Isoform Analysis of LC-MS/MS Data from
MultidimensionalFractionation of the Serum ProteomeAlexei L.
Krasnoselsky,* Vitor M. Faca, Sharon J. Pitteri, Qing Zhang, and
Samir M. HanashFred Hutchinson Cancer Research Center, 1100
Fairview Avenue North, Seattle, Washington 98109Received November
8, 2007Abstract: We developed a visualization approach for
theidentication of protein isoforms, precursor/mature pro-tein
combinations, and fragments from LC-MS/MS analy-sis of
multidimensional fractionation of serum and plasmaproteins. We also
describe a pattern recognition algorithmto automatically detect and
ag potentially heterogeneousspecies of proteins in proteomic
experiments that
involveextensivefractionationandresultinalargenumberofidentiedserumorplasmaproteinsinanexperiment.Examples
are given of proteins with known isoforms thatvalidate our approach
and present a subset of precursor/matureproteinpairsthat
weredetectedwiththisap-proach. Potential applications
includeidenticationofdifferentially expressed isoforms in disease
states.Keywords: Protein fractionation visualization LC-MS/MS
isoformsIntroductionWith rapid proliferation of proteomic data,
there is a needfor tools that allow computational data mining and
visualiza-tion of complex data sets. There are many software
packagesavailable for processing proteomics data and displaying
results(for recent review, see Palagi et al.).1However, there is a
paucityofvisualizationtoolsthataresimpleandeasilyadaptabletoevolving
proteomic data formats. Visualization tools combineseveral sources
of information for intelligent data mining. Thehuman eye is
particularly suited to identify complex patternsand features,
provided that the information is presented in astructured visual
way and limited to a few patterns at a time.The gene expression
red-green heat maps serve as an exampleof simple and yet effective
method of representation of complexdata.2Proteins exist in plasma
and tissue sources in multiple formsthat result fromalternative
splicing (isoforms), precursor/matureproteincombinations,
ordifferentpatternsofglyco-sylation. Most proteins are secreted as
precursor proteins fromwhich biologically active forms are
generated upon proteolyticcleavage (e.g., see Khatib and
Geraldine).3For biomarkerdiscovery,
itisimportanttoassessthepresenceofisoformsthat may differ in their
levels in a disease related manner as inthe case of phosphorylation
and glycosylation, among numer-ous post-translational modications.
We present here a visu-alizationapproachfor multidimensional
proteomicdatatoassist inthesearchfor proteinisoforms,
precursor/matureprotein combinations, and fragments. Along with the
visualiza-tion tool, we also describe a simple pattern
recognitionalgorithm that we developed to automatically detect and
agpotentiallyheterogeneous species of proteins
inproteomicexperiments that involve extensive fractionation and
result ina large number of identied proteins in one
experiment.MethodsProtein Separation and Mass Spectrometry
Analysis.
Serumandplasmaproteinsamplesweresubjectedtofractionationfollowed by
LC-MS/MS analysis of tryptic digests from indi-vidual fractions.
The full procedure, designated Intact ProteinAnalysis System (IPAS)
has been previously described by Facaet al.4Briey, after
immunodepletion, acrylamide-labeledsamples5were fractionated by
anion-exchange into 12 fractionsand subsequently by reversed-phase
into 12 fractions, repre-senting a total of 144 fractions that were
analyzed individuallyby shotgun LC-MS/MS. In-solution tryptic
digestion wasperformed overnight with lyophilized aliquots from the
reversed-phase(seconddimension) fractionationstep.
TheresultingpeptidemixtureswereanalyzedbyaLTQ-FTICRmassspec-trometer
(Thermo-Finnigan) coupledwitha NanoAcquity-nanoow chromatography
system (Waters). Spectra were ac-quired in a data-dependent mode in
m/z range of 400-1800,including selection of the 5 most abundant +2
or +3 ions ofeachMSspectrumfor MS/MSanalysis.
Acquireddatawasautomatically processedby the Computational
ProteomicsAnalysisSystem(CPAS)6pipeline.
ThispipelineincludestheX!Tandemsearchalgorithm7withcometscoremoduleplug-in,8PeptideProphet9peptide
validation, and ProteinProphet10protein inference tool. The tandem
mass spectra were searchedagainst version 3.12 of the human IPI
database.11All identica-tions with a PeptideProphet probability
greater than 0.75 wereselected and the subsequent protein
identications wereltered at a 5% error
rate.HeterogeneityDetectionAlgorithm. Theconceptbehindcluster
detection is as follows. For each protein (single IPI or aprotein
group of multiple IPI numbers considered to representthe same
protein), the data were assembled into a n m gridof fractions,
where n corresponds to the number of fractionsderived in
ion-exchange chromatography (represented on theX-axis) and m
corresponds to the number of fractions
derivedinRP-HPLC(representedontheY-axis).Thedimensionsforthe two
data sets used in this article are 12 12 for one data* To whom
correspondence should be addressed. Tel: (206) 667-1250, fax:(206)
667-2537, E-mail: [email protected] reveals data.
EdwardR. TufteinThe Visual Display ofQuantitative Information.2546
Journal of Proteome Research 2008, 7, 25462552 10.1021/pr7007219
CCC: $40.75 2008 American Chemical SocietyPublished on Web
04/18/2008Figure1.Visualizationofproteomicdatain2-Dfractionationexperimentswithdifferentialsamplelabeling,Thedatashownisforprotein
HFAC (hepatocyte growth factor activator). (A) The peptide and
ratio map of the 2-D chromatography fractionation. The
gridrepresents the 2-D chromatography fractionation (12 12
fractions). The X-axis represents 12 fractions of ion-exchange
chromatographyand Y-axis, 11 fractions of RP HPLC. Each node of the
grid shows the fraction location. The peptides are shown as
concentric circlesof different colors (the full list of identied
peptides is shown in the inset), whereby the size of the circle
indicates a relative distanceofthepeptidefromtheN-terminusofthefull
proteinsequence. Thesizeofthecirclecorrespondstothesequential
orderofthepeptides starting from the N-terminus. The range for each
peptide represents the starting and ending position in the protein
sequence,scaled to 0-1. Values are provided for ratios between two
samples being compared based on differential acrylamide
labeling,5wherecase samples are labeled with C13 acrylamide and
control samples with C12 acrylamide. (B) Histogram of the ratios
obtained for thisprotein in an experiment in which a comparison is
made between two samples (in all 132 fractions). (C) Total MS
events map for 2-Dseparation. Each node of the grid shows the
number of MS events summed up across all peptides, while the size
of the circle reectsvisually that number.IsoformAnalysis of
LC-MS/MSData technical notesJournal of Proteome Research Vol. 7,
No. 6, 2008 2547set, and 1211 for the other. The
patterndetectionisperformedat thepeptidelevel. For eachfraction,
abinarypeptide separation map is derived by assigning 1 to a
fractionwhere the peptide was identied and 0 where it was not.
Themapservesasinput totheproteinheterogeneitydetectionalgorithm,
which consists of two steps. First, the fractionationpattern is
smoothed by a 2 2 kernel, whereby each
fractionxijisassignedasumofthevaluesinthekernel:
Si,j)xi,j+xi+1,j+xi,j+1+xi+1,j+1. The rationale for smoothing is to
reducetheMSsamplingeffect that might result inoverestimationof the
number of clusters. The clusters are dened by selectingthe nodes
with the values equal or exceeding k (kmax) 4) andseparatedby a
gapof at least g fractions (g)2 for thisfractionation experiment
based on the chromatographic reso-lution of the system). The number
of identied peptide clustersis then averaged across all peptides
for a given protein to resultin a cluster score assigned to this
protein. The output consistsof all proteinsrankedbythecluster
scorewiththeclusterstatistics described on peptide
level.DataVisualization.
Thevisualizationapplicationrequiresseveralinputdatamatrices.Foreachprotein(singleIPIoraprotein
group of multiple IPI numbers considered to
representthesameprotein), then mdatamatrixof fractions
(asdescribedabove),alongwiththeratiovectorandavectorofnumber of
spectral events for each peptide in each fraction ispassed to the
software. Avector of scaled 0-1
sequencepositioninformationispassedtotheapplicationaswell.Allpreprocessing
of the data is accomplished prior to passing
thedatatothevisualizationtool.Theoutputsoftheapplicationinclude
three gures, saved as picture les (jpeg format): thegure that
combines fractionation, ratio, and peptide
sequenceinformation(suchasFigure1A), histogramof
theratios(ifavailable, see Figure 1B), and a gure of total spectral
eventsfor each fraction (such as Figure 1C). The Matlab code for
theapplication is available upon request from the
author.ResultsVisualization of the IPAS Proteomics Data. The data
gener-atedincomparativeproteomicsexperimentsthatutilizeex-tensiveproteinfractionationcontaininformationrelatedtoisoforms
that could be mined, but is generally not systemati-callyanalyzed.
Suchinformationisintrinsictothelocations(fractions) in which
proteins were identied. Thus, chromato-graphic properties contain
information that could be used
tomakeinferencesaboutsubspecies/isoformsof proteinsthatelute
differently but may be the products of the same gene. Inthisstudy,
weanalyzeddatafrom132serumfractionsthatresulted from 2-D
fractionation of intact (undigested) proteins.Figure 1A shows a
representation of the 2-D fractionation as agridwiththenodes
denotingthefractions. Theparticularidentied peptides in a protein
could be used to infer cleavagesas in the case of surface proteins
that shed their extracellulardomains. We have devised a way of
capturing this
informationonthefractionationgrid,wherebyasetofconcentriccirclesrepresent
the sequentially organized peptides. The circles
arescaledinsuchawaythat thesizeof thecircleindicatesarelative
distance from the N-terminus of the protein, with thepeptide
represented by the smallest circle being closest to theN-terminus
and the largest circle denoting the peptide closestto the
C-terminus. Such visualization aids in immediatediscerning a
fragment: if a set of peptides appears as doughnut-shaped in one or
more fractions (such as fraction withcoordinates [x ) 2, y ) 5] on
Figure 1A), such a set of peptideswould be derived from the
C-terminal portion of the protein.If the peptides in a given
fraction are represented by a set ofsmall circles (relative toall
the peptides identiedinthefractions, as shown in the gure inset),
such as in the fractionwith coordinates [x ) 7, y ) 3], then the
fragment is derivedfrom the N-terminal portion of the protein.
Thus, visualizationallows animmediategraspof four characteristics
for eachprotein: the two chromatographic properties, the
distributionof peptides along the sequence, and in comparative
quantita-tive studies the differential ratio. Furthermore, the
samevisualization approach can be used for representing
thenumberofMSeventsforagivenproteininagivenfraction(Figure 1B).
Additional informationis provided inanac-companying histogram of
all ratios for a given protein in theexperiment (Figure
1C).AutomatedDetectionof ChromatographicClusters. Wedeveloped a
simple pattern recognition algorithm (see Meth-ods) to identify and
ag proteins that show distinct chromato-graphic clusters, suchas
showninFigure 2A. The clusteridentication occurs on the peptide
level, and the number ofclustersisthenaveragedacrossall
thepeptidesforasingleFigure2. Relationshipbetweennumber of
peptidesandtheaverage number of clusters per protein. (A) The
average clusterscore (number of identied chromatographic clusters
averagedacrossallthepeptidesperprotein)isplottedagainstthetotalnumberofuniquepeptidesforthecorrespondingprotein.
(B)The histogram of average cluster scores across all proteins
withtwo or more unique peptides.technical notes Krasnoselsky et
al.2548 Journal of Proteome Research Vol. 7, No. 6, 2008protein to
derive a protein score. Figure 2A shows that there
isnocorrelationbetweentheaveragenumberofpeptidesandthe number of
identied clusters. The increase in number ofclusters for proteins
identied with a single peptide in multiplefractions is most likely
due to incorrect IDs. The
single-peptidehitswerenotincludedinsubsequentanalysis.
Theanalysisshowsthatoutof1224proteinswithmorethanoneuniquepeptidecoverage295proteinsshowedchromatographichet-erogeneity
on the peptide levels. Such heterogeneity could bedue to multiple
factors that include MS sampling, precursor/mature protein,
multichain proteins connected by S-S bridges,splice isoforms, PTM
modications, and proteolytic fragments.Thealgorithmags all
theseinstances as longas theyaremanifested in discontinuous elution
prole for a given protein.The histogramin Figure 2B shows that the
majority ofheterogeneous proteins show less than two clusters per
protein(averaged number of identied peptide clusters). This
isreasonable given the limited resolution of the system (11
12fraction grid).Identicationof
ProteinsandTheirCleavageProducts.Most proteins are synthesized in
vivo in the form of inactiveprecursor that is cleaved upon a
physiological event locally orwith their extracellular release. We
have analyzed
humanplasmaforpresenceofsuchprecursor/matureproteinpairsusing our
pattern detection algorithm to ag potential
isoforms.Outof295proteinsthatwereaggedasheterogeneous, 176(or 60%)
were consistent with precursors. Figure 1A shows anexample of the
detection of the full-length precursor and thematureformof
hepatocytegrowthfactor activator
(HGFA),identiedintheIPASexperimentwith14peptides.Ascouldbe
observedfromFigure 1A, the proteinspecies elute asseparate clusters
that correspond to the mature protein as
wellasthecorrespondingprecursorpartremoveduponcleavage(seeFigure3forexplanation).
Thedetectionalgorithmagsthis protein as heterogeneous and fragments
may be discernedupon inspection of the plot. The precursor for HGFA
does notconvert single chainHGFtoits biologically active
form.12However, cleavage of pre-HGFAat
R407-I408andR372-V373converts it to its active two-chain form.
Figure 1A shows thatwe detect several forms. The
R407-I408corresponds totheposition 0.62 on 0-1 scale from N- to
C-terminus of 655 aminoacid-long HGFA, and R372-V373 to 0.57,
respectively. Indeed, weidentied two sets of fractions that
correspond to the precursorpart that is removed in the mature form
(sequence 36:372 or0.06:0.57) as well as the two chains of the
mature protein itself(0.57-0.6and0.72-0.98, peptides 9-14).
Interestingly,
theshortchainofthematureprotein(peptide8)yieldedonlyasingle
identied peptide, which elutes separately from the longchain of the
HGFA.Analysis of Protein Isoforms. Proteins that result in
alterna-tive splicing can produce isoforms that are distinguishable
inIPAS experiments. Here, we show one such example, bulin-1(FBLN1).
Fibulin-1isanextracellular matrixproteinthat isknown to have four
different isoforms (for recent review, seeGallagher et al.13). In
the IPAS experiment described here, wehave identied peptides that
map to FBLN1 and identify at leasttwo groups of isoforms: isoform C
and isoforms B and D. Thelatter are indistinguishable by the
identiedpeptides andreferred here as isoform B/D. Figure 4 exhibits
the fractionationpattern of FBLN1. The differences between the
isoforms lie inthe C-terminal portion of FBLN1. Figure 4A exhibits
thefractions in which the isoforms B/D were identied by
uniquepeptides (peptides 14and15), whereas theisoformCwasidentied
by its corresponding C-terminal peptides (peptides11 and 12 on
Figure 4B). Isoform B/D elutes in the earlier ion-exchange
andreverse-phase HPLCfractions. There is alsosome, albeit
incomplete, separationof isoformsbyreverse-phase HPLC for the late
eluting ion-exchange fractions.
Analysisofthepeptidecompositionshowsnoevidenceoftheearliereluting
fractions resulting from fragmentation of the later full-length
protein. Such differences might be due to variation
intheglycosylationpatternFBLN1. Thecontributionof
eachisoformtotheoverall FBLN1ratiocouldnotbeassessedinthis study
due to the origin of the Cys-containing peptides fromthe region of
FBLN1 sequence common to all known isoforms.However, thepresenceof
several isoformsthatarepartiallyresolved chromatographically is
demonstrated.Autilityofthevisualizationalgorithmcouldbeillustratedon
Figure 5A where two subspecies of coagulation factor F11are shown.
The detection algorithm ags F11 (IPI00008556) asa
chromatographically heterogeneous protein with two distinctspecies
(Figure 5A). The Swiss-Prot annotation (P03951) indi-Figure 3.
Hepatocyte growth factor activator protein, The sequence of the
precursor is shown with a signal peptide in black
letters,prepropeptide removed in mature protein in red letters,
short chain in blue letters, and long chain in green letters. The
underlinedpeptides denote those identied by mass-spectrometry in
132 fractions.IsoformAnalysis of LC-MS/MSData technical
notesJournal of Proteome Research Vol. 7, No. 6, 2008 2549catesthat
twospliceisoformshavebeenidentiedfor thisprotein. However, the
visualization plot suggests that the
twochromatographicspeciesareunlikelytobespliceisoforms,because the
missing sequence in isoform 2 that distinguishesit from isoform 1
is present in both clusters (the sequence
mapstotherangeof0.17-0.30). Analternativeexplanationisthedifference
in glycosylation pattern (F11 is heavily
glycosylated).DiscussionFractionationbasedonchromatographicpropertiesyieldsangerprint
of aproteinthat is determinedbystructuralvariations in the protein.
High resolution HPLC systems,
suchasmodernreverse-phaseandion-exchangeHPLC,yield2-Dfractionation
patterns that allow inferences to be made
regard-ingsingleproteinheterogeneity.
Wehaveutilizedthischro-matographic pattern information, along with
sequence map-ping of identied peptides, to gain insight into
potentialfragmentationpatterns, spliceisoforms,
orothersourcesofproteinheterogeneitythat might befoundinasample.
Toreduce data complexity and allow an easier grasp of
multidi-mensional proteomic data, we developed a visualization
methodthat combines three sources of information (four dimensionsof
data) in one two-dimensional plot. Along with the
visualiza-tiontool, we alsodevelopeda simple
patternrecognitionalgorithm to automatically detect and ag
potentially hetero-geneous species of proteins in experiments such
as IPAS, whichinvolve extensive fractionation and identify more
than athousand serum or plasma proteins in one experiment.4Given
that proteins are identied based on matching of theircorresponding
peptide mass spectra to sequence databases, theFigure 4. Fibulin 1
isoforms, (A) Total MS events map for 2-D separation. The grid
represents the 2-D chromatography fractionation (12 12 fractions).
The X-axis represents 12 fractions of ion-exchange chromatography
and Y-axis, 12 fractions of RP HPLC. Each node ofthe grid shows the
number of MS events corresponding to FBLN1, while the size of the
circle reects visually that number. (B) Thepeptide and ratio map of
the 2-D chromatography fractionation. Each node of the grid shows
the fraction location as in (A). Informationis provided regarding
fractions in which FBLN1 was found and the related peptides that
were identied (full list is displayed in thegure inbox). Peptides
are shown as concentric circles of different colors, whereby the
size of the circle indicates a relative distanceof the peptide from
the N-terminus of the full protein sequence.technical notes
Krasnoselsky et al.2550 Journal of Proteome Research Vol. 7, No. 6,
2008isoform identication process is dependent on accurate
peptideidentications. The goal of the automated detection
algorithmwe have developed is to reduce data complexity by
eliminatingproteins that do not show heterogeneity and leaving it
to theresearcher, aided by the visualization tool, to make
naldecisions about the agged proteins. It is desirable to
estimateafalse-discoveryrateforthelistofproteinsdeemedhetero-geneous
by the algorithm. To address this problem, theavailability of a
benchmark set of known heterogeneousproteins that are resolved by
chromatography would be usefulto develop an algorithm for FDR
estimation. In this publication,we provide two examples, whereby an
observed
heterogeneousnatureofproteins(HGFAandFBLN1)couldbeindicativeofthe
true precursor/mature protein (in the case of HGFA) anddifferent
splice isoforms (in the case of FBLN1) to be presentinthesamples.
However, thedenitiveassessmentrequiresbiochemical evidence to
validate the nding of distinct speciesfor the same protein.
Nevertheless, as shown in this paper, inthe example of coagulation
factor F11, using our visualizationsoftware tool enables the
researcher to rule out a
hypothesis,suchasthepresenceofalternativelysplicedisoformsinthecase
of F11.Our approach allows us to start compiling a list of
proteinsthat could serve as benchmark set for performance
evaluationof futureisoformdetectionalgorithms. Figure5shows
anexampleof twosuchproteins, F11andLCAT. Compilingacomprehensive
data set for benchmarking of isoform detectionalgorithmis beyond
the scope of this paper and will beaddressedinfuturepublications.
Suchaproteinset
shouldsatisfyatleastthefollowingcriteria:thespeciesofaproteinshould
(a) be well-dened and characterized biochemically; (b)be detectable
in normal plasma in quantities that allow goodFigure 5. Protein
heterogeneity for LCAT and F11, The peptide map of the 2-D
chromatography fractionation. Each node of the gridshows the
fraction location as in 4A). (A) F11; peptides 6-9 are present in
both chromatographically distinct clusters. Region 0.1-0.30of the
sequence of the F11 protein is missing in alternatively spliced
isoform 2 (see text for details). (B) LCAT protein;
N-glycosylationof LCAT has been shown by
mass-spectrometry.14IsoformAnalysis of LC-MS/MSData technical
notesJournal of Proteome Research Vol. 7, No. 6, 2008 2551peptide
coverage in MS; and (c) have large enough differencesto be
separable by common methods of protein fractionation.In conclusion,
we have developed a visualization tool to aidin making inferences
about heterogeneity of proteins identiedin proteomics experiments
that utilize extensive fractionation.We also provide a simple
algorithm to detect and ag potentialsplice isoforms,
mature/precursor protein combinations, andother types of protein
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