data extraction and analysis for lc-ms based proteomics · 2016-01-06 · data extraction and...

Data Extraction and Analysis for LC-MS Based Proteomics

Instructors

Jake Jaffe2, Deep Jaitly1, and Matt Monroe1

Co-Organizers

Josh Adkins1 and Dick Smith1

1 Pacific Northwest National Laboratory, Richland, WA 993542 The Broad Institute, Cambridge, MA 02142

Course OutlineIntroduction

BiosPipelinesData and Tools Availability

Feature discovery in LC-MS datasetsFeature discovery in individual spectraFeature definition over elution time

Identifying LC-MS Features using an AMT tag DBBreakAMT tag Pipeline DemoMAPQUANT, PEPPeR and GenePatternPanel Discussion

QuestionsFuture Directions

Jake Jaffe – Short BioPh.D. Biology in 2004

Harvard UniversityDr. George Church and Dr. Howard Berg, advisors

Research Scientist at The Broad Institute of MIT and Harvard

Proteomics Platform

Co-developer of the Platform for Experimental Proteomic Pattern Recognition (PEPPeR)

Landmark Matching AlgorithmProteogenomic Mapping

Does both Laboratory and Computational BiologyLikes coffee a lot

Would like to thank organizers for inviting him to Seattle!

Deep Jaitly – Short BioMaster’s of Mathematics in 2000

University of WaterlooDr. Ming Li and Dr. Paul Kearney, advisors

Lead algorithm developer for the PNNL proteomics teamAuthor or co-author of over 10 peer-reviewed articles>10 years experience in programming and algorithm development

C++, C#, & VB .NETMatLabJava

4 years industrial experience at Caprion with emphasis on algorithm development for LC-MS and LC-MS/MS data

Matt Monroe – Short BioPh.D. Analytical Chemistry in 2002

University of North Carolina, Chapel HillDr. James Jorgenson, advisor

Administers and expands SQL Server databases and associated software that supports the AMT tag pipelineAuthor or Co-author of over 30 peer-reviewed articles>15 years experience in programming

VB6 / VB .NETSQLLabView

Key member of PNNL’s proteomics data analysis pipeline

X!Tandem or SEQUESTw filtering& Archive

Upstreamseparations

Complex mixture of proteins

TandemMS spectra

ParentMS spectra

CIDLC-MS/MS

Shotgun or MuDPIT Proteomics

High-throughput LC-FTICR-MS Analysis (AMT) tag

Accurate Mass and Time Tag Approach

SEQUEST and/or X!Tandem Results•Filtering•Calculate Exact Mass•Normalize Observed Elution Time

μLC- FTICR-MS Peak-Matched Results

Compare Abundancesacross Multiple ProteomesShi, Adkins, et. al., J. Bio. Chem. 2006, 29131-29140.

Complex samples

Accurate Mass and Time (AMT) Tag Data Processing Pipeline

Automated sample processing

Sample blocking

Sample blocking& randomization

LCMSWarp

SLiCScoreQA/QC

trends

QA/QC trends

SEQUESTX!Tandem

MASIC

Decon2Ls VIPER

STARSuite ExtractorQ Rollup

Mini-proteome

PRISM: G.R. Kiebel et. al. Proteomics 2006, 6, 1783-1790.

Example Data for the AMT tag Pipeline Demo

Salmonella typhimurium, LC-MS/MSGrown in LB (Luria-Bertani) up to log phaseSoluble portion of cell lysis“Mini-AMT tag” database, composed of 25 SCX fractions analyzed by LC-MS/MSMass and time tag database composed from searches using X!Tandem (Log E_Value ≤ -2)Linear alignment of datasets for AMT tag database

LC-MSDifferent sample, grown and prepared in the same conditionsLC-FTICR-MS analysis (11T FTICR)Non-linear alignment and peak matching to the database

IdentificationSampleSet

QuantificationSampleSet

Identity-centricLCMSMethod

Pattern-centricLCMSMethod

FeatureDetection

LandmarkMatching

PeakMatching

MS/MSInterpretation

y1

y2

y3 y

4

y5 b4b3

b2

b1

T A I F

MarkerDiscovery

Targeted MS/MSIdentifications

PEPPeR Pipeline

Software & DataAMT tag Pipeline Software

http://ncrr.pnl.gov/

http://www.proteomicsresource.org/

Salmonella typhimurium data resource

http://www.broad.mit.edu/cancer/software/genepattern/

PEPPeR, software within GenePattern

Funding for Tool DevelopmentNIH

National Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteNational Institute of General Medical SciencesNational Institute of Diabetes & Digestive & Kidney Diseases

DOE Office of Biological and Environmental Research

Other Excellent Software Resourceshttp://www.ms-utils.org/ (Magnus Palmblad)

http://open-ms.sourceforge.net/index.php (European consortium)

http://tools.proteomecenter.org/SpecArray.php (ISB)

http://fiehnlab.ucdavis.edu/staff/kind/Metabolomics/Peak_Alignment/(Tobias Kind with Oliver Fiehn)

http://www.proteomecommons.org/tools.jsp(Phil Andrews and Jayson Falkner)

Course OutlineIntroductionFeature discovery in LC-MS datasets

Feature discovery in individual spectraFeature definition over elution time


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kolker_19Oct04_Pegasus_0804-4_FT100k-res #265 RT: 24.14 AV: 1 NL: 1.39E4T: FTMS + p NSI Full ms [ 300.00-2000.00]

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328.23 759.05

511.73564.19

408.31

638.21 1103.01

943.96770.88

1991.14

742.19

1291.701144.81

1838.461589.94954.38

time (min)

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kolker_19Oct04_Pegasus_0804-4_FT100k-res #498 RT: 37.66 AV: 1 NL: 1.81E6T: FTMS + p NSI Full ms [ 300.00-2000.00]

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927.49

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754.47368.72

658.80

841.32 1097.501000.37

1991.071202.70 1867.131484.40 1629.98

kolker_19O ct04_Pegasus_0804-4_FT100k-res #991 RT: 66.77 AV: 1 NL: 1.06E6T: FTMS + p NSI Full m s [ 300.00-2000.00]

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901.32624.12 759.06 918.35324.22 1103.02 1345.68 1986.661594.78 1789.34

QC Standards (12 protein digest)

Mass spectra capture the changing composition of peptides eluting from the column

LC-MS dataComplex peptide mixture on a column is separated by liquid chromatography over a period of timeChanging composition of the mobile phase causes different peptides to elute at different timesThe components eluting from a column is sampled continuously by sequential mass spectra

Structure of LC-MS DataEach compound is observed as an isotopic pattern in a mass spectrum which is dependent on its chemical composition, charge and resolution of instrument

Peptide: VKHPSEIVNVGDEINVK

Parent Protein: gi|16759851 30S ribosomal protein S1

Charge: 2+m/z: 939.0203Monoisotopic Mass: 1876.0054 Da

939.51939.00

940.01

940.51

941.01 941.51

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100

939 939.5 940 940.5 941 941.5m/z

Theoretical Profile

Structure of LC-MS DataA mass spectrum of a complex mixture contains overlaid distributions of several different compounds

748.40

899.48

822.47

949.17

599.991103.03

459.48530.21 1282.13

1343.10

2.5e+6

5.0e+6

7.5e+6

1.00e+7

1.25e+7

1.50e+7

500 750 1000 1250m/z

scan 1844

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Structure of LC-MS DataA mass spectrum of a complex mixture contains overlaid distributions of several different compounds.

Structure of LC-MS DataWith LC as the first dimension, each compound is observed over multiple spectra, showing a three-dimensional pattern of m/z, elution time and abundance

Salmonella typhimurium dataset

Peptide: VKHPSEIVNVGDEINVK

Parent Protein: gi|16759851 30S ribosomal protein S1

Charge: 2+m/z: 939.0203Monoisotopic Mass: 1876.0054 Da

Elution range: Scans 1539 - 1593

Feature Discovery in LC-MS dataGoal: Infer (mass, elution time, intensity) of compounds that are present in data obtained from an LC-MS dataset

Since their identities are unknown, the compounds are more appropriately termed features to refer to the idea that these are inferred from a three dimensional pattern

2D view of an LC-MS analysis of Salmonella typhimurium

Feature Discovery in LC-MS dataSequential process of finding features in each mass spectrum is followed by grouping of features over multiple spectra together

2D views of an LC-MS dataset in different stages of processing

raw dataCollapsed

monoisotopicfeatures in all spectra

LC-MS featuresdeisotoping Elution profile discovery

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Feature discovery in individual spectraDeisotoping

Process of converting a mass spectrum (m/z, intensity) into a list of species (mass, abundance, charge)

Deisotoping a mass spectrum of 4 overlapping species

charge Monoisotopic MW abundance2 1546.856603 5334672 1547.705048 1946072 1547.887682 6719472 1548.799612 426939

Deisotoping routine for a peakAlgorithm to detect peptides in a complex spectrum

avg. mass = 1876.02

Charge detectionalgorithm2

theoretical spectrum

Fitness value

Averagine3

estimated empirical formula:

C83 H124 N23 O25 S1

Mercury4

charge = 2

observed spectrum

1. Horn, D.M., Zubarev, R.A., McLafferty, F.W. Automated Reduction and Interpretation of High Resolution Electrospray Mass Spectra of Large Molecules. J. Am. Soc. Mass Spectrom. 2000, 11, 320-332.2. Senko, M. W.; Beu, S. C.; McLafferty, F. W. Automated assignment of charge states from resolved isotopic peaks for multiplycharged ions. J. Am. Soc. Mass Spectrom. 1995, 6, 52–56.3. Senko, M. W.; Beu, S. C.; McLafferty, F. W. Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions. J. Am. Soc. Mass Spectrom. 1995, 6, 229–233.4. Rockwood, A. L.; Van Orden, S. L.; Smith, R. D. Rapid Calculation of Isotope Distributions. Anal. Chem. 1995, 67, 2699–2704.

Deisotoping entire spectrum –Modification of THRASH1

SpectrumCalculate

background intensity

Find peaks in spectrum

Choose most abundant peak

S/N, intensity > thresholds

Determine its charge

Guess empirical formula for mass

= (mz-1.00782)*CS

Generate theoretical

profile, initialize fit = ∞

Calculate fitscore

fit improves?

Calculate fit Fit improves?

fit better thanthreshold ?

m/z of peak = mz

yes

Done

no

charge = CS

Empirical formula=

CnHmNxOySz

Fit score = fitnew

fit = fitnew

yes

noUnshift

theoretical profile

yes

noFit score = fitnew

fit = fitnew

yes

no

Delete isotopic peaks from peak list, points in spectrum,

& add to deisotoped results

Delete isotopic peaks from peak list & profile in spectrum

Shift theoretical

profile by +1Da

Shift theoretical

profile by -1Da

1. Horn, D.M., Zubarev, R.A., McLafferty, F.W. Automated Reduction and Interpretation of High Resolution Electrospray Mass Spectra of Large Molecules. J. Am. Soc. Mass Spectrom. 2000, 11, 320-332.

Modified THRASH RoutineAlgorithm to detect peptides in a complex spectrum1. Discover all peaks in a spectrum above a specified S/N and keep in

unprocessed list2. Select most abundant peak still unprocessed3. Compute charge for peak using charge detection algorithm4. Compute average mass from observed m/z and predicted charge value5. Use “Averagine” algorithm to guess empirical formula based on mass

and average composition of peptides in database6. Use Mercury algorithm to generate theoretical spectrum from the

predicted empirical formula, charge of peak, and resolution of peak7. Calculate fitness value for similarity between theoretical and observed

spectrum8. Perform “THRASHING” by overlaying theoretical and observed spectra

after applying “isotopic” one dalton shift to the theoretical spectrum. Keep best fit

9. If successful fit was observed, delete isotopic peaks and associated profile of height above specified threshold of most intense ions using the theoretical pattern as template. Otherwise remove current peak only from list of unprocessed peaks.

10. While unprocessed peaks remain, repeat steps starting at step 2.

Charge Detection AlgorithmPatterson (Autocorrelation) algorithm to detect charge of a peak in a complex spectrum

1. Zhang, Z; Marshall, A.G. A Universal Algorithm for Fast and Automated Charge State Deconvolution of Electrospray Mass-to-Charge Ratio Spectra. J. Am. Soc. Mass Spectrom. 1998, 9, 225-233.

2. Senko, M. W.; Beu, S. C.; McLafferty, F. W. Automated assignment of charge states from resolved isotopic peaks for multiplycharged ions. J. Am. Soc. Mass Spectrom. 1995, 6, 52–56.

3. Labowsky, M; Whitehouse, C.; Fenn, J.B. Rapid Commun. Mass Spectrom. 1993, 7, 71-84.4. Reinhold, B.B.; Reinhold, V.N. Electrospray Ionization Mass Spectrometry: Deconvolution by an Entropy-Based Algorithm. J. Am. Soc. Mass

Spectrom. 1992, 3, 207-215.5. Mann, M.; Meng, C.K.; Fenn, J.B. Interpreting Mass Spectra of Multiply Charged Ions. Analytical Chemistry. Aug. 1, 1989, 61, 1702-1708.

938.5 939 939.5 940 940.5 941 941.50

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nP(Δmz) = ΣI(mzi) * I(mzi+ Δmz)

Averagine AlgorithmAlgorithm to guess an average empirical formula for a given mass

Uses average composition of all peptides in peptide database as the empirical formula for all peptidesProtein database Averagine formula: C4.9384 H7.7583 N1.3577 O1.4773 S0.0417 , Mass = 111.1254Average Mass of 1877.025 would give a multiplier of 1877.025/111.1254

1. Senko, M. W.; Beu, S. C.; McLafferty, F. W. Determination of monoisotopic masses and ion populations for large biomoleculesfrom resolved isotopic distributions. J. Am. Soc. Mass Spectrom. 1995, 6, 229–233.

10.0417*1877.025/111.1254S251.4773*1877.025/111.1254O

231.3577*1877.025/111.1254NRemainder = 112H844.9834*1877.025/111.1254CAtomicityCopiesElement

Empirical formula used for theoretical profile = C83 H112 N23 O25 S1

Theoretical Isotopic ProfileMercury algorithm to generate a theoretical profile for a compound

Treat each element’s isotopic distribution as a sum of delta (δ) functions

1. Rockwood, A. L.; Van Orden, S. L.; Smith, R. D. Rapid Calculation of Isotope Distributions. Anal. Chem. 1995, 67, 2699–2704.2. Kubinyi, H. Calculation of isotope distributions in mass spectrometry. A trivial solution for a non-trivial problem. Analytica Chemica

Acta. 1991, 247, 107-119.

0.99759 δ(m-15.99491) + 0.000374 δ(m-16.99913) + 0.002036δ(m-17.99916)

Oxygen

0.9502 δ(m-31.97207) + 0.0075 δ(m-32.97145)+ 0.0421 δ(m-33.96786) + 0.0002 δ(m-35.96708)

Sulphur

0.996337 δ(m-14.00307 ) + 0.003663 δ(m-15.00011 )Nitrogen

0.99985 δ(m-1.007825) + 0.00015 δ(m-2.014102)Hydrogen

0.98893 δ(m-12) + 0.01107 δ(m-13.00336)Carbon

Isotope distribution FunctionElement

Relative isotope abundance Isotope Mass


Treat each element’s isotopic distribution as a sum of delta (δ) functionsConvert distribution function into frequency domain: delta functions convert to simple exponential functions

0.99759 e15.99491(i2π)μ + 0.000374 e16.99913(i2π)μ + 0.002036e17.99916 (i2π)μ

Oxygen

0.9502 e31.97207 (i2π)μ + 0.0075 e32.97145 (i2π)μ + 0.0421e33.96786(i2π)μ + 0.0002 e35.96708(i2π)μ

Sulphur

0.996337 e14.00307(i2π)μ + 0.003663 e15.00011(i2π)μNitrogen

0.99985 e1.007825(i2π)μ + 0.00015 e2.014102(i2π)μHydrogen

0.98893 e12(i2π)μ + 0.01107 e13.00336(i2π)μCarbon

Frequency Spectrum Function (fElem(μ))Element


Treat each element’s isotopic distribution as a sum of delta (δ) functionsConvert distribution function into frequency domain: delta functions convert to simple exponential functions. Calculate the isotopic profile for a compound from the convolution of isotopic distributions of individual atoms and the imposition of a peak shape reflecting resolution of instrumentCompute convolution using multiplication in the frequency domain and by applying a Fourier transform

F(m) = FT [s(μ) fC(μ)n fH(μ)m fN(μ)x fO(μ)y fS(μ)z]

For the empirical formula CnHmNxOySz

Frequency spectra of elementsInverse transform of shape function

Fit FunctionsFit functions to quantitate quality of match between theoretical and observed profiles

• Least square area1: Σ (ti-oi)2 / Σti2

• Least square peak: Σ (Tj-Oj)2 / ΣTj2

• Chi-square area: Σ (ti-oi)2 / Σti• Chi-square peaks2: Σ (Ti-Oi)2 / ΣTi

Threshold intensity for points to be scored

ti: theoretical intensity of ithpoint

oi: observed intensity of ithpoint (after normalizing)

Tj: theoretical intensity of jth“isotopic” peak

Oj: observed intensity of jth“isotopic” peak

1. Horn, D.M., Zubarev, R.A., McLafferty, F.W. Automated Reduction and Interpretation of High Resolution Electrospray Mass Spectra of Large Molecules. J. Am. Soc. Mass Spectrom. 2000, 11, 320-332.

2. Senko, M. W.; Beu, S. C.; McLafferty, F. W. Determination of monoisotopic masses and ion populations for large biomoleculesfrom resolved isotopic distributions. J. Am. Soc. Mass Spectrom. 1995, 6, 229–233.

Chemical Labeling with TagsSpecify a static tag to be applied to Averagine formula

Changes the Averagine formula generated

346.01

348.01

347.02

346.70

2.5e+3

5.0e+3

7.5e+3

1.00e+4

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+TOF MS: 0.359 min from bromoadenosine.wiff Agilent

Scan # 18

Subtract average mass of tag

autocorrelation charge = 1 Average mass = 345.01

autocorrelation charge = 1 mass = 265.1065

C5 H174 N1 O1 S0

Calculate Averagine formula

C5 H174 N1 O1 S0 Br1

Add tag formula to Averagine formula

Interesting profile because of the isotopic distribution of bromine (51% 78.91833 , 49% 80.91629)

Chemical Labeling with TagsSpecify a static tag to be applied to Averagine formula

Changes the Averagine formula generated

346.02348.02

349.02347.02

2.5e+3

5.0e+3

7.5e+3

1.00e+4

346 347 348 349m/z

inte

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+TOF MS: 0.359 min from bromoadenosine.wiff Agilent

Scan # 18

C5 H174 N1 O1 Br 1Theoretical distribution

Subtract average mass of tag

autocorrelation charge = 1 Average mass = 345.01

autocorrelation charge = 1 mass = 265.1065

C5 H174 N1 O1 S0

Calculate Averagine formula

C5 H174 N1 O1 S0 Br1

Add tag formula to Averagine formula

16O/18O MixturesOverlapping isotope patterns separated by 4 Da

If peaks exist 4 Da before current peak, those are processed first, and only the first four isotopic peaks are removed

656.84

658.85657.34

657.84

659.35

658.35

659.85

660.35658.59

d=0.502

d=0.501

d=0.501

d=0.502

d=0.501

d=0.502d=1.002 d=1.022

5.0e+5

1.00e+6

1.50e+6

2.00e+6

2.50e+6

3.00e+6

657 658 659 660 661

m/z

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Isotopic CompositionChanging natural abundances


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890.3 890.5 890.8 891 891.3 891.5

m/z

Isotopic distribution of peptide with similar mass and charge (16+), but with natural isotopic distribution of atoms

890.32

890.45

890.58890.38

890.50

890.70890.25 890.630

d=0.062 d=0.065

d=0.056 d=0.071

2.5e+6

5.0e+6

7.5e+6

1.00e+7

1.25e+7

890.3 890.4 890.5 890.6 890.7

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Inte

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13C, 15N depleted media – isotopic composition of atoms is different from those found in nature. Distribution of isotopes of Sulfur predominates the distribution shown below


Changing 12C/13C, 14N/15N isotopic abundances from those in nature to approriate ones results in a better fitAs shown, estimated isotopic abundances were still not perfect

m/z

890.32890.45

890.38890.51

890.57

890.63890.70

890.76

.

0.0 .

0 0.

d=0.062d=0.065

d=0.056d=0.071

d=0.054 d=0.073

2.5e+6

5.0e+6

7.5e+6

1.00e+7

1.25e+7

890.3 890.4 890.5 890.6 890.7

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LC-MS Feature Discovery

• Black dots indicate individual m/z values• Green dots signify successfully deisotoped data• Shades of red indicate data intensity

• Black dots indicate individual m/z values• Green dots signify successfully deisotoped data• Shades of red indicate data intensity

Feature definition over elution timeDeisotoping collapses original data into data lists

Goal: Given series of deisotoped mass spectra, group related data across elution time

Look for repeated monoisotopic mass values in sequential spectra, allowing for missing dataCan also look for expected chromatographic peak shape

46.740.01561872.86621871.8631873.0910.0296936.93894510072150080.40.01051512.75331512.7531513.7620.0706757.885450399321500

111.20.00651181.65411181.6541182.3790.0198591.83435698962150057.520.00881376.71451376.7151377.640.0446689.36456306572150039.060.0096729.1045729.1045729.54610.024730.111766147711500109.010.00761282.63411282.6341283.4170.0253642.32436639542150092.090.0091374.76951374.771375.6940.0384688.3927340702150079.220.00862023.05492022.0522023.3750.02675.024698876131500120.360.0165942.9742942.9742943.55180.1025943.981512136071150077.940.0061124.6361124.6361125.3220.012563.325322978222150074.750.0137863.4846863.4846864.00730.0156864.491924228291150074.040.02221102.0261102.0261102.6980.11111103.033261491311500718.830.0106758.0576758.0576758.52220.0716759.0649277293311500

signal noisefwhmmost abu.

mwmonoiso

mwaverage mwfitmzabundancechargescan num

Feature definition over elution timeCan visualize deisotoped data in two-dimensions

Time

Mas

s

S. typhimurium dataset on 11T FTICR

Charge state view

Feature definition over elution time

• Plotting monoisotopic mass,but color is based on charge of the original data point seen

• Monoisotopic Mass =(m/z x charge) - 1.00728 x charged

Time

Mas

s

Feature definition over elution timeZoom-in view of species

Time

Mas

s

Same species in multiple spectra need to be grouped together Related peaks found using a

weighted Euclidean distance; considers:

MassAbundanceElution timeIsotopic Fit


Grouping uses single linkage clusteringForm connections between data points in n-dimensionsCompute the Euclidean distance between two points

distance = Sqrt { [weightmass x (massa – massb)]2 + [weightabu x (LogAbua – LogAbub)]2 +[weightET x (ETa – ETb)]2 +[weightfit x (fita– Fitb)]2 }

If distance < threshold, combine points together


Determine 6 separate groupsTypically require 2 or 3 points per group


Feature definition over elution timeFeature detail

Median Mass: 1904.9399 Da (more tolerant to outliers than average)Elution Time: Scan 1757 (0.363 NET)Abundance: 1.7x107 counts (area under desired SIC)

See both 2+ and 3+ dataStats typically come from the most abundant charge state (2+)

Scan number

Monoisotopic Mass

1,904.850

1,904.870

1,904.890

1,904.910

1,904.930

1,904.950

1,904.970

1,740 1,745 1,750 1,755 1,760 1,765 1,770 1,775 1,780 1,785 1,790

5 ppm

1 2 3Charge:

Selected Ion Chromatograms

0.0E00

5.0E+5

1.0E+6

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2.0E+6

Abu

ndan

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ount

s) Both2+ data3+ data

Second exampleLC-MS feature eluting over 7.5 minutes


Clustering algorithm allows for missing data, common with chromatographic tailing

Second example, feature detailMedian Mass: 2068.1781 DaElution Time: Scan 1809 (0.380 NET)Abundance: 8.7x107 counts (area under 3+ SIC)

This example has primarily 3+ data; previous had even mix of 2+ and 3+ data


Scan number

Monoisotopic Mass

2,068.075

2,068.095

2,068.115

2,068.135

2,068.155

2,068.175

2,068.195

1,775 1,800 1,825 1,850 1,875 1,900 1,925 1,950 1,975 2,000 2,025 2,050

1 2 3Charge:

5 ppm

0.0E+0

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4.0E+6

Abu

ndan

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ount

s)

Both2+ data3+ data

Selected Ion Chromatograms

Feature definition over elution timeRefining the features

Require data spans at least 3 spectraExclude grouped feature if it is too long (e.g. ≥ 15% of dataset)

Scan number

1,612.650

1,612.670

1,612.690

1,612.710

1,612.730

1,612.750

1,540 1,545 1,550 1,555 1,560 1,565 1,570 1,575 1,580 1,585 1,590 1,595 1,600 1,605 1,610 1,615

1,612.770

0.0E+0

1.0E+6

2.0E+6

3.0E+6

4.0E+6

Sometimes the Euclidean distance results in undesirable clusteringSplit if elution profile indicates two or more entities with a mass difference ≥ threshold (e.g. 4 ppm)Necessary since hard to define clustering weights and distance constraints that work in all situations

9 ppm

Feature definition over elution timeExample: S. typhimurium dataset on 11T FTICR

• 100 minute LC-MS analysis (3360 mass spectra)• 67 cm, 150 μm I.D. column with 5 μm C18 particles• 78,641 deisotoped peaks• Group into 5910 LC-MS Features

Isotopic Pairs ProcessingPaired features typically have identical sequences, with and without an isotopic label

e.g. 16O/18O pairs or 14N/15N pairs

Data prior to finding features

LC-FTICR-MS

Control(16O water)

Perturbed(18O water)

Isotopic Pairs ProcessingData after finding paired features

4 Da pair spacing due to incorporation of two 18O atoms

LC-FTICR-MS

Control(16O water)

Perturbed(18O water)

Paired feature example: 16O/18O data

Isotopic Pairs Processing

Monoisotopic Mass

Scan number

1,235.0

1,236.2

1,237.4

1,238.6

1,239.8

1,241.0

1,242.2

1,243.4

1,244.6

1,245.8

1,247.0

2,688 2,700 2,712 2,724 2,7360.0E+00

5.0E+04

1.0E+05

1.5E+05

2.0E+05

2700 2710 2720 2730

Pair #424; Charge used = 2AR = 1.78 (LightArea÷Heavyarea); orAR = 1.34 ± 0.2 (scan-by-scan)

4.0085 Da

Scan number

Monoisotopic Mass

1,279.0

1,280.2

1,281.4

1,282.6

1,283.8

1,285.0

1,286.2

1,287.4

1,288.6

1,289.8

1,291.0

3,010 3,026 3,042 3,058

4.0085 Da

0.0E+00

1.0E+06

2.0E+06

3.0E+06

4.0E+06

3010 3020 3030 3040 3050 3060 3070

Pair #460; Charge used = 2AR = 0.13 (LightArea÷Heavyarea); orAR = 0.12 ± 0.02 (scan-by-scan)

Compute AR using ratio of areas, or Compute AR scan-by-scan, then average AR values (members must co-elute)

Scan number

Monoisotopic Mass

2,925.0

2,934.0

2,943.0

2,952.0

2,961.0

2,970.0

2,979.0

2,988.0

2,997.0

3,006.0

3,015.0

1,695 1,698 1,701 1,704 1,707 1,710 1,713 1,716 1,719 1,722 1,725 1,728 1,731

Paired feature example: 14N/15N dataPair members often do not co-eluteUse bulk area ratio, or re-align pair members then compute AR scan-by-scan

Isotopic Pairs Processing

AR = 1.17 (LightArea÷Heavyarea)

1.0E+6

2.0E+6

3.0E+6

4.0E+6

5.0E+6

30.9 Da, corresponding to 31 N atomsMatching AMT: GILSGEFDHIPEQAFYMVGSIDEAVEKEmpirical formula: C134H201N31O44S

Feature definition over elution timeNumerous options for clustering data to form LC-MS features and for finding paired features

Accurate Mass and Time (AMT) tagUnique peptide sequence whose monoisotopic mass and normalized elution time are accurately knownAMT tags also track any modified residues in peptide

AMT tag DBCollection of AMT tags

AMT tag Approach articlesR.D. Smith et. al. Proteomics 2002, 2, 513-523.J.D.S. Zimmer, M.E. Monroe et. al., Mass Spec. Reviews 2006, 25, 450-482.L. Shi, J.N. Adkins, et. al., J. of Biological Chem. 2006, 281, 29131-29140.

Assembling an AMT tag DB

What can we use an AMT tag DB for?Query LC-MS/MS data to answer questions

How many distinct peptides were observed passing filter criteria?Which peptides were observed most often by LC-MS/MS?How many proteins had 2 or more partially or fully tryptic peptides?

Correlate LC-MS features to the AMT tagsAnalyze multiple, related samples by LC-MS using a high mass accuracy mass spectrometer

e.g. Time course study, 5 data points with 3 points per sampleCharacterize the LC-MS features

Deisotope to obtain monoisotopic mass and chargeCluster in time dimension to obtain abundance information

Match to AMT tags to identify peptidesAlign in mass and time dimensionsMatch mass and time of LC-MS features to mass and time of AMT tags

Assembling an AMT tag DB

Assembling an AMT tag DBCharacterizing AMT tags

Analyze samples by LC-MS/MS10 minute to 180 minute LC separationsObtain 1000's of MS/MS fragmentation spectra for each sample

Analyze spectra using SEQUEST, X!Tandem, etc.SEQUEST: http://www.thermo.com/bioworks/ X!Tandem: http://www.thegpm.org/TANDEM/index.htmlR. Craig and R.C. Beavis, Bioinformatics 2004, 20, 1466-1467.

Collate results

List of peptide

and proteinmatches

AID_STM_019_110804_19_LTQ_16Dec04_Earth_1004-10 #11195 RT: 44.76 AV: 1 NL: 2.79E5T: ITMS + c NSI d Full ms2 [email protected] [ 160.00-1265.00]

200 300 400 500 600 700 800 900 1000 1100 1200m/z

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e A

bund

ance

552.47

774.25

445.94987.28873.30

717.22580.74 866.10703.01437.21 1004.39

360.21 678.22231.21 1086.31973.13 1178.33

Assembling an AMT tag DBAMT tag example

R.VKHPSEIVNVGDEINVK.VObserved in scan 11195 of dataset #19 in an SCX fractionation series

3+ speciesMatch 30 b/y ionsX!Tandem hyperscore = 80X!Tandem Log(E_Value) = -5.9

y3b8++

y4

b9++

b10++

y5

b11++

y6

b13++

y7

b16++y8 y9

y10b7++


R.VKHPSEIVNVGDEINVK.VObserved in scan 11195 of dataset #19 in an SCX fractionation series

3+ speciesMatch 30 b/y ionsX!Tandem hyperscore = 80X!Tandem Log(E_Value) = -5.9

Assembling an AMT tag DBAlign related datasets using elution times of observed peptides

One option: utilize NET prediction algorithm to create theoretical dataset to align against

NET Prediction uses position and ordering of amino acid residues to predict normalized elution time

0.76488.043-6.5R.TFAISPGHMNQLRAESIPEAVIAGASALVLTSYLVR.C

0.58973.961-8.9R.KVAAQIPNGSTLFIDIGTTPEAVAHALLGHSNLR.I

0.43862.803-11.6K.KTGVLAQVQEALKGLDVR.E

0.51962.583-7.3K.RFNDDGPILFIHTGGAPALFAYHPHV.-

0.41553.003-8.2R.GIIKVGEEVEIVGIK.E

0.22436.915-8.8R.LVHGEEGLVAAKR.I

0.16733.958-6.1R.AARPAKYSYVDENGETK.T

Predicted NET

Elution Time

X!TandemLog (E_Value)Peptide

K. Petritis, L.J. Kangas, et al., Analytical Chemistry 2003, 75, 1039-1048. K. Petritis, L.J. Kangas, et al., Analytical Chemistry 2006, 78, 5026-5039.

0

0.2

0.4

0.6

0.8

1

20 40 60 80 100Elution Time (minutes)

Pre

dict

ed N

ET

y = 0.01081x -0.1829R2 = 0.95

Example: 506 unique peptides used for alignment; Log(E_Value) ≤ -6

Assembling an AMT tag DBAlign related datasets using elution times of observed peptides

One option: utilize NET prediction algorithm to create theoretical dataset to align against

NET Prediction uses position and ordering of amino acid residues to predict normalized elution time

Alignment yields NET values based on observed elution timesObserved NET = Slope×(Observed Elution Time) + Intercept

VKHPSEIVNVGDEINVKElution time: 44.923 minutesPredicted NET: 0.292Observed NET: 0.303


R.VKHPSEIVNVGDEINVK.VObserved in 7 (of 25) LC-MS/MS datasets in the SCX fractionation series

Analysis 1, scan 11195 3+, hyperscore 80, Obs. NET 0.303

Compute monoisotopic mass: 1876.0053 DaAverage Normalized Elution Time: 0.3021 (StDev 0.0021)




Assembling an AMT tag DBMass and Time Tag Database

Repository for AMT tagsMass, elution time, modified residues, and supporting information for each AMT tag

Allows samples of unknown composition to be matched quickly and efficiently, without needing to perform tandem MSAssembled by analyzing a control set of samples, cataloging each peptide identification until subsequent analyses no longer provide new identifications

0.0050.5572533.23048MYGHLKGEVA…QER36843675

0.0110.4592590.281511WVKVDGWDN…FER36715875

0.0020.3791960.06025HRDLLGATNP…TLR36609588

0.0050.2351175.61463SSALNTLTNQK17683899

0.0000.1431338.68261MTGRELKPHDR1662039

Observed NET

StDev

Average Observed

NET

Calculated Monoisotopic

MassLC-MS/MS Obs. CountPeptideMT Tag ID

Assembling an AMT tag DBMini AMT tag DB

Database constructed from a relatively small number of datasetse.g. 25 SCX fractionation samples from S. typhimurium, each analyzed by LC-MS/MS and then by X!TandemProtein database: S_typhimurium_LT2_2004-09-19

4550 proteins and 1.4 million residues

>STM1834 putative YebN family transport protein (yebN) {Salmonella typhimurium LT2}

MFAGGSDVFNGYPGQDVVMHFTATVLLAFGMSMDAFAASIGKGATLHKPKFSEALRTGLI

FGAVETLTPLIGWGLGILASKFVLEWNHWIAFVLLIFLGGRMIIEGIRGGSDEDETPLRR

HSFWLLVTTAIATSLDAMAVGVGLAFLQVNIIATALAIGCATLIMSTLGMMIGRFIGPML

GKRAEILGGVVLIGIGVQILWTHFHG

>STM1835 23S rRNA m1G745 methyltransferase (rrmA) {Salmonella typhimurium LT2}

MSFTCPLCHQPLTQINNSVICPQRHQFDVAKEGYINLLPVQHKRSRDPGDSAEMMQARRA

FLDAGHYQPLRDAVINLLRERLDQSATAILDIGCGEGYYTHAFAEALPGVTTFGLDVAKT

AIKAAAKRYSQVKFCVASSHRLPFADASMDAVIRIYAPCKAQELARVVKPGGWVVTATPG

PHHLMELKGLIYDEVRLHAPYTEQLDGFTLQQSTRLAYHMQLTAEAAVALLQMTPFAWRA

RPDVWEQLAASAGLSCQTDFNLHLWQRNR

Assembling an AMT tag DBDatabase Relationships

Minimum information required:Single table with Mass and NET

T_Mass_Tags

PK Mass_Tag_ID

PeptideMonoisotopic_MassNET

Expanded schema:

T_Proteins

PK Ref_ID

ReferenceDescription

T_Mass_Tags

PK Mass_Tag_ID

PeptideMonoisotopic_Mass

T_Mass_Tags_NET

PK,FK1 Mass_Tag_ID

Avg_GANETCnt_GANETStD_GANET

T_Mass_Tags_to_Protein_Map

PK,FK1 Mass_Tag_IDPK,FK2 Ref_ID

PK := Primary KeyFK := Foreign Key

Assembling an AMT tag DBMicrosoft Access DB Relationships

Full schema to track individual peptide observations

V_Filter_Set_Overview_Ex

Filter_TypeFilter_Set_IDExtra_InfoFilter_Set_NameFilter_Set_Description

T_Analysis_Description

PK Job

DatasetDataset_IDDataset_Created_DMSDataset_Acq_Time_StartDataset_Acq_Time_EndDataset_Scan_CountExperimentCampaignOrganismInstrument_ClassInstrumentAnalysis_ToolParameter_File_NameSettings_File_NameOrganism_DB_NameProtein_Collection_ListProtein_Options_ListCompletedResultTypeSeparation_Sys_TypeScanTime_NET_SlopeScanTime_NET_InterceptScanTime_NET_RSquaredScanTime_NET_Fit

T_Mass_Tags

PK Mass_Tag_ID

PeptideMonoisotopic_MassMultiple_ProteinsCreatedLast_AffectedNumber_Of_PeptidesPeptide_Obs_Count_Passing_FilterHigh_Normalized_ScoreHigh_Peptide_Prophet_ProbabilityMod_CountMod_DescriptionPMT_Quality_Score

T_Mass_Tags_NET

PK,FK1 Mass_Tag_ID

Min_GANETMax_GANETAvg_GANETCnt_GANETStD_GANETStdError_GANETPNET

T_Proteins

PK Ref_ID

ReferenceDescriptionProtein_SequenceProtein_Residue_CountMonoisotopic_MassProtein_Collection_IDLast_Affected

T_Mass_Tags_to_Protein_Map

PK,FK1 Mass_Tag_IDPK,FK2 Ref_ID

Mass_Tag_NameCleavage_StateFragment_NumberFragment_SpanResidue_StartResidue_EndRepeat_CountTerminus_StateMissed_Cleavage_Count

T_Peptides

PK Peptide_ID

FK1 Analysis_IDScan_NumberNumber_Of_ScansCharge_StateMHMultiple_ProteinsPeptide

FK2 Mass_Tag_IDGANET_ObsScan_Time_Peak_ApexPeak_AreaPeak_SN_Ratio

T_Score_Discriminant

PK,FK1 Peptide_ID

Peptide_Prophet_FScorePeptide_Prophet_Probability

T_Score_Sequest

PK,FK1 Peptide_ID

XCorrDelCnSpDelM

T_Score_XTandem

PK,FK1 Peptide_ID

HyperscoreLog_EValueDeltaCn2Y_ScoreY_IonsB_ScoreB_IonsDelMIntensityNormalized_Score

Assembling an AMT tag DBExample data

1876.00533VKHPSEIVNVGDEINVK24847Monoisotopic_MassPeptideMass_Tag_ID

R.VKHPSEIVNVGDEINVK.VR.VKHPSEIVNVGDEINVK.VR.VKHPSEIVNVGDEINVK.VR.VKHPSEIVNVGDEINVK.VR.VKHPSEIVNVGDEINVK.VR.VKHPSEIVNVGDEINVK.VR.VKHPSEIVNVGDEINVK.V

Peptide

29421206392248477626329159206391248477255629118206390248476908129667206389248476538621090520638824847615113994520638724847574613111952063862484753428

Charge State

Scan NumberJobMass Tag

IDPeptide_ID

-11.2760.376263-13.777872556-12.826969081-12.8077.265386-12.857461511-4.9269.257461-5.8980.253428

Log(E_Value) HyperscorePeptide_ID

2.11E-0370.302124847StD_GANETCnt_GANETAvg_GANETMass_Tag_ID

T_Mass_Tags_NETT_Mass_Tags

T_Peptides T_Score_XTandem

Assembling an AMT tag DBProcessing stepsThermo-Finnigan LTQ .Raw files

MS/MS spectra files

Convert to .Dta using Extract_MSn.exe. Concatenate .Dta files into _Dta.txt file using Perl script. Improved application (under development): Decon_MSn.exe

X!Tandem Results

Process _Dta.txt files with X!Tandem(round 1 partially tryptic; round 2 dynamic oxidized methionine)

Tab delimited text files

Convert X!Tandem .XML files to tab-delimited files using the Peptide Hit Results Processor application

Summarized result files

Microsoft Access DB

Align datasets using MTDB Creator application

Load into database

Assembling an AMT tag DBPHRP Relationships

Results_Info

PK Result_ID

FK1 Unique_Seq_IDGroup_IDScanChargePeptide_MHPeptide_HyperscorePeptide_Expectation_Value_Log(e)Multiple_Protein_CountPeptide_SequenceDeltaCn2y_scorey_ionsb_scoreb_ionsDelta_MassPeptide_Intensity_Log(I)

Result_To_Seq_Map

PK,FK1 Unique_Seq_IDPK,FK2 Result_ID

Seq_Info

PK Unique_Seq_ID

Mod_CountMod_DescriptionMonoisotopic_Mass

Mod_Details

PK,FK1 Unique_Seq_ID

Mass_Correction_TagPosition

Seq_to_Protein_Map

PK,FK1 Unique_Seq_IDPK Protein_Name

Cleavage_StateTerminus_StateProtein_Expectation_Value_Log(e)Protein_Intensity_Log(I)

Assembling an AMT tag DBDatabase histograms – filtered on Log(E_Value) ≤ -2

Peptide Mass Histogram

0

200

400

600

800

1000

1200

1400

500 1500 2500 3500 4500

Peptide Mass

Freq

uenc

y

NET Histogram

0

100

200

300

400

500

600

0 0.2 0.4 0.6 0.8 1

Normalized Elution Time

Freq

uenc

y

X!Tandem Hyperscore Histogram

0

200

400

600

800

1000

1200

20 40 60 80 100 120

Hyperscore

Freq

uenc

y

0

5000

10000

15000

20000

0 5 10 15 20 25Dataset Count

Pep

tide

Cou

nt

0

15000

30000

45000

60000

0 100 200 300 400 500 600Dataset Count

Pep

tide

Cou

nt

AMT Tag DB Growth TrendTrend for Mini AMT Tag DB

25 SCX fractionation datasets of a single growth condition

Trend for Mature AMT Tag DB

521 different samples from ~25 different growth conditionsSlope of curve decreases as more datasets added and fewer new peptides are seen

Filtered on Log(E_Value) ≤ -2

Filtered on Peptide Prophet Probability ≥ 0.99

Identifying LC-MS FeaturesVIPER software

Visualize and find features in LC-MS dataMatch features to peptides (AMT tags)Graphical User Interface and automated analysis mode

Peak Matching StepsLoad LC-MS peak lists from Decon2LSFilter dataFeature definition over elution timeSelect AMT tags to match againstOptionally, find paired features (e.g. 16O/18O pairs)Align LC-MS features to AMT tags using LCMSWarpBroad AMT tag DB searchSearch tolerance refinementFinal AMT tag DB searchReport results

Identifying LC-MS Features

AMT Tag database selection


Connect to mass tag system (MTS) if inside PNNL or use Standalone Microsoft Access DB

Alignment using LCMSWarp

Calculated monoisotopic mass

Average observed NET

AMTs

Deisotoped monoisotopic mass

Observed scan number

LC-MS Features

Align scan number (i.e. elution time) of features to NETs of peptides in given AMT tag database

Match mass and NET of AMT tags to mass and scan number of MS featuresUse LCMSWarp algorithm to find optimal alignment to give the most matches

Scan number

Alig

nmen

t S

core Best score = 0.00681

Scan = 1113Shift = 113


N. Jaitly, M.E. Monroe et. al., Analytical Chemistry 2006, 78, 7397-7409.

LCMSWarp computes a similarity score from conserved local mass and retention time patterns

Alignment Function

Heatmap of similarity score between LC-MS features and AMT tags (z-score representation)

Alignment using LCMSWarpSimilarity scores between LC-MS features and AMT tags are used to generate a score graph of similarityBest alignment is found using a dynamic programming algorithm that determines the transformation function with maximum likelihood

AMT tag

NET

MS Scan Number

S. typhimurium on 11T

N. Jaitly, M.E. Monroe et. al., Analytical Chemistry 2006, 78, 7397-7409.

Alignment using LCMSWarpTransformation function is used to convert from scan number to NET

Features centered at same scan number get the same obs. NET valueWhen matching LC-MS Features to AMTs, we will search +/- a NET tolerance, which effectively allows for LC-MS features to shift around a little in elution time

0.16790.16790.16790.16770.16450.16330.16090.15940.15890.15890.1569

LC-MS Feature

NET

0.168210560.169710560.186210560.165210550.18310420.151910370.150910270.165310210.150710190.162610190.15191011

MatchingAMT tag

NET

LC-MS Feature

Scan

00.10.20.30.40.50.60.70.80.9

750 1250 1750 2250 2750 3250LC-MS Feature Scan

LC-M

S F

eatu

re N

ET

Alignment using LCMSWarpNET Residual Plots

Difference between NET of LC-MS feature and NET of matching AMT tag

Indicates quality of alignment between features and AMT tags

This data shows nearly linear alignment between features and AMTs, but the algorithm can easily account for non-linear trends

NET Residuals if a linear mapping is used NET Residuals after LCMSWarp

AM

T ta

g N

ET

MS Scan Number


Non-linear alignment example #1

Identical LC separation system, but having column flow irregularities


AMT tag

NET

MS Scan Number


NET Residuals after LCMSWarp

NET Residuals if a linear mapping is used


PMT Tag DB from C18 LC-MS/MS analyses using ISCO-based LC (exponential dilution gradient)LC-MS analysis used C18 LC-MS via Agilent linear gradient pump




S. oneidensis on LTQ-Orbitrap


PMT Tag DB from C18 LC-MS/MS analyses using ISCO-based LCLC-MS analysis used C18 LC-MS via Agilent linear gradient pump




QC Standards (12 protein digest) on LTQ-Orbitrap

Alignment using LCMSWarpLCMSWarp Features

Fast and robustPrevious method used least-squares regression, iterating through a large range of guesses (slow and often gave poor alignment)

Requires that a reasonable number of LC-MS features match the AMT Tag DB

S. typhimurium on 11Tmatch against 18,617 S. typhimurium PMTs

S. typhimurium on 11Tmatch against 65,193 S. oneidensis PMTs

Alignment using LCMSWarpIn addition to aligning data in time, we can also recalibrate the masses of the LC-MS features

Possible because mass and time values are available for both LC-MS features and AMT tags

Two options for mass re-calibrationBulk linear correctionPiece-wise correction via LCMSWarp

Visualize mass differences using mass error histogram or mass residual plot

Match TolerancesMass: ±25 ppmNET: ±0.05 NET

Mass Error HistogramList of binned mass error values

Difference between feature's mass and matching AMT tag's massBin values to generate a histogramTypically observe background false positive level

3.60.005691573.8321573.838111.80.018481571.8921571.910712.20.019121571.8311571.849811.30.017701571.7261571.7432511.10.017451570.8831570.9005

Mass Error (ppm)

Delta Mass (Da)

AMT Tag Mass (Da)

LC-MS Feature

Mass (Da)

100

200

300

400

-10 0 10 20

Count (LC-MS Features)

Mass Error (ppm)

Likely false positive

identifications

Likely true positive

identifications

Option 1: Bulk linear correctionUse location of peak in mass error histogram to adjust masses of all featuresShift by ppm mass; absolute shift amount increases as monoisotopic mass increases

Shift all masses -11.6 ppm:

Δmass= -11.6ppm x massold

1x106 ppm/Da

For 1+ feature at 1570.9005 Da,Δmass = -0.0182 Da

For 3+ feature at 2919.4658 Da,Δmass = -0.0339 Da

Mass Calibration

100

200

300

400

-10 0 10 20

Count (LC-MS Features)

Mass Error (ppm)

Peak Center of mass: 11.6 ppmPeak Width: 2 ppm at 60% of maxPeak Height: 404 counts/binNoise level: 19 counts/bin

Peak Center of mass: 11.6 ppmPeak Width: 2 ppm at 60% of maxPeak Height: 404 counts/binNoise level: 19 counts/bin

Mass Calibration

MS Scan Number

Mass Residual

Mass Error (ppm) vs. Scan Number

Option 2: Piece-wise correction via LCMSWarpExamine sections of the data to determine a custom mass shift for each sectionOne option is to divide into time sections

Mass Error (ppm) vs. Scan Number after correction

MS Scan Number


Mass Calibration

Mass Error (ppm) vs. m/z

m/z

Mass Residual

Option 2: Piece-wise correction via LCMSWarpSecond option is to divide into m/z sectionsLCMSWarp utilizes a hybrid correction based on both mass error vs. time and mass error vs. m/z

Mass Error (ppm) vs. m/zafter correction

m/z


Mass CalibrationComparison of the three methods

Mass error histogram gets taller, narrower, and more symmetricLinear Mass error vs. m/z Mass error vs. time Hybrid

Not all datasets show the same trends, but Hybrid mass recalibration is generally superior

0

100

200

300

400

500

600

700

-5 -4 -3 -2 -1 0 1 2 3 4 5Mass Error (ppm)

Bin

cou

nt

LCMSWarp_Hybrid

LCMSWarp_vs_time

LCMSWarp_vs_mz

Linear Correction


0

200

400

600

800

1000

1200

1400

1600

-5 -4 -3 -2 -1 0 1 2 3 4 5Mass Error (ppm)

Bin

cou

nt

LCMSWarp_Hybrid

LCMSWarp_vs_time

LCMSWarp_vs_mz

Linear Correction

S. oneidensis on LTQ-FT

Peak Matching StepsLoad LC-MS peak lists from Decon2LSFilter dataFeature definition over elution timeSelect AMT tags to match againstOptionally, find paired features (e.g. 16O/18O pairs)Align LC-MS features to AMT tags using LCMSWarpBroad AMT tag DB searchSearch tolerance refinementFinal AMT tag DB searchReport results


Identifying LC-MS FeaturesMatch Features to LC-MS/MS IDsS. typhimurium DB, from 25 LC-MS/MS analyses

18,617 AMT tags, all fully or partially trypticLook for AMT tags within a broad mass range, e.g., ±25 ppm and ±0.05 NET of each feature

Average observed NET

S. typhimurium on 11T FTICRS. typhimurium AMT Tag Database

18,617 AMT tags 5,934 features5,934 features4,678 features have match,matching 6,242 AMT tags

Observed NET

Search tolerance refinementCan use mass error and NET error histograms to determine optimal search tolerances

Examine distribution of errors to determine optimal tolerance using expectation maximization algorithm

Examine distribution of errors to determine optimal tolerance using expectation maximization algorithm

±1.76 ppm

Repeat search with final search tolerances5,934 features3,866 features with matches3,958 out of 18,617 AMT tags matched using ±1.76 ppm



Match TolerancesMass: ±1.76 ppmNET: ±0.0203 NET

Observed NET

NET

Monoisotopic Mass

1,767.960

1,767.964

1,767.968

1,767.972

1,767.976

1,767.980

1,767.984

0.350 0.358 0.366 0.374 0.382 0.390 0.398 0.407

Given feature can match more than one AMT tagNeed measure of ambiguity

1767.9727 DaNET: 0.383

1767.9727 DaNET: 0.383

0.3921767.9664R.SIGIAPDVLICRGDRAI.P36259992

0.3801767.9730K.DLETIVGLQTDAPLKR.A105490

0.3731767.9777T.RALMQLDEALRPSLR.S35896216

NETMass (Da)PeptideAMT Tag IDMatch TolerancesMass: ±4 ppmNET: ±0.02 NET

Δ mass = 2.8 ppmΔ NET = -0.010

Δ mass = 0.17 ppmΔ NET = -0.003

Δ mass = -3.5 ppmΔ NET = 0.009


σmj = 4 ppm, σtj = 0.025

2

2

2

22 )()(

tj

tji

mj

mjiij

tmd

σμ

σμ −

+−

=⎟⎠

⎞⎜⎝

⎛−

−=

∑=

−

−

N

kiktkmk

ijtjmjij

d

dp

1

21

21

)2/exp()(

)2/exp()(

σσ

σσ

38837.2Sum:0.145521.4

0.7027042.5

0.166273.3

pijNumerator

3.2670.3921767.966436259992

0.0900.3801767.9730105490

3.0120.3731767.977735896216

dij2NETMass (Da)AMT Tag ID

K.K. Anderson, M.E. Monroe, andD.S. Daly. Proteome Science 2006, 4, DOI:10.1186/1477-5956-4-1.

dij

NET

Monoisotopic Mass

1,767.960

1,767.964

1,767.968

1,767.972

1,767.976

1,767.980

1,767.984

0.350 0.358 0.366 0.374 0.382 0.390 0.398 0.407


0.70

0.16

0.14


SLiC: Spatially Localized Confidence ScoreMeasures uniqueness of match

0.062.150.140.3921767.9664R.SIGIAPDVLICRGDRAI.P36259992

0.973.680.700.3801767.9730K.DLETIVGLQTDAPLKR.A105490

0.613.130.160.3731767.9777T.RALMQLDEALRPSLR.S35896216

Avg Disc

ScoreAverage

XCorrSLiC ScoreNETMass (Da)PeptideAMT Tag ID

NET

Monoisotopic Mass

1,767.960

1,767.964

1,767.968

1,767.972

1,767.976

1,767.980

1,767.984

0.350 0.358 0.366 0.374 0.382 0.390 0.398 0.407

0.16

0.14

0.70


K.K. Anderson, M.E. Monroe, andD.S. Daly. Proteome Science 2006, 4, DOI:10.1186/1477-5956-4-1.

Effect of search tolerances on Mass Error histogramIf mass error plot not centered at 0, then narrow mass windows exclude valid dataDecreasing mass and/or NET tolerance reduces background false positive level

Search tolerance refinement

0

100

200

300

400

-6 -4 -2 0 2 4 6

Mass Error (ppm)

Cou

nt (F

eatu

res)

±25 ppm; ±0.05 NET±25 ppm; ±0.02 NET±3 ppm; ±0.02 NET±1.5 ppm; ±0.02 NET

0

25

50

75

100

-6 -4 -2 0 2 4 6Mass Error (ppm)

Cou

nt (F

eatu

res)

±25 ppm; ±0.05 NET±25 ppm; ±0.02 NET±3 ppm; ±0.02 NET±1.5 ppm; ±0.02 NET

Mass error histograms with linear mass correction

Mass error histograms with LCMSWarp mass correction

Peak Matching StepsLoad LC-MS peak lists from Decon2LSFilter dataFeature definition over elution timeOptionally, find paired features (e.g. 16O/18O pairs)Align LC-MS features to AMT tags using LCMSWarpBroad AMT tag DB search

±25 ppm and ±0.05 NETSearch tolerance refinementFinal AMT tag DB search

e.g. ±1.8 ppm and ±0.02 NETReport results


Automated processing using VIPERProcessing steps and parameters defined in .Ini file

Separate .Ini file for 14N/15N pairs and 16O/18O pairs

Automated Peak Matching

Browsable result folders for visual QC of each datasetS. typhimurium on 11T FTICR

Data Searched Data With Matches

Mass Errors Before Refinement Mass Errors After Refinement

2D Plot MetricsReasonable number of matchesNET range ≈ 0 to 1

2D Plot MetricsReasonable number of matchesNET range ≈ 0 to 1

Peak Matching Results

Mass Error Histogram Metrics

Well defined, symmetric mass error peak centered at 0 ppm

Mass Error Histogram Metrics

Well defined, symmetric mass error peak centered at 0 ppm


Total Ion Chromatogram (TIC)

NET Errors Before Refinement NET Errors After Refinement

Base Peak Intensity (BPI) Chromatogram


NET Error Histogram Metrics

Well defined, symmetric NET error peak centered at 0

NET Error Histogram Metrics

Well defined, symmetric NET error peak centered at 0

Chromatogram Metrics

Narrow peaks evenly distributed throughout separation window

Chromatogram Metrics

Narrow peaks evenly distributed throughout separation window



NET Alignment Surface MetricsShould show a smooth, bright yellow, diagonal line

NET Alignment Surface MetricsShould show a smooth, bright yellow, diagonal line

NET Alignment Residual MetricsData after recalibration should be narrowly distributed around zero

NET Alignment Residual MetricsData after recalibration should be narrowly distributed around zero

Peak Matching ResultsWhat about the unmatched LC-MS features?

Could align LC-MS features across datasets Find the unmatched ones that show interesting trends

m/z

scan #

Generate list of the mass and elution times for the interesting featuresRe-analyze the sample to perform targeted LC-MS/MSAlignment example for 36 datasets using prototype software tool

After alignment

Similar approaches and software tools: High Res LC-MSSpecArray (Pep3D, mzXML2dat, PepList, PepMatch, PepArray)

X.-J. Li, et. al. Mol Cell Proteomics 2005, 4, 1328-1340.msInspect

M. Bellew et. al. Bioinformatics 2006, 22, 1902-1909.PEPPeR

J. Jaffe et.al. Mol. Cell. Proteomics 2006, 5, 1927-1941.XCMS (for Metabolite profiling)

C.A. Smith et. al. Analytical Chemistry 2006, 78, 779-787.Surromed label-free quantitation software (MassView)

W. Wang et al. Analytical Chemistry 2003, 75, 4818-4826.


Similar approaches and software tools: Low Res LC-MSSignal maps software

A. Prakash et. al. Mol. Cell Proteomics 2006, 5, 423-432.Informatics platform for global proteomic profiling using LC-MS

D. Radulovic, et al. Mol. Cell. Proteomics 2004, 3, 984-997.Computational Proteomics Analysis System (CPAS)

A. Rauch et. al. J. Proteome Research 2006, 5, 112-121.


Alternative Approaches to Mass Spec Features:

Picking, Assignment, and Statistical Discovery Tools

Jacob D. JaffeThe Broad Institute of Harvard and MIT

Proteomics Platform

Section OutlineMAPQUANT: an image-based feature picking engine

PEPPeR: a self-contained web-based Biomarker Discovery pipeline

GenePattern: a suite of analysis and visualiztiontools that works with just about anything

A picture is worth 1000 parameters…

VARMIX_C_01 RT: 20.00 - 40.00 Mass: 350.00 - 1200.00 NL: 5.83E7 F:

20 22 24 26 28 30 32 34 36 38 40Time (min)

400

500

600

700

800

900

1000

1100

1200

m/z

MAPQUANT

MAPQUANT treats LCMS runs as images

Can leverage tried and true image processing techniques

Scripting language allows fine tuning and optimization for your data

Supporting both hi-res (FTMS) and lo-res (ITMS) platforms

High computational overheadSupports parallelization on a Linux cluster

Leptos KC, Sarracino DA, Jaffe JD, Krastins B, Church GM. MapQuant: open-source software for large-scale protein quantification. Proteomics. 2006 Mar;6(6):1770-82.

Common Image OperationsStructuring Elements

Morphological Operations

Source: http://rkb.home.cern.ch/rkb/AN16pp/node178.html

Opening with a Cross Element

Opening with an Ellipse Element

Closing with a Cross Element

Filtering and SmoothingEases ‘jagged’ peaks

Gaussian, Boxcar, and Savitsky-Golay Filters

Rough Peak

Gaussian Smoothed Peak

SegmentationFind the rough peak borders (think paint bucket!)

Break into many subtasks

Peak Centering/ModelingFind the Center: Structuring Elements Again

Raster structure element over segmentFind local maxima of products

Model as a 3D Gaussian/oidModel peaks to contain 95% of abundanceAltered model available to better fit chromatographic tailing effects

Picked Peaks – Gaussian/oid Models

Isotope ClustersFind features that are close in time and not more than 1 m/z unit apart

Single-linkage clustering

Deconvolve the clustered peaksIterative – once a successful deconvolution is made, these peaks are subtracted and the residual is deconvolved again until no more deconvolution can be done

Clustered, Deisotoped, Deconvolved

MAPQUANT Last WordsChain these operations into scripts for automated processing

Break LCMS run into m/z stripes for parallelization on a cluster

Output:Parameterized peaks with m/z, r.t., abundance, z, carbon content

Also visualization tools based on scripting language for method development / trouble shooting

Cross-platform: Win32 and Linux

PEPPeR: Platform for Experimental Proteomics Pattern Recognition

Jaffe JD, Mani DR, Leptos KC, Church GM, Gillette MA, Carr SA. PEPPeR, a Platform for Experimental Proteomic Pattern Recognition. Mol Cell Proteomics. 2006 Oct;5(10):1927-1941.

Multiple LCMS Experiments: Good with the Bad

There is a lot of information in therePeptide/protein IDsQuantitative dataStatistical assessment

The information may be noisyRetention time driftInstrument response noise

Are there methods to leverage this information?Without ‘perfect’ chromatography?Without strict alignment?

PEPPeR Concepts – Samples and Data Acquisition

PEPPeR Concepts – Data Processing

PEPPeR Concepts – Processing Continued…

PEPPeR Concepts – Analysis and Follow up

Landmark Matching: Identity Propagation

Use accurate mass, relative retention order comparison to identify peaks

Current ExperimentA

B

C

X

Y

m/z=999.4991

m/z=999.4996

Landmark Matching: Identity PropagationUse accurate mass, relative retention order comparison to identify peaks

Current ExperimentA

B

C

X

Y

m/z=999.4991

m/z=999.4996

Comparison Experiment

A

B

C

M

N

APEPTIDEKm/z=999.4993

APDITEPEKm/z=999.4993


Current ExperimentA

B

C

X

Y

m/z=999.4991

m/z=999.4996


A

B

C

M

N



Is X = M ?


Current ExperimentA

B

C

X

Y

m/z=999.4991

m/z=999.4996


A

B

C

M

N



Is X = N ?

Landmark Scoring and Confidence

S = ξ(Λ− i ,Λ0 ) + ξ(Λ0 ,Λi )[ ]i=1

w

∑

⎪⎪⎩

⎪⎪⎨

⎧

⎩⎨⎧−

−>+<−>

<

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n(n)m(m)mnnm

nm

nm

if 0 if 1

)()( and )()( if 5.0)()( if

)()( if 1

),(σμσμδττ

ττ

ττ

ξ

Let:Λ be a list of peptides observed in the comparison experiment ordered

by elution time. Here, elution time is defined by the centroid of all MS/MS scans leading to the identification of the peptide.Λ0 is defined as the position of the putative assignment in Λ

μ(x) be the centroid of elution time of peptide xin the comparison experiment (in scans)

σ(x) be the standard deviation of elution time of peptide xin the comparison experiment (in scans)

τ(x) be the centroid of elution time of peptide xin the current experiment (in seconds)

δ be the average retention time peak width, such that peptides eluting within δ sec are considered to be co-eluting (typically δ = 30 s)

w the number of peptides to consider before and afterthe putative assignment on the landmark list (typically w = 3)

landmarkzmoverall PPP /=))(1))(|/(1()()|/(

)()|/()/|(

landmarkPlandmarkzmPlandmarkPlandmarkzmPlandmarkPlandmarkzmP

zmlandmarkPPlandmark

−−+

==

Nuts and bolts: How it worksMatch features to sequenced peptides in a single LCMS run

Refine/recalibrate m/z tolerance

Re-match features to sequenced peptides in a single LCMS run

Now compare list of all features to Basis Set for mass, relative elution order matches given landmarks as reference points – propagation of identified features across multiple experiments

Peak Matching: Recognizing Identical Features

Retention Time

m/z

}

}

Use landmarks to derive corrections and tolerances for clustering of features across LCMS experiments

Break down the problem to make it parallelizable


Retention Time

m/z

Expe

rimen

ts


Retention Time

m/z


Gaussian mixture model (GMM) with parameters determined by maximizing Likelihood ratio using Expectation Maximization (EM)Number of clusters determined using Bayesian Information Criterion (BIC)Coalesce clusters if M/Z and RT variation is within tolerance


Parameterized PeaksPeak ID m/z R.T. z Run 1 Run 2 Run 3 Run … Identity

1 490.3144 62.0 3 607.6 544.2 581.0 …2 743.3549 56.2 3 694.4 682.6 691.4 …3 999.4991 22.5 2 209.6 247.6 232.6 … APEPTIDEK4 396.7187 20.5 3 321.7 344.9 318.5 …5 934.6045 31.7 2 722.7 753.0 701.3 …6 678.1993 32.4 3 371.2 387.2 441.4 …7 999.4994 56.8 2 857.1 811.0 750.5 … APDITEPEK8 526.6502 46.0 3 183.6 169.0 155.2 …9 1105.3597 69.4 3 1130.1 1075.7 1075.1 …

10 1292.0880 34.5 2 709.7 614.0 656.0 …

Experimental Parameters

Thermo LTQ-FT Mass SpectrometerResolution 100,0001 MS, 3 MS/MS for Quantification Experiments1 MS, 10 MS/MS for Identification Experiments

Agilent 1100 Nano-flow Chromatograph12.5 cm x 75 μm Reprosil 3 μm C18AQ material200 nl/min, 50’ RP gradient, 15 μm tip opening

SpectrumMill for MS/MS searches0.05 Da tolerance, Autovalidation

MapQuant for feature detectionLeptos et. al, Proteomics 6:1770 (2006)

Calibration and Landmark PerformanceScale Mixture

A B C D E F G H IAprotinin 1 2 3 10 20 30 100 200 300Ribonuclease A 300 1 2 3 10 20 30 100 200Myoglobin 200 300 1 2 3 10 20 30 100beta-Lactoglobulin 100 200 300 1 2 3 10 20 30alpha Casein 30 100 200 300 1 2 3 10 20Carbonic anhydrase 20 30 100 200 300 1 2 3 10Ovalbumin 10 20 30 100 200 300 1 2 3Fibrinogen 3 10 20 30 100 200 300 1 2BSA 2 3 10 20 30 100 200 300 1Transferrin 100 100 100 100 100 100 100 100 100Plasminogen 30 30 30 30 30 30 30 30 30beta-Galactosidase 10 10 10 10 10 10 10 10 10

All concentrations in fmol/ul (nM)Inject 1 ul x 5 replicates each

Peaks with IDs (avg. per run):165 ⇒ 281 +70%

False positive rate:93% p < 0.005100% p < 0.05

False negative rate:~2%

Measurement of Ratios with VariabilityVariability Mixture

α β α β α β α β α βAprotinin 100 5 100 5 100 5 100 5 100 5

Ribonuclease A 100 100 100 100 100 100 100 100 100 100Myoglobin 100 100 100 100 100 100 100 100 100 100

beta-Lactoglobulin 50 1 50 1 50 1 50 1 50 1alpha Casein 100 10 100 10 100 10 100 10 100 10

Carbonic anhydrase 100 100 100 100 100 100 100 100 100 100Ovalbumin 5 10 5 10 5 10 5 10 5 10Fibrinogen 25 25 25 25 25 25 25 25 25 25

BSA 200 200 200 200 200 200 200 200 200 200Transferrin 10 5 10 5 10 5 10 5 10 5

Plasminogen 2.5 25 2.5 25 2.5 25 2.5 25 2.5 25beta-Galactosidase 1 10 1 10 1 10 1 10 1 10

All concentrations in fmol/ul (nM)Inject 1 ul x 5 replicates each

Person EPerson A Person B Person C Person D

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

βG Pls Ov CA Alb RNAse Myo Fbr β Tfn Cas Apr βLG

log 2

ratio

KnownMeasured

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

βG Pls Ov Fbr β CA BSA Myo RNAse Tfn Cas Apr βLG

log 2

ratio

KnownMeasured

Complex Variability Mixture:

Mix α + Mitochondrial Protein from 2 wk. mouse liver

Mix β + Mitochondrial Protein from 6 wk. mouse liver

1 prep each sample, 6 injections each

* *

*

*

*

**

~

~

*

*

*

Discovery of Novel Markers with PEPPeR

α1

α3α2

α5α4

β1

β3β2

β5β4

αβ

gi Number Species Name223424 E. coli RNA polymerase β'

38491462 E. coli GroEL42144 E. coli NusA42818 E. coli RNA polymerase β42900 E. coli Ribosomal protein S1

26249756 E. coli Argininosuccinate synthase8099322 B. taurus κ-casein

B-Galactosidase had 1:10 ratio!Casein had 10:1 ratio!

peptides / m/z features

Designed accurate mass ‘inclusion lists’ to hit these targets

Confident IDs of previously identified peptides agree 100% of the time (59/59)

60 novel confident peptide IDs25 belong to proteins in the mix

24/25 are changing

35 are from proteins not designedto be in the mixture

PEPPeR and GenePattern

GenePattern is a suite of tools originally developed for microarray analysis

AIM: reproducible research through well-defined processing pipelines

Many analysis modules availablePEPPeR: Landmark Matching and Peak MatchingDaisy-chainable into pipelinesFeed into statistical tools

PEPPeR in GenePattern

Insert your favorite stuff here…Landmark Matching is platform agnostic

Need to get your data into a few simple flat-file formats and then zip them up togetherSearch engines i.e. SEQUEST, SpectrumMill, Mascot, etc.Peak Pickers: MAPQUANT, msInspect, DeCyder, etc.Some helper apps can be found with the PEPPeR bundle on the GenePattern website

All works via web-client interfaceJust press go

Landmark Matching Output

The main output is a zipped directory of all the processed files. This can be used as input into the PeakMatch module.

It is a good idea to check the error log to make sure that everything was processed correctly.

Peak Matching Interface

GenePattern Downstream ToolsDifferential analysis/marker selection

Gene/Class neighborsComparative marker selectionGene Set Enrichment Analysis

Class Prediction – supervised learning – with cross-validationRegression treesK-nearest neighborsNeural networksSupport Vector Machine

Class Discovery – unsupervised learningHierarchical clusteringSelf-organizing mapsPrincipal Component Analysis

Data VisualizationHeat Maps, etc.

Summary – what I hope you learnedOne basis for peak picking based on image processing techniques (MAPQUANT)

PEPPeR: Landmark Matching and Peak MatchingKeep track of all of those pesky peaks that you picked!

GenePattern: A web-based tool to coordinate reproducible research

An entrée into downstream discovery methods in an automated pipeline (more GenePattern)

AcknowledgementsBroad Proteomics:

Steve CarrD.R. ManiVincent Fusaro

Broad GenePattern Team:Michael ReichJosh Gould

Church Lab, Harvard Medical SchoolGeorge ChurchKyriacos Leptos

URLs:PEPPeR / GenePattern:

http://www.broad.mit.edu/cancer/software/genepattern/http://www.broad.mit.edu/cancer/software/genepattern/desc/proteomics.html

MAPQUANT:http://arep.med.harvard.edu/MapQuant/

data extraction and analysis for lc-ms based proteomics · 2016-01-06 · data extraction and...

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