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Gawel DR, Serra-Musach J, Lilja S, Aagesen J, Arenas A, Asking B, Bengnér M, Björkander J, Biggs S, Ernerudh J, Hjortswang H, Karlsson JE, Köpsen M, Lee EJ, Lentini A, Li X, Magnusson M, Martínez-Enguita D, Matussek A, Nestor CE, Schäfer S, Seifert O, Sonmez C, Stjernman H, Tjärnberg A, Wu S, Åkesson K, Shalek AK, Stenmarker M, Zhang H, Gustafsson M, Benson M. A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med 2019; 11:47. [PMID: 31358043 PMCID: PMC6664760 DOI: 10.1186/s13073-019-0657-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 07/10/2019] [Indexed: 12/17/2022] Open
Abstract
Background Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. Conclusions Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease. Electronic supplementary material The online version of this article (10.1186/s13073-019-0657-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Danuta R Gawel
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Jordi Serra-Musach
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Sandra Lilja
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Jesper Aagesen
- Department of Internal Medicine, Region Jönköping County, Jönköping, Sweden
| | - Alex Arenas
- Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain
| | - Bengt Asking
- Department of Surgery, Region Jönköping County, Jönköping, Sweden
| | - Malin Bengnér
- Office for Control of Communicable Diseases, Region Jönköping County, Jönköping, Sweden
| | - Janne Björkander
- Department of Internal Medicine, Region Jönköping County, Jönköping, Sweden
| | - Sophie Biggs
- Division of Rheumatology, Autoimmunity, and Immune Regulation, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Jan Ernerudh
- Department of Clinical Immunology and Transfusion Medicine, Linköping University, Linköping, Sweden
| | - Henrik Hjortswang
- Department of Gastroenterology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Jan-Erik Karlsson
- Department of Internal Medicine, Region Jönköping County, Jönköping, Sweden.,Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Mattias Köpsen
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Eun Jung Lee
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden.,Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - Antonio Lentini
- Department of Clinical and Experimental Medicine, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
| | - Xinxiu Li
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Mattias Magnusson
- Division of Rheumatology, Autoimmunity, and Immune Regulation, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - David Martínez-Enguita
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Andreas Matussek
- Clinical Microbiology, Region Jönköping County, Jönköping, Sweden.,Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Karolinska University Hospital Huddinge, Stockholm, Sweden.,Karolinska University Laboratory, Karolinska University Hospital, Solna, Sweden
| | - Colm E Nestor
- Department of Clinical and Experimental Medicine, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
| | - Samuel Schäfer
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Oliver Seifert
- Department of Dermatology and Venereology, Region Jönköping County, Jönköping, Sweden.,Department of Clinical and Experimental Medicine, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
| | - Ceylan Sonmez
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Henrik Stjernman
- Department of Internal Medicine, Region Jönköping County, Jönköping, Sweden
| | - Andreas Tjärnberg
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Simon Wu
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Karin Åkesson
- Department of Clinical and Experimental Medicine, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.,Futurum - Academy for Health and Care, Department of Pediatrics, Region Jönköping County, Jönköping, Sweden
| | - Alex K Shalek
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.,Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Margaretha Stenmarker
- Futurum - Academy for Health and Care, Department of Pediatrics, Region Jönköping County, Jönköping, Sweden.,Department of Pediatrics, Institution for Clinical Sciences, Göteborg, Sweden
| | - Huan Zhang
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden.
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Mikael Benson
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden.
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Abstract
Recent advancements in high-throughput technologies such as mass spectrometry have led to an increase in the rate at which data is generated and accumulated. As a result, standard statistical methods no longer suffice as a way of analyzing such gigantic amounts of data. Network analysis, the evaluation of how nodes relate to one another, has over the years become an integral tool for analyzing high throughput proteomic data as they provide a structure that helps reduce the complexity of the underlying data.Computational tools, including pathway databases and network building tools, have therefore been developed to store, analyze, interpret, and learn from proteomics data. These tools enable the visualization of proteins as networks of signaling, regulatory, and biochemical interactions. In this chapter, we provide an overview of networks and network theory fundamentals for the analysis of proteomics data. We further provide an overview of interaction databases and network tools which are frequently used for analyzing proteomics data.
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Affiliation(s)
- David Chisanga
- Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciencesy, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Shivakumar Keerthikumar
- Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Suresh Mathivanan
- Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Naveen Chilamkurti
- Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciencesy, La Trobe University, Melbourne, VIC, 3086, Australia.
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