Andrew D. Couse, Sarah J. Cox-Vazquez*, Subhadip Ghatak, Jonathan C. Trinidad and David E. Clemmer*,
{"title":"Delineating Bovine Milk Derived Microvesicles from Exosomes Using Proteomics","authors":"Andrew D. Couse, Sarah J. Cox-Vazquez*, Subhadip Ghatak, Jonathan C. Trinidad and David E. Clemmer*, ","doi":"10.1021/acs.jproteome.4c00352","DOIUrl":null,"url":null,"abstract":"<p >In the work presented herein, a simple serial-pelleting purification strategy combined with a mass spectrometry-based proteomics analysis was developed as a means of discerning differences in extracellular vesicle (EV) populations found in bovine milk samples. A sequence of ultracentrifugation speeds was used to generate changes in the abundances of EV populations, allowing for the identification of associated proteins. A metric was developed to determine the relative abundances of proteins in large EVs (>200 nm) and small EVs (<200 nm). Of the 476 proteins consistently found in this study, 340 are associated with vesicular components. Of these, 156 were heavily enriched in large EVs, 155 shared between large and small EVs, and 29 heavily enriched in small EVs. Additionally, out of 68 proteins annotated as exosome proteins, 32 were enriched in large EVs, 27 shared between large and small EVs, 5 enriched in small EVs, and 7 were found to be nonvesicular contaminant proteins. The top correlated proteins in the small EV group were predominantly membrane-bound proteins, whereas the top correlated proteins in the large EV group were mostly cytosolic enzymes for molecular processing. This method provides a means of assessing the origins of vesicle components and provides new potential marker proteins within discrete vesicle populations.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Proteome Research","FirstCategoryId":"99","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00352","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
In the work presented herein, a simple serial-pelleting purification strategy combined with a mass spectrometry-based proteomics analysis was developed as a means of discerning differences in extracellular vesicle (EV) populations found in bovine milk samples. A sequence of ultracentrifugation speeds was used to generate changes in the abundances of EV populations, allowing for the identification of associated proteins. A metric was developed to determine the relative abundances of proteins in large EVs (>200 nm) and small EVs (<200 nm). Of the 476 proteins consistently found in this study, 340 are associated with vesicular components. Of these, 156 were heavily enriched in large EVs, 155 shared between large and small EVs, and 29 heavily enriched in small EVs. Additionally, out of 68 proteins annotated as exosome proteins, 32 were enriched in large EVs, 27 shared between large and small EVs, 5 enriched in small EVs, and 7 were found to be nonvesicular contaminant proteins. The top correlated proteins in the small EV group were predominantly membrane-bound proteins, whereas the top correlated proteins in the large EV group were mostly cytosolic enzymes for molecular processing. This method provides a means of assessing the origins of vesicle components and provides new potential marker proteins within discrete vesicle populations.
期刊介绍:
Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".