Nekesa C. Oliver, Min Ji Choi, Albert B. Arul, Marsalas D. Whitaker, Renã A. S. Robinson
{"title":"建立大规模血浆蛋白质组样品制备的质量控制指标","authors":"Nekesa C. Oliver, Min Ji Choi, Albert B. Arul, Marsalas D. Whitaker, Renã A. S. Robinson","doi":"10.1021/acsmeasuresciau.3c00070","DOIUrl":null,"url":null,"abstract":"Large-scale plasma proteomics studies have been transformed due to the multiplexing and automation of sample preparation workflows. However, these workflows can suffer from reproducibility issues, a lack of standardized quality control (QC) metrics, and the assessment of variation before liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. The incorporation of robust QC metrics in sample preparation workflows ensures better reproducibility, lower assay variation, and better-informed decisions for troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of patient samples (<i>N</i> = 808) using tandem mass tag (TMT) 16-plex batches (<i>N</i> = 58). The proteomic workflow consisted of protein depletion, protein digestion, TMT labeling, and fractionation. Five QC sample types (QC<sub>std</sub>, QC<sub>dig</sub>, QC<sub>pool</sub>, QC<sub>TMT</sub>, and QC<sub>BSA</sub>) were created to measure the performance of sample preparation prior to the final LC–MS/MS analysis. We measured <10% CV for individual sample preparation steps in the proteomic workflow based on data from various QC sample steps. The establishment of robust measures for QC of sample preparation steps allowed for greater confidence in prepared samples for subsequent LC–MS/MS analysis. This study also provides recommendations for standardized QC metrics that can assist with future large-scale cohort sample preparation workflows.","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"90 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Establishing Quality Control Metrics for Large-Scale Plasma Proteomic Sample Preparation\",\"authors\":\"Nekesa C. Oliver, Min Ji Choi, Albert B. Arul, Marsalas D. Whitaker, Renã A. S. Robinson\",\"doi\":\"10.1021/acsmeasuresciau.3c00070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Large-scale plasma proteomics studies have been transformed due to the multiplexing and automation of sample preparation workflows. However, these workflows can suffer from reproducibility issues, a lack of standardized quality control (QC) metrics, and the assessment of variation before liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. The incorporation of robust QC metrics in sample preparation workflows ensures better reproducibility, lower assay variation, and better-informed decisions for troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of patient samples (<i>N</i> = 808) using tandem mass tag (TMT) 16-plex batches (<i>N</i> = 58). The proteomic workflow consisted of protein depletion, protein digestion, TMT labeling, and fractionation. Five QC sample types (QC<sub>std</sub>, QC<sub>dig</sub>, QC<sub>pool</sub>, QC<sub>TMT</sub>, and QC<sub>BSA</sub>) were created to measure the performance of sample preparation prior to the final LC–MS/MS analysis. We measured <10% CV for individual sample preparation steps in the proteomic workflow based on data from various QC sample steps. The establishment of robust measures for QC of sample preparation steps allowed for greater confidence in prepared samples for subsequent LC–MS/MS analysis. This study also provides recommendations for standardized QC metrics that can assist with future large-scale cohort sample preparation workflows.\",\"PeriodicalId\":29800,\"journal\":{\"name\":\"ACS Measurement Science Au\",\"volume\":\"90 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Measurement Science Au\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1021/acsmeasuresciau.3c00070\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Measurement Science Au","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1021/acsmeasuresciau.3c00070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Establishing Quality Control Metrics for Large-Scale Plasma Proteomic Sample Preparation
Large-scale plasma proteomics studies have been transformed due to the multiplexing and automation of sample preparation workflows. However, these workflows can suffer from reproducibility issues, a lack of standardized quality control (QC) metrics, and the assessment of variation before liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. The incorporation of robust QC metrics in sample preparation workflows ensures better reproducibility, lower assay variation, and better-informed decisions for troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of patient samples (N = 808) using tandem mass tag (TMT) 16-plex batches (N = 58). The proteomic workflow consisted of protein depletion, protein digestion, TMT labeling, and fractionation. Five QC sample types (QCstd, QCdig, QCpool, QCTMT, and QCBSA) were created to measure the performance of sample preparation prior to the final LC–MS/MS analysis. We measured <10% CV for individual sample preparation steps in the proteomic workflow based on data from various QC sample steps. The establishment of robust measures for QC of sample preparation steps allowed for greater confidence in prepared samples for subsequent LC–MS/MS analysis. This study also provides recommendations for standardized QC metrics that can assist with future large-scale cohort sample preparation workflows.
期刊介绍:
ACS Measurement Science Au is an open access journal that publishes experimental computational or theoretical research in all areas of chemical measurement science. Short letters comprehensive articles reviews and perspectives are welcome on topics that report on any phase of analytical operations including sampling measurement and data analysis. This includes:Chemical Reactions and SelectivityChemometrics and Data ProcessingElectrochemistryElemental and Molecular CharacterizationImagingInstrumentationMass SpectrometryMicroscale and Nanoscale systemsOmics (Genomics Proteomics Metabonomics Metabolomics and Bioinformatics)Sensors and Sensing (Biosensors Chemical Sensors Gas Sensors Intracellular Sensors Single-Molecule Sensors Cell Chips Arrays Microfluidic Devices)SeparationsSpectroscopySurface analysisPapers dealing with established methods need to offer a significantly improved original application of the method.