{"title":"Design of experiments approach for systematic optimization of a single-shot diaPASEF plasma proteomics workflow applicable for high-throughput.","authors":"Shawn J Rice, Chandra P Belani","doi":"10.1002/prca.202300006","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Plasma is an abundant source of protein biomarkers. Mass spectrometry (MS) is an effective means to measure a large number of proteins in a single run. The recent development of data-independent acquisition with parallel accumulation and serial fragmentation (diaPASEF) on a trapped ion mobility spectrometer (TIMS) affords deep proteomic coverage with short liquid chromatography gradients. In this work, we utilized a process optimization approach, design of experiments (DoE), to maximize precursor identification for a plasma proteomic diaPASEF workflow.</p><p><strong>Experimental design: </strong>A partial factorial design was used to screen 11 sample preparation factors and six diaPASEF MS acquisition factors. Selected factors were optimized using the response surface method.</p><p><strong>Results: </strong>Three important sample preparation factors and the two important MS acquisition factors were identified in the screening experiments and were selected for separate optimization experiments. The optimal parameters were compared to our standard plasma proteomics workflows using either a 1-h or overnight trypsin digestion. The optimized method outperformed the 1-h digestion, and it was similar in performance to the overnight digestion, however, the optimized method could be completed in a day.</p><p><strong>Conclusion and clinical relevance: </strong>We have used DoE to report an optimized plasma proteomics workflow for diaPASEF, however, established methods are already highly optimized, and resources may be better spent on running samples than comprehensive optimization.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/prca.202300006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/31 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Plasma is an abundant source of protein biomarkers. Mass spectrometry (MS) is an effective means to measure a large number of proteins in a single run. The recent development of data-independent acquisition with parallel accumulation and serial fragmentation (diaPASEF) on a trapped ion mobility spectrometer (TIMS) affords deep proteomic coverage with short liquid chromatography gradients. In this work, we utilized a process optimization approach, design of experiments (DoE), to maximize precursor identification for a plasma proteomic diaPASEF workflow.
Experimental design: A partial factorial design was used to screen 11 sample preparation factors and six diaPASEF MS acquisition factors. Selected factors were optimized using the response surface method.
Results: Three important sample preparation factors and the two important MS acquisition factors were identified in the screening experiments and were selected for separate optimization experiments. The optimal parameters were compared to our standard plasma proteomics workflows using either a 1-h or overnight trypsin digestion. The optimized method outperformed the 1-h digestion, and it was similar in performance to the overnight digestion, however, the optimized method could be completed in a day.
Conclusion and clinical relevance: We have used DoE to report an optimized plasma proteomics workflow for diaPASEF, however, established methods are already highly optimized, and resources may be better spent on running samples than comprehensive optimization.