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ABRF 2017 Annual Meeting Workflow Interest Network (WIN) Presentation A QC And Benchmark Study Of LC-MS/MS Methods Among MS Laboratories.

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Presentation on theme: "ABRF 2017 Annual Meeting Workflow Interest Network (WIN) Presentation A QC And Benchmark Study Of LC-MS/MS Methods Among MS Laboratories."— Presentation transcript:

1 ABRF 2017 Annual Meeting Workflow Interest Network (WIN) Presentation A QC And Benchmark Study Of LC-MS/MS Methods Among MS Laboratories

2 The ABRF Workflow Interest Network (WIN)
A new research group, the Workflow Interest Network (WIN), was established in Our current focuses are 1) to collaborate with other ABRF members and mass spectrometry-based research groups to identify key factors that contribute to poor reproducibility and inter-laboratory variability, and 2) to propose benchmarks for MS-based proteomics analysis as well as quality control procedures to improve reproducibility.

3 Current Members Of The WIN Group
Emily Chen (Chair) – Columbia University LeeAnn Higgins – University of Minnesota Theresa McLaughlin – Stanford University Achim Treumann – Newcastle University Sheng Zhang – Cornell University Allis Chien (EB Liaison) – Stanford University

4 Causative & Predictive
Proposed Research Study 2016– 2017: A QC & Benchmarke Study Among MS Laboratories MS Data Acquisition C18 Mass Spectrometer Data Acquisition Simple To Implement Repeatability & Reproducibility Cost Effective Causative & Predictive

5 MS Core Facility QC Procedures Questionnaire Results
Q1: Q2:

6 MS Core Facility QC Procedures Questionnaire Results
Q4:

7 MS Core Facility QC Procedures Questionnaire Results

8 MS Core Facility QC Procedures Questionnaire Results

9 Proposed Research Study 2016– 2017 : Two Phases
Phase 1 Study: The goal was to examine the LC-MS/MS performance among 11 MS-based core laboratories, using two sets of peptide standards and a complex lysate. Phase 2 study will be launch shortly after the ABRF meeting. An announcement will be made via . We need your participation!

10 Proposed Research Study 2016– 2017: Phase 1 Design
Clean and calibrate the instrument & Clean the column 2. RT standard 15 peptide mix (10fmole) 15 min x 5 = 200 min 3. Digested Hela lysate + RT peptides (100ng + 10fmol) 100 min (suggested gradient) 4. RT standard 15 peptide mix 15 min 6. Digested Hela lysate + RT peptides (100ng + 10fmol) 100 min (Lab gradient) 5. Blank (1ul H2O) – 15 min (same gradient) 8. Submit raw files 7. RT standard 15 peptide mix

11 Proposed Research Study 2016– 2017: Phase 1 Design
15 minutes Suggested LC Gradient For The Peptide Standard a. Thermo RT peptide mix (10fmol) b. Biognosys iRT peptide mix (1:50)

12 Proposed Research Study 2016– 2017: Phase 1 Design
Suggested LC Gradient For The Hela Digested Lysate (100ng) + Spiked-in Peptide Standard

13 Proposed Research Study 2016– 2017: Questions To Address In Phase 1 Study
Are suggested concentrations of peptide standards and digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? Is the suggested gradient for digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? How consistent are the retention time and mass accuracy of a select peptide standard among different laboratories? What are ID-free parameters that are indicative of LC and/or instrument performance? What is the reasonable expectation of repeatability within the laboratory and reproducibility among different laboratories for a selected QC sample? Can we build a benchmark for commonly used mass spectrometers for proteomics analysis?

14 Proposed Research Study 2016– 2017: Participating Labs In Phase 1 Study
Participating Labs & Instruments: Orbitrap Fusion/Lumos Tribrid Orbitrap Elite/Velos Columbia University (Fusion) University of Cornell (Elite) University of Cornell (Fusion) University of Minnesota (Velos) UCD (Lumos) Stanford University (Elite) University of Minnesota (Fusion) University of Minnesota CancerCenter (Velos) University of Minnesota Cancer Center (Fusion) UW Medicine at South Lake Union  Proteomics Resource (Fusion & Lumos) QExative Plus Triple TOF 6600 Wayne State University Newcastle University Thermo Demo Lab (CA) UW Medicine at South Lake Union  Proteomics Resource The Rockefeller University (Laboratory of Cellular and Structural Biology)

15 Proposed Research Study 2016– 2017: Phase 1 Study
Q1: Are suggested concentrations of peptide standards and digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? Raw File From The Instrument QuiC from Biognosys

16 ID-Independent Matrices
Proposed Research Study 2016– 2017: Phase 1 Results ID-Independent Matrices hela

17 Proposed Research Study 2016– 2017: Phase 1 Study
Q1: Are suggested concentrations of peptide standards and digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? Lab 4 Lab 2 Lab 3 Lab 1-ref Lab 5 Lab 6 Fusion/Lumos (Retention Time)

18 Proposed Research Study 2016– 2017: Phase 1 Study
Q1: Are suggested concentrations of peptide standards and digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? Fusion/Lumos (Mass Accuracy) Lab 4 Lab 2 Lab 3 Lab 1-ref Lab 5 Lab 6

19 Proposed Research Study 2016– 2017: Phase 1 Study
Q1: Are suggested concentrations of peptide standards and digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? Elite/Velos (Retention Time) Lab 1-ref Lab 2 Lab 3 Lab 4 Lab 5

20 Proposed Research Study 2016– 2017: Phase 1 Study
Q1: Are suggested concentrations of peptide standards and digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? Elite/Velos (Mass Accuracy) Lab 1-ref Lab 2 Lab 3 Lab 4 Lab 5

21 Proposed Research Study 2016– 2017: Phase 1 Study
Q1: Are suggested concentrations of peptide standards and digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? QuiC from Biognosys Lab 1-ref (Fusion) Lab 2 (Fusion) Lab 3 (Fusion) Lab 1-ref (Fusion) Lab 2 (Fusion) iRT view RT view Q3: How consistent are the retention time and mass accuracy of a select peptide standard among different laboratories?

22 Proposed Research Study 2016– 2017: Phase 1 Study
Q3: How consistent are the retention time and mass accuracy of a select peptide standard among different laboratories? iRT only iRT + Hela QuiC from Biognosys Lab 1 Fusion Lab 2 Velos Lab 3 Elite Lab 4 Velos Lab 5 Fusion iRT only iRT + Hela TIC view iRT view

23 Proposed Research Study 2016– 2017: Phase 1 Study
Q3: How consistent are the retention time and mass accuracy of a select peptide standard among different laboratories? QuiC from Biognosys iRT only iRT + Hela Lab 1 Fusion Lab 2 Velos Lab 3 Elite Lab 4 Velos Lab 5 Fusion iRT only iRT + Hela FWHM MS1 Mass Accuracy

24 ID-dependent Matrices
Proposed Research Study 2016– 2017: Phase 1 Results ID-dependent Matrices hela

25 Proposed Research Study 2016– 2017: Phase 1 Study
Q2: Is the suggested gradient for digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? 100ng Digested Hela lysate QE Plus 2531 2486 Lab5: A Lab5: B 2488 2519 Lab6: A Lab6: B 2224 2320 Lab7: A Lab7: B 2174 >90% Repeatability Lab2: A Lab2: B 3332 3186 Lab3: A Lab3: B 3205 3250 Lab4: A Lab4: B 2849 2797 Lab1: A Lab1: B 3133 2774 359 349 100ng Digested Hela lysate Fusion/Lumos A: suggested gradient B: lab gradient

26 Proposed Research Study 2016– 2017: Phase 1 Study
Q2: Is the suggested gradient for digested HeLa lysate suitable for evaluating the performance of different mass spectrometers in different laboratories? Lab2: Lab Gradient Lab2: Suggested Gradient Lab3: Lab Gradient Lab3: Suggested Gradient Lab4: Lab Gradient Lab4: Suggested Gradient Lab5: Lab Gradient Lab5: Suggested Gradient Lumos Fusion QE Plus Elite Q5: What is the reasonable expectation of repeatability within the laboratory and reproducibility among different laboratories for a selected QC sample? 56% - 90%

27 Proposed Research Study 2016– 2017: How Will We Use The Big Data?
Polynomial Regression Analysis: log(Number of ID spectra) versus log(Max ms2 injection time) The regression equation is log(Number of ID spectra) = log(Max ms2 injection time) log(Max ms2 injection time)^2 S = R-Sq = 82.4% R-Sq(adj) = 78.9% Analysis of Variance Source DF SS MS F P Regression Error Total

28 Proposed Research Study 2016– 2017: How Will We Use The Big Data?
Q6: Can we build a benchmark for commonly used mass spectrometers for proteomics analysis? *100ng Digested Hela lysate Top 20% 2230 proteins Top 50% Bottom 20% 2021 proteins Performance Meter Velos/Elite Top 20% 3332 proteins Top 50% Bottom 20% 2797 proteins Performance Meter Fusion/Lumos Top 20% 2531 proteins Top 50% Bottom 20% 2224 proteins Performance Meter QE Plus

29 ACKNOWLEDGEMENT WIN members Phase 1 Participating Labs
Special Thanks to WIN members Phase 1 Participating Labs EB Liaison – Allis Chien ABRF The Biognosys Team Fadi Abdi Ian Lienert Florian Marty We need more WIN members! Please participate in the phase 2 study! Caitlin Needel

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