many of these have precipitated exciting discoveries in the immunology of type 1 diabetes (T1D). This review will cover the first papers to use scRNAseq to characterise human lymphocyte phenotypes in T1D in the peripheral blood, pancreatic lymph nodes and islets. These have revealed specific genes such as IL-32 that are differentially expressed in islet -specific T cells in T1D. scRNAseq has also revealed wider gene expression patterns that are involved in T1D and can predict its development even predating autoantibody production. Single cell sequencing of TCRs has revealed V genes and CDR3 motifs that are commonly used to target islet autoantigens, although truly public TCRs remain elusive. Little is known about BCR repertoires in T1D, but scRNAseq approaches have revealed that insulin binding BCRs commonly use specific J genes, share motifs between donors and frequently demonstrate poly-reactivity. This review will also summarise new developments in scRNAseq technology, the insights they have given into other diseases and how they could be leveraged to advance research in the type 1 diabetes field to identify novel biomarkers and targets for immunotherapy.
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Affiliation(s)
- Stephanie J. Hanna
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Danijela Tatovic
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Terri C. Thayer
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Department of Biological and Chemical Sciences, School of Natural and Social Sciences, Roberts Wesleyan College, Rochester, NY, United States
| | - Colin M. Dayan
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
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Rao A, Barkley D, França GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature 2021; 596:211-220. [PMID: 34381231 PMCID: PMC8475179 DOI: 10.1038/s41586-021-03634-9] [Citation(s) in RCA: 486] [Impact Index Per Article: 162.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/11/2021] [Indexed: 02/08/2023]
Abstract
Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.
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Affiliation(s)
- Anjali Rao
- Institute for Computational Medicine, NYU Langone Health, New York, NY, USA
| | - Dalia Barkley
- Institute for Computational Medicine, NYU Langone Health, New York, NY, USA
| | - Gustavo S França
- Institute for Computational Medicine, NYU Langone Health, New York, NY, USA
| | - Itai Yanai
- Institute for Computational Medicine, NYU Langone Health, New York, NY, USA.
- Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY, USA.
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35
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Anchang CG, Xu C, Raimondo MG, Atreya R, Maier A, Schett G, Zaburdaev V, Rauber S, Ramming A. The Potential of OMICs Technologies for the Treatment of Immune-Mediated Inflammatory Diseases. Int J Mol Sci 2021; 22:ijms22147506. [PMID: 34299122 PMCID: PMC8306614 DOI: 10.3390/ijms22147506] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 01/08/2023] Open
Abstract
Immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel diseases and inflammatory arthritis (e.g., rheumatoid arthritis, psoriatic arthritis), are marked by increasing worldwide incidence rates. Apart from irreversible damage of the affected tissue, the systemic nature of these diseases heightens the incidence of cardiovascular insults and colitis-associated neoplasia. Only 40–60% of patients respond to currently used standard-of-care immunotherapies. In addition to this limited long-term effectiveness, all current therapies have to be given on a lifelong basis as they are unable to specifically reprogram the inflammatory process and thus achieve a true cure of the disease. On the other hand, the development of various OMICs technologies is considered as “the great hope” for improving the treatment of IMIDs. This review sheds light on the progressive development and the numerous approaches from basic science that gradually lead to the transfer from “bench to bedside” and the implementation into general patient care procedures.
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Affiliation(s)
- Charles Gwellem Anchang
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Cong Xu
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Maria Gabriella Raimondo
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Raja Atreya
- Department of Internal Medicine 1, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany;
| | - Andreas Maier
- Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany;
| | - Georg Schett
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Vasily Zaburdaev
- Max-Planck-Zentrum für Physik und Medizin, 91054 Erlangen, Germany;
- Department of Biology, Mathematics in Life Sciences, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Simon Rauber
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Andreas Ramming
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
- Correspondence: ; Tel.: +49-9131-8543048; Fax: +49-9131-8536448
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Bae S, Choi H, Lee DS. Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images. Nucleic Acids Res 2021; 49:e55. [PMID: 33619564 PMCID: PMC8191797 DOI: 10.1093/nar/gkab095] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/10/2021] [Accepted: 02/03/2021] [Indexed: 12/26/2022] Open
Abstract
Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patterns with spatially resolved gene expression data to extract morphological context markers. Morphological features that correspond to spatial maps of the transcriptome were extracted by image patches surrounding each spot and were subsequently represented by image latent features. The molecular profiles correlated with the image latent features were identified. The extracted genes could be further analyzed to discover functional terms and exploited to extract clusters maintaining morphological contexts. We apply our approach to spatial transcriptomic data from different tissues, platforms and types of images to demonstrate an unbiased method that is capable of obtaining image-integrated gene expression trends.
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Affiliation(s)
- Sungwoo Bae
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong Soo Lee
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Manning JE, Lewis JW, Marsh LJ, McGettrick HM. Insights Into Leukocyte Trafficking in Inflammatory Arthritis - Imaging the Joint. Front Cell Dev Biol 2021; 9:635102. [PMID: 33768093 PMCID: PMC7985076 DOI: 10.3389/fcell.2021.635102] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 02/11/2021] [Indexed: 01/13/2023] Open
Abstract
The inappropriate accumulation and activation of leukocytes is a shared pathological feature of immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Cellular accumulation is therefore an attractive target for therapeutic intervention. However, attempts to modulate leukocyte entry and exit from the joint have proven unsuccessful to date, indicating that gaps in our knowledge remain. Technological advancements are now allowing real-time tracking of leukocyte movement through arthritic joints or in vitro joint constructs. Coupling this technology with improvements in analyzing the cellular composition, location and interactions of leukocytes with neighboring cells has increased our understanding of the temporal dynamics and molecular mechanisms underpinning pathological accumulation of leukocytes in arthritic joints. In this review, we explore our current understanding of the mechanisms leading to inappropriate leukocyte trafficking in inflammatory arthritis, and how these evolve with disease progression. Moreover, we highlight the advances in imaging of human and murine joints, along with multi-cellular ex vivo joint constructs that have led to our current knowledge base.
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Affiliation(s)
| | | | | | - Helen M. McGettrick
- Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
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Lakhanpal A, Smith MH, Donlin LT. Rheumatology in the era of precision medicine: synovial tissue molecular patterns and treatment response in rheumatoid arthritis. Curr Opin Rheumatol 2021; 33:58-63. [PMID: 33229974 DOI: 10.1097/bor.0000000000000767] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE OF REVIEW A critical unmet need in rheumatoid arthritis (RA) is the identification of biomarkers that predict which of the available medications will be most effective for an individual in order to lower disease activity sooner than is afforded by the current treat-to-target approach. Here we will discuss recent reports examining the potential for synovial tissue molecular, cellular, and spatial profiling in defining objective measures of treatment response and therein developing personalized medicine for RA. RECENT FINDINGS Recent high-dimensional molecular profiling of RA synovium has provided unprecedented resolution of the cell types and pathways in tissues affected by rheumatic diseases. Heightened attention to tissue architecture is also emerging as a means to classify individual disease variation that may allow patients to be further stratified by therapeutic response. Although this wealth of data may have already pinpointed promising biomarkers, additional studies, likely including tissue-based functional drug response assays, will be required to demonstrate how the complex tissue environment responds. SUMMARY Molecular, cellular, and more recently spatial profiling of the RA synovium are uncovering fundamental features of the disease. Current investigations are examining whether this information will provide meaningful biomarkers for individualized medicine in RA.
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Affiliation(s)
| | | | - Laura T Donlin
- Arthritis and Tissue Degeneration Program and the David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery
- Weill Cornell Medical College and Graduate School, New York, New York, USA
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A clinically validated human capillary blood transcriptome test for global systems biology studies. Biotechniques 2020; 69:289-301. [PMID: 32772558 DOI: 10.2144/btn-2020-0088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
To prevent and treat chronic diseases, including cancer, a global application of systems biology is needed. We report here a whole blood transcriptome test that needs only 50 μl of capillary (fingerprick) blood. This test is suitable for global applications because the samples are preserved at ambient temperature for up to 4 weeks and the RNA preservative inactivates all pathogens, enabling safe transportation. Both the laboratory and bioinformatic steps are automated and performed in a clinical lab, which minimizes batch effects and creates unbiased datasets. Given its clinical testing performance and accessibility to traditionally underrepresented and diverse populations, this test offers a unique ability to reveal molecular mechanisms of disease and enable longitudinal, population-scale studies.
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40
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Bergenstråhle J, Larsson L, Lundeberg J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 2020; 21:482. [PMID: 32664861 PMCID: PMC7386244 DOI: 10.1186/s12864-020-06832-3] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 06/15/2020] [Indexed: 03/10/2023] Open
Abstract
BACKGROUND Recent advancements in in situ gene expression technologies constitute a new and rapidly evolving field of transcriptomics. With the recent launch of the 10x Genomics Visium platform, such methods have started to become widely adopted. The experimental protocol is conducted on individual tissue sections collected from a larger tissue sample. The two-dimensional nature of this data requires multiple consecutive sections to be collected from the sample in order to construct a comprehensive three-dimensional map of the tissue. However, there is currently no software available that lets the user process the images, align stacked experiments, and finally visualize them together in 3D to create a holistic view of the tissue. RESULTS We have developed an R package named STUtility that takes 10x Genomics Visium data as input and provides features to perform standardized data transformations, alignment of multiple tissue sections, regional annotation, and visualizations of the combined data in a 3D model framework. CONCLUSIONS STUtility lets the user process, analyze and visualize multiple samples of spatially resolved RNA sequencing and image data from the 10x Genomics Visium platform. The package builds on the Seurat framework and uses familiar APIs and well-proven analysis methods. An introduction to the software package is available at https://ludvigla.github.io/STUtility_web_site/ .
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Affiliation(s)
- Joseph Bergenstråhle
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65, Solna, Sweden
| | - Ludvig Larsson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65, Solna, Sweden
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65, Solna, Sweden.
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Vollmann-Zwerenz A, Leidgens V, Feliciello G, Klein CA, Hau P. Tumor Cell Invasion in Glioblastoma. Int J Mol Sci 2020; 21:ijms21061932. [PMID: 32178267 PMCID: PMC7139341 DOI: 10.3390/ijms21061932] [Citation(s) in RCA: 143] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/02/2020] [Accepted: 03/09/2020] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a particularly devastating tumor with a median survival of about 16 months. Recent research has revealed novel insights into the outstanding heterogeneity of this type of brain cancer. However, all GBM subtypes share the hallmark feature of aggressive invasion into the surrounding tissue. Invasive glioblastoma cells escape surgery and focal therapies and thus represent a major obstacle for curative therapy. This review aims to provide a comprehensive understanding of glioma invasion mechanisms with respect to tumor-cell-intrinsic properties as well as cues provided by the microenvironment. We discuss genetic programs that may influence the dissemination and plasticity of GBM cells as well as their different invasion patterns. We also review how tumor cells shape their microenvironment and how, vice versa, components of the extracellular matrix and factors from non-neoplastic cells influence tumor cell motility. We further discuss different research platforms for modeling invasion. Finally, we highlight the importance of accounting for the complex interplay between tumor cell invasion and treatment resistance in glioblastoma when considering new therapeutic approaches.
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Affiliation(s)
- Arabel Vollmann-Zwerenz
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
| | - Verena Leidgens
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
| | - Giancarlo Feliciello
- Fraunhofer-Institute for Toxicology and Experimental Medicine, Division of Personalized Tumor Therapy, 93053 Regensburg, Germany; (G.F.); (C.A.K.)
| | - Christoph A. Klein
- Fraunhofer-Institute for Toxicology and Experimental Medicine, Division of Personalized Tumor Therapy, 93053 Regensburg, Germany; (G.F.); (C.A.K.)
- Experimental Medicine and Therapy Research, University of Regensburg, 93053 Regensburg, Germany
| | - Peter Hau
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
- Correspondence: ; Tel.: +49-941-941-8083; Fax: +49-941-941-363013
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