Implementation of statistical process control for proteomic experiments via LC MS/MS

Abstract

Statistical process control (SPC) is a robust set of tools that aids in the visualization, detection, and identification of assignable causes of variation in any process that creates products, services, or information. A tool has been developed termed Statistical Process Control in Proteomics (SProCoP) which implements aspects of SPC (e.g., control charts and Pareto analysis) into the Skyline proteomics software. It monitors five quality control metrics in a shotgun or targeted proteomic workflow. None of these metrics require peptide identification. The source code, written in the R statistical language, runs directly from the Skyline interface, which supports the use of raw data files from several of the mass spectrometry vendors. It provides real time evaluation of the chromatographic performance (e.g., retention time reproducibility, peak asymmetry, and resolution), and mass spectrometric performance (targeted peptide ion intensity and mass measurement accuracy for high resolving power instruments) via control charts. Thresholds are experiment- and instrument-specific and are determined empirically from user-defined quality control standards that enable the separation of random noise and systematic error. Finally, Pareto analysis provides a summary of performance metrics and guides the user to metrics with high variance. The utility of these charts to evaluate proteomic experiments is illustrated in two case studies.

Figure 1

A) A diagram summarizing the workflow for using SPC in a proteomics experiment. QC data is imported into Skyline where a control chart matrix, Pareto analysis and box plots are outputted. These charts illustrate a representative output from SProCoP when using a high resolving power mass spectrometer. The box plots of MMA would not be displayed if using a low RP mass spectrometer. B) The process is systematically evaluated approximately every 4–5 injections. C) Scan cycle consists of a MS1 full scan followed by targeted MS/MS or SRM scans.

Figure 2

A) A bar graph of the integrated peak areas of a representative peptide (FFVAPFPEVFGK) across the study. B) Use of a control chart to visualize and quantify the systematic shift of the peptide abundance to outside the empirically defined threshold. C) Other peptides that were monitored show a similar trend. Black arrow marks the first QC that was run after the bath gas was replenished.

Figure 3

Control charts for peak areas (1^st row) and retention time (2^nd row) for 5 targeted peptides in an experiment where the traps (n=3) were changed every 5 QC runs. Thresholds were established on the first trap (n=5 QC standards). The majority of peak areas were within the thresholds (± 3 sd); however, the abundance of the GASIVDK peptide increased significantly using the final trap. The retention times shifted significantly for all peptides and is easily seen in the control charts and summarized in the Pareto Chart. The majority of non-conformers were related to retention time reproducibility (80%).