1. Table 1. Quality Parameters Being Considered for Evaluation
Developing Procedures to Benchmark and Optimize the Performance of Mass Spectrometers in Individual Proteomics Laboratories
Emily Chen1, LeeAnn Higgins2, Achim Treumann3, Sheng Zhang4, Theresa McLaughlin5, Allis Chien5
1Columbia University, New York, NY; 2University of Minnesota, St Paul, MN; 3Newcastle University, Newcastle, UK; 4Cornell University, Ithaca, NY; 5Stanford University, Stanford, CA
Overview
The Workflow Interest Network (WIN) is an ABRF Research Group composed of volunteer scientists who believe in inter-laboratory repeatability and reproducibility. The WIN's mission is to help scientists and resource facilities improve reproducibility of scientific data by optimizing analytical parameters and providing recommendations for reproducible inter-laboratory workflows. Initially the group will focus on qualitative and quantitative experiments using mass spectrometry based technology. Ultimately, we welcome scientists from other types of technological platform to join our efforts and share their experiences of developing reproducible workflows in their fields.
ABRF 2017
Annual Meeting
March 25-28
2017
Introduction
Preliminary Results
Future
In this ABRF study we are developing a set of robust procedures that simplify instrument performance optimization among proteomics core facilities independent of instrument platform and geographic location.
Commercially available standardized mixtures are relatively affordable and broadly available. A workflow that simplifies the collection and analysis of system suitability data using these standard mixtures would be useful for the proteomics community.
In phase 2 of our study, we will recruit >40 participating laboratories, provide a template for each type of instrument derived from the phase 1 study, and ask each participant to perform an experiment similar that from phase 1. We will collect raw files and instrument/LC parameters from each participating laboratory, perform ID-free matrix multivariate analysis, compare ID-free analysis with actual protein identification data, and identity factors that have the most impact on inter-laboratory variability.
Retention Time Reproducibility
Mass Accuracy
Lab 4
Lab 6
Lab 6
Lab 1-ref
Lab 2
Lab 1-ref
Lab 4
Lab 5
Lab 5
Lab 2
Lab 3
Lab 3
Fusion/Lumos
Fusion/Lumos
Table 1. Quality Parameters Being Considered for Evaluation
ID-Free Metrics
Sample
Monitoring Parameters
Information
Peptide RT mix std
Retention Time (RT)
LC stability
FWHM
LC performance - peptide separation
TIC/Base peak
LC reproducibility
MS1 mass accuracy
Instrument performance
MS2 mass accuracy
Peak intensity
Median ms2 injection time
Digested cell lysate
std
Retention Time (RT) of spiked peptide stds
MS2 Repeat Sampling Frequency
ID-Dependent Metrics
# of identified PSM
(fixed FDR & search algorithm)
Charge distribution of peptides
# of missed cleavages (proteolysis)
Sample preparation
Materials and Methods
Aliquots of Pierce Peptide RT mix and Hela cell lysate digest were analyzed.
Participating laboratories were asked to
Clean the LC column, clean and calibrate the instrument, then perform
5 injections of the 15 peptide RT mix (10fmole on column)
1 injection of the Digested Hela lysate spiked with RT mix peptides (100ng digest + 10fmol peptide mix, 100 min suggested gradient)
1 injection of the Peptide RT mix
1 injection blank water
1 injection Hela lysate
1 injection Peptide RT mix
Participants were asked to generate a dataset using a standardized HPLC gradient. However, they were also encouraged to generate a dataset using their own optimized gradient if they believed it would provide better quality data.
Lab 3
Lab 4
Lab 5
Lab 2
Lab 5
Lab 1-ref
Lab 1-ref
Lab 2
Lab 3
Lab 4
Elite/Velos
Elite/Velos
Lab 4
Lab 3
Lab 4
Lab 3
Lab 1-ref
Lab 5
Lab 1-ref
Lab 2
Lab 5
Lab 2
QE/Other
QE/Other
Chromatographic retention time (RT) is a quality parameter that can be readily observed. RTs vary among instrument systems, in part due to variations in dead volume. However, RTs for runs on the same instrument are quite reproducible. Deviations indicating column contamination or other system issues can be addressed promptly.
Other quality parameters such as mass accuracy, fill time, or Peptide Spectral Matches (see Table 1) are difficult to monitor without specialized software and/or data processing. Issues affecting data acquisition take longer to be recognized and addressed with currently available software tools and workflows.
Participating Laboratories
11 laboratories in the Americas and Europe participated in Phase 1 of the study. Instrument platforms included the Orbitrap Elite, Fusion, Velos, Q-Exactive, and Triple TOF.
Acknowledgements
The Workflow Interest Group (WIN) thanks Thermo Fisher Scientific and Biognosys for providing materials used in this study.
References
Walzer M et al, Mol Cell Proteomics, 2014 Aug;13(8):
Kinsinger C et al, Mol Cell Proteomics, 2011 Nov; 10(12): mcp.O