1. 1 The world leader in serving science DIA: the Why, How, and When…Really…

2. 2 Outline: 20 Minutes to Clarity Why? How? When?

3. 3 WHY: What’s all the fuss about? Potential to quantify all detectable peptides with high sensitivity Continuous collection of MS2 spectra across peak for whole mass range Qualitative AND quantitative results All product ions, all precursors – integrate and/or search against library Minimal up front method development (relative to SRM) Same data acquisition method applicable to all sample sets, all sample complexities Selectivity can be optimized post-acquisition to offset sample complexity More transitions with proper relative ratios  more selectivity

4. 4 Sounds AMAZING… What’s the catch? In a word… Interference. Intensive post-acquisition data processing (software-dependent), manual target validation, quantitative error modeling, and statistical analysis required Spectral libraries (preferably with retention time markers) required for qualitative analysis and greatly facilitate quantitative analysis ie, results from DIA experiments will be dependent on the quality of your prior deep-dig DDA analysis of sample type and spectral library creation Reproducible chromatography and retention time characterization/ calibration critical for selectivity and validation Not compatible with fast chromatography as duty cycle increases, so must average peak width efforts to improve selectivity at MS method level result in longer cycle times, in turn requiring further “dumb-ing down” of chromatography Mass accuracy & precursor range isolation width have huge impact on results by nature, this method introduces reproducible interferences Very large raw files generated

5. 5 HOW: Recommended DIA Workflow and Data Processing Biological Fluid Samples DDA Experiment DIA experiment Targeted protein selection with Proteome Discoverer Targeted data extraction (Retention time, m/z & fragment ion distribution) Spectral library generated with Pinpoint/Skyline based on PD Search results Validation, Normalization, & Statistical Analysis

6. 6 I really need a spectral library?

7. 7 Spectral Libraries Improve DIA Data Interpretation Spectral Libraries provide information on the following: Which peptides per protein have been previously identified from a true tandem mass spectrum What is the relative abundance of the specific peptides to others from the targeted protein Determining the precursor charge states for each targeted peptide Determining the product ion distribution – which fragments should be seen Determining the relative retention time for each targeted peptides If no spectral libraries, how to increase effectiveness? Increase the mass tolerance (i.e. tighten ppm values used for data extraction) Incorporate an alternative approach for correlating peptide sequence with measured retention time – SSRCalc, PRTC peptides

8. 8 Generating a Spectral Library in Pinpoint Spectral library is generated from imported PD results Only peptides identified with good quality ms/ms spectra are used for the spectral library **Coming soon… Crystal node in PD for more statistically rigorous generation of custom spectral libraries**

9. 9 DIA Assay Development Based on Spectral Library Up to eight fragment ions used for sequence confirmation Most intense three fragment ions used for quantification

10. 10 Comparing the Library Spectrum with DIA Data y3 y4 y6 y5 y7 y8 b3 Dot product correlation coefficient of 0.91 P-value score of 1.45e-3

11. 11 Processing DIA Data Without Spectral Libraries Targeting KGNVATEISTER without knowledge of product ion distributions using ± 25 ppm

12. 12 Comparative Integration Times for FVTQAEGAK 5.85 minutes 8.42 minutes Spectral libraries provides an indication of product ion distribution – which fragments should be more abundant as well as provides additional means of verification through product ion distribution overlap.

13. 13 101214161820222426283032343638404244 Retention Time Landmarks in Library Improve Robustness of Target Identification in DIA Experiment Sample PRTC Peptides Library PRTC Peptides

14. 14 OK fine, I’ll make good libraries. Now about collecting the data, and turning it into reliable information…

15. 15 Internal Standards Data needs to be normalized across all files (replicates and experiments) to ensure detection of TRUE biological changes. This is not trivial! Inclusion of internal standards (N15 labeled sample, synthetic heavy labeled target peptides) makes post-acquisition normalization and data interpretation much more straightforward

16. 16 Collecting Your DIA Data…Pick Your Poison: Basic DIA (repeated large isolation windows) Lower selectivity, more interference, easier to set up http://vimeo.com/85084985 https://skyline.gs.washington.edu/labkey/_webdav/home/software/Skyline/%4 0files/tips/SkylineDIAMiniTutorial_2_1.pdf (DIA & msxDIA)https://skyline.gs.washington.edu/labkey/_webdav/home/software/Skyline/%4 0files/tips/SkylineDIAMiniTutorial_2_1.pdf MsxDIA Higher selectivity due to randomized, smaller, multiplexed isolation windows More sophisticated processing (requires de-multiplexing) available in Skyline only See Skyline link above WiSIM DIA Available on Fusion only, version 1.1 of software Novel workflow based on high res MS1 quant, low res MS2 verification Higher throughput, less interference, easier to set up and process

17. 17 Effects of Resolution Setting for DIA Acquisition

18. 18 Impact of Isolation Window Width Gallien S, Elodie Duriez E, Demeure K, Domon B. Selectivity of LC-MS/MS analysis: Implication for proteomics experiments. J Prot. 2012 http://dx.doi.org/10.1016/j.jprot.2012.11.005,http://dx.doi.org/10.1016/j.jprot.2012.11.005 Controlled isolation width and HR limit signal interference Controlled isolation width and HR limit signal interference 300 target peptides 2200 fragments Tolerance +/-10 ppm Q Exactive

19. 19 Interferences: Impact of Isolation Window, Resolution Targeted data extraction of the MS/MS spectra generated by data independent acquisition: a new concept for consistent and accurate proteome analysis Gillet et al., MCP 2012, doi: 10.1074/mcp.O111.016717 SRM DIA MS E

20. 20 Simultaneous Qualification and Quantification Using Pinpoint Quan using three most intense fragment ions Qual using eight most intense fragment ions Quan out put

21. 21 Comparison of DIA Processing Software https://skyline.gs.washington.edu/labkey/project/home/software/Skyline/begin.view

22. 22 PinpointSkylineSpectronaut Requirem ents No special requirements 1.Standard Calibration Kit (see Con below) 2.Data format conversion 3.Spectral library Pro1..msf file can be imported directly as the spectral library 2.Thermo specific software supporting all targeted proteomics experiments, including SRM, PRM, targeted DDA, and DIA experiments 3.Can filter out peptides by using FDR filter in the.msf file while importing the.msf file 4.Using RT from the spectral library for peptide filter 5.Direct view of the product ion distribution vs. spectral library 6.CV% and Calibration curves are automatically calculated 1.Free Open-Source Windows client for building SRM, MRM, PRM, targeted MS/MS, targeted DDA and DIA/SWATH, as well as msxDIA 2.Very stable and be able to handling big data sets in one shot 3.Be able to generate inclusion mass lists for DIA experiments 4..msf file from P.D.1.4 can be imported directly as the spectral library 5.The peptides in the.msf file must be pre- filtered before importing 6.Fast Developments and Features implementation 1.Free academic license 2.Supports Q Exactive (and TripleTOF 5600 & 5600+) 3.Application Note on QE - quantitative proteomics discovery. Comparison of DIA and Shotgun techniques 4.Using FDR for peptide validation Con1.Does not de-multiplex msxDIA data 2.Have difficulty to handle big data sets (facing crashes while loading QE data sets in one shot) 3.Many settings in the workbook are not saved after iterations (still some SW bugs) 1.RT from the library is not used as peptide validation factor yet 2. The peptide validation score is still not very understood 1..raw data files have to be converted into.htrms file - Conversion time is substantial 2. For small molecules Spectronaut is not the solution 3.Standard Calibration Kit : a peptide mix comes at a cost to customer Comparison of DIA Processing Software

23. 23 And this really works even better on the Fusion???

24. 24 Orbitrap R:240,000 Linear Trap SIM 450 - 630 amu SIM 630 - 810 amu SIM 810 -990 amu ---- 15 sequential cid ms/ms scans with 12 Da isolation Windows 456 m/z 468 m/z 600 m/z 612 m/z 624 m/z 636 m/z 648 m/z 780 m/z 792 m/z 804 m/z 816 m/z 828 m/z 960 m/z 972 m/z 984 m/z 15 sequential cid ms/ms scans with 12 Da isolation Windows 15 sequential cid ms/ms scans with 12 Da isolation Windows WiSIM-DIA Experiment on the Orbitrap FUSION How it works Three high-resolution, accurate-mass (HR/AM) selected ion monitoring (SIM) scans (240,000 FWHM) with wide isolation windows (180 amu) were used to cover all precursor ions of 450 – 990 m/z. In parallel with each SIM scan, 15 sequential ion trap MS/MS with 12 amu isolation windows were acquired to cover the associated 180 amu SIM mass range. Instrument method template will be available in OT Fusion 1.1

25. 25 WiSIM-DIA Data Processing Pinpoint 1.3 software A spectral library is established using PD search results from the short gun data dependent discovery experiments. For quantification, the XICs of isotope C 12 and C 13 precursor ions per targeted peptide are extracted from the HR/AM SIM data with ± a 5 ppm window. For peptide sequence confirmation, eight most intense fragment ions (b and y types) detected from discovery data are extracted from cid ms/ms with ± 600 ppm window and used to match the spectral library. A peptide with a P-value of correlation with library spectra that is less than 0.1 is considered to be confirmed with high confidence by the spectral library match.

26. 26 Establishing a Spectral Library Spectral library is generated from imported PD results Only peptides identified with good quality of ms/ms spectra are used for the spectral library

27. 27 Targeted Assay Development Relying on Spectral Library Up to eight fragment ions used for sequence confirmation Isotope C 12 and C 13 precursor ions used for Quan

28. 28 Simultaneous Qualification and Quantification Using Pinpoint Quan using Isotope C 12 and C 13 precursor ions Qual using eight most intense fragment ions Quan out put

29. 29 Success of Targeted Peptide Quantitation in HeLa Cell Lysate Digest

30. 30 Advantages of WiSIM-DIA By using precursor ions collected on SIM with extremely high resolving power of 240,000 for quantification, high sensitivity and high selectivity are achieved through separation of most background interferences from analyte signal. Down to 10 attomole LOD/LOQ Up to 4 orders of linear dynamic range Capability to quantify peptides which have poor fragmentation efficiency, such as large peptides, labile peptides and peptides with PTM.

31. 31 WHEN: Areas of Application for DIA Sample from immuno-precipitation for protein-protein interaction studies (network topology) (Anne-Claude Gingras Mount Sinai, Toronto) PanOmics studies to elucidate enzymatic pathways (medium to high abundance proteins) (Ruedi Aebersold, ETH, Zürich) Simplified and enriched PTM fractions, including glycans Comprehensive coverage of biopharmaceutical peptide maps from enriched growth media (MS e Waters apps note) Small molecule applications in metabolomics, clinical, food and environmental markets in need of comprehensive sample coverage

32. 32 Background slides… Info courtesy of… Scott Peterman Andreas Hühmer Reiko Kiyonami Benjamin Orsburn Lani Cardasis

33. 33 DIA Data Processing Pinpoint 1.3 Spectral library established using DDA discovery data Simultaneous peptide sequence confirmation and quantification Three most intense fragment ions (b and y types) for quantification Eight to ten most intense fragment ions (b and y types) for confirmation Automatic quality control of transitions to eliminate transitions with significant interferences ± 5 ppm XIC window

34. 34 Adding in Retention Times for Further Confirmation Library Retention Time (min) DIA Experimentally Determined Retention Time (min) 50 ppm 10 ppm The retention times from the libraries were determined for each targeted peptide based on a true tandem MS and matched values. Using these RT values locally (specific peptide) means nothing, but comparing against all other targeted peptides builds confidence. Courtesy of Scott Peterman

35. 35 Building in Targeted Peptide Lists DIA Select peptides/ proteins Assign m/z values to each peptides Build acquisition method Acquire data Process data Qualitative analysis Quantitative analysis 1. Biological hypothesis used to select targeted proteins Spectral Library 2. Determine peptides per targeted protein Do you have spectral libraries? Yes Build target lists based on spectral library information Peptide sequences Precursor charge states Most abundant product ion m/z values Product ion distribution values Retention time values No Build target list based on in silico assumptions Generate all possible peptides Assume most likely precursor charge states Predict product ions based on sequence Predict retention times based on hydrophobicity factors Lastly – peptide selection is driven by the biological hypothesis – if determining the protein level, any unique peptide will do, if the assay is driven by site-specific targeting (i.e. SNPs, PTMs, truncation) then the peptide covering this site must be targeted. Courtesy of Scott Peterman

36. 36 Target Peptide Verification Accurate mass values for each precrusor/product ions Extract product ion XICs using a specified mass tolerance (theoretical m/z value +/- set mass window e.g. 10 ppm) Overlaid XICs for multiple mass values per targeted show co-variance based on user- defined mass tolerance values Measured retention times align with either spectral library retention times or other means of predicting retention times (SSRCalc) Determine the AUC values per product ion and calculate product ion distribution for comparison to library/expected distribution DIA Select peptides/ proteins Assign m/z values to each peptides Build acquisition method Acquire data Process data Qualitative analysis Quantitative analysis Courtesy of Scott Peterman

37. 37 Importance of Mass Accuracy for Data Interpretation Y = 1.3998x – 2.5084 R 2 = 0.9943 YYWGGQYTWDMAK Courtesy of Scott Peterman

38. 38 Importance of Mass Accuracy for Data Interpretation Courtesy of Scott Peterman

39. 39 Importance of Mass Accuracy for Data Interpretation YYWGGQYTWDMAK Courtesy of Scott Peterman