1. Automatic Analysis of Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) Data Hyejin Yoon School of Informatics Indiana University Bloomington December 5, 2008 Advisor: Dr. Haixu Tang

2. Outline 1. Introduction 2. Motivation 4. IMS-MS Analyzer 5. Results 3. Workflow of IMS-MS Data Analysis 6. Future Work 7. References 8. Acknowledgements

3. Mass Spectrometry (MS) Generic mass spectrometry (MS)- based proteomics experiment [Ruedi Aebersold et al.]  Measures molecular mass (mass-to-charge ratio) of a sample  Mass spectrum  Tandem MS (MS/MS)

4. Application of MS  Molecule identification/quantitation accurate molecular weight confirm the molecular formula substitution of a amino acid or post-translational modification  Structural and sequence information from MS/MS

5. Liquid Chromatography – Mass Spectrometry  MS Combined with Liquid Chromatography (LC) LC-MS, LC-MS/MS  Advantages Provides a steady stream of different samples More precise Higher confident  Limitation Molecule at low abundance levels Low depth of coverage for complex samples Slow: Liquid phase A schematic diagram of LC-MS [http://www.childrenshospital.org/cfapps/research/data_admin/Site602/mainpageS602P0.html]

6.  Ion mobility spectrometry (IMS) Fast: Gas phase Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) E Buffer Gas DETECTOR Gate High-throughput proteomics platform based on ion- mobility time-of-flight mass spectrometry [Belov et. al. ASMS]

7.  IMS-MS Distinguish different ions having identical mass-to-charge ratios Separates out conformers Increases depth of coverage, confidence Used to measure cross-section Reduces noise Fast separation: Gas phase Advantages of IMS-MS A schematic diagram of IMS-MS [Hoaglund CS, et al. 1998]

8.  IMS-MS “Frame” 3-dimensional data: drift time, m/z, intensity 2D Color map Rarely done so far, Few analysis SW LC-IMS-MS LC coupled to MS-MS 4-dimensional data frame, drift time, m/z, intensity Multiple frames Advantage  Multiple measurements per LC peak  Increasing peak capacity  Increase depth of coverage  Reproducible, increase confidence MS vs. IMS-MS  MS Mass Spectrum 2-dimensional data: m/z, intensity Many tools to analyze LC-MS

9. Motivation for Automatic IMS-MS Analysis  Challenging data analysis, due to multi-dimensional nature of data  Need for an automatic data analysis tool for the studies using IMS-MS/LC-IMS-MS instruments  Visualize IMS-MS, LC-IMS-MS data m/z, drift time space Mass, drift time space  Feature/Peak detection Deisotope isotopic distributions to get monoisotopic mass & charge state Identify IMS-MS peaks using two dimensions (mass/ drift time)  User-friendly

10. Workflow of IMS-MS Analysis IMS-MS / LC-IMS-MS System IMS-MS / LC-IMS-MS System Biological sample mixture Biological sample mixture Visualization & Feature-finding Algorithm Visualization & Feature-finding Algorithm Peak-picking Algorithm Peak-picking Algorithm Visualization & Deisotoping Algorithm Visualization & Deisotoping Algorithm IMS-MS Analyzer Feature List IMS-MS Data IMS-MS Data IMS-MS Peak List IMS-MS Peak List Monoisotope (peak) List Monoisotope (peak) List LC-IMS-MS Data LC-IMS-MS Data Monoisotope (peak) Lists Monoisotope (peak) Lists Feature Lists Feature Lists IMS-MS Peak Lists IMS-MS Peak Lists

11. IMS-MS Analyzer: 2D Color Map and Deisotoping Visualization & Feature-finding Algorithm Visualization & Feature-finding Algorithm Peak-picking Algorithm Peak-picking Algorithm Visualization & Deisotoping Algorithm Visualization & Deisotoping Algorithm IMS-MS Analyzer Feature List IMS-MS Data IMS-MS Data Peak List Monoisotope (peak) List Monoisotope (peak) List LC-IMS-MS Data LC-IMS-MS Data Monoisotope (peak) Lists Monoisotope (peak) Lists Feature Lists Feature Lists Peak Lists Peak Lists

12. 2D Color Map and Zoom :::: :::: Input (drift scan, TOF bin, intensity) calibration coefficients drift time, m/z, color code Plot drift time vs. m/z vs. intensity

13. 2D Color Map and Zoom

14. Single drift scan view

16. Single Drift Scan Processing  Peak-picking on spectra Remove spectral noise  Deisotoping Algorithm THRASH [Horn et al. 2000] algorithm Detect accurate monoisotopic mass and charge state

17. THRASH on a frame  THRASH entire frame THRASH scan by scan a peak list in the form of monoisotopic masses observed across continuous drift-times. Results saved as a csv file

18. IMS-MS Analyzer: THRASH 2D map and Feature Finding Visualization & Feature-finding Algorithm Visualization & Feature-finding Algorithm Peak-picking Algorithm Peak-picking Algorithm Visualization & Deisotoping Algorithm Visualization & Deisotoping Algorithm IMS-MS Analyzer Feature List IMS-MS Data IMS-MS Data Peak List Monoisotope (peak) List Monoisotope (peak) List LC-IMS-MS Data LC-IMS-MS Data Monoisotope (peak) Lists Monoisotope (peak) Lists Feature Lists Feature Lists Peak Lists Peak Lists

19. THRASH 2D map 2D map of drift time vs. m/z THRASH frame 2D map of drift-time vs. monoisotopic mass

20. Feature Finding   Feature: a drift profile for a specific mass value   Preliminary step to Identify IMS-MS peaks   Sliding Window approach Cluster monoisotopic ions located across continuous drift-times Report representative monoisotopic mass, drift-time value, maximum intensity, total intensity, charge and range of drift-time that correspond to a particular feature   Feature profile view   Manually visualizing Gaussian fitting to the feature

21. Feature Finding

22. IMS-MS Analyzer: Peak-Picking Visualization & Feature-finding Algorithm Visualization & Feature-finding Algorithm Peak-picking Algorithm Peak-picking Algorithm Visualization & Deisotoping Algorithm Visualization & Deisotoping Algorithm IMS-MS Analyzer Feature List IMS-MS Data IMS-MS Data IMS-MS Peak List IMS-MS Peak List Monoisotope (peak) List Monoisotope (peak) List LC-IMS-MS Data LC-IMS-MS Data Monoisotope (peak) Lists Monoisotope (peak) Lists Feature Lists Feature Lists Peak Lists Peak Lists

23. Peak-Picking  Overlapping peaks: isomeric molecules or conformational change in a molecules  Apply Gaussian mixture models  Use Expectation-Maximization (EM) algorithm  Goodness-of-fit to find the best fitting Gaussian mixture  Choose Gaussian means to represent IMS-MS peaks

24. Peak-picking Examples

25. Gaussian Mixture Models (GMMs)  There are k components of Gaussian  i ’ th component: w i  Mean of component w i : μ i  Each component generates data from a Gaussian function with mean μ i and variance σ i 2  Each datapoint is generated according to probability of component i: P(w i ) N(μ i, σ i 2 )  We need to find μ 1, μ 2, …, μ k which give maximum likelihood

26. EM Algorithm  Alternate between Expectation (E) step and Maximization (M) step  E step computes an expectation of the likelihood by including the unobserved variables as if they were observed  M step computes the maximum likelihood estimates of the parameters by maximizing the expected likelihood found on the E step  Begin next round of the E step using the parameters found on the M step and repeat the process

27.  On the t’th iteration let our estimates be  E step  M step EM for GMMs

28.  How well the model fits a set of observed data  Discrepancy between observed values and the values expected under the model  Based on goodness-of-fit we determine the best fitting Gaussian mixture within user specified max components Goodness-of-Fit

29. Peak-picking

30. Peak-picking Results

31. IMS-MS Analyzer: LC-IMS-MS Processing Visualization & Feature-finding Algorithm Visualization & Feature-finding Algorithm Peak-picking Algorithm Peak-picking Algorithm Visualization & Deisotoping Algorithm Visualization & Deisotoping Algorithm IMS-MS Analyzer Feature List IMS-MS Data IMS-MS Data Peak List Monoisotope (peak) List Monoisotope (peak) List LC-IMS-MS Data LC-IMS-MS Data Monoisotope (peak) Lists Monoisotope (peak) Lists Feature Lists Feature Lists IMS-MS Peak Lists IMS-MS Peak Lists

32. Analyzing LC-IMS-MS data  Data set of multiple frames  4D data  Binary search algorithm to find the target frame  Processing all frames automatically : :

33. 2D Map of LC-IMS-MS

34. THRASH/peak-picking of LC-IMS-MS

35. Results IMS-MS sample (Cellobiose) LC-IMS-MS sample (Human Plasma) # of Deisotoped ions 5370~266 per frame # of IMS-MS peaks 350~18 per frame

36. Future Work Biological sample LC-IMS-MS Systems LC-IMS-MS Systems LC-IMS-MS dataset LC-IMS-MS dataset IMS-MS/MS dataset IMS-MS/MS dataset Precursor Feature/Peak List Precursor Feature/Peak List Fragment Peak List Fragment Peak List MS/MS Spectra + Precursor information MS/MS Spectra + Precursor information Downstream Computational Analysis - Protein identification - Protein quantitation - Biological pathway reconstruction Precursor Peak List Precursor Peak List Drift Profile Aligner De- isotoping Peak Picking Feature Detector Feature Detector Fragment Feature/Peak List Fragment Feature/Peak List

37. References  Aebersold R, Mann M, Mass spectrometry-based proteomics, Nature. 2003 Mar 13;422(6928):198-207  Guerrera IC, Kleiner O. Application of mass spectrometry in proteomics, Biosci Rep. 2005 Feb-Apr;25(1-2):71-93.  Clemmer DE, Jarrold MF, Ion mobility measurements and their applications to clusters and biomolecules, J Mass Spectrom. 1997;32: 577-592.  Hoaglund CS, Valentine SJ, Sporleder CR, Reilly JP, Clemmer DE, Three-dimensional ion mobility/TOFMS analysis of electrosprayed biomolecules, Anal Chem. 1998 Jun 1;70(11):2236-42.  Baker ES, Clowers BH, Li F, Tang K, Tolmachev AV, Prior DC, Belov ME, Smith RD, Ion Mobility Spectrometry–Mass Spectrometry Performance Using Electrodynamic Ion Funnels and Elevated Drift Gas Pressures, J Am Soc Mass Spectrom. 2007 Jul;18(7):1176-87.  Horn DM, Zubarev RA, McLafferty FW, Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules, J Am Soc Mass Spectrom. 2000 Apr;11(4):320-32.  http://www.astbury.leeds.ac.uk/facil/MStut/mstutorial.htm  http://www.childrenshospital.org/cfapps/research/data_admin/Site602/mainpageS602P0.ht ml  http://www.autonlab.org/tutorials/gmm.html

38. Acknowledgements  Prof. Haixu Tang, School of Informatics  Lab-mates Anoop Mayampurath, Mina Rho, Jun Ma, Yong Li, Paul Yu, Chao Ji, Indrani Sarkar  Chemistry Department Stephen Valentine Manny Plasenci Ruwan Thushara Kurulugama Prof. David E. Clemmer  Faculty and staff, School of Informatics