An Automated Pipeline to Monitor System Performance in Liquid Chromatography–Tandem Mass Spectrometry Proteomic Experiments

Abstract

We report the development of a completely automated pipeline to monitor system suitability in bottom-up proteomic experiments. LC–MS/MS runs are automatically imported into Skyline and multiple identification-free metrics are extracted from targeted peptides. These data are then uploaded to the Panorama Skyline document repository where metrics can be viewed in a web-based interface using powerful process control techniques, including Levey–Jennings and Pareto plots. The interface is versatile and takes user input, which allows the user significant control over the visualization of the data. The pipeline is vendor and instrument-type neutral, supports multiple acquisition techniques (e.g., MS 1 filtering, data-independent acquisition, parallel reaction monitoring, and selected reaction monitoring), can track performance of multiple instruments, and requires no manual intervention aside from initial setup. Data can be viewed from any computer with Internet access and a web browser, facilitating sharing of QC data between researchers. Herein, we describe the use of this pipeline, termed Panorama AutoQC, to evaluate LC–MS/MS performance in a range of scenarios including identification of suboptimal instrument performance, evaluation of ultrahigh pressure chromatography, and identification of the major sources of variation throughout years of peptide data collection.

Michael S. Bereman, Joshua Beri, Vagisha Sharma, Cory Nathe, Josh Eckels, Brendan MacLean, and Michael J. MacCoss. An Automated Pipeline to Monitor System Performance in Liquid Chromatography–Tandem Mass Spectrometry Proteomic Experiments. Journal of Proteome Research 2016 15 (12), 4763-4769, DOI: 10.1021/acs.jproteome.6b00744

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