Liquid chromatography-mass spectrometry (LC-MS)-based proteomics studies of large sample cohorts can easily require from months to years to complete. Acquiring consistent, high-quality data in such large-scale studies is challenging because of normal variations in instrumentation performance over time, as well as artifacts introduced by the samples themselves, such as those because of collection, storage and processing. Existing quality control methods for proteomics data primarily focus on post-hoc analysis to remove low-quality data that would degrade downstream statistics; they are not designed to evaluate the data in near real-time, which would allow for interventions as soon as deviations in data quality are detected. In addition to flagging analyses that demonstrate outlier behavior, evaluating how the data structure changes over time can aide in understanding typical instrument performance or identify issues such as a degradation in data quality because of the need for instrument cleaning and/or re-calibration. To address this gap for proteomics, we developed Quality Control Analysis in Real-Time (QC-ART), a tool for evaluating data as they are acquired to dynamically flag potential issues with instrument performance or sample quality. QC-ART has similar accuracy as standard post-hoc analysis methods with the additional benefit of real-time analysis. We demonstrate the utility and performance of QC-ART in identifying deviations in data quality because of both instrument and sample issues in near real-time for LC-MS-based plasma proteomics analyses of a sample subset of The Environmental Determinants of Diabetes in the Young cohort. We also present a case where QC-ART facilitated the identification of oxidative modifications, which are often underappreciated in proteomic experiments.
Bioinformatics software
Biostatistics
Quality control and metrics
Quantification
Data evaluation
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Author contributions: B.A.S., E.S.N., L.M.B., B.-J.W.-R., and T.O.M. designed research; B.A.S., E.S.N., L.M.B., A.M.T., C.K.A., T.C., M.A.G., M.E.M., R.J.M., D.J.O., P.D.P., and A.A.S. performed research; B.A.S., E.S.N., L.M.B., A.M.T., C.K.A., M.E.M., D.J.O., P.D.P., B.-J.W.-R., and T.O.M. analyzed data; B.A.S., E.S.N., B.-J.W.-R., and T.O.M. wrote the paper; R.D.S. and T.S.G. contributed new reagents/analytic tools.
The TEDDY Study Group is funded by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, and UC4 DK100238 and by Contract no. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Centers for Disease Control and Prevention (CDC), and JDRF. This work is supported in part by the National Institutes of Health/National Center for Advancing Translational Sciences Clinical and Translational Science Awards UL1 TR000064 (University of Florida) and the University of Colorado (UL1 TR001082), and TEDDY grant UC4 DK100238. Proteomics measurements were obtained using capabilities developed partially under National Institutes of Health grant P41GM103493 and were performed in the Environmental Molecular Sciences Laboratory, a U.S. Department Of Energy (DOE) sponsored national scientific user facility at Pacific Northwest National Laboratory (PNNL) in Richland, WA. Battelle operates PNNL for the DOE under contract DE-AC05-76RLO01830.
