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Automatic Analysis of Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) Data

Automatic Analysis of Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) Data. Hyejin Yoon. Advisor: Dr. Haixu Tang. School of Informatics Indiana University Bloomington. December 5, 2008. Outline. 1. Introduction. 2. Motivation. 3. Workflow of IMS-MS Data Analysis.

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Automatic Analysis of Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) Data

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

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

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

  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 dataframe, drift time, m/z, intensity Multiple frames Advantage Multiple measurements per LC peak Increasing peak capacity Increase depth of coverage Reproducible, increase confidence MS Mass Spectrum 2-dimensional data:m/z, intensity Many tools to analyze LC-MS MS vs. IMS-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 thestudies 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 Analyzer Monoisotope (peak) List Feature List IMS-MS Peak List IMS-MS Data IMS-MS / LC-IMS-MS System Visualization & Deisotoping Algorithm Visualization & Feature-finding Algorithm Peak-picking Algorithm LC-IMS-MS Data Monoisotope (peak) Lists Biological sample mixture Feature Lists IMS-MS Peak Lists

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

  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

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

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

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

  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: wi • Mean of component wi : μi • Each component generates data from a Gaussian function with mean μi and variance σi2 • Each datapoint is generated according to • probability of component i: P(wi) • N(μi, σi2)  We need to find μ1, μ2, …, μk 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. EM for GMMs • On the t’th iteration let our estimates be • E step • M step

  28. Goodness-of-Fit • 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 mixturewithin user specified max components

  29. Peak-picking

  30. Peak-picking Results

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

  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

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

  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.html • http://www.autonlab.org/tutorials/gmm.html

  38. 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 Acknowledgements

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