Correction to: A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases

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Abstract

Correction to: Genome Med 11, 47 (2019) https://doi.org/10.1186/s13073-019-0657-3 It was highlighted that in the original article [1] there was an omission in the Funding section. This Correction article shows the correct Funding section.

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A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases

Danuta R Gawel, Jordi Serra-Musach, Sandra Lilja … (2019)

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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Author and article information

Contributors

Huan Zhang: huan.zhang@liu.se

Mikael Benson: mikael.benson@liu.se

Journal

Journal ID (nlm-ta): Genome Med

Journal ID (iso-abbrev): Genome Med

Title: Genome Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-994X

Publication date (Electronic): 28 April 2020

Publication date PMC-release: 28 April 2020

Publication date Collection: 2020

Volume: 12

Electronic Location Identifier: 37

Affiliations

[1 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Centre for Personalized Medicine, , Linköping University, ; Linköping, Sweden

[2 ]Department of Internal Medicine, Region Jönköping County, Jönköping, Sweden

[3 ]GRID grid.410367.7, ISNI 0000 0001 2284 9230, Departament d’Enginyeria Informàtica i Matemàtiques, , Universitat Rovira i Virgili, ; Tarragona, Spain

[4 ]Department of Surgery, Region Jönköping County, Jönköping, Sweden

[5 ]Office for Control of Communicable Diseases, Region Jönköping County, Jönköping, Sweden

[6 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Division of Rheumatology, Autoimmunity, and Immune Regulation, Department of Clinical and Experimental Medicine, , Linköping University, ; Linköping, Sweden

[7 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Department of Clinical Immunology and Transfusion Medicine, , Linköping University, ; Linköping, Sweden

[8 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Department of Gastroenterology and Department of Clinical and Experimental Medicine, , Linköping University, ; Linköping, Sweden

[9 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Department of Medical and Health Sciences, , Linköping University, ; Linköping, Sweden

[10 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Bioinformatics, Department of Physics, Chemistry and Biology, , Linköping University, ; Linköping, Sweden

[11 ]GRID grid.15444.30, ISNI 0000 0004 0470 5454, Department of Otorhinolaryngology, , Yonsei University College of Medicine, ; Seoul, South Korea

[12 ]Clinical Microbiology, Region Jönköping County, Jönköping, Sweden

[13 ]Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Karolinska University Hospital Huddinge, Stockholm, Sweden

[14 ]GRID grid.24381.3c, ISNI 0000 0000 9241 5705, Karolinska University Laboratory, Karolinska University Hospital, ; Solna, Sweden

[15 ]Department of Dermatology and Venereology, Region Jönköping County, Jönköping, Sweden

[16 ]GRID grid.5640.7, ISNI 0000 0001 2162 9922, Department of Clinical and Experimental Medicine, Faculty of Medicine and Health Sciences, , Linköping University, ; Linköping, Sweden

[17 ]Futurum – Academy for Health and Care, Department of Pediatrics, Region Jönköping County, Jönköping, Sweden

[18 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, ; Cambridge, MA USA

[19 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Chemistry, , Massachusetts Institute of Technology, ; Cambridge, MA USA

[20 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, ; Cambridge, MA USA

[21 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA

[22 ]GRID grid.461656.6, ISNI 0000 0004 0489 3491, Ragon Institute of MGH, MIT and Harvard, ; Cambridge, MA USA

[23 ]Department of Pediatrics, Institution for Clinical Sciences, Göteborg, Sweden

Article

Publisher ID: 732

DOI: 10.1186/s13073-020-00732-7

PMC ID: 7189719

PubMed ID: 32345376

SO-VID: 23a7fb9e-d205-4d00-a50f-89424dc8818a

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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