{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:11:10Z","timestamp":1791245470843,"version":"4.1.0"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"European Commission grant","award":["848028\u2260767 2"],"award-info":[{"award-number":["848028\u2260767 2"]}]},{"name":"Swedish Cancer Society","award":["CAN 2017\/411"],"award-info":[{"award-number":["CAN 2017\/411"]}]},{"DOI":"10.13039\/501100004047","name":"Karolinska Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004047","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genome Med"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision\/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn\u2019s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn\u2019s disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio\u2019s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/SDTC-CPMed\/scDrugPrio\">https:\/\/github.com\/SDTC-CPMed\/scDrugPrio<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13073-024-01314-7","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T10:03:01Z","timestamp":1710928981000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases"],"prefix":"10.1186","volume":"16","author":[{"given":"Samuel","family":"Sch\u00e4fer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Smelik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleg","family":"Sysoev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yelin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Desir\u00e9","family":"Eklund","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Lilja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mika","family":"Gustafsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holger","family":"Heyn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Julia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Istv\u00e1n A.","family":"Kov\u00e1cs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Loscalzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Marsal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxiu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danuta","family":"Gawel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7753-9181","authenticated-orcid":false,"given":"Mikael","family":"Benson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"issue":"1","key":"1314_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13073-019-0701-3","volume":"12","author":"B Bj\u00f6rnsson","year":"2019","unstructured":"Bj\u00f6rnsson B, Borrebaeck C, Elander N, Gasslander T, Gawel DR, Gustafsson M, et al. Digital twins to personalize medicine. Genome Med. 2019;12(1):4.","journal-title":"Genome Med"},{"key":"1314_CR2","doi-asserted-by":"crossref","unstructured":"Kayal M, Ungaro RC, Bader G, Colombel J-F, Sandborn WJ, Stalgis C. Net remission rates with biologic treatment in Crohn\u2019s disease: a reappraisal of the clinical trial data. Clin Gastroenterol Hepatol. 2022.","DOI":"10.1016\/j.cgh.2022.02.044"},{"issue":"10","key":"1314_CR3","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1111\/apt.15706","volume":"51","author":"H Alric","year":"2020","unstructured":"Alric H, Amiot A, Kirchgesner J, Tr\u00e9ton X, Allez M, Bouhnik Y, et al. The effectiveness of either ustekinumab or vedolizumab in 239 patients with Crohn\u2019s disease refractory to anti-tumour necrosis factor. Aliment Pharmacol Ther. 2020;51(10):948\u201357.","journal-title":"Aliment Pharmacol Ther"},{"key":"1314_CR4","doi-asserted-by":"crossref","unstructured":"Shalek AK, Benson M. Single-cell analyses to tailor treatments. Sci Transl Med. 2017;9(408).","DOI":"10.1126\/scitranslmed.aan4730"},{"issue":"7","key":"1314_CR5","doi-asserted-by":"publisher","first-page":"e68876","DOI":"10.1371\/journal.pone.0068876","volume":"8","author":"C Breynaert","year":"2013","unstructured":"Breynaert C, Dresselaers T, Perrier C, Arijs I, Cremer J, Van Lommel L, et al. Unique gene expression and MR T2 relaxometry patterns define chronic murine dextran sodium sulphate colitis as a model for connective tissue changes in human Crohn\u2019s disease. PLoS ONE. 2013;8(7):e68876.","journal-title":"PLoS ONE"},{"issue":"1","key":"1314_CR6","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s13073-019-0657-3","volume":"11","author":"DR Gawel","year":"2019","unstructured":"Gawel DR, Serra-Musach J, Lilja S, Aagesen J, Arenas A, Asking B, et al. A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med. 2019;11(1):47.","journal-title":"Genome Med"},{"issue":"2","key":"1314_CR7","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s41591-019-0733-7","volume":"26","author":"D Kim","year":"2020","unstructured":"Kim D, Kobayashi T, Voisin B, Jo J-H, Sakamoto K, Jin S-P, et al. Targeted therapy guided by single-cell transcriptomic analysis in drug-induced hypersensitivity syndrome: a case report. Nat Med. 2020;26(2):236\u201343.","journal-title":"Nat Med"},{"key":"1314_CR8","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.csbj.2022.11.055","volume":"21","author":"C-Y Hsieh","year":"2023","unstructured":"Hsieh C-Y, Wen J-H, Lin S-M, Tseng T-Y, Huang J-H, Huang H-C, et al. scDrug: From single-cell RNA-seq to drug response prediction. Comput Struct Biotechnol J. 2023;21:150\u20137.","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"1314_CR9","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1186\/s13073-021-01000-y","volume":"13","author":"C Suphavilai","year":"2021","unstructured":"Suphavilai C, Chia S, Sharma A, Tu L, Da Silva RP, Mongia A, et al. Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures. Genome Med. 2021;13(1):189.","journal-title":"Genome Med"},{"issue":"1","key":"1314_CR10","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1186\/s13073-021-01001-x","volume":"13","author":"C Fustero-Torre","year":"2021","unstructured":"Fustero-Torre C, Jimenez-Santos MJ, Garcia-Martin S, Carretero-Puche C, Garcia-Jimeno L, Ivanchuk V, et al. Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data. Genome Med. 2021;13(1):187.","journal-title":"Genome Med"},{"key":"1314_CR11","doi-asserted-by":"crossref","unstructured":"Ianevski A, Lahtela J, Javarappa KK, Sergeev P, Ghimire BR, Gautam P, et al. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Sci Adv. 2021;7(8).","DOI":"10.1126\/sciadv.abe4038"},{"issue":"3","key":"1314_CR12","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1016\/j.cell.2016.06.017","volume":"166","author":"F Iorio","year":"2016","unstructured":"Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740\u201354.","journal-title":"Cell"},{"issue":"2","key":"1314_CR13","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1038\/s43018-019-0018-6","volume":"1","author":"SM Corsello","year":"2020","unstructured":"Corsello SM, Nagari RT, Spangler RD, Rossen J, Kocak M, Bryan JG, et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nature Cancer. 2020;1(2):235\u201348.","journal-title":"Nature Cancer"},{"issue":"D1","key":"1314_CR14","doi-asserted-by":"publisher","first-page":"D1144","DOI":"10.1093\/nar\/gkaa1084","volume":"49","author":"SL Freshour","year":"2021","unstructured":"Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, et al. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021;49(D1):D1144\u201351.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"1314_CR15","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13073-022-01048-4","volume":"14","author":"X Li","year":"2022","unstructured":"Li X, Lee EJ, Lilja S, Loscalzo J, Sch\u00e4fer S, Smelik M, et al. A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets. Genome Med. 2022;14(1):48.","journal-title":"Genome Med"},{"key":"1314_CR16","doi-asserted-by":"publisher","first-page":"10331","DOI":"10.1038\/ncomms10331","volume":"7","author":"E Guney","year":"2016","unstructured":"Guney E, Menche J, Vidal M, Barabasi AL. Network-based in silico drug efficacy screening. Nat Commun. 2016;7:10331.","journal-title":"Nat Commun"},{"issue":"1","key":"1314_CR17","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0092-8674(00)00015-5","volume":"102","author":"TR Hughes","year":"2000","unstructured":"Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, et al. Functional discovery via a compendium of expression profiles. Cell. 2000;102(1):109\u201326.","journal-title":"Cell"},{"issue":"1","key":"1314_CR18","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1038\/s41467-019-09186-x","volume":"10","author":"F Cheng","year":"2019","unstructured":"Cheng F, Kovacs IA, Barabasi AL. Network-based prediction of drug combinations. Nat Commun. 2019;10(1):1197.","journal-title":"Nat Commun"},{"issue":"Database issue","key":"1314_CR19","doi-asserted-by":"publisher","first-page":"D668","DOI":"10.1093\/nar\/gkj067","volume":"34","author":"DS Wishart","year":"2006","unstructured":"Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34(Database issue):D668\u201372.","journal-title":"Nucleic Acids Res"},{"issue":"13","key":"1314_CR20","doi-asserted-by":"publisher","first-page":"i277","DOI":"10.1093\/bioinformatics\/btn182","volume":"24","author":"A \u00d6zg\u00fcr","year":"2008","unstructured":"\u00d6zg\u00fcr A, Vu T, Erkan G, Radev DR. Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics. 2008;24(13):i277\u201385.","journal-title":"Bioinformatics"},{"issue":"5","key":"1314_CR21","first-page":"1","volume":"1695","author":"G Csardi","year":"2006","unstructured":"Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal Complex Systems. 2006;1695(5):1\u20139.","journal-title":"InterJournal Complex Systems"},{"issue":"52","key":"1314_CR22","doi-asserted-by":"publisher","first-page":"E12201","DOI":"10.1073\/pnas.1810452115","volume":"115","author":"CFA Negre","year":"2018","unstructured":"Negre CFA, Morzan UN, Hendrickson HP, Pal R, Lisi GP, Loria JP, et al. Eigenvector centrality for characterization of protein allosteric pathways. Proc Natl Acad Sci U S A. 2018;115(52):E12201\u20138.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"1314_CR23","doi-asserted-by":"crossref","unstructured":"Ashtiani M, Mirzaie M, Jafari M. CINNA: an R\/CRAN package to decipher central informative nodes in network analysis. Bioinformatics (Oxford, England). 2018;35.","DOI":"10.1101\/168757"},{"issue":"2","key":"1314_CR24","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1038\/s41592-019-0667-5","volume":"17","author":"R Browaeys","year":"2020","unstructured":"Browaeys R, Saelens W, Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods. 2020;17(2):159\u201362.","journal-title":"Nat Methods"},{"issue":"7","key":"1314_CR25","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1016\/j.cell.2017.10.044","volume":"171","author":"SV Puram","year":"2017","unstructured":"Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell. 2017;171(7):1611-24.e24.","journal-title":"Cell"},{"issue":"11","key":"1314_CR26","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","volume":"13","author":"P Shannon","year":"2003","unstructured":"Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u2013504.","journal-title":"Genome Res"},{"issue":"3","key":"1314_CR27","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1038\/s43016-021-00243-7","volume":"2","author":"IF de Valle","year":"2021","unstructured":"de Valle IF, Roweth HG, Malloy MW, Moco S, Barron D, Battinelli E, et al. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. Nature Food. 2021;2(3):143\u201355.","journal-title":"Nature Food"},{"issue":"1","key":"1314_CR28","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1002\/art.21517","volume":"54","author":"M Magnusson","year":"2006","unstructured":"Magnusson M, Zare F, Tarkowski A. Requirement of type I interferon signaling for arthritis triggered by double-stranded RNA. Arthritis Rheum. 2006;54(1):148\u201357.","journal-title":"Arthritis Rheum"},{"issue":"4","key":"1314_CR29","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1038\/nmeth.4179","volume":"14","author":"TM Gierahn","year":"2017","unstructured":"Gierahn TM, Wadsworth MH, Hughes TK, Bryson BD, Butler A, Satija R, et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods. 2017;14(4):395\u20138.","journal-title":"Nat Methods"},{"key":"1314_CR30","unstructured":"Illumina. bcl2fastq and bcl2fastq2 Conversion Software 2013 [Available from: https:\/\/support.illumina.com\/sequencing\/sequencing_software\/bcl2fastq-conversion-software.html."},{"key":"1314_CR31","unstructured":"Picard toolkit. Broad Institute, GitHub repository. 2019."},{"issue":"1","key":"1314_CR32","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1093\/bioinformatics\/bts635","volume":"29","author":"A Dobin","year":"2013","unstructured":"Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15\u201321.","journal-title":"Bioinformatics"},{"key":"1314_CR33","doi-asserted-by":"crossref","unstructured":"Aterido A, Ca\u00f1ete JD, Tornero J, Ferr\u00e1ndiz C, Pinto JA, Gratac\u00f3s J, et al. Genetic variation at the glycosaminoglycan metabolism pathway contributes to the risk of psoriatic arthritis but not psoriasis. Ann Rheum Dis. 2019;78(3).","DOI":"10.1136\/annrheumdis-2018-214158"},{"issue":"8","key":"1314_CR34","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1002\/art.21972","volume":"54","author":"W Taylor","year":"2006","unstructured":"Taylor W, Gladman D, Helliwell P, Marchesoni A, Mease P, Mielants H. Classification criteria for psoriatic arthritis: development of new criteria from a large international study. Arthritis Rheum. 2006;54(8):2665\u201373.","journal-title":"Arthritis Rheum"},{"issue":"9","key":"1314_CR35","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1136\/ard.2008.094946","volume":"68","author":"CT Ritchlin","year":"2009","unstructured":"Ritchlin CT, Kavanaugh A, Gladman DD, Mease PJ, Helliwell P, Boehncke WH, et al. Treatment recommendations for psoriatic arthritis. Ann Rheum Dis. 2009;68(9):1387\u201394.","journal-title":"Ann Rheum Dis"},{"issue":"1","key":"1314_CR36","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/nbt.4042","volume":"36","author":"HM Kang","year":"2018","unstructured":"Kang HM, Subramaniam M, Targ S, Nguyen M, Maliskova L, McCarthy E, et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat Biotechnol. 2018;36(1):89\u201394.","journal-title":"Nat Biotechnol"},{"issue":"23","key":"1314_CR37","doi-asserted-by":"publisher","first-page":"4569","DOI":"10.1093\/bioinformatics\/btab358","volume":"37","author":"X Huang","year":"2021","unstructured":"Huang X, Huang Y. Cellsnp-lite: an efficient tool for genotyping single cells. Bioinformatics. 2021;37(23):4569\u201371.","journal-title":"Bioinformatics"},{"issue":"1","key":"1314_CR38","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1186\/s13059-019-1865-2","volume":"20","author":"Y Huang","year":"2019","unstructured":"Huang Y, McCarthy DJ, Stegle O. Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. Genome Biol. 2019;20(1):273.","journal-title":"Genome Biol"},{"issue":"6","key":"1314_CR39","doi-asserted-by":"publisher","first-page":"e8746","DOI":"10.15252\/msb.20188746","volume":"15","author":"MD Luecken","year":"2019","unstructured":"Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746.","journal-title":"Mol Syst Biol"},{"issue":"7","key":"1314_CR40","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","volume":"177","author":"T Stuart","year":"2019","unstructured":"Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888-902.e21.","journal-title":"Cell"},{"issue":"5","key":"1314_CR41","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","volume":"36","author":"A Butler","year":"2018","unstructured":"Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411\u201320.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"1314_CR42","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41592-021-01336-8","volume":"19","author":"MD Luecken","year":"2022","unstructured":"Luecken MD, B\u00fcttner M, Chaichoompu K, Danese A, Interlandi M, Mueller MF, et al. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods. 2022;19(1):41\u201350.","journal-title":"Nat Methods"},{"issue":"1","key":"1314_CR43","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1038\/s41467-018-07931-2","volume":"10","author":"G Eraslan","year":"2019","unstructured":"Eraslan G, Simon LM, Mircea M, Mueller NS, Theis FJ. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun. 2019;10(1):390.","journal-title":"Nat Commun"},{"issue":"1","key":"1314_CR44","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"issue":"1","key":"1314_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1758-2946-6-10","volume":"6","author":"D Krstajic","year":"2014","unstructured":"Krstajic D, Buturovic LJ, Leahy DE, Thomas S. Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform. 2014;6(1):1\u201315.","journal-title":"J Cheminform"},{"key":"1314_CR46","doi-asserted-by":"crossref","unstructured":"Efron B, Tibshirani RJ. An introduction to the bootstrap: CRC press; 1994.","DOI":"10.1201\/9780429246593"},{"key":"1314_CR47","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Machine Learn Res. 2011;12:2825\u201330.","journal-title":"J Machine Learn Res"},{"issue":"1","key":"1314_CR48","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1186\/s13059-015-0844-5","volume":"16","author":"G Finak","year":"2015","unstructured":"Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16(1):278.","journal-title":"Genome Biol"},{"issue":"1","key":"1314_CR49","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ygeno.2017.08.007","volume":"110","author":"B Zhao","year":"2018","unstructured":"Zhao B, Erwin A, Xue B. How many differentially expressed genes: a perspective from the comparison of genotypic and phenotypic distances. Genomics. 2018;110(1):67\u201373.","journal-title":"Genomics"},{"issue":"1","key":"1314_CR50","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1038\/s41467-019-14118-w","volume":"11","author":"D Schafflick","year":"2020","unstructured":"Schafflick D, Xu CA, Hartlehnert M, Cole M, Schulte-Mecklenbeck A, Lautwein T, et al. Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis. Nat Commun. 2020;11(1):247.","journal-title":"Nat Commun"},{"key":"1314_CR51","doi-asserted-by":"crossref","unstructured":"Schafflick D, Hartlenert M, Schulte-Mecklenbeck A, Lautwein T, Wolbert J, Horste GM. Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis. Gene Expression Omnibus (GEO). 2019. https:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE138266.","DOI":"10.1101\/403527"},{"issue":"D1","key":"1314_CR52","doi-asserted-by":"publisher","first-page":"D939","DOI":"10.1093\/nar\/gkaa980","volume":"49","author":"S Tweedie","year":"2021","unstructured":"Tweedie S, Braschi B, Gray K, Jones TEM, Seal RL, Yates B, et al. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. 2021;49(D1):D939\u201346.","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"1314_CR53","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1016\/j.cell.2019.08.008","volume":"178","author":"JC Martin","year":"2019","unstructured":"Martin JC, Chang C, Boschetti G, Ungaro R, Giri M, Grout JA, et al. Single-cell analysis of Crohn\u2019s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy. Cell. 2019;178(6):1493-508.e20.","journal-title":"Cell"},{"key":"1314_CR54","unstructured":"Cho J, Martin J, Merad M, Kenigsberg E, Giri M. Single-cell analysis of Crohn\u2019s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy. Gene Expression Omnibus (GEO). 2019. https:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE134809."},{"issue":"2","key":"1314_CR55","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1111\/imm.13370","volume":"164","author":"Y Shi","year":"2021","unstructured":"Shi Y, Xu M, Pan S, Gao S, Ren J, Bai R, et al. Induction of the apoptosis, degranulation and IL-13 production of human basophils by butyrate and propionate via suppression of histone deacetylation. Immunology. 2021;164(2):292\u2013304.","journal-title":"Immunology"},{"issue":"2","key":"1314_CR56","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1002\/cyto.a.24479","volume":"101","author":"C He","year":"2022","unstructured":"He C, Gao S, Zhao X, Shi Y, Tang Y, Cao Y, et al. An efficient and cost-effective method for the purification of human basophils. Cytometry A. 2022;101(2):150\u20138.","journal-title":"Cytometry A"},{"key":"1314_CR57","doi-asserted-by":"publisher","first-page":"946202","DOI":"10.3389\/fimmu.2022.946202","volume":"13","author":"H Li","year":"2022","unstructured":"Li H, Tang Y, Ren J, Bai R, Hu L, Jia W, et al. Identification of novel B-1 transitional progenitors by B-1 lymphocyte fate-mapping transgenic mouse model Bhlhe41 (dTomato-Cre). Front Immunol. 2022;13:946202.","journal-title":"Front Immunol"},{"issue":"5","key":"1314_CR58","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1038\/nprot.2007.173","volume":"2","author":"DD Brand","year":"2007","unstructured":"Brand DD, Latham KA, Rosloniec EF. Collagen-induced arthritis. Nat Protoc. 2007;2(5):1269\u201375.","journal-title":"Nat Protoc"},{"issue":"1","key":"1314_CR59","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1186\/s13075-018-1658-0","volume":"20","author":"SH Park","year":"2018","unstructured":"Park SH, Rhee J, Kim SK, Kang JA, Kwak JS, Son YO, et al. BATF regulates collagen-induced arthritis by regulating T helper cell differentiation. Arthritis Res Ther. 2018;20(1):161.","journal-title":"Arthritis Res Ther"},{"issue":"2","key":"1314_CR60","doi-asserted-by":"publisher","first-page":"R84","DOI":"10.1186\/ar4526","volume":"16","author":"D Woetzel","year":"2014","unstructured":"Woetzel D, Huber R, Kupfer P, Pohlers D, Pfaff M, Driesch D, et al. Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation. Arthritis Res Ther. 2014;16(2):R84.","journal-title":"Arthritis Res Ther"},{"key":"1314_CR61","doi-asserted-by":"crossref","unstructured":"Woetzel D, Huber R, Kupfer P, Pohlers D, Pfaff M, Driesch D, H\u00e4upl T, Koczan D, Stiehl P, Guthke R, Kinne RW. Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation. Gene Expression Omnibus (GEO). 2014. https:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE55235.","DOI":"10.1186\/ar4526"},{"issue":"1","key":"1314_CR62","doi-asserted-by":"publisher","first-page":"2755","DOI":"10.1038\/s41467-018-05044-4","volume":"9","author":"S Tasaki","year":"2018","unstructured":"Tasaki S, Suzuki K, Kassai Y, Takeshita M, Murota A, Kondo Y, et al. Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission. Nat Commun. 2018;9(1):2755.","journal-title":"Nat Commun"},{"key":"1314_CR63","unstructured":"Tasaki S, Toyoshiba H, Takeuchi T. Whole blood gene expression of rheumatoid arthritis. Gene Expression Omnibus (GEO). 2018. https:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE93272."},{"issue":"D1","key":"1314_CR64","doi-asserted-by":"publisher","first-page":"D1005","DOI":"10.1093\/nar\/gky1120","volume":"47","author":"A Buniello","year":"2019","unstructured":"Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D1005\u201312.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"1314_CR65","doi-asserted-by":"publisher","first-page":"D793","DOI":"10.1093\/nar\/gkn665","volume":"37","author":"J Amberger","year":"2009","unstructured":"Amberger J, Bocchini CA, Scott AF, Hamosh A. McKusick\u2019s Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 2009;37(1):D793\u20136.","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"1314_CR66","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1002\/pro.3715","volume":"28","author":"M Kanehisa","year":"2019","unstructured":"Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947\u201351.","journal-title":"Protein Sci"},{"issue":"5795","key":"1314_CR67","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1126\/science.1132939","volume":"313","author":"J Lamb","year":"2006","unstructured":"Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929\u201335.","journal-title":"Science"},{"issue":"7803","key":"1314_CR68","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1038\/s41586-020-2188-x","volume":"580","author":"K Luck","year":"2020","unstructured":"Luck K, Kim D-K, Lambourne L, Spirohn K, Begg BE, Bian W, et al. A reference map of the human binary protein interactome. Nature. 2020;580(7803):402\u20138.","journal-title":"Nature"},{"issue":"10","key":"1314_CR69","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1038\/nrrheum.2009.181","volume":"5","author":"PC Taylor","year":"2009","unstructured":"Taylor PC, Feldmann M. Anti-TNF biologic agents: still the therapy of choice for rheumatoid arthritis. Nat Rev Rheumatol. 2009;5(10):578\u201382.","journal-title":"Nat Rev Rheumatol"},{"key":"1314_CR70","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jaut.2016.10.005","volume":"77","author":"M Rizzi","year":"2017","unstructured":"Rizzi M, Lorenzetti R, Fischer K, Staniek J, Janowska I, Troilo A, et al. Impact of tofacitinib treatment on human B-cells in vitro and in vivo. J Autoimmun. 2017;77:55\u201366.","journal-title":"J Autoimmun"},{"key":"1314_CR71","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3389\/fimmu.2019.00201","volume":"10","author":"S Hausser-Kinzel","year":"2019","unstructured":"Hausser-Kinzel S, Weber MS. The role of B cells and antibodies in multiple sclerosis, neuromyelitis optica, and related disorders. Front Immunol. 2019;10:8.","journal-title":"Front Immunol"},{"issue":"4","key":"1314_CR72","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1002\/art.38995","volume":"67","author":"J Belasco","year":"2015","unstructured":"Belasco J, Louie JS, Gulati N, Wei N, Nograles K, Fuentes-Duculan J, et al. Comparative genomic profiling of synovium versus skin lesions in psoriatic arthritis. Arthritis Rheumatol. 2015;67(4):934\u201344.","journal-title":"Arthritis Rheumatol"},{"issue":"5","key":"1314_CR73","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1136\/annrheumdis-2020-218186","volume":"80","author":"A Nerviani","year":"2021","unstructured":"Nerviani A, Boutet MA, Tan WSG, Goldmann K, Purkayastha N, Lajtos TA, et al. IL-23 skin and joint profiling in psoriatic arthritis: novel perspectives in understanding clinical responses to IL-23 inhibitors. Ann Rheum Dis. 2021;80(5):591\u20137.","journal-title":"Ann Rheum Dis"},{"key":"1314_CR74","doi-asserted-by":"publisher","first-page":"154960","DOI":"10.1016\/j.cyto.2019.154960","volume":"127","author":"EJ Lee","year":"2020","unstructured":"Lee EJ, Lilja S, Li X, Sch\u00e4fer S, Zhang H, Benson M. Bulk and single cell transcriptomic data indicate that a dichotomy between inflammatory pathways in peripheral blood and arthritic joints complicates biomarker discovery. Cytokine. 2020;127:154960.","journal-title":"Cytokine"},{"key":"1314_CR75","unstructured":"FDA U. Paving the way for personalized medicine. FDA's Role in a new Era of Medical Product Development US Department of Health and Human Services. 2013:1\u201361."},{"issue":"1","key":"1314_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-05116-5","volume":"9","author":"F Cheng","year":"2018","unstructured":"Cheng F, Desai RJ, Handy DE, Wang R, Schneeweiss S, Barab\u00e1si A-L, et al. Network-based approach to prediction and population-based validation of in silico drug repurp4osing. Nat Commun. 2018;9(1):1\u201312.","journal-title":"Nat Commun"},{"issue":"3","key":"1314_CR77","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1038\/s41588-020-00774-y","volume":"53","author":"F Cheng","year":"2021","unstructured":"Cheng F, Zhao J, Wang Y, Lu W, Liu Z, Zhou Y, et al. Comprehensive characterization of protein\u2013protein interactions perturbed by disease mutations. Nat Genet. 2021;53(3):342\u201353.","journal-title":"Nat Genet"},{"issue":"7971","key":"1314_CR78","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1038\/s41586-023-06311-1","volume":"619","author":"K Kanemaru","year":"2023","unstructured":"Kanemaru K, Cranley J, Muraro D, Miranda AMA, Ho SY, Wilbrey-Clark A, et al. Spatially resolved multiomics of human cardiac niches. Nature. 2023;619(7971):801\u201310.","journal-title":"Nature"},{"issue":"1","key":"1314_CR79","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41540-017-0009-0","volume":"3","author":"J Menche","year":"2017","unstructured":"Menche J, Guney E, Sharma A, Branigan PJ, Loza MJ, Baribaud F, et al. Integrating personalized gene expression profiles into predictive disease-associated gene pools. npj Syst Biol Appl. 2017;3(1):10.","journal-title":"npj Syst Biol Appl"},{"key":"1314_CR80","doi-asserted-by":"crossref","unstructured":"Nelson SML, Nguyen TM, McDonald JWD, MacDonald JK. Natalizumab for induction of remission in Crohn's disease. Cochrane Database of Systematic Reviews. 2018(8).","DOI":"10.1002\/14651858.CD006097.pub3"},{"issue":"3","key":"1314_CR81","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/0022-2836(70)90057-4","volume":"48","author":"SB Needleman","year":"1970","unstructured":"Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 1970;48(3):443\u201353.","journal-title":"J Mol Biol"},{"issue":"1","key":"1314_CR82","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","volume":"147","author":"TF Smith","year":"1981","unstructured":"Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol. 1981;147(1):195\u20137.","journal-title":"J Mol Biol"},{"issue":"22","key":"1314_CR83","doi-asserted-by":"publisher","first-page":"10915","DOI":"10.1073\/pnas.89.22.10915","volume":"89","author":"S Henikoff","year":"1992","unstructured":"Henikoff S, Henikoff JG. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A. 1992;89(22):10915\u20139.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"1314_CR84","doi-asserted-by":"publisher","first-page":"10775","DOI":"10.1038\/srep10775","volume":"5","author":"A Koussounadis","year":"2015","unstructured":"Koussounadis A, Langdon SP, Um IH, Harrison DJ, Smith VA. Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system. Sci Rep. 2015;5(1):10775.","journal-title":"Sci Rep"},{"issue":"2","key":"1314_CR85","doi-asserted-by":"publisher","first-page":"e17258","DOI":"10.1371\/journal.pone.0017258","volume":"6","author":"J Gillis","year":"2011","unstructured":"Gillis J, Pavlidis P. The impact of multifunctional genes on\" guilt by association\" analysis. PLoS ONE. 2011;6(2):e17258.","journal-title":"PLoS ONE"},{"key":"1314_CR86","doi-asserted-by":"crossref","unstructured":"Lin A, Giuliano CJ, Palladino A, John KM, Abramowicz C, Yuan ML, et al. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci Transl Med. 2019;11(509).","DOI":"10.1126\/scitranslmed.aaw8412"},{"key":"1314_CR87","doi-asserted-by":"crossref","unstructured":"Rusu A, Tanase C, Pascu GA, Todoran N. Recent Advances Regarding the Therapeutic Potential of Adapalene. Pharmaceuticals (Basel). 2020;13(9).","DOI":"10.3390\/ph13090217"},{"issue":"12 Suppl 1","key":"1314_CR88","first-page":"I284","volume":"106","author":"NK Chanani","year":"2002","unstructured":"Chanani NK, Cowan DB, Takeuchi K, Poutias DN, Garcia LM, del Nido PJ, et al. Differential effects of amrinone and milrinone upon myocardial inflammatory signaling. Circulation. 2002;106(12 Suppl 1):I284\u20139.","journal-title":"Circulation"},{"issue":"7549","key":"1314_CR89","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1038\/520609a","volume":"520","author":"NJ Schork","year":"2015","unstructured":"Schork NJ. Personalized medicine: time for one-person trials. Nature. 2015;520(7549):609\u201311.","journal-title":"Nature"},{"key":"1314_CR90","unstructured":"Sch\u00e4fer S, Lilja S, Smelik M, Eklund D, Li X, Sysoev O, Zhang H, Zhao Y, Kov\u00e1cs IA, Gustafsson M, Gawel D, Wang H, Benson M. Network analyses of single cell-based digital twins for individualized treatment of antigen induced arthritis in mice. Gene Expression Omnibus (GEO). 2023. https:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE193536."},{"key":"1314_CR91","doi-asserted-by":"publisher","unstructured":"Sch\u00e4fer S. scDrugPrio data sets and metadata. Figshare+ 2024. https:\/\/doi.org\/10.25452\/figshare.plus.c.7094116.","DOI":"10.25452\/figshare.plus.c.7094116"},{"key":"1314_CR92","unstructured":"Sch\u00e4fer S. scDrugPrio codes and vignettes. GitHub 2024. https:\/\/github.com\/SDTC-CPMed\/scDrugPrio."}],"container-title":["Genome Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13073-024-01314-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13073-024-01314-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13073-024-01314-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T10:05:21Z","timestamp":1710929121000},"score":1,"resource":{"primary":{"URL":"https:\/\/genomemedicine.biomedcentral.com\/articles\/10.1186\/s13073-024-01314-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,20]]},"references-count":92,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1314"],"URL":"https:\/\/doi.org\/10.1186\/s13073-024-01314-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.11.08.566249","asserted-by":"object"}]},"ISSN":["1756-994X"],"issn-type":[{"value":"1756-994X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,20]]},"assertion":[{"value":"2 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All psoriatic arthritis patients and healthy controls provided written informed consent to participate in the study. The study was approved by Hospital Universitari Vall d'Hebron Clinical Research Ethics Committee with reference number 20\/0022. Protocols were reviewed and approved by the local institutional review board of each participating centre. This research conformed to the principles of the Helsinki Declaration. All experimental procedures related to the AIA mouse model were performed according to the guidelines provided by the Swedish Animal Welfare Act and approved by the Ethical Committee for Research on Animals in Stockholm, Sweden (N271-14). All experimental procedures related to the CIA mouse model were performed in accordance with protocols approved by the Animal Experimental Ethics Committee of Xuzhou Medical University (202012A162).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"MB is the scientific founder of Mavatar, Inc. DRG is employed by Mavatar, Inc. H.H. is co-founder and shareholder of Omniscope, SAB member of Nanostring and MiRXES, and consultant to Moderna and Singularity. JL is coscientific founder of Scipher Medicine. The remaining authors declare that they do not have any competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"42"}}