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dc.contributor.authorO'Rourke, Matthew B.
dc.contributor.authorJanuszewski, Andrzej S.
dc.contributor.authorSullivan, David R.
dc.contributor.authorLengyel, Imre
dc.contributor.authorStewart, Alan J.
dc.contributor.authorArya, Swati
dc.contributor.authorMa, Ronald
dc.contributor.authorGalande, Sanjeev
dc.contributor.authorHardikar, Anandwardhan A.
dc.contributor.authorJoglekar, Mugdha V.
dc.contributor.authorKeech, Anthony C.
dc.contributor.authorJenkins, Alicia J.
dc.contributor.authorMolloy, Mark P.
dc.date.accessioned2023-03-23T12:30:07Z
dc.date.available2023-03-23T12:30:07Z
dc.date.issued2023-05-16
dc.identifier283550981
dc.identifierf01f5225-d757-459e-a2c3-55e8dedad716
dc.identifier85150893753
dc.identifier.citationO'Rourke , M B , Januszewski , A S , Sullivan , D R , Lengyel , I , Stewart , A J , Arya , S , Ma , R , Galande , S , Hardikar , A A , Joglekar , M V , Keech , A C , Jenkins , A J & Molloy , M P 2023 , ' Optimised plasma sample preparation and LC-MS analysis to support large-scale proteomic analysis of clinical trial specimens : application to the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial ' , Proteomics - Clinical Applications , vol. 17 , no. 3 , 2200106 . https://doi.org/10.1002/prca.202200106en
dc.identifier.issn1862-8346
dc.identifier.otherORCID: /0000-0001-7978-9507/work/132763903
dc.identifier.otherORCID: /0000-0003-4580-1840/work/132764481
dc.identifier.urihttps://hdl.handle.net/10023/27246
dc.descriptionFunding: This work was performed by funding from The University of Sydney (CIA Jenkins) and funds provided by the National Health and Medical Research Council (Australia) APP1147897.en
dc.description.abstractPurpose: Robust, affordable plasma proteomic biomarker workflows are needed for large-scale clinical studies. We evaluated aspects of sample preparation to allow LC-MS analysis of more than 1500 samples from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial of adults with type 2 diabetes. Methods: Using LC-MS with data-independent acquisition we evaluated four variables: plasma protein depletion, EDTA or citrated anti-coagulant blood collection tubes, plasma lipid depletion strategies and plasma freeze-thaw cycles. Optimised methods were applied in a pilot study of FIELD participants. Results: LC-MS of undepleted plasma conducted over a 45 min gradient yielded 172 proteins after excluding immunoglobulin isoforms. Cibachrome-blue-based depletion yielded additional proteins but with cost and time expenses, while immunodepleting albumin and IgG provided few additional identifications. Only minor variations were associated with blood collection tube type, delipidation methods and freeze-thaw cycles. From 65 batches involving over 1500 injections, the median intra-batch quantitative differences in the top 100 proteins of the plasma external standard was less than 2%. Fenofibrate altered seven plasma proteins. Conclusions and Clinical Relevance: A robust plasma handling and LC-MS proteomics workflow for abundant plasma proteins has been developed for large-scale biomarker studies that balances proteomic depth with time and resource costs.
dc.format.extent10
dc.format.extent744083
dc.language.isoeng
dc.relation.ispartofProteomics - Clinical Applicationsen
dc.subjectPlasmaen
dc.subjectMass spectrometryen
dc.subjectProteomicsen
dc.subjectBiomarkeren
dc.subjectDiabetesen
dc.subjectFenofibrateen
dc.subjectRC Internal medicineen
dc.subjectQH301 Biologyen
dc.subjectATC-NDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectMCCen
dc.subject.lccRCen
dc.subject.lccQH301en
dc.titleOptimised plasma sample preparation and LC-MS analysis to support large-scale proteomic analysis of clinical trial specimens : application to the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trialen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.contributor.institutionUniversity of St Andrews. University of St Andrewsen
dc.identifier.doi10.1002/prca.202200106
dc.description.statusPeer revieweden


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