Comparison

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.children
shospital.org/cfapps/research/data_admin/Site602/m
ainpageS602P0.html
6
Ion Mobility Spectrometry Mass Spectrometry
(IMS-MS)

• Ion mobility spectrometry (IMS)
• Fast Gas phase

High-throughput proteomics platform based on
ion-mobility time-of-flight mass spectrometry
Belov et. al. ASMS
7
Advantages of IMS-MS

• 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

A schematic diagram of IMS-MS Hoaglund CS, et
al. 1998
8
MS vs. IMS-MS

• IMS-MS
• Frame
• 3-dimensional datadrift 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 datam/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 Analyzer
Feature List
IMS-MS Peak List
Monoisotope (peak) List
IMS-MS Data
IMS-MS / LC-IMS-MS System
Visualization Feature-finding Algorithm
Peak-picking Algorithm
Visualization Deisotoping Algorithm
LC-IMS-MS Data
Monoisotope (peak) Lists
Biological sample mixture
Feature Lists
IMS-MS Peak Lists
11
IMS-MS Analyzer2D Color Map and Deisotoping
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 AnalyzerTHRASH 2D map and Feature Finding
19
THRASH 2D map
2D map of drift-time vs. monoisotopic mass

• 2D map of drift time vs. m/z

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 AnalyzerPeak-Picking
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
• ith component wi
• Mean of component wi µi
• Each component generates data from a Gaussian
  function with mean µi and variance si2
• Each datapoint is generated according to
• probability of component i P(wi)
• N(µi, si2)
• ? 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 tth 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 mixture within user specified
  max components

29
Peak-picking
30
Peak-picking Results
31
IMS-MS AnalyzerLC-IMS-MS Processing
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 537 0266 per frame
of IMS-MS peaks 35 018 per frame
36
Future Work
37
References

• Aebersold R, Mann M, Mass spectrometry-based
  proteomics, Nature. 2003 Mar 13422(6928)198-207
• Guerrera IC, Kleiner O. Application of mass
  spectrometry in proteomics, Biosci Rep. 2005
  Feb-Apr25(1-2)71-93.
• Clemmer DE, Jarrold MF, Ion mobility measurements
  and their applications to clusters and
  biomolecules, J Mass Spectrom. 199732 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
  170(11)2236-42.
• Baker ES, Clowers BH, Li F, Tang K, Tolmachev AV,
  Prior DC, Belov ME, Smith RD, Ion Mobility
  SpectrometryMass Spectrometry Performance Using
  Electrodynamic Ion Funnels and Elevated Drift Gas
  Pressures, J Am Soc Mass Spectrom. 2007
  Jul18(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 Apr11(4)320-32.
• http//www.astbury.leeds.ac.uk/facil/MStut/mstutor
  ial.htm
• http//www.childrenshospital.org/cfapps/research/d
  ata_admin/Site602/mainpageS602P0.html
• 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