automatic analysis of ion mobility spectrometry – mass spectrometry (ims-ms) data
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Automatic Analysis of
Ion Mobility Spectrometry – Mass Spectrometry
(IMS-MS) Data
Hyejin Yoon
School of InformaticsIndiana University Bloomington
December 5, 2008
Advisor: Dr. Haixu Tang
Outline
1. Introduction 1. Introduction
2. Motivation 2. Motivation
4. IMS-MS Analyzer 4. IMS-MS Analyzer
5. Results 5. Results
3. Workflow of IMS-MS Data Analysis 3. Workflow of IMS-MS Data Analysis
6. Future Work 6. Future Work
7. References 7. References
8. Acknowledgements 8. Acknowledgements
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)
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
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]
Ion mobility spectrometry (IMS)
Fast: Gas phase
Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS)
E
Buffer GasDETECTOR
Gate
High-throughput proteomics platform based on ion-mobility time-of-flight mass spectrometry
[Belov et. al. ASMS]
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]
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
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
Workflow of IMS-MS Analysis
IMS-MS / LC-IMS-MS
System
IMS-MS / LC-IMS-MS
System
Biological samplemixture
Biological samplemixture
Visualization &
Feature-findingAlgorithm
Visualization &
Feature-findingAlgorithm
Peak-pickingAlgorithm
Peak-pickingAlgorithm
Visualization&
DeisotopingAlgorithm
Visualization&
DeisotopingAlgorithm
IMS-MS Analyzer
Feature ListFeature ListIMS-MS
DataIMS-MS
Data IMS-MSPeak ListIMS-MS
Peak List
Monoisotope(peak)
List
Monoisotope(peak)
List
LC-IMS-MS Data
LC-IMS-MS Data
Monoisotope(peak)Lists
Monoisotope(peak)Lists
FeatureLists
FeatureLists
IMS-MSPeak Lists
IMS-MSPeak Lists
IMS-MS Analyzer:2D Color Map and Deisotoping
Visualization &
Feature-findingAlgorithm
Visualization &
Feature-findingAlgorithm
Peak-pickingAlgorithm
Peak-pickingAlgorithm
Visualization&
DeisotopingAlgorithm
Visualization&
DeisotopingAlgorithm
IMS-MS Analyzer
Feature ListFeature ListIMS-MS
DataIMS-MS
Data Peak ListPeak ListMonoisotope
(peak)List
Monoisotope(peak)
List
LC-IMS-MS Data
LC-IMS-MS Data
Monoisotope(peak)Lists
Monoisotope(peak)Lists
FeatureLists
FeatureLists
PeakListsPeakLists
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
2D Color Map and Zoom
Single drift scan view
Single drift scan view
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
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
IMS-MS Analyzer:THRASH 2D map and Feature Finding
Visualization &
Feature-findingAlgorithm
Visualization &
Feature-findingAlgorithm
Peak-pickingAlgorithm
Peak-pickingAlgorithm
Visualization&
DeisotopingAlgorithm
Visualization&
DeisotopingAlgorithm
IMS-MS Analyzer
Feature ListFeature ListIMS-MS
DataIMS-MS
Data Peak ListPeak ListMonoisotope
(peak)List
Monoisotope(peak)
List
LC-IMS-MS Data
LC-IMS-MS Data
Monoisotope(peak)Lists
Monoisotope(peak)Lists
FeatureLists
FeatureLists
PeakListsPeakLists
THRASH 2D map
2D map of drift time vs. m/z
THRASH frame
2D map of drift-time vs. monoisotopic mass
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
Feature Finding
IMS-MS Analyzer:Peak-Picking
Visualization &
Feature-findingAlgorithm
Visualization &
Feature-findingAlgorithm
Peak-pickingAlgorithm
Peak-pickingAlgorithm
Visualization&
DeisotopingAlgorithm
Visualization&
DeisotopingAlgorithm
IMS-MS Analyzer
Feature ListFeature ListIMS-MS
DataIMS-MS
Data IMS-MSPeak ListIMS-MS
Peak List
Monoisotope(peak)
List
Monoisotope(peak)
List
LC-IMS-MS Data
LC-IMS-MS Data
Monoisotope(peak)Lists
Monoisotope(peak)Lists
FeatureLists
FeatureLists
PeakListsPeakLists
Peak-Picking
Overlapping peaks: isomeric molecules or conformational change in a molecules
Apply Gaussian mixture models Use Expectation-Maximization (EM) algorithmGoodness-of-fit to find the best fitting
Gaussian mixtureChoose Gaussian means to represent IMS-MS
peaks
Peak-picking Examples
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 σi
2
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
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
On the t’th iteration let our estimates be
E step
M step
EM for GMMs
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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
Peak-picking
Peak-picking Results
IMS-MS Analyzer:LC-IMS-MS Processing
Visualization &
Feature-findingAlgorithm
Visualization &
Feature-findingAlgorithm
Peak-pickingAlgorithm
Peak-pickingAlgorithm
Visualization&
DeisotopingAlgorithm
Visualization&
DeisotopingAlgorithm
IMS-MS Analyzer
Feature ListFeature ListIMS-MS
DataIMS-MS
Data Peak ListPeak ListMonoisotope
(peak)List
Monoisotope(peak)
List
LC-IMS-MS Data
LC-IMS-MS Data
Monoisotope(peak)Lists
Monoisotope(peak)Lists
FeatureLists
FeatureLists
IMS-MSPeak Lists
IMS-MSPeak Lists
Analyzing LC-IMS-MS data
Data set of multiple frames4D dataBinary search algorithm to
find the target frameProcessing all frames
automatically
:
:
2D Map of LC-IMS-MS
THRASH/peak-picking of LC-IMS-MS
Results
IMS-MS sample
(Cellobiose)
LC-IMS-MS sample
(Human Plasma)
# of Deisotoped ions
537 0~266 per frame
# of IMS-MS peaks
35 0~18 per frame
Future Work
Biological sample
LC-IMS-MSSystems
LC-IMS-MSSystems
LC-IMS-MS dataset
LC-IMS-MS dataset
IMS-MS/MSdataset
IMS-MS/MSdataset
PrecursorFeature/Peak
List
PrecursorFeature/Peak
List
FragmentPeak ListFragmentPeak List
MS/MS Spectra
+ Precursor information
MS/MS Spectra
+ Precursor information
DownstreamComputationalAnalysis- Protein identification- Protein quantitation- Biological pathway reconstruction
Precursor Peak List
Precursor Peak List
Drift ProfileAligner
De-isotoping
Peak Picking
FeatureDetector
FeatureDetector
FragmentFeature/Peak
List
FragmentFeature/Peak
List
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
AcknowledgementsProf. 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