Tsantilas Kristine A, Merrihew Gennifer E, Robbins Julia E, Johnson Richard S, Park Jea, Plubell Deanna L, Canterbury Jesse D, Huang Eric, Riffle Michael, Sharma Vagisha, MacLean Brendan X, Eckels Josh, Wu Christine C, Bereman Michael S, Spencer Sandra E, Hoofnagle Andrew N, MacCoss Michael J
Department of Genome Sciences, University of Washington, Washington 98195, United States.
Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States.
bioRxiv. 2024 Aug 11:2024.04.12.589318. doi: 10.1101/2024.04.12.589318.
A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.
在从规划到分析的工作流程的每个阶段，对自下而上蛋白质组学数据的质量、可重复性和变异性进行全面评估都是必要的。我们分享了一些应用适应性质量控制（QC）措施来评估样品制备、系统功能和定量分析的小案例。使用靶向方法对系统适用性样品进行纵向重复测量，我们还分享了在三个仪器平台上使用这些样品来识别严重系统故障并在数月至数年的时间内跟踪功能的示例。在蛋白质和肽水平纳入的内部QC使我们的团队能够评估样品制备问题，并区分系统故障和样品特异性问题。与我们的实验样品一起制备的外部QC样品用于在评估生物学表型之前，在批次校正和标准化过程中验证我们结果的一致性和定量潜力。我们将这些控制与快速分析（Skyline）、纵向QC指标（AutoQC）和基于服务器的数据存储（PanoramaWeb）相结合。我们建议，这种综合的QC方法是各团队进行快速质量控制评估的有用起点，以确保利用宝贵的仪器时间收集尽可能高质量的数据。数据可在Panorama Public和ProteomeXchange上获取，标识符为PXD051318。
