Retention time prediction with irt for scheduled MRM and SWATH MS
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1 Retention time prediction with irt for scheduled MRM and SWATH MS EuPa course, Oliver Rinner, PhD CEO Biognosys
2 Summary Why to schedule MRM acquisition? How to schedule? Prediction of retention time Empirical, SSRcalc, irt Indexed retention time (irt) Tools for irt and scheduling Outlook: On the fly calibration (Thermo) Virtual scheduling in DIA/SWATH Copyright Biognosys 2
3 Elements of an MRM assay Retention time is an essential part of a MRM assay Triple quadrupole mass spectrometer Peptide sample LC Ionization Peptide mass Collision energy Fragment mass Retention Time MRM assay Copyright Biognosys 3
4 Mass [m/z] MRM - unscheduled time Copyright Biognosys 4
5 Mass [m/z] MRM - scheduled time Copyright Biognosys 5
6 RT-window size affects the number of concurrent transitions Copyright Biognosys 6
7 Smaller RT windows more accurate quantification 150 peptides, 1232 transitions, 30 min. nano-lc HELA SILAC light/heavy cells mixed at 1:5 ratio theoretical log2 intensity ratio 2.32 Longer dwell time per peptide translates into increased quantification accuracy Escher, Reiter et. al; Proteomics 2012, 12(8)
8 Prediction of retention time HOW TO SCHEDULE Copyright Biognosys 8
9 Scheduling means: telling the instrument what to acquire when Example Analyst, in general scheduling means indicating expected RT and window width. Either dwell time or cycle time can be fixed (varies for different vendors) Copyright Biognosys 9
10 Retention time prediction Sources of empirical RT variance: 1. Peptide intrinsic retention on resin Stable physical property 2. Specific set-up of LC system Column length, matrix, gradient etc. 3. Residual variance A. Systematic shifts from sample to sample B. Fluctuations in pumps, mixer etc. Copyright Biognosys 10
11 Example variance source 2: Absolute retention times of peptides can vary in different matrices Plasma Buffer Lysate Example: 11 peptides measured in three different matrices
12 Example variance source 3: Fluctuation of retention time with repeat injections 260 runs on micro-flow LC system Matrix: Total digest plant leafs Retention time stability: Median 1.7% CV (0.6% for target peptides, ~5% for extremely hydrophilic peptides) Copyright Biognosys 12
13 Option 1: Direct approach empirical determination of RT prior to analysis Method: Run MRM in unscheduled mode with all targets, write down RTs and program scheduled method Pro: Straightforward, captures variance sources 1 and 2 Con: Time consuming, needs to be repeated for all targets if set-up changes. Requires easy detectability of targets Conclusion: Good for few targets not useful for high multiplexing Copyright Biognosys 13
14 Option 2: In silico calculation of RT using SSRcalc Method: Calculate hydrophobicity (HI) in silico. Run calibrant peptides, measure their RT Perform linear regression HI -> RT Calculate HI->RT for all analytes using parameters from fit Pro: No pre-experiments required (except calibration run), captures variance sources 2, not so well source 1 Con: RT prediction is not accurate because HI is only an approximation for peptide intrinsic retention -> large RT windows required Conclusion: Good for few targets not useful for high multiplexing Copyright Biognosys 14
15 In silico retention time prediction is not accurate enough for direct scheduled MRM Modified from Pierce ( Prediction of BSA peptide RTs with Pinpoint (based on SSRcalc) leads to overall good correlation but high variation in single cases Copyright Biognosys 15
16 Option 3: Semi-empirical calculation of RT using irt Method: Assign empirical irt value to each peptide once (replaces HI). Run calibrant peptides, measure their RT Perform linear regression irt-> RT of calibrants Calculate irt->rt for all analytes using parameters from fit Pro: Captures variance sources 1 and 2; once irt has been determined once, no pre-runs are required Con: irt needs to be determined once Conclusion: Combination of SSRcalc and empirical approach allows efficient multiplexed MRM Copyright Biognosys 16
17 irt concept I irt concept combines the advantages of in silico & empirical approaches: Fast & Accurate RT prediction irt is a fixed number assigned to each peptide Dimensionless Relative to reference peptides Easily storable in databases and transferable between laboratories and machines Escher, Reiter et. al; Proteomics 2012, 12(8)
18 irt concept II Calibration run Every target peptide has an irt representing its retention time relative to the standard peptides (irt Kit) independent of the LC gradient Known irts for target peptides allows setting up scheduled MRM methods using a single calibration run at the current LC setup Escher, Reiter et. al; Proteomics 2012, 12(8)
19 irt vs. SSRCalc 150 peptides measured in MRM mode (Thermo TSQ Vantage with easy nlc nanoflow LC system), 1232 transitions, in HELA SILAC heavy/light mixture Retention times predicted on a 90 min LC gradient using SSRCalc and irt irt prediction is ~5 times more accurate than SSRCalc prediction Smaller RT windows can be used for scheduled MRM Escher, Reiter et. al; Proteomics 2012, 12(8)
20 irt values are stable across LC systems Eksigent LC-QTRAP-30min Gradient Eksigent LC-Orbitrap-50min Gradient Proxeon LC-FT-50min Gradient Proxeon LC-TSQ-60min Gradient A single calibration run is sufficient when setting up new LC systems or methods Retention time prediction for any given peptide can be carried out using a linear equation of the format RT = m * irt + q Easy transfer of MRM methods using irt across machines and laboratories irt varies for different particle sizes though -> irt should be determined with the resins used in the analytical runs Escher Reiter et. al; Proteomics 2012, 12(8)
21 irt: case study Quantification of 150 peptides on a 30min gradient Copyright Biognosys 21
22 irt reference peptides Biognosys retention time calibration standard peptides (irt Kit) Mixture of 11 synthetic peptides eluting across the entire gradient Spiked into sample before injection Copyright Biognosys 22
23 irt calculator, Skyline TOOLS FOR RT PREDICTION WITH IRT Copyright Biognosys 23
24 Examples, tools for scheduled MRM SSRcalc irt Webtool: irt calculator: Skyline irt in skyline: Copyright Biognosys 24
25 Copyright Biognosys 25
26 Copyright Biognosys 26
27 irt is implemented in Skyline Facilitates method transfer between laboratories Positions irt as a widely used standard Skyline implementation by B. MacLean and J. Chilton from Escher, Reiter et. al; Proteomics 2012, 12(8) Copyright Biognosys 27
28 irt as a score, on the fly calibration, virtual scheduling in SWATH OUTLOOK Copyright Biognosys 28
29 On the fly calibration On the fly calibration can capture some variance of source 3 by compensating for additive shifts in RT on the fly. For a description see: Differences in loading can lead to systematic shifts in RT. Overloading leads to earlier RT. On-the-fly calibration adapts schedule based on difference of predicted vs. measured RT of e.g. irt peptides. Copyright Biognosys 29
30 Virtual scheduling in SWATH-MS (HRM) RT window based on irt Empirical retention time Retention time (RT) prediction Allows to zoom into SWATH data Increases processing speed Optimizes sensitivity Copyright Biognosys 30
31 Spectronaut TM Specifically developed for SWATH data analysis, free for academic use Fully automated calibration of irt, peak detection and scoring parameters Up to 100x increase in speed allows processing of > transitions in less than 5 minutes 1 Powerful peak scoring based on advanced mprophet algorithms 2 Intuitive data visualization for browsing of all ion traces seconds after loading the run 1 Bernhardt et al. (2012), Spectronaut - A fast and efficient algorithm for MRM-like processing of data independent acquisition (SWATH-MS) data 2 Reiter et al., Nature Methods (2011)
32 High content data visualizaton of SWATH data with Spectronaut Copyright Biognosys 32
33 HRM Calibration Kit for SWATH-MS Enables automated in-run parameter calibration HRM Calibration Kit Set of non-naturally occurring synthetic peptides evenly spread over LC-gradient Performs auto-calibration of irt, mass, peak picking parameters without intervention Copyright Biognosys 33
34 In-run RT calibration with irt increases number of ids in SWATH MS Effect of optimization of RT extraction window Copyright Biognosys 34
35 Summary Scheduling improves quantification and multiplexing in MRM Only empirical RT and irt provide sufficient accuracy for efficient scheduling irt is a peptide intrinsic property that is stable across platforms, gradients, sample matrices irt can be used for alignment, scoring, and data splicing in SWATH-MS Copyright Biognosys 35
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