1
|
Ozcelik F, Dundar MS, Yildirim AB, Henehan G, Vicente O, Sánchez-Alcázar JA, Gokce N, Yildirim DT, Bingol NN, Karanfilska DP, Bertelli M, Pojskic L, Ercan M, Kellermayer M, Sahin IO, Greiner-Tollersrud OK, Tan B, Martin D, Marks R, Prakash S, Yakubi M, Beccari T, Lal R, Temel SG, Fournier I, Ergoren MC, Mechler A, Salzet M, Maffia M, Danalev D, Sun Q, Nei L, Matulis D, Tapaloaga D, Janecke A, Bown J, Cruz KS, Radecka I, Ozturk C, Nalbantoglu OU, Sag SO, Ko K, Arngrimsson R, Belo I, Akalin H, Dundar M. The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution. Funct Integr Genomics 2024; 24:138. [PMID: 39147901 DOI: 10.1007/s10142-024-01417-9] [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: 07/02/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.
Collapse
Affiliation(s)
- Firat Ozcelik
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Mehmet Sait Dundar
- Department of Electrical and Computer Engineering, Graduate School of Engineering and Sciences, Abdullah Gul University, Kayseri, Turkey
| | - A Baki Yildirim
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Gary Henehan
- School of Food Science and Environmental Health, Technological University of Dublin, Dublin, Ireland
| | - Oscar Vicente
- Institute for the Conservation and Improvement of Valencian Agrodiversity (COMAV), Universitat Politècnica de València, Valencia, Spain
| | - José A Sánchez-Alcázar
- Centro de Investigación Biomédica en Red: Enfermedades Raras, Centro Andaluz de Biología del Desarrollo (CABD-CSIC-Universidad Pablo de Olavide), Instituto de Salud Carlos III, Sevilla, Spain
| | - Nuriye Gokce
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Duygu T Yildirim
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Nurdeniz Nalbant Bingol
- Department of Translational Medicine, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey
| | - Dijana Plaseska Karanfilska
- Research Centre for Genetic Engineering and Biotechnology, Macedonian Academy of Sciences and Arts, Skopje, Macedonia
| | | | - Lejla Pojskic
- Institute for Genetic Engineering and Biotechnology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Mehmet Ercan
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Miklos Kellermayer
- Department of Biophysics and Radiation Biology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Izem Olcay Sahin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | | | - Busra Tan
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Donald Martin
- University Grenoble Alpes, CNRS, TIMC-IMAG/SyNaBi (UMR 5525), Grenoble, France
| | - Robert Marks
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Satya Prakash
- Department of Biomedical Engineering, University of McGill, Montreal, QC, Canada
| | - Mustafa Yakubi
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Tommaso Beccari
- Department of Pharmeceutical Sciences, University of Perugia, Perugia, Italy
| | - Ratnesh Lal
- Neuroscience Research Institute, University of California, Santa Barbara, USA
| | - Sehime G Temel
- Department of Translational Medicine, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey
- Department of Medical Genetics, Bursa Uludag University Faculty of Medicine, Bursa, Turkey
- Department of Histology and Embryology, Faculty of Medicine, Bursa Uludag University, Bursa, Turkey
| | - Isabelle Fournier
- Réponse Inflammatoire et Spectrométrie de Masse-PRISM, University of Lille, Lille, France
| | - M Cerkez Ergoren
- Department of Medical Genetics, Near East University Faculty of Medicine, Nicosia, Cyprus
| | - Adam Mechler
- Department of Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, Australia
| | - Michel Salzet
- Réponse Inflammatoire et Spectrométrie de Masse-PRISM, University of Lille, Lille, France
| | - Michele Maffia
- Department of Experimental Medicine, University of Salento, Via Lecce-Monteroni, Lecce, 73100, Italy
| | - Dancho Danalev
- University of Chemical Technology and Metallurgy, Sofia, Bulgaria
| | - Qun Sun
- Department of Food Science and Technology, Sichuan University, Chengdu, China
| | - Lembit Nei
- School of Engineering Tallinn University of Technology, Tartu College, Tartu, Estonia
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Dana Tapaloaga
- Faculty of Veterinary Medicine, University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Andres Janecke
- Department of Paediatrics I, Medical University of Innsbruck, Innsbruck, Austria
- Division of Human Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - James Bown
- School of Science, Engineering and Technology, Abertay University, Dundee, UK
| | | | - Iza Radecka
- School of Science, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK
| | - Celal Ozturk
- Department of Software Engineering, Erciyes University, Kayseri, Turkey
| | - Ozkan Ufuk Nalbantoglu
- Department of Computer Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey
| | - Sebnem Ozemri Sag
- Department of Medical Genetics, Bursa Uludag University Faculty of Medicine, Bursa, Turkey
| | - Kisung Ko
- Department of Medicine, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Reynir Arngrimsson
- Iceland Landspitali University Hospital, University of Iceland, Reykjavik, Iceland
| | - Isabel Belo
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Hilal Akalin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
| | - Munis Dundar
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
| |
Collapse
|
2
|
Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024; 23:2700-2722. [PMID: 38451675 PMCID: PMC11296931 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
Collapse
Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| |
Collapse
|
3
|
Yin Z, Huang W, Li K, Fernie AR, Yan S. Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38990529 DOI: 10.1111/tpj.16924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/24/2024] [Accepted: 07/01/2024] [Indexed: 07/12/2024]
Abstract
Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.
Collapse
Affiliation(s)
- Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
- Institute of Advanced Science Facilities, Shenzhen, 518107, Guangdong, China
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Kun Li
- Guangdong Key Laboratory of Crop Genetic Improvement, Crop Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| |
Collapse
|
4
|
Karpov OA, Stotland A, Raedschelders K, Chazarin B, Ai L, Murray CI, Van Eyk JE. Proteomics of the heart. Physiol Rev 2024; 104:931-982. [PMID: 38300522 DOI: 10.1152/physrev.00026.2023] [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: 07/03/2023] [Revised: 12/25/2023] [Accepted: 01/14/2024] [Indexed: 02/02/2024] Open
Abstract
Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.
Collapse
Affiliation(s)
- Oleg A Karpov
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Aleksandr Stotland
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Koen Raedschelders
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Blandine Chazarin
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Lizhuo Ai
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Christopher I Murray
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| |
Collapse
|
5
|
Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 2024; 21:1153-1165. [PMID: 38997593 DOI: 10.1038/s41592-024-02327-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 05/28/2024] [Indexed: 07/14/2024]
Abstract
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
Collapse
Affiliation(s)
- Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Deep Piction GmbH, Munich, Germany.
| |
Collapse
|
6
|
Kaltenecker D, Al-Maskari R, Negwer M, Hoeher L, Kofler F, Zhao S, Todorov M, Rong Z, Paetzold JC, Wiestler B, Piraud M, Rueckert D, Geppert J, Morigny P, Rohm M, Menze BH, Herzig S, Berriel Diaz M, Ertürk A. Virtual reality-empowered deep-learning analysis of brain cells. Nat Methods 2024; 21:1306-1315. [PMID: 38649742 PMCID: PMC11239522 DOI: 10.1038/s41592-024-02245-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 03/12/2024] [Indexed: 04/25/2024]
Abstract
Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.
Collapse
Affiliation(s)
- Doris Kaltenecker
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Rami Al-Maskari
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
| | - Moritz Negwer
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Luciano Hoeher
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Florian Kofler
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Shan Zhao
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Mihail Todorov
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Zhouyi Rong
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Johannes Christian Paetzold
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marie Piraud
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Geppert
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Pauline Morigny
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Maria Rohm
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Bjoern H Menze
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Department for Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Stephan Herzig
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Chair Molecular Metabolic Control, TU Munich, Munich, Germany
| | - Mauricio Berriel Diaz
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany.
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Ali Ertürk
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
- Deep Piction, Munich, Germany.
| |
Collapse
|
7
|
Aoki J, Isokawa M. Understanding Cellular, Molecular, and Functional Specificity, Heterogeneity, and Diversity of the Endocannabinoid System. Cells 2024; 13:1049. [PMID: 38920677 PMCID: PMC11201661 DOI: 10.3390/cells13121049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/27/2024] Open
Abstract
The endocannabinoid system (ECS) is a widely recognized lipid messenger system involved in many aspects of our health and diseases [...].
Collapse
Affiliation(s)
- Jun Aoki
- Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan;
- Center for Biosystems Dynamics Research, Laboratory for Cell Signaling Dynamics, RIKEN, Kobe 650-0047, Japan
| | - Masako Isokawa
- Forefront Research Center, Graduate School of Science, Osaka University, Osaka 560-0043, Japan
| |
Collapse
|
8
|
Hudson JA, Ferrand RA, Gitau SN, Mureithi MW, Maffia P, Alam SR, Shah ASV. HIV-Associated Cardiovascular Disease Pathogenesis: An Emerging Understanding Through Imaging and Immunology. Circ Res 2024; 134:1546-1565. [PMID: 38781300 DOI: 10.1161/circresaha.124.323890] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Cardiac abnormalities were identified early in the epidemic of AIDS, predating the isolation and characterization of the etiologic agent, HIV. Several decades later, the causation and pathogenesis of cardiovascular disease (CVD) linked to HIV infection continue to be the focus of intense speculation. Before the widespread use of antiretroviral therapy, HIV-associated CVD was primarily characterized by HIV-associated cardiomyopathy linked to profound immunodeficiency. With increasing antiretroviral therapy use, viral load suppression, and establishment of immune competency, the effects of HIV on the cardiovascular system are more subtle. Yet, people living with HIV still face an increased incidence of cardiovascular pathology. Advances in cardiac imaging modalities and immunology have deepened our understanding of the pathogenesis of HIV-associated CVD. This review provides an overview of the pathogenesis of HIV-associated CVD integrating data from imaging and immunologic studies with particular relevance to the HIV population originating from high-endemic regions, such as sub-Saharan Africa. The review highlights key evidence gaps in the field and suggests future directions for research to better understand the complex HIV-CVD interactions.
Collapse
Affiliation(s)
- Jonathan A Hudson
- Kings College London BHF Centre, School of Cardiovascular and Metabolic Medicine & Sciences, United Kingdom (J.A.H.)
| | - Rashida A Ferrand
- Department of Clinical Research (R.A.F.), London School of Hygiene and Tropical Medicine, United Kingdom
- Biomedical Research and Training Institute, Harare, Zimbabwe (R.A.F.)
| | - Samuel N Gitau
- Department of Radiology, Aga Khan University Nairobi, Kenya (S.N.G.)
| | - Marianne Wanjiru Mureithi
- Department of Medical Microbiology and Immunology, Faculty of Health Sciences (M.W.M.), University of Nairobi, Kenya
| | - Pasquale Maffia
- School of Infection and Immunity, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom (P.M.)
- Department of Pharmacy, School of Medicine and Surgery, University of Naples Federico II, Italy (P.M.)
- Africa-Europe Cluster of Research Excellence in Non-Communicable Diseases and Multimorbidity, African Research Universities Alliance and The Guild of European Research-Intensive Universities, Glasgow, United Kingdom (P.M.)
| | - Shirjel R Alam
- Department of Cardiology, North Bristol NHS Trust, United Kingdom (S.R.A.)
| | - Anoop S V Shah
- Department of Non-Communicable Disease Epidemiology (A.S.V.S.), London School of Hygiene and Tropical Medicine, United Kingdom
- Department of Cardiology, Imperial College NHS Trust, London, United Kingdom (A.S.V.S.)
| |
Collapse
|
9
|
Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| |
Collapse
|
10
|
Zheng J, Wu YC, Cai X, Phan P, Er EE, Zhao Z, Lee SSY. Correlative multiscale 3D imaging of mouse primary and metastatic tumors by sequential light sheet and confocal fluorescence microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594162. [PMID: 38798657 PMCID: PMC11118317 DOI: 10.1101/2024.05.14.594162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Three-dimensional (3D) optical microscopy, combined with advanced tissue clearing, permits in situ interrogation of the tumor microenvironment (TME) in large volumetric tumors for preclinical cancer research. Light sheet (also known as ultramicroscopy) and confocal fluorescence microscopy are often used to achieve macroscopic and microscopic 3D images of optically cleared tumor tissues, respectively. Although each technique offers distinct fields of view (FOVs) and spatial resolution, the combination of these two optical microscopy techniques to obtain correlative multiscale 3D images from the same tumor tissues has not yet been explored. To establish correlative multiscale 3D optical microscopy, we developed a method for optically marking defined regions of interest (ROIs) within a cleared mouse tumor by employing a UV light-activated visible dye and Z-axis position-selective UV irradiation in a light sheet microscope system. By integrating this method with subsequent tissue processing, including physical ROI marking, reversal of tissue clearing, tissue macrosectioning, and multiplex immunofluorescence, we established a workflow that enables the tracking and 3D imaging of ROIs within tumor tissues through sequential light sheet and confocal fluorescence microscopy. This approach allowed for quantitative 3D spatial analysis of the immune response in the TME of a mouse mammary tumor following cancer immunotherapy at multiple spatial scales. The workflow also facilitated the direct localization of a metastatic lesion within a whole mouse brain. These results demonstrate that our ROI tracking method and its associated workflow offer a novel approach for correlative multiscale 3D optical microscopy, with the potential to provide new insights into tumor heterogeneity, metastasis, and response to therapy at various spatial levels.
Collapse
|
11
|
Babu E, Sen S. Explore & actuate: the future of personalized medicine in oncology through emerging technologies. Curr Opin Oncol 2024; 36:93-101. [PMID: 38441149 DOI: 10.1097/cco.0000000000001016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings. RECENT FINDINGS To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation. SUMMARY The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.
Collapse
Affiliation(s)
- Erald Babu
- UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai, Kalina Campus, Mumbai, Maharashtra, India
| | | |
Collapse
|
12
|
Feng S, Calinawan A, Pugliese P, Wang P, Ceccarelli M, Petralia F, Gosline SJC. Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution. CELL REPORTS METHODS 2024; 4:100708. [PMID: 38412834 PMCID: PMC10921018 DOI: 10.1016/j.crmeth.2024.100708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 10/23/2023] [Accepted: 01/18/2024] [Indexed: 02/29/2024]
Abstract
Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.
Collapse
Affiliation(s)
- Song Feng
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Anna Calinawan
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
| | | | - Pei Wang
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
| | | | | | | |
Collapse
|
13
|
Pascual-Reguant A, Kroh S, Hauser AE. Tissue niches and immunopathology through the lens of spatial tissue profiling techniques. Eur J Immunol 2024; 54:e2350484. [PMID: 37985207 DOI: 10.1002/eji.202350484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/22/2023]
Abstract
Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.
Collapse
Affiliation(s)
- Anna Pascual-Reguant
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
- Spatial Genomics, Centre Nacional d'Anàlisi Genòmica, Barcelona, 08028, Spain
| | - Sandy Kroh
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
| | - Anja E Hauser
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
| |
Collapse
|
14
|
Liu Y, Tan Y, Zhang Z, Yi M, Zhu L, Peng W. The interaction between ageing and Alzheimer's disease: insights from the hallmarks of ageing. Transl Neurodegener 2024; 13:7. [PMID: 38254235 PMCID: PMC10804662 DOI: 10.1186/s40035-024-00397-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/31/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Ageing is a crucial risk factor for Alzheimer's disease (AD) and is characterised by systemic changes in both intracellular and extracellular microenvironments that affect the entire body instead of a single organ. Understanding the specific mechanisms underlying the role of ageing in disease development can facilitate the treatment of ageing-related diseases, such as AD. Signs of brain ageing have been observed in both AD patients and animal models. Alleviating the pathological changes caused by brain ageing can dramatically ameliorate the amyloid beta- and tau-induced neuropathological and memory impairments, indicating that ageing plays a crucial role in the pathophysiological process of AD. In this review, we summarize the impact of several age-related factors on AD and propose that preventing pathological changes caused by brain ageing is a promising strategy for improving cognitive health.
Collapse
Affiliation(s)
- Yuqing Liu
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Yejun Tan
- School of Mathematics, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Zheyu Zhang
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Min Yi
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Lemei Zhu
- Academician Workstation, Changsha Medical University, Changsha, 410219, People's Republic of China
| | - Weijun Peng
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China.
| |
Collapse
|
15
|
Chau CW, Sugimura R. Organoids in COVID-19: can we break the glass ceiling? J Leukoc Biol 2024; 115:85-99. [PMID: 37616269 DOI: 10.1093/jleuko/qiad098] [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: 01/30/2023] [Revised: 07/24/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
COVID-19 emerged in September 2020 as a disease caused by the virus SARS-CoV-2. The disease presented as pneumonia at first but later was shown to cause multisystem infections and long-term complications. Many efforts have been put into discovering the exact pathogenesis of the disease. In this review, we aim to discuss an emerging tool in disease modeling, organoids, in the investigation of COVID-19. This review will introduce some methods and breakthroughs achieved by organoids and the limitations of this system.
Collapse
Affiliation(s)
- Chiu Wang Chau
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, 21 Sassoon Rd, Pokfulam 99077, Hong Kong
| | - Ryohichi Sugimura
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, 21 Sassoon Rd, Pokfulam 99077, Hong Kong
- Centre for Translational Stem Cell Biology, 17 Science Park W Ave, Science Park 999077, Hong Kong
| |
Collapse
|
16
|
Kim J, Dwivedi G, Boughton BA, Sharma A, Lee S. Advances in cellular and tissue-based imaging techniques for sarcoid granulomas. Am J Physiol Cell Physiol 2024; 326:C10-C26. [PMID: 37955119 DOI: 10.1152/ajpcell.00507.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023]
Abstract
Sarcoidosis embodies a complex inflammatory disorder spanning multiple systems, with its origin remaining elusive. It manifests as the infiltration of inflammatory cells that coalesce into distinctive noncaseous granulomas within afflicted organs. Unraveling this disease necessitates the utilization of cellular or tissue-based imaging methods to both visualize and characterize the biochemistry of these sarcoid granulomas. Although hematoxylin and eosin stain, standard in routine use alongside cytological stains have found utility in diagnosis within clinical contexts, special stains such as Masson's trichrome, reticulin, methenamine silver, and Ziehl-Neelsen provide additional varied perspectives of sarcoid granuloma imaging. Immunohistochemistry aids in pinpointing specific proteins and gene expressions further characterizing these granulomas. Finally, recent advances in spatial transcriptomics promise to divulge profound insights into their spatial orientation and three-dimensional (3-D) molecular mapping. This review focuses on a range of preexisting imaging methods employed for visualizing sarcoid granulomas at the cellular level while also exploring the potential of the latest cutting-edge approaches like spatial transcriptomics and matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), with the overarching goal of shedding light on the trajectory of sarcoidosis research.
Collapse
Affiliation(s)
- Junwoo Kim
- Department of Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
| | - Girish Dwivedi
- Department of Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
- Department of Cardiology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Berin A Boughton
- Australian National Phenome Centre, Murdoch University, Murdoch, Western Australia, Australia
| | - Ankur Sharma
- Onco-Fetal Ecosystem Laboratory, Harry Perkins Institute of Medical Research, Nedlands, Western Australia, Australia
- Curtin Medical School, Curtin University, Perth, Western Australia, Australia
| | - Silvia Lee
- Department of Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, Murdoch, Western Australia, Australia
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
- Curtin Medical School, Curtin University, Perth, Western Australia, Australia
| |
Collapse
|
17
|
Lin HC, Makhlouf A, Vazquez Echegaray C, Zawada D, Simões F. Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs. Development 2023; 150:dev202300. [PMID: 38078653 PMCID: PMC10753584 DOI: 10.1242/dev.202300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.
Collapse
Affiliation(s)
- Hsiu-Chuan Lin
- Department of Biosystems Science and Engineering, ETH Zürich, 4057 Basel, Switzerland
| | - Aly Makhlouf
- MRC Laboratory of Molecular Biology, University of Cambridge, Cambridge CB2 0QH, UK
| | - Camila Vazquez Echegaray
- Molecular Medicine and Gene Therapy, Lund Stem Cell Centre, Wallenberg Centre for Molecular Medicine, Lund University, 221 84 Lund, Sweden
| | - Dorota Zawada
- First Department of Medicine, Cardiology, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, 81675 Munich, Germany
- German Center for Cardiovascular Research (DZHK), Munich Heart Alliance, 80636 Munich, Germany
- Regenerative Medicine in Cardiovascular Diseases, First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, 81675 Munich, Germany
| | - Filipa Simões
- Department of Physiology, Anatomy and Genetics, Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford OX3 7TY, UK
| |
Collapse
|
18
|
Walsh LA, Quail DF. Decoding the tumor microenvironment with spatial technologies. Nat Immunol 2023; 24:1982-1993. [PMID: 38012408 DOI: 10.1038/s41590-023-01678-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
Collapse
Affiliation(s)
- Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
19
|
Sigmund F, Berezin O, Beliakova S, Magerl B, Drawitsch M, Piovesan A, Gonçalves F, Bodea SV, Winkler S, Bousraou Z, Grosshauser M, Samara E, Pujol-Martí J, Schädler S, So C, Irsen S, Walch A, Kofler F, Piraud M, Kornfeld J, Briggman K, Westmeyer GG. Genetically encoded barcodes for correlative volume electron microscopy. Nat Biotechnol 2023; 41:1734-1745. [PMID: 37069313 PMCID: PMC10713455 DOI: 10.1038/s41587-023-01713-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 02/14/2023] [Indexed: 04/19/2023]
Abstract
While genetically encoded reporters are common for fluorescence microscopy, equivalent multiplexable gene reporters for electron microscopy (EM) are still scarce. Here, by installing a variable number of fixation-stable metal-interacting moieties in the lumen of encapsulin nanocompartments of different sizes, we developed a suite of spherically symmetric and concentric barcodes (EMcapsulins) that are readable by standard EM techniques. Six classes of EMcapsulins could be automatically segmented and differentiated. The coding capacity was further increased by arranging several EMcapsulins into distinct patterns via a set of rigid spacers of variable length. Fluorescent EMcapsulins were expressed to monitor subcellular structures in light and EM. Neuronal expression in Drosophila and mouse brains enabled the automatic identification of genetically defined cells in EM. EMcapsulins are compatible with transmission EM, scanning EM and focused ion beam scanning EM. The expandable palette of genetically controlled EM-readable barcodes can augment anatomical EM images with multiplexed gene expression maps.
Collapse
Affiliation(s)
- Felix Sigmund
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Oleksandr Berezin
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Sofia Beliakova
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Bernhard Magerl
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Martin Drawitsch
- Research Group, Circuits of Birdsong, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Alberto Piovesan
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Filipa Gonçalves
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Silviu-Vasile Bodea
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Stefanie Winkler
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Zoe Bousraou
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Martin Grosshauser
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Eleni Samara
- Department Circuits-Computation-Models, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Jesús Pujol-Martí
- Department Circuits-Computation-Models, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | | | - Chun So
- Department of Meiosis, Max Planck Institute for Multidisciplinary Sciences, Goettingen, Germany
| | - Stephan Irsen
- Max Planck Institute for Neurobiology of Behavior-caesar (MPINB), Bonn, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Marie Piraud
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Joergen Kornfeld
- Research Group, Circuits of Birdsong, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Kevin Briggman
- Max Planck Institute for Neurobiology of Behavior-caesar (MPINB), Bonn, Germany
| | - Gil Gregor Westmeyer
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany.
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany.
| |
Collapse
|
20
|
Tüshaus J, Sakhteman A, Lechner S, The M, Mucha E, Krisp C, Schlegel J, Delbridge C, Kuster B. A region-resolved proteomic map of the human brain enabled by high-throughput proteomics. EMBO J 2023; 42:e114665. [PMID: 37916885 PMCID: PMC10690467 DOI: 10.15252/embj.2023114665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023] Open
Abstract
Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).
Collapse
Affiliation(s)
- Johanna Tüshaus
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Amirhossein Sakhteman
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Severin Lechner
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Matthew The
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Eike Mucha
- Bruker Daltonics GmbH & Co. KGBremenGermany
| | | | - Jürgen Schlegel
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Claire Delbridge
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Bernhard Kuster
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
- German Cancer Consortium (DKTK), Munich SiteHeidelbergGermany
| |
Collapse
|
21
|
Zhang X, Li X, Xiong X. Applying proteomics in metabolic dysfunction-associated steatotic liver disease: From mechanism to biomarkers. Clin Res Hepatol Gastroenterol 2023; 47:102230. [PMID: 37931846 DOI: 10.1016/j.clinre.2023.102230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/08/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), which represents the most common cause of liver disease, is emerging as a major health problem around the world. However, the molecular events that underline the pathogenesis and the progression of MASLD remain to be fully elucidated. Advanced stages of MASLD is strongly associated with liver-related outcomes and overall mortality. Despite this, highly accurate, sensitive, and non-invasive diagnostic tools are currently not aviailable, yet no FDA approved drugs for MASLD. The advance of proteomics has enable the study of protein expression, post-translational modifications (PTMs), subcellular distribution, and interactions. In this review, we discuss insights gained from the recent proteomics studies that shed new light on the pathogenesis, diagnosis and potential theraputic targets of MASLD.
Collapse
Affiliation(s)
- Xiaofu Zhang
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, 180 Fenlin Road, Xuhui District, Shanghai 200032, China
| | - Xiaoying Li
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, 180 Fenlin Road, Xuhui District, Shanghai 200032, China
| | - Xuelian Xiong
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, 180 Fenlin Road, Xuhui District, Shanghai 200032, China.
| |
Collapse
|
22
|
Barabino S, Lombardi S, Zilocchi M. Keep in touch: a perspective on the mitochondrial social network and its implication in health and disease. Cell Death Discov 2023; 9:417. [PMID: 37973903 PMCID: PMC10654391 DOI: 10.1038/s41420-023-01710-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
Mitochondria have been the focus of extensive research for decades since their dysfunction is linked to more than 150 distinct human disorders. Despite considerable efforts, researchers have only been able to skim the surface of the mitochondrial social complexity and the impact of inter-organelle and inter-organ communication alterations on human health. While some progress has been made in deciphering connections among mitochondria and other cytoplasmic organelles through direct (i.e., contact sites) or indirect (i.e., inter-organelle trafficking) crosstalk, most of these efforts have been restricted to a limited number of proteins involved in specific physiological pathways or disease states. This research bottleneck is further narrowed by our incomplete understanding of the cellular alteration timeline in a specific pathology, which prevents the distinction between a primary organelle dysfunction and the defects occurring due to the disruption of the organelle's interconnectivity. In this perspective, we will (i) summarize the current knowledge on the mitochondrial crosstalk within cell(s) or tissue(s) in health and disease, with a particular focus on neurodegenerative disorders, (ii) discuss how different large-scale and targeted approaches could be used to characterize the different levels of mitochondrial social complexity, and (iii) consider how investigating the different expression patterns of mitochondrial proteins in different cell types/tissues could represent an important step forward in depicting the distinctive architecture of inter-organelle communication.
Collapse
Affiliation(s)
- Silvia Barabino
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126, Milan, Italy.
| | - Silvia Lombardi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126, Milan, Italy
| | - Mara Zilocchi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126, Milan, Italy.
| |
Collapse
|
23
|
Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
Collapse
Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
| |
Collapse
|
24
|
Reitz CJ, Kuzmanov U, Gramolini AO. Multi-omic analyses and network biology in cardiovascular disease. Proteomics 2023; 23:e2200289. [PMID: 37691071 DOI: 10.1002/pmic.202200289] [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: 03/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.
Collapse
Affiliation(s)
- Cristine J Reitz
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Uros Kuzmanov
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Anthony O Gramolini
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| |
Collapse
|
25
|
Oliva M, Lister R. Exploring the identity of individual plant cells in space and time. THE NEW PHYTOLOGIST 2023; 240:61-67. [PMID: 37483019 PMCID: PMC10952157 DOI: 10.1111/nph.19153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/17/2023] [Indexed: 07/25/2023]
Abstract
In recent years, single-cell genomics, coupled to imaging techniques, have become the state-of-the-art approach for characterising biological systems. In plant sciences, a variety of tissues and species have been profiled, providing an enormous quantity of data on cell identity at an unprecedented resolution, but what biological insights can be gained from such data sets? Using recently published studies in plant sciences, we will highlight how single-cell technologies have enabled a better comprehension of tissue organisation, cell fate dynamics in development or in response to various stimuli, as well as identifying key transcriptional regulators of cell identity. We discuss the limitations and technical hurdles to overcome, as well as future directions, and the promising use of single-cell omics to understand, predict, and manipulate plant development and physiology.
Collapse
Affiliation(s)
- Marina Oliva
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular SciencesUniversity of Western AustraliaPerthWA6009Australia
| | - Ryan Lister
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular SciencesUniversity of Western AustraliaPerthWA6009Australia
- The Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical ResearchThe University of Western AustraliaPerthWA6009Australia
| |
Collapse
|
26
|
Kolabas ZI, Kuemmerle LB, Perneczky R, Förstera B, Ulukaya S, Ali M, Kapoor S, Bartos LM, Büttner M, Caliskan OS, Rong Z, Mai H, Höher L, Jeridi D, Molbay M, Khalin I, Deligiannis IK, Negwer M, Roberts K, Simats A, Carofiglio O, Todorov MI, Horvath I, Ozturk F, Hummel S, Biechele G, Zatcepin A, Unterrainer M, Gnörich J, Roodselaar J, Shrouder J, Khosravani P, Tast B, Richter L, Díaz-Marugán L, Kaltenecker D, Lux L, Chen Y, Zhao S, Rauchmann BS, Sterr M, Kunze I, Stanic K, Kan VWY, Besson-Girard S, Katzdobler S, Palleis C, Schädler J, Paetzold JC, Liebscher S, Hauser AE, Gokce O, Lickert H, Steinke H, Benakis C, Braun C, Martinez-Jimenez CP, Buerger K, Albert NL, Höglinger G, Levin J, Haass C, Kopczak A, Dichgans M, Havla J, Kümpfel T, Kerschensteiner M, Schifferer M, Simons M, Liesz A, Krahmer N, Bayraktar OA, Franzmeier N, Plesnila N, Erener S, Puelles VG, Delbridge C, Bhatia HS, Hellal F, Elsner M, Bechmann I, Ondruschka B, Brendel M, Theis FJ, Erturk A. Distinct molecular profiles of skull bone marrow in health and neurological disorders. Cell 2023; 186:3706-3725.e29. [PMID: 37562402 PMCID: PMC10443631 DOI: 10.1016/j.cell.2023.07.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 04/24/2023] [Accepted: 07/07/2023] [Indexed: 08/12/2023]
Abstract
The bone marrow in the skull is important for shaping immune responses in the brain and meninges, but its molecular makeup among bones and relevance in human diseases remain unclear. Here, we show that the mouse skull has the most distinct transcriptomic profile compared with other bones in states of health and injury, characterized by a late-stage neutrophil phenotype. In humans, proteome analysis reveals that the skull marrow is the most distinct, with differentially expressed neutrophil-related pathways and a unique synaptic protein signature. 3D imaging demonstrates the structural and cellular details of human skull-meninges connections (SMCs) compared with veins. Last, using translocator protein positron emission tomography (TSPO-PET) imaging, we show that the skull bone marrow reflects inflammatory brain responses with a disease-specific spatial distribution in patients with various neurological disorders. The unique molecular profile and anatomical and functional connections of the skull show its potential as a site for diagnosing, monitoring, and treating brain diseases.
Collapse
Affiliation(s)
- Zeynep Ilgin Kolabas
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Graduate School of Systemic Neurosciences (GSN), Munich, Germany
| | - Louis B Kuemmerle
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Robert Perneczky
- Division of Mental Health in Older Adults and Alzheimer Therapy and Research Center, Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, 80336 Munich, Germany; German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Benjamin Förstera
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Selin Ulukaya
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Mayar Ali
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Graduate School of Systemic Neurosciences (GSN), Munich, Germany; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Saketh Kapoor
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Laura M Bartos
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ozum Sehnaz Caliskan
- Institute for Diabetes and Obesity, Helmholtz Center Munich and German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Zhouyi Rong
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Hongcheng Mai
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Luciano Höher
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Denise Jeridi
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Muge Molbay
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Igor Khalin
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | | | - Moritz Negwer
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | | | - Alba Simats
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Olga Carofiglio
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Mihail I Todorov
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Izabela Horvath
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; School of Computation, Information and Technology (CIT), TUM, Boltzmannstr. 3, 85748 Garching, Germany
| | - Furkan Ozturk
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Selina Hummel
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Gloria Biechele
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Artem Zatcepin
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marcus Unterrainer
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany; Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Johannes Gnörich
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jay Roodselaar
- Charité - Universitätsmedizin Berlin, Department of Rheumatology and Clinical Immunology, Berlin, Germany; Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Joshua Shrouder
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Pardis Khosravani
- Biomedical Center (BMC), Core Facility Flow Cytometry, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Benjamin Tast
- Biomedical Center (BMC), Core Facility Flow Cytometry, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Lisa Richter
- Biomedical Center (BMC), Core Facility Flow Cytometry, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Laura Díaz-Marugán
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Doris Kaltenecker
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Diabetes and Cancer, Helmholtz Munich, Munich, Germany
| | - Laurin Lux
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Ying Chen
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Shan Zhao
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- Division of Mental Health in Older Adults and Alzheimer Therapy and Research Center, Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, 80336 Munich, Germany; Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK; Institute of Neuroradiology, University Hospital LMU, Munich, Germany
| | - Michael Sterr
- Institute of Diabetes and Regeneration Research, Helmholtz Diabetes Center, Helmholtz Zentrum München, Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Ines Kunze
- Institute of Diabetes and Regeneration Research, Helmholtz Diabetes Center, Helmholtz Zentrum München, Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Karen Stanic
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Vanessa W Y Kan
- Institute of Clinical Neuroimmunology, University Hospital Munich, Ludwig-Maximilians University Munich, Munich, Germany
| | - Simon Besson-Girard
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Graduate School of Systemic Neurosciences (GSN), Munich, Germany
| | - Sabrina Katzdobler
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Carla Palleis
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Julia Schädler
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Johannes C Paetzold
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Department of Computing, Imperial College London, London, UK
| | - Sabine Liebscher
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Institute of Clinical Neuroimmunology, University Hospital Munich, Ludwig-Maximilians University Munich, Munich, Germany; Biomedical Center (BMC), Medical Faculty, Ludwig-Maximilians Universität Munich, Munich, Germany
| | - Anja E Hauser
- Charité - Universitätsmedizin Berlin, Department of Rheumatology and Clinical Immunology, Berlin, Germany; Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Ozgun Gokce
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Diabetes Center, Helmholtz Zentrum München, Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany; TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Hanno Steinke
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Corinne Benakis
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Christian Braun
- Institute of Legal Medicine, Faculty of Medicine, LMU Munich, Germany
| | - Celia P Martinez-Jimenez
- Helmholtz Pioneer Campus (HPC), Helmholtz Munich, Neuherberg, Germany; TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Katharina Buerger
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
| | - Nathalie L Albert
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Günter Höglinger
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Christian Haass
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anna Kopczak
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim Havla
- Institute of Clinical Neuroimmunology, University Hospital Munich, Ludwig-Maximilians University Munich, Munich, Germany; Biomedical Center (BMC), Medical Faculty, Ludwig-Maximilians Universität Munich, Munich, Germany
| | - Tania Kümpfel
- Institute of Clinical Neuroimmunology, University Hospital Munich, Ludwig-Maximilians University Munich, Munich, Germany; Biomedical Center (BMC), Medical Faculty, Ludwig-Maximilians Universität Munich, Munich, Germany
| | - Martin Kerschensteiner
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Institute of Clinical Neuroimmunology, University Hospital Munich, Ludwig-Maximilians University Munich, Munich, Germany; Biomedical Center (BMC), Medical Faculty, Ludwig-Maximilians Universität Munich, Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Mikael Simons
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Arthur Liesz
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Graduate School of Systemic Neurosciences (GSN), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Natalie Krahmer
- Institute for Diabetes and Obesity, Helmholtz Center Munich and German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | | | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Nikolaus Plesnila
- Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Suheda Erener
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Victor G Puelles
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Pathology, Aarhus University Hospital, Aarhus, Denmark
| | - Claire Delbridge
- Institute of Pathology, Department of Neuropathology, Technical University Munich, TUM School of Medicine, Munich, Germany
| | - Harsharan Singh Bhatia
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Farida Hellal
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Markus Elsner
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany
| | - Ingo Bechmann
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Benjamin Ondruschka
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Brendel
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Mathematics, Technische Universität München, Garching bei München, Germany
| | - Ali Erturk
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Neuherberg, Munich, Germany; Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians University Munich, Munich, Germany; Graduate School of Systemic Neurosciences (GSN), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
| |
Collapse
|
27
|
Aoki J, Isokawa M. Large-Scale Multi-Omic Approaches and Atlas-Based Profiling for Cellular, Molecular, and Functional Specificity, Heterogeneity, and Diversity in the Endocannabinoid System. Cells 2023; 12:cells12050814. [PMID: 36899950 PMCID: PMC10000389 DOI: 10.3390/cells12050814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
The endocannabinoid system (ECS) is a widely-recognized lipid messenger system involved in many aspects of our our lives in health and diseases [...].
Collapse
Affiliation(s)
- Jun Aoki
- Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan
- Laboratory for Cell Signaling Dynamics, Center for Biosystems Dynamics Research, RIKEN, Kobe 650-0047, Japan
| | - Masako Isokawa
- Forefront Research Center, Graduate School of Science, Osaka University, Osaka 560-0043, Japan
- Correspondence:
| |
Collapse
|
28
|
Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Comput Struct Biotechnol J 2023; 21:940-955. [PMID: 38213887 PMCID: PMC10781722 DOI: 10.1016/j.csbj.2023.01.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
Collapse
Affiliation(s)
- Liangchen Yue
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Feng Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Jiongsong Hu
- University of South China, Hengyang, Hunan 421001, China
| | - Pin Yang
- Anhui Medical University, Hefei 230022, Anhui, China
| | - Yuxiang Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Junguo Dong
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Wenjie Shu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xingxu Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310029, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| |
Collapse
|