Skyline - SRM Method Builder

Skyline - SRM Method Builder

Tutorial Webinar #7 iRT Retention Time Prediction with Skyline With Brendan MacLean (Principal Developer) Agenda Welcome from the Skyline team! iRT Retention Time Prediction Introduction with Brendan MacLean Overview of iRT key concepts Tutorial with Brendan MacLean Calibrating and building an iRT library Using the iRT library for retention time prediction Audience Q&A submit questions to Google Form: https://skyline.gs.washington.edu/labkey/qa4s

kyline.url Prior Knowledge and Consistency Based on empirical measurement Powerful enough to be used cross-lab / cross experiment More powerful run-to-run Relative ion abundance Library: Spectral and chromatogram Prediction: Zhang, Anal. Chem., 2004 Retention time Library: iRT (and AMT)

Prediction: Krokhin, Anal. Chem., 2006 (SSRCalc) Chromatography-based Quantification Hypothesis testing (Verification) SRM PRM MS1 chromatogram extraction (DDA) Data independent acquisition (DIA) Acquisition Targeted Survey More Selective PRM DIA Less Selective

SRM DDA Scheduling & Detection Sourc e Extraction & Detection Retention Time Alignment by ID in DDA Retention Times for SIVPSGASTGVHEALEMR precursor - 614.3122+++ precursor [M+1] - 614.6465+++ precursor [M+2] - 614.9805+++ 37 36 35 34 33 32

31 Replicate Logical Order precursor [M+3] - 615.3144+++ Retention Times for SIVPSGASTGVHEALEMR precursor - 614.3122+++ precursor [M+1] - 614.6465+++ precursor [M+2] - 614.9805+++ 37 36 35 34 33 32 31 Replicate

Acquired Time Order precursor [M+3] - 615.3144+++ Retention Times for SIVPSGASTGVHEALEMR precursor - 614.3122+++ precursor [M+3] - 615.3144+++ precursor [M+1] - 614.6465+++ precursor [M+2] - 614.9805+++ 37.0 36.5 36.0 35.5 35.0 34.5 Replicate Aligned Times iRT Standard Peptides 1.4 1.2 1.0

0.8 0.6 0.4 0.2 0.0 10 20 30 40 50 60 70 50 60 70 Retention Time 900 800 700 600 500 400

300 200 100 0 10 20 30 40 Retention Time Escher, Proteomics, 2012 Retention Time Alignment by Standards 35 Reteniont Time A 30 f(x) = 0.38 x + 5.61 R = 1 25 20 15

10 10 20 30 40 Retention Time B 50 60 70 iRT Standard Attributes 10-20 peptides Consistently measurable in sample Spanning gradient range of interest Biognosys Pierce

Sigma Aldrich Heavy reference peptides Analyte peptides ApoA1 Defining an iRT Scale Retention time independent Regression 35 Peptides slope = 0.15, intercept = 15.61window = 0.5r = 1 30 25 20 15 10 -40 -20 0 20

40 iRT-C18 60 80 100 120 Defining an iRT Scale Points on a line (score = time * slope + intercept) 120 100 f(x) = 11.11 x 22.22 R = 1 80 Score 60 40 20

0 10 12 14 16 18 20 Measured Time 22 24 26 28 30 Defining an iRT Scale Points on a line (score = time * slope + intercept) 120 100

f(x) = 6.76 x 105.55 R = 1 80 60 Score 40 20 0 10 15 20 -20 -40 Measured Time 25 30

35 Building an iRT Library 1.4 * 1.2 1.0 0.8 0.6 * * * * 0.4 * * * 0.2 *

* * 0.0 15 20 25 Retention Time 30 Building an iRT Library Regression Refined Outliers 40 35 Regression Predictor Peptides Refined slope = 0.15, intercept = 15.09window = 1.0r = 0.9991 slope = 0.01, intercept = 22.27window = 17.7r = 0 slope = 0.15, intercept = 15.09window = 5.0r = 0.052

30 25 20 15 10 5 0 -100 -50 0 50 Score iRT-C18 100 150 Building an iRT Library f(x) = 00 10

R = 0 15 20 25 30 35 -10 -20 Score -30 -40 iRT Standards Linear (iRT Standards) Outliers -50 -60 -70

-80 Measured Time Building an iRT Library Regression 40 35 Predictor Peptides slope = 0.15, intercept = 15.09window = 0.5r = 0.9999 slope = 0.15, intercept = 15.09window = 5.0r = 0.9999 30 25 20 15 10 5 0

-50 0 50 iRT-C18 Score 100 150 Using the iRT Library (Prediction) Regression Refined Outliers 80 70 Regression Predictor Peptides Refined 50 100 slope = 0.40, intercept = 24.77window = 1.2r = 0.9998

slope = 0.02, intercept = 1.77window = 43.5r = 0.061 slope = 0.40, intercept = 24.77window = 5.0r = 0.061 60 50 40 30 20 10 0 -50 0 Score iRT-C18 150 Using the iRT Library to Measure Regression 80

70 Predictor Peptides slope = 0.39, intercept = 24.85window = 2.1r = 0.9989 slope = 0.40, intercept = 24.66window = 5.0r = 0.9989 60 50 40 30 20 10 0 -40 -20 0 20 40

60 iRT-C18 80 100 120 140 SSRCalc Predictor Correlation Regression Refined 80 Regression Peptides Refined Outliers slope = 1.46, intercept = 2.10window = 2.6r = 0.998 slope = 1.50, intercept = 2.07window = 13.5r = 0.9531 70 60 50

40 30 20 10 0 0 10 20 SSRCalc 3.0 (100A) 30 40 50 Tutorial Calibrating, building and using an iRT library Learn More iRT Retention Time Prediction Tutorial

Webinar #8: TBD Workshop and ASMS Skyline User Group Meeting at ASMS Tuesday, June 16 May 31 at Old Post Office, St. Louis, MO Workshop in Rio de Janiero, August 31-September 2 Workshop in Puerto Vallarta, November Weeklong Course at PRBB, Barcelona, November 15-20 Questions? Ask any questions you have on iRT at the following form:

https://skyline.gs.washington.edu/labkey/qa4skyline.url Take the post-webinar survey: https://skyline.gs.washington.edu/labkey/survey4webinar.url Tutorial Webinar #7 This ends this Skyline Tutorial Webinar. Please give us feedback on the webinar at the following survey: https://skyline.gs.washington.edu/labkey/survey4webinar.url A recording of todays meeting will be available shortly at the Skyline website. We look forward to seeing you at a future Skyline Tutorial Webinar.

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