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Elkjaer ML, Hartebrodt A, Oubounyt M, Weber A, Vitved L, Reynolds R, Thomassen M, Rottger R, Baumbach J, Illes Z. Single-Cell Multi-Omics Map of Cell Type-Specific Mechanistic Drivers of Multiple Sclerosis Lesions. Neurol Neuroimmunol Neuroinflamm 2024; 11:e200213. [PMID: 38564686 PMCID: PMC11073880 DOI: 10.1212/nxi.0000000000200213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/19/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND AND OBJECTIVES In progressive multiple sclerosis (MS), compartmentalized inflammation plays a pivotal role in the complex pathology of tissue damage. The interplay between epigenetic regulation, transcriptional modifications, and location-specific alterations within white matter (WM) lesions at the single-cell level remains underexplored. METHODS We examined intracellular and intercellular pathways in the MS brain WM using a novel dataset obtained by integrated single-cell multi-omics techniques from 3 active lesions, 3 chronic active lesions, 3 remyelinating lesions, and 3 control WM of 6 patients with progressive MS and 3 non-neurologic controls. Single-nucleus RNA-seq and ATAC-seq were combined and additionally enriched with newly conducted spatial transcriptomics from 1 chronic active lesion. Functional gene modules were then validated in our previously published bulk tissue transcriptome data obtained from 73 WM lesions of patients with progressive MS and 25 WM of non-neurologic disease controls. RESULTS Our analysis uncovered an MS-specific oligodendrocyte genetic signature influenced by the KLF/SP gene family. This modulation has potential associations with the autocrine iron uptake signaling observed in transcripts of transferrin and its receptor LRP2. In addition, an inflammatory profile emerged within these oligodendrocytes. We observed unique cellular endophenotypes both at the periphery and within the chronic active lesion. These include a distinct metabolic astrocyte phenotype, the importance of FGF signaling among astrocytes and neurons, and a notable enrichment of mitochondrial genes at the lesion edge populated predominantly by astrocytes. Our study also identified B-cell coexpression networks indicating different functional B-cell subsets with differential location and specific tendencies toward certain lesion types. DISCUSSION The use of single-cell multi-omics has offered a detailed perspective into the cellular dynamics and interactions in MS. These nuanced findings might pave the way for deeper insights into lesion pathogenesis in progressive MS.
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Affiliation(s)
- Maria L Elkjaer
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Anne Hartebrodt
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Mhaned Oubounyt
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Anna Weber
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lars Vitved
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Richard Reynolds
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Mads Thomassen
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Richard Rottger
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jan Baumbach
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Zsolt Illes
- From the Department of Neurology (M.L.E., A.W., Z.I.), Odense University Hospital; BRIDGE (M.L.E., A.W., M.T., Z.I.), Department of Clinical Research; Department of Molecular Medicine (M.L.E., A.W., L.V., Z.I.), University of Southern Denmark, Odense, Denmark; Biomedical Network Science Lab (A.H.), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Department of Mathematics and Computer Science (A.H., Richard Rottger, J.B.), University of Southern Denmark, Odense, Denmark; Institute for Computational Systems Biology (M.O., J.B.), University of Hamburg, Germany; Department of Brain Sciences (Richard Reynolds), Imperial College, London, United Kingdom; and Clinical Genome Center (M.T.), Research Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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Matschinske J, Späth J, Bakhtiari M, Probul N, Kazemi Majdabadi MM, Nasirigerdeh R, Torkzadehmahani R, Hartebrodt A, Orban BA, Fejér SJ, Zolotareva O, Das S, Baumbach L, Pauling JK, Tomašević O, Bihari B, Bloice M, Donner NC, Fdhila W, Frisch T, Hauschild AC, Heider D, Holzinger A, Hötzendorfer W, Hospes J, Kacprowski T, Kastelitz M, List M, Mayer R, Moga M, Müller H, Pustozerova A, Röttger R, Saak CC, Saranti A, Schmidt HHHW, Tschohl C, Wenke NK, Baumbach J. The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach. J Med Internet Res 2023; 25:e42621. [PMID: 37436815 PMCID: PMC10372562 DOI: 10.2196/42621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/13/2023] [Accepted: 02/26/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. OBJECTIVE Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. METHODS The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. RESULTS FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. CONCLUSIONS FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Linda Baumbach
- University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | | | | | | | - Nina C Donner
- Concentris Research Management gGmbH, Fürstenfeldbruck, Germany
| | | | | | | | | | | | | | - Jan Hospes
- Research Institute AG & Co KG, Vienna, Austria
| | - Tim Kacprowski
- Technical University Braunschweig and Hannover Medical School, Brunswick, Germany
| | | | - Markus List
- Technical University Munich, Munich, Germany
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