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

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 &


Peak-pickingAlgorithm


Visualization&

DeisotopingAlgorithm

Visualization&


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


FeatureLists

FeatureLists

IMS-MSPeak Lists

IMS-MSPeak Lists

IMS-MS Analyzer:2D Color Map and Deisotoping

Visualization &


Visualization &




Visualization&


Visualization&


IMS-MS Analyzer


DataIMS-MS

Data Peak ListPeak ListMonoisotope

(peak)List

Monoisotope(peak)

List

LC-IMS-MS Data

LC-IMS-MS Data



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 &


Visualization &




Visualization&


Visualization&


IMS-MS Analyzer


DataIMS-MS


(peak)List

Monoisotope(peak)

List

LC-IMS-MS Data

LC-IMS-MS Data



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 &


Visualization &




Visualization&


Visualization&


IMS-MS Analyzer


DataIMS-MS

Data IMS-MSPeak ListIMS-MS

Peak List

Monoisotope(peak)

List

Monoisotope(peak)

List

LC-IMS-MS Data

LC-IMS-MS Data



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 &


Visualization &




Visualization&


Visualization&


IMS-MS Analyzer


DataIMS-MS


(peak)List

Monoisotope(peak)

List

LC-IMS-MS Data

LC-IMS-MS Data



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

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

Documents

imsms analysisimsms

ms http

ms datamz

ms data analysis

workflow of ims

analysis swlcims

ms hoaglund cs

gas phaseadvantages