Louise Bundgaard*, Filip Årman, Emma Åhrman, Marie Walters, Ulrich auf dem Keller, Johan Malmström and Stine Jacobsen,
{"title":"An Equine Protein Atlas Highlights Synovial Fluid Proteome Dynamics during Experimentally LPS-Induced Arthritis","authors":"Louise Bundgaard*, Filip Årman, Emma Åhrman, Marie Walters, Ulrich auf dem Keller, Johan Malmström and Stine Jacobsen, ","doi":"10.1021/acs.jproteome.4c0012510.1021/acs.jproteome.4c00125","DOIUrl":null,"url":null,"abstract":"<p >In human proteomics, substantial efforts are ongoing to leverage large collections of mass spectrometry (MS) fragment ion spectra into extensive spectral libraries (SL) as a resource for data independent acquisition (DIA) analysis. Currently, such initiatives in equine research are still missing. Here we present a large-scale equine SL, comprising 6394 canonical proteins and 89,329 unique peptides, based on data dependent acquisition analysis of 75 tissue and body fluid samples from horses. The SL enabled large-scale DIA-MS based quantification of the same samples to generate a quantitative equine protein distribution atlas to infer dominant proteins in different organs and body fluids. Data mining revealed 163 proteins uniquely identified in a specific type of tissue or body fluid, serving as a starting point to determine tissue-specific or tissue-type-specific proteins. We showcase the SL by highlighting proteome dynamics in equine synovial fluid samples during experimental lipopolysaccharide-induced arthritis. A fuzzy c-means cluster analysis pinpointed SERPINB1, ATRN, NGAL, LTF, MMP1, and LBP as putative biomarkers for joint inflammation. This SL provides an extendable resource for future equine studies employing DIA-MS.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jproteome.4c00125","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"99","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00125","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In human proteomics, substantial efforts are ongoing to leverage large collections of mass spectrometry (MS) fragment ion spectra into extensive spectral libraries (SL) as a resource for data independent acquisition (DIA) analysis. Currently, such initiatives in equine research are still missing. Here we present a large-scale equine SL, comprising 6394 canonical proteins and 89,329 unique peptides, based on data dependent acquisition analysis of 75 tissue and body fluid samples from horses. The SL enabled large-scale DIA-MS based quantification of the same samples to generate a quantitative equine protein distribution atlas to infer dominant proteins in different organs and body fluids. Data mining revealed 163 proteins uniquely identified in a specific type of tissue or body fluid, serving as a starting point to determine tissue-specific or tissue-type-specific proteins. We showcase the SL by highlighting proteome dynamics in equine synovial fluid samples during experimental lipopolysaccharide-induced arthritis. A fuzzy c-means cluster analysis pinpointed SERPINB1, ATRN, NGAL, LTF, MMP1, and LBP as putative biomarkers for joint inflammation. This SL provides an extendable resource for future equine studies employing DIA-MS.