Schilling - Longitudinal SSS

MSstatsQC: Longitudinal system suitability monitoring and quality control for targeted proteomic experiments

  • Organism: Homo sapiens, Bovine
  • Instrument: Triple Quadrupoles
  • SpikeIn: No
Selected Reaction Monitoring (SRM) is a powerful tool for targeted detection and quantification of peptides in complex matrices. An important objective of SRM is to obtain peptide quantifications that are (1) suitable for the purpose of the investigation, and (2) reproducible across laboratories and runs. The first objective is achieved by system suitability tests (SST), which verify that mass spectrometric instrumentation performs as specified. The second objective is achieved by quality control (QC), which provides in-process quality assurance of the sample profile. A common aspect of SST and QC is the longitudinal nature of the data. Although SST and QC have received a lot of attention in the proteomic community, the currently used statistical methods are fairly limited. This manuscript improves upon the statistical methodology for SST and QC that is currently used in proteomics. It adapts the modern methods of longitudinal statistical process control, such as simultaneous and time weighted control charts and change point analysis, to SST and QC of SRM experiments, discusses their advantages, and provides practical guidelines. Evaluations on simulated datasets, and on datasets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium, demonstrated that these methods substantially improve our ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems. We implemented the methods in an open-source R-based software package MSstatsQC and its web-based graphical user interface. They are available for use stand-alone, or for integration with automated pipelines. Although the examples focus on targeted proteomics, the statistical methods in this manuscript apply more generally to quantitative proteomics.
Experiment Description
CPTAC study 9.1 (sites 54, 56A, 65 and 86) [Abbatiello et al. Mol Cell Proteomics 2015] were used to evaluate longitudinal SST monitoring algorithms for SRM proteomic workflows. The study designed a reference sample with 15 peptides to characterize instrument performance in terms of peak area, retention time and FWHM metrics. The study acquired guide sets from this sample, as well as longitudinal measurements collected during two months in 11 labs. The number of time points in a lab varied between 36 and 89. The datasets were processed with Skyline to generate a table of quantitative metrics for input to MSstatsQC. We used these datasets to evaluate the ability of SPC to discover abnormalities such as retention time and peak area drifts (Site 54), retention time fluctuation (Site 86), and chromatographic shape problems (Site 65), and to illustrate effects of guide set selection on the performance of detecting a retention time drift (Site 56A). Guide sets are manually determined from in-control observations for each site.
Sample Description
pre-digested 6-protein mix
Created on 6/21/17, 8:21 AM

MSstatsQC: Longitudinal system suitability monitoring and quality control for targeted proteomic experiments

Authors: Eralp Dogu, Sara Mohammad-Taheri, Susan E. Abbatiello, Michael S. Bereman, Brendan MacLean, Birgit Schilling*, Olga Vitek*


Acquisition of System Suitability Samples (SSS) over course of 1 week on Triple Quadrupole Instruments.

SSS were interspersed into biomarker plasma assessment study samples throughout the week (Abbatiello et al., MCP 2015), study total runs ~ 300 acquistions

6 sites selected to show SSS performance (QC), monitoring digested 6 protein mix (15 peptides) as part of the larger study: 

Comments and notes about uploaded files (also see slides attached):

  • 86:  Site86_Study9.1_SSS_QCED,   quite nice and robust SSS performance throughout the study, basically for all monitored peptides
  • 54:  Site54_Study9-1_SSS_111611_QCED,  quite nice SSS, very nice and robust for majority of peptides, One ‘empty’ run called SSS_run_109, also Slight ‘random’ problems with 2 hydrophobic, late eluting peptides VGP and also FFV (note a 3 letter code is used for any of the 15 monitored peptides, first 3 amino acids)
  • 65:  Study9.1 Site65 SSS runs_QCED,  quite nice SSS, some slight variation that for the study heavy peptide spikes could account for. 
  • 56C:  Study9_1_Site56C_SSS_Final_101711_QCED,  quite nice SSS,  Some of the SSS runs in the beginning have signal loss on some late eluting peptides (VGP, FFV),  Most other peptides are fine, even for problem peptides things stabilized after several runs in the beginning, and the study could be performed fine.
  • 56A:  Study9p1_site_56A_5500_SSS_runs_final_QCED,  overall ok.  some sensitivity loss observed over the study, notes from operator :
    1. Changed the chip in pos 1 before the run # 155
    2. Cleaned the Mass Spec before the run # 255.; sensitivity jumped up nicely after cleaning to anticipated performance. 
  • 90:  Study9S_Site90_SSS_QCED, SSS is borderline as signal goes up and down ....  heavy peptides in study can account for a lot, but LOD/LOQ suffered a little bit

  Attached Files  
 SSS longitudinal_0322_2016_v1.pptx

Clustergrammer Heatmap
Study9.1 Site65 SSS 08:19:276222211050
Study9.1 Site65 SSS runs_start at run 08:19:276222211036 08:19:277151575116 08:19:27715157589 08:19:27715157546
Study9.1 Site65 SSS 08:19:26615157550 08:19:26715157540 08:19:26715157568