Quantitative Proteomic Analysis of Microdissected Breast CancerTissues: Comparison of Label-Free and SILAC-based Quantificationwith Shotgun, Directed, and Targeted MS ApproachesNing Qing Liu,*,†,§ Lennard J. M. Dekker,‡ Christoph Stingl,‡ Coskun Guzel,‡ Tommaso De Marchi,†
John W. M. Martens,†,∥ John A. Foekens,†,§,∥ Theo M. Luider,‡ and Arzu Umar*,†,§,∥
†Department of Medical Oncology, Erasmus MC Cancer Institute and ‡Department of Neurology, Erasmus University MedicalCenter, Dr. Molewaterplein 50, Be-401, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands§Netherlands Proteomics Centre, H. R. Kruytgebouw, East 6.11, Padualaan 8, 3584 CH Utrecht, The Netherlands∥Center for Translational Molecular Medicine, High Tech Campus 84, 5656 AG Eindhoven, The Netherlands
*S Supporting Information
ABSTRACT: Quantitative proteomics plays an important role invalidation of breast-cancer-related biomarkers. In this study, wesystematically compared the performance of label-free quantifica-tion (LFQ) and SILAC with shotgun and directed methods forquantifying breast-cancer-related markers in microdissectedtissues. We show that LFQ leads to slightly higher coefficient ofvariation (CV) for protein quantification (median CV = 16.3%)than SILAC quantification (median CV = 13.7%) (P < 0.0001),but LFQ method enables ∼60% more protein quantification and isalso more reproducible (∼20% more proteins were quantified inall replicate samples). Furthermore, we describe a method toaccurately quantify multiple proteins within one pathway, that is,“focal adhesion pathway”, in trace amounts of breast cancer tissuesusing a SILAC-based SRM assay. Using this SILAC-based SRM assay, we precisely quantified five “focal adhesion” proteins withgood quantitative precision (CV range: 2.4−5.9%) in replicate whole tissue lysate samples and replicate microdissected samples(CV range: 5.8−16.1%). Our results show that in microdissected breast cancer tissues LFQ in combination with shotgunproteomics performed the best overall and is therefore suitable for both biomarker discovery and validation in these types ofspecimens. The SILAC-based SRM method can be used for the development of clinically relevant protein assays in tumorbiopsies.
KEYWORDS: quantitative proteomics, breast cancer, laser capture microdissection, label-free quantification, SILAC,shotgun proteomics, accurate inclusion mass screening, selected reaction monitoring
■ INTRODUCTION
Screening novel disease-related biomarkers is of utmostimportance for clinical cancer research. Highly sensitive andspecific biomarkers to a large extent facilitate monitoringdisease progression1 and predict therapy response2 forpersonalized treatment. Protein markers can easily be trans-ferred to clinical practice with relatively low cost. In addition,proteins are the most relevant functional units toward cancerphenotypes, which may directly indicate molecular mechanismsof underlying pathological process. Liquid chromatography−tandem mass spectrometry (LC-MS/MS) outperforms othertechniques for protein detection and quantification due to itsreliability and applicability for global proteome profiling.Therefore, LC-MS/MS-based proteomic techniques start tobecome a major platform for biomarker discovery.LC-MS/MS-based quantitative methods can be categorized
on the basis of labeling strategies or quantitative MS
approaches. Labeling strategies can be subdivided intometabolic (stable isotope labeling in cell culture/SILAC3),chemical (isotope coded affinity tag/ICAT,4 isobaric tags forrelative and absolute quantitation/iTRAQ,5 TMT,6 anddimethyl7), and enzymatic (O188) labeling methods. Inaddition, label-free quantification (LFQ) is being increasinglyused. This method performs quantification on the signal ofeluted peptides detected by MS (ion intensity9,10) or number ofMS/MS spectra recorded by tandem MS (spectral counting11).For LFQ, protein abundance can be normalized to thebackground proteome signal of measured samples under theassumption of a linear relation between amount of inputproteins and total ion intensity or number of MS/MS spectra.This enables reliable quantification of proteins in a label-free
manner. When using labeling strategies, a universal internalstandard can be added to all samples in an LC-MS/MSexperiment. The use of a universal standard circumventstechnical bias introduced during LC-MS/MS profiling orsample handling. Precise and accurate protein quantificationis achieved by computing the protein ratio between samplesand standard.Quantitative methods can also be classified according to MS
acquisition techniques and analyzer principles. Three majorstrategies to acquired quantitative MS data are known asshotgun, directed, and targeted approaches.12 Shotgunproteomics is a global method that allows identification andquantification of peptides without required prior knowledge ofthe actual sample composition. First, peptide precursors areacquired in an MS full scan over a specified m/z range, and themost abundant precursors are selected for subsequentfragmentation. Selection of precursor occurs by “on-the-fly”according to specific rules that are defined by the acquisitionsoftware. Typically, the most intensive precursors are triggeredfirst and the remaining precursors are triggered with decreasingintensity. Acquisition of MS and MS/MS spectra occurssequentially in a shotgun measurement, and recorded MS ionintensities are used for quantification. Directed MS analyses(e.g., accurate inclusion mass screening/AIMS13) differ fromshotgun acquisition only in the way in which precursor ions areselected for fragmentation. This selection is independent ofprecursor intensity. Predefined precursors are preferentially orexclusively selected for fragmentation on basis of their presence.Therefore, more reproducible identification of preselectedpeptides can be expected. Targeted analyses (e.g., selectedreaction monitoring/SRM14) allow only fragment ions ofselected peptides to reach the MS detector. Because in targetedanalyses quantification is performed on a small set of peptides,great sensitivity and reproducibility can be achieved.Selection of appropriate quantitative methods is critical for
finding disease-related biomarkers. In general, label-free andshotgun proteomics are considered to be a good strategy forbiomarker discovery.12 This combination allows for a largenumber of protein identifications with relatively high sequencecoverage15,16 but modest quantitative precision.12 Labeling-based quantification together with directed or targetedapproaches has recently been used for validation ofbiomarkers.1,17 Absolute quantification14 (AQUA) using SRMtechnique and isotopically labeled synthetic peptides canaccurately quantify peptides of interest and are considered asa key method for biomarker validation.12 However, only limitednumbers of putative protein markers will be routinelyquantified using AQUA due to high costs of isotopicallylabeled peptide standards. Therefore, a reliable, high-throughput, and cost-efficient quantitative method is highlydesirable to quantify a large panel of protein markers (e.g.,protein signatures) and disease-related pathways.Breast cancer tissues are highly heterogeneous, and they
consist of many different cell types, such as stromalcompartments, infiltrates, and tumor cells. Furthermore, theproportion of different cells varies dramatically betweenindividual tumor tissues as well as in different parts of thesame tissue. This heterogeneity introduces an extra bias toconfound tissue specific proteins as putative protein markers.Such bias can be largely avoided using laser capturemicrodissection (LCM) technology.18,19 Hence, it has becomea routine technique in our MS-based tissue proteomicsstudies.2,20,21 However, LCM-derived samples typically yield
submicrogram quantities of proteins from a tumor tissue, whichis below the detection limit of protein assays such asbicinchoninic acid assay and thus hinders accurate determi-nation of protein concentration. In addition, isolation efficiencyof proteins largely varies between breast cancer tissues withdifferent morphology (e.g., tumor area20); therefore, variationin protein quantification can be introduced due to varying inputof proteins. Thus, it is important to assess the performance ofdifferent quantitative methods in LCM tissues.In this study, we systematically evaluated the quantitative
performance of LFQ and SILAC in combination with shotgunand directed quantitative MS in LCM breast cancer tissues. Inaddition, we developed a SILAC-based SRM assay for preciseprotein quantification. We precisely quantified five proteins ofthe “focal adhesion” pathway as proof-of principle. Our resultssuggest that LFQ with shotgun method achieves a good balancebetween identification and quantitative precision and can beuseful for large-scale validation of protein markers. SILAC-based SRM assays may aid in the development of clinicallyapplicable assays for validated protein markers in a rapid andcost-efficient manner.
■ MATERIALS AND METHODS
Tumor Tissues
Eight fresh frozen breast cancer tissues were selected from ourliquid N2 bank, of which four were estrogen-receptor-α (ER)positive (+) and the other four were negative (−), asdetermined by ELISA assay. From all tissues, tumor cellswere isolated using LCM following a published protocol.20
Replicate LCM samples were prepared from one of the ER−tissues (biological replicates), which was also used for thepreparation of whole tissue lysates (WTLs) (technicalreplicates). This study was approved by the Medical EthicsCommittee of the Erasmus Medical Center Rotterdam, TheNetherlands (MEC 02.953) and was performed in accordancewith the Code of Conduct of the Federation of MedicalScientific Societies in The Netherlands.Sample Preparation
Isolation of tumor cells was performed using an in-houseoptimized protocol of cryosectioning, followed by LCM.20,21 Inbrief, 8 μm tissue cryosections were fixed in ice-cold 70%ethanol, dehydrated in 100% ethanol, and stored at −80 °Cuntil further processing. Prior to LCM, slides were brieflywashed in tap water, stained for 30 s in hematoxylin, washedagain in tap water, subsequently dehydrated in 50, 70, 95 andtwice 100% ethanol for 15 s each and 60 s for a final 100%ethanol step, and air-dried. Halt protease and phosphataseinhibitor cocktail (Thermo Scientific, Rockford, IL) was addedto tap water and 50% and 70% ethanol solutions at a 1:100dilution (v/v) to inhibit nonspecific cleavage caused byendogenous enzymes during LCM processing. LCM wasimmediately performed after staining using a PALM MicroBe-am device (Carl Zeiss Microscopy, Bernried, Germany). Foreach tissue section, an area of ∼500 000 μm2 (∼4000 tumorcells)18 was collected in ZEISS opaque adhesive caps (CarlZeiss MicroImaging, Munich, Germany). Dissected cells weregently suspended in 20 μL of 0.1% RapiGest (Waters, Milford,MA) and stored at −80 °C until further processing.LCM and WTL tumor samples were processed as previously
described.20 Cells were lysed by sonication in 0.1% RapiGestsolution using an Ultrasonics Disruptor Sonifier II (model W-250/W-450, Branson Ultrasonics, Danbury, CT) for 1 min at
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70% amplitude. Proteins were subsequently denatured at 95 °Cfor 5 min. Denatured proteins were further reduced at 60 °Cfor 30 min with a final concentration of 5 mM dithiothreitol(DTT) (Sigma, Saint Louis, MO), and alkylated in the dark for30 min with a final concentration of 15 mM iodoacetamide(IAA) (Thermo Scientific). Fully unfolded proteins weredigested with trypsin for 4 h at 37 °C in accordance with theinstructions of the manufacturer using MS-grade porcinemodified trypsin gold (Promega, Madison, WI) at a 1:4 (w/v) and 1:50 (w/v) ratio for LCM and WTL samples,respectively. Digestion was terminated by incubation with0.5% Trifluoroacetic acid (TFA) at 37 °C for 30 min.Undissolved cellular debris were removed by centrifugation at14 000 rpm for 15 min, and supernatant was transferred to anew Eppendorf Lobind tube and stored at −80 °C until MSanalysis.
Cell Culture and SILAC Reference
Four breast cancer cell lines, ZR-75-1, SK-BR-3, MDA-MB-231,and MDA-MB-468, were purchased from American TypeCulture Collection (Manassas, VA). The four cell lines weremaintained in SILAC-labeled RPMI medium in which naturallysine and arginine were replaced by Lys-8 and Arg-10 (DundeeCell Products, Dundee, U.K.), and were cultured under 5%CO2 and 95% air. SILAC medium was further supplementedwith 10% (v/v) dialyzed fetal bovine serum (Dundee CellProducts), 1% glutamax-I (Invitrogen, Paisley, U.K.), and 1%antibiotics (100 μg/mL penicillin G and 80 μg/mLstreptomycin) and titrated using 200 mg/L of L-proline(Sigma-Aldrich) to prevent probable arginine-to-prolineconversion.22 The cells were cultured in SILAC medium formore than 10 doublings to attain complete labeling.The labeled cell lines were lysed and digested following the
same in-solution digestion protocol to create a universal SILACreference.20 Labeling efficiency of the cell lines was checked toensure that all cell lines obtained >95% incorporation rate.23
Labeled tryptic peptides of the four cell lines were then mixedat equivalent ratio to create a universal SILAC standard. 50,100, 200, and 400 ng of SILAC reference were spiked intriplicate into each of the LCM samples to determine theamount of standard to achieve best precision of quantification.The optimal amount of SILAC standard was then used forfurther SILAC-based quantitative experiments. LFQ sampleswere not spiked with SILAC standard.
LC-MS/MS Analysis
Nano-LC-Orbitrap-MS/MS was performed on an nLC system(Ultimate 3000, Dionex, Amsterdam, The Netherlands)hyphenated online with a hybrid linear ion trap/Orbitrapmass spectrometer ((LTQ-Orbitrap-XL, ThermoElectron, Bre-men, Germany) following a slightly modified procedure, aspreviously described.24 For each sample, a volume of 20 μL wasfirst loaded onto a trap column (PepMap C18, 300 μm I.D. × 5mm, 5 μm particle size, 100 Å pore size; Dionex, Amsterdam,The Netherlands) for concentration and desalting using 0.1%TFA (in water) as loading solvent at a flow rate of 20 μL/min.The trap column was then switched online to directly connectwith a reversed-phase 75-μm I.D. × 50 cm fused silica capillarycolumn packed with 3 μm C18 particles (PepMap, Dionex,Amsterdam, The Netherlands), and peptides were graduallyeluted with a flow rate of 250 nL/min at 40 °C columntemperature using the following binary gradient: The gradientstarted with 100% mobile phase A (97.9% H2O, 2% acetonitrile,0.1% formic acid) to 25% mobile phase B (80% acetonitrile,
19.02% H2O, 0.08% formic acid) over the first 120 min; then, asteeper gradient was used to further increase mobile phase B to50% in the next 60 min. The eluted peptides were directlysprayed with a voltage of 1.5 kV into the online coupled LTQ-Orbitrap-XL MS using electrospray ionization (ESI) equippedwith a metal-coated nano ESI emitters (New Objective,Woburn, MA). Mass spectra were acquired over a mass-to-charge ratio (m/z) range 400−1800 at a resolving power of 30000 at 400 m/z. Target of automatic gain (AGC) was set at 106
ions, and mass was locked at 445.120025 u protonated with(Si(CH3)2O))6). On the basis of this full scan, the top fiveintensive ions were consecutively isolated (AGC target set to104 ions) and fragmented by collisional activated dissociation(CAD) applying 35% normalized collision energy in the linearion trap. Parent ions within a mass window of ±5 ppm ofdissociation were then excluding for MS/MS fragmentation inthe next 3 min or until the precursor ion intensity fell below asignal-to-noise ratio (S/N) of 1.5 for more than 10 scans (earlyexpiration). Full scan and MS/MS fragmentation spectra werepartially simultaneously acquired in Orbitrap and linear ion trapparts.AIMS was used for directed LC-MS analyses as previously
described,13 with minor modification to obtain optimalsensitivity on local MS instrument. The protein list of the“focal adhesion” pathway was obtained from the KEGGpathway database (http://www.genome.jp/kegg/pathway.html). Peptides selected for AIMS had to be unique to oneof the protein groups and fully tryptic. No modification wasconsidered other than SILAC labeling and carbamidomethyla-tion of cysteines. Selected peptides had to be observed in LFQand SILAC experiments of the eight LCM samples. Allpreviously observed charge states were included, and peptideswere within an m/z range of 400 and 1800. No retention timeprediction was associated with a given peptide in the inclusionlist. The inclusion list (n = 578 entries) can be found in TableS1 in the Supporting Information. The top five ion precursorswere isolated from the inclusion list within an m/z width of 20ppm. The remaining of LC-MS settings were identical to whatwas used in the global screening mode.
Targeted SRM Analysis of “Focal Adhesion” Proteins
LCM and WTL replicate samples mixed with equal amount ofSILAC standard were measured by LC-SRM-MS to targetproteins involved in the “focal adhesion” pathway. Peptideswere selected from proteins detected by shotgun measurementsfollowing these described rules: (1) unique to one protein, (2)fully tryptic, (3) 8−25 amino acid residues, (4) excluding first25 amino acids at the N-terminus of proteins, (5) excludingragged ends (tryptic peptides cleaved between R/K, K/R, R/R,and K/K), (6) excluding peptides with C, M, RP, and KP, (7)maximal three variable modifications, and (8) maximal oneneutral loss. From these peptides, we selected SRM transitionsusing the following criteria: (1) precursor ions with twocharges, (2) daughter ions are y ions with 1 charge, (3) m/zrange of precursor ions is between 50 Th and 1500 Th, (4)mass tolerance of precursor ions is set at 0.055 Th, and (5)mass tolerance of daughter ions is set at 0.5 Th. Peptideseligible for the above-mentioned criteria were selected for SRManalysis.A selective reaction monitoring (SRM) method for the
selected peptides was developed and optimized on a nano-ACQUITY UPLC system (Waters) that was connected to aXevo TQ-S triple quadrupole mass spectrometer (Waters). Five
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μL of each sample was trapped on a symmetry 2-cm C18
nanoACQUITY column (particle size: 5 μm, ID: 180 μm)(Waters) and washed for 5 min at a flow rate of 8 μL/min witha solution of 99% solvent A and 1% solvent B; solvent Aconsisted of 0.1% formic acid in water and solvent B consistedof 0.1% formic acid in acetonitrile (Biosolve, Valkenswaard,The Netherlands). Subsequently, samples were separated on a20 cm BEH 300 C18 column (particle size: 1.7 μm, ID: 75 μm)
(Waters) at a flow rate of 300 nL/min. The used gradientincreased from 1 to 20% solvent B in 60 min and in the next 30min of the gradient to 60% solvent B. Then, the column waswashed for 5 min with 80% solvent B and equilibrated for 25min with 99% solvent A. The column was online connected tothe Z-spray nano source of the Xevo TQ-S mass spectrometer(Waters). The following source parameters were used: acapillary voltage of 3.00 kV, a cone voltage of 50 V, a source
Figure 1. Flowchart summarizing the experimental design of systematic comparison between different quantitative methods and development ofMRM assay for quantification of focal adhesion pathway.
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offset of 50 V, and a source temperature of 70 °C.Fragmentation was induced by CAD with argon gas, whichwas inserted with a flow rate of 0.15 mL/min. For each peptidethe optimal collision energy was calculated by the Skylinesoftware package (version 1.3).25 The three best performingtransitions for each measured peptide were experimentallydetermined and used in the optimized assay. Skyline softwarewas used to analyze the data. The ratios between the area ofheavy labeled internal standard and the peptide were used tocalculate the coefficient of variation (CV) for the measuredpeptides and proteins.
Data Analysis
Recorded MS files were analyzed using MaxQuant software(version 1.1.1.36). An initial search was set at a precursor masswindow of 7 ppm. The search followed an enzymatic cleavagerule of Trypsin/P and allowed maximal two missed cleavagesites. Carbamidomethylation of cysteines was defined as fixedmodification, while protein N-terminal acetylation andmethionine oxidation were defined as variable modificationsfor database searching. To construct the MS/MS peak list file,up to top eight peaks per 100 Da window were extracted andsubmitted to search against a concatenated forward and reverseversion of the UniProtKB/Swiss-Prot human database(generated from version 2011_03). The cutoff of global falsediscovery rate (FDR) for peptide and protein identification wasset to 0.01, and only peptides with ≥7 amino acid residues wereallowed for identification. Minimally one unique peptide wasrequired for protein identification.A retention time window of 2 min was applied to match the
same accurate masses between each of the replicate samples orbetween experimental LCM samples. The option of second
identifications26 was selected to allow identifying coelutedpeptides from given MS/MS spectra. To analyze label-free data,we performed LFQ algorithm27 in MaxQuant on the identifiedpeptides to quantify protein abundance. SILAC labels of R10and K8 were chosen to analyze SILAC data. For labeling check,a variable modification of heavy proline (P6) was defined and a“requantify” option was not selected.
Statistical Analysis and Biological Interpretation
Statistical analyses between different methods of quantificationwere performed by Microsoft Office Excel 2010. The numberof quantified peptides and proteins between differentquantitative methods was tested using a paired two-tailedStudent’s t test. CVs between different quantitative methodswere compared with an unpaired two-tailed Student’s t test.Box plots of CVs were made by box plot template (Vertex42,2009) supported by Microsoft Excel. Gene ontology ofidentified proteins was annotated using ArrayTrack.28 Pathwaysrelated to ER status were identified using Gene Set EnrichmentAnalysis (GSEA, version 2.0)29 and searched against MolecularSignatures Database (MSigDB) of KEGG pathways (version3.0).30 Scatter plots were made using GraphPad Prism 5(version 5.01).
■ RESULTS
Optimization of SILAC Reference for Use in LCM Samples
We selected four breast cancer cell lines (ZR-75-1, SK-BR-3,MDA-MB-468, MDA-MB-231) representing different breastcancer subtypes31 to cover the majority of proteome of breastcancer epithelial cells (Figure 1, Table S2 in the SupportingInformation). Cell lines were labeled with >95% labeling
Figure 2. Proper amount of SILAC standard required for LCM samples. (A) Histogram shows average ratio distribution of peptides (left panel) andproteins (right panel) quantified from triplicate LCM samples (gray) and eight different LCM samples (black) with 200 ng of SILAC standard. (B)CVs of peptide ratios quantified from triplicate LCM samples with different amount of SILAC standard (only peptides quantified in threemeasurements were considered.) (C) CVs of protein ratios quantified from triplicate LCM samples with different amount of SILAC standard (onlyproteins quantified in three measurements were considered). (*: P < 0.05, ****: P < 0.0001, NS: P > 0.05).
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efficiency and then mixed in equivalent ratio to create auniversal SILAC-labeled standard (Figure 1, Table S2 in theSupporting Information). Detailed experimental design of thisstudy is present in Figure 1.In our tissue proteomics pipeline, we typically microdissect
∼4000 tumor cells, which leads to submicrogram quantities ofextracted proteins. It is difficult to accurately determine theexact protein concentration by all available protein assays. ForSILAC-based quantification, universal standard should bespiked into the sample at approximately 1:1 ratio to achievethe most precise quantification. We estimated that 200−400 ngof proteins can be extracted from ∼4000 microdissected cells.To confirm this estimation, we designed a novel method todetermine the amount of SILAC standard needed to achieve a1:1 ratio. SILAC standards of 50, 100, 200, and 400 ng werespiked into each of the triplicate LCM control samples,respectively. In this way, we could construct a calibration curveto calculate the approximate amount of SILAC standardrequired for a LCM sample. Histograms of LCM controlsamples spiked with 200 ng of standard showed a centraldistribution of Log2 (peptide ratio) around 0 (LCM tissue/SILAC standard, average ratio of triplicate measurements) onboth peptide (Figure 2A, left panel, grey histogram) andprotein levels (Figure 2A, right panel, grey histogram). At lowerconcentration of SILAC standard, the histogram peak wasshifted to the right side (positive Log2 ratio of LCM tissue/SILAC standard), while at 400 ng of SILAC standard the peakshifted to the left side (negative Log2 ratio of LCM tissue/SILAC standard) (Figure S1A in the Supporting Information).
The calibration curves derived from the histograms also showedthat the Log2 ratio between LCM tissue and 200 ng of SILACstandard was close to 0 on both peptide and protein levels(Figure S1B in the Supporting Information). We observed asimilar distribution pattern in a set of 8 experimental LCMsamples from different biological sources at peptide level(Figure 2A, left panel, black histogram) and protein level(Figure 2A, right panel, black histogram). Moreover, >98% ofquantified peptide and protein pairs were distributed within arange of ±5 Log2 ratio using 200 ng of SILAC standard. Thus, a1:1 ratio of LCM sample and SILAC standard was achieved at200 ng.To evaluate the precision of quantification between LCM
samples mixed with different amounts of SILAC standard, weplotted CVs of quantified peptides and proteins. As expected,triplicate LCM samples mixed with 200 ng of SILAC standardwere quantified with significantly lower CVs on peptide level(median CV = 17.07%) than those mixed with 50, 100, or 400ng of SILAC standard (median CV = 20.30%, P < 0.0001;17.87%, P < 0.0001; 17.36%, P < 0.05, respectively) (Figure2B). At protein level, only LCM samples spiked with 50 ng ofSILAC standard showed significantly higher CVs (median CV= 18.5%, P < 0.0001) compared with LCM samples spiked with200 ng of SILAC standard (median CV = 13.3%), but nosignificant difference was observed in LCM samples mixed with100 or 400 ng of SILAC standard (median CV = 15.8 and14.0%, respectively) (Figure 2C). These results indicate thatthe sample-to-SILAC standard ratio has a large influence onprecision of quantification at peptide level but is less crucial at
Figure 3. Evaluation of LFQ and SILAC quantification in replicate LCM samples. (A) CVs of peptide and protein ratios quantified from replicateLCM samples (n = 4, only proteins quantified in ≥3 measurements were considered, left panel) and experimental LCM samples (n = 8, onlyproteins quantified in ≥4 measurements were considered, right panel). (B) Number of quantified peptides and proteins from replicate LCM samples(n = 4, left panel) and experimental LCM samples (n = 8, right panel). (***: P < 0.001, ****: P < 0.0001).
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the protein level due to normalization. On the basis of theseresults, we concluded that 200 ng SILAC standard should bespiked into experimental LCM samples for the remainingexperiments in this study.Technical and Functional Comparison of Breast CancerProteome Based on LFQ and SILAC Quantification
For global quantification, LFQ- and SILAC-based approacheswere compared to determine which method gives the overallbest performance in combination with our tissue proteomicspipeline. First, quantitative precision was tested in quad-ruplicate measurements of LCM samples using LFQ andSILAC methods. Overall CV of peptides quantified by SILAC(median CV = 14.2%) was significantly lower than that by LFQmethod (median CV = 31.7%) in LCM samples (P < 0.0001)(Figure 3A, left panel). This difference became smaller afterprotein estimation and normalization: median CV of SILACquantified proteins was 13.7%, while LFQ-quantified proteinsshowed a median CV of 16.3% (P < 0.0001) (Figure 3A, leftpanel). Because LCM samples were biological replicates, theabove-described difference could have been introduced duringthe LCM procedure. To eliminate variation introduced byLCM, LFQ- and SILAC-based quantifications were alsocompared using WTL samples (technical replicate). Similarto LCM samples, median CV of peptides quantified by LFQ(17.4%) was much higher than that of SILAC-quantifiedpeptides (8.1%, P < 0.0001) (Figure S2A in the SupportingInformation). After protein quantification and normalization,
median CV of LFQ abundance decreased to 13.2%, and medianCV of SILAC abundance slightly increased to 8.5% (P <0.0001) (Figure S2A in the Supporting Information). Next, thenumber of quantified peptides and proteins was examinedbetween two types of quantitative methods. On average, LFQquantified ∼30% more peptides (9741 ± 63, mean ± SD) thanSILAC (7499 ± 287) in each of the four LCM replicates (P <0.001), resulting in 60% more protein quantification in LFQ(1624 ± 27) than SILAC (1036 ± 31, P < 0.0001) (Figure 3B,left panel and Table S3 in the Supporting Information). Thesame trend was also observed in the four WTL replicates, inwhich 6254 ± 48 peptides and 1201 ± 8 proteins werequantified by LFQ compared with 5202 ± 254 peptides (P <0.01) and 809 ± 27 proteins (P < 0.0001) by SILAC (FigureS2B and Table S3 in the Supporting Information). Furthercomparison between reproducibility of LFQ and SILACquantification showed that LFQ method more reproduciblyquantified the same peptides and proteins between differentmeasurements in replicate LCM samples, replicate WTLsamples, and experimental LCM samples (Figure S3 in theSupporting Information).Next, we compared the performance of LFQ and SILAC
methods in eight different experimental LCM samples,obtained from four ER+ and four ER− breast cancer tissues.As expected, median CVs of quantified peptides and proteins inexperimental LCM samples were higher compared with themedian CVs of those from replicate LCM samples using the
Figure 4. Functional comparison of proteins quantified by LFQ and SILAC method in eight different LCM samples. (A) GO annotations of proteinsquantified by LFQ and SILAC methods based on their cellular location. Inserted Venn diagram indicating unique and overlapping quantifiedproteins from LFQ and SILAC method. All proteins in this analysis were quantified in at least one of the eight samples. (B) Venn diagram indicatingunique and commonly differentially expressed proteins or genes (P < 0.05) between ER+ and ER− breast tumors using different quantitativeprocedures. Proteins used in this analysis were quantified in at least four experimental samples. (C) Venn diagram showing unique and commonenriched KEGG pathways (NES > 1.4) identified by protein or gene expression between ER+ and ER− breast tumors using different quantitativemethods. The proteins used in this analysis were quantified in at least four experimental samples.
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same quantitative methods. Median CVs of peptides quantifiedby LFQ and SILAC method were 60.33 and 47.05%,respectively, corresponding to median CVs of 62.10 and41.67% for quantified protein abundance (Figure 3A, rightpanel). However, as observed in replicate LCM samples,significantly more peptides and proteins were quantified usingLFQ method (8403 ± 296 of peptides and 1863 ± 73 ofproteins, mean ± SD) than SILAC method (5247 ± 445 ofpeptides and 804 ± 54 of proteins, mean ± SD) (Figure 3B,right panel and Table S3 in the Supporting Information).Furthermore, protein abundances of four ER+ and four ER−
experimental LCM samples quantified by LFQ and SILACmethod were investigated for gene ontological (GO)annotations and biological significance. GO annotationsclassified all of the proteins based on their cellular location.Proteins quantified by LFQ and SILAC method showed acomparable distribution of proteins from different cellularcomponents (Figure 4A). The major part of the proteomequantified by LFQ and SILAC method was overlapping,indicating that core proteomes between tissue proteins andSILAC cells are similar (Figure 4A, inserted Venn diagram).Next, differentially expressed proteins between ER+ and ER−samples were determined using both LFQ and SILACabundance data. The resulting candidate proteins were furthercompared with a gene expression data set previously generatedfrom different LCM samples32 to eliminate potential falsepositive identifications caused by our small sample set. In total,320 and 87 differentially expressed proteins were quantifiedusing a cutoff of P < 0.05 in LFQ and SILAC samples,respectively, of which 52 proteins were quantified in bothsamples (Figure 4B and Table S4A in the SupportingInformation), all of which were expressed in the same directionbetween ER+ and ER− samples. In addition, 51 differentiallyexpressed proteins in LFQ samples also had a significant changeat the level of gene expression, while only six candidate proteinsfrom SILAC samples were also differentially expressed at the
gene level (Figure 4B and Table S4B in the SupportingInformation), from which only 1/51 and 1/6 candidateproteins from LFQ and SILAC were expressed in a differentdirection than gene expression data between ER+ and ER−samples, respectively. We also compared whether specificpathways were overrepresented in one of the experimentalgroups by searching the LFQ, SILAC, and public geneexpression data sets against the KEGG pathway databaseusing GSEA. Biologically significant pathways between ER+and ER− breast cancer tissues were determined based on theirnormalized enrichment score (NES). In total, 16, 8, and 44significant pathways were identified from the LFQ, SILAC, andgene expression data, respectively, with a cutoff of NES >1.4(Figure 4C and Table S4C in the Supporting Information). Sixsignificant pathways were shared by LFQ and gene expressiondata sets, and five significant pathways were in commonbetween LFQ and SILAC data, while only one overlappedbetween SILAC and gene expression data (Figure 4C andTable S4D in the Supporting Information).In conclusion, SILAC quantification provides better precision
than LFQ, although the difference is smaller after globalnormalization at protein level, while LFQ enabled a largernumber of peptide and protein quantifications than SILACquantification. In both methods, intratissue variation (CVs fromreplicate LCM samples) is lower than intertissue variation (CVsfrom experimental LCM samples), indicating that the majorityof peptides and proteins could be reliably quantified from LCMsamples using both LFQ- and SILAC-based quantitativemethods. A higher number of proteins quantified by LFQmight aid to identify more breast-cancer-specific markers incontrast with those quantified by SILAC method. Therefore,pathway-analysis-based data from LFQ could substantially bemore valuable than those from SILAC quantification.
Table 1. Enriched KEGG Pathway in ER+ and ER− Breast Cancer Tissues
pathway pathway sizea gene set sizeb NESc nominal p valued FDR q valuee
aPathway size: number of genes involved in a given pathway. bGene set size: number of genes in input data set tested for a given gene set. cNES(normalized enrichment score): a given ES is normalized across analyzed gene sets (a primary statistics to assess enrichment), and in this studyabsolute NES > 1.4 was required as enrichment. dNominal p value: statistical significance of enrichment score, not adjusted for gene set size andmultiple testing error. eFalse discovery rate (FDR) q value: probability of normalized enrichment score to be a false positive finding (major score forFDR control).
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Quantification of Focal Adhesion Pathway with Shotgunand Directed LC-MS Methods
GSEA analysis revealed the 16 differentially expressed pathwaysbetween ER+ and ER− breast cancer tissues by LFQ (Table 1,Figure S4A in the Supporting Information). Some of theseputative pathways share similar expression patterns of proteinsand form two major clusters (Figure S4A in the SupportingInformation, blue and red rectangle). We selected two breast-cancer-related pathways for detailed investigation due to theirspecific role in breast cancer progression. In the first cluster(Figure S4A in the Supporting Information, red rectangle),“oxidative phosphorylation” pathway is overrepresented in ER+breast cancer with an NES of 1.486 (Table 1 and Figure S4B inthe Supporting Information, left panel). In the second cluster(Figure S4A in the Supporting Information, blue rectangle),“focal adhesion” pathway showed a significant enrichment inER− samples with an NES of −1.656 (Table 1 and Figure S4Bin the Supporting Information, right panel). The “focaladhesion” pathway was also confirmed by gene expressiondata (Table S4D in the Supporting Information). Therefore, weselected this pathway for further quantitative comparison usingdifferent methods.
Next, we evaluated the precision of different approaches toquantify “focal adhesion” pathway. Because we observed thatSILAC quantification was more precise but quantified fewerpeptides and proteins than LFQ, we applied AIMS to increasethe number of quantified peptides and proteins for the “focaladhesion” pathway. Overall, CV of peptides (median CV =32.76%) quantified by LFQ was significantly higher than usingSILAC-shotgun (SILAC-SG, median CV = 14.07%) inquadruplicate LCM samples in shotgun measurements (P <0.0001) (Figure 5A). No significant difference in CV wasobserved between SILAC-SG (median CV = 14.07%) andSILAC-AIMS (median CV = 14.16%) (Figure 5A). In contrast,CVs of the corresponding proteins were dramatically decreasedafter normalization, and no statistical difference was observedanymore between the different quantification methods (Figure5A). A similar pattern of peptide and protein quantitativeprecision was also observed in replicate WTL samples (FigureS5A). LFQ significantly quantified more peptides (345 ± 4 forLCM samples and 322 ± 4 for WTL samples, mean ± SD) andproteins (30 ± 1 for LCM samples and 27 ± 0 for WTLsamples, mean ± SD) from the “focal adhesion” pathway thanSILAC-SG (peptides: 243 ± 15 for LCM samples and 238 ± 17for WTL samples, protein: 23 ± 2 for LCM samples and 24 ± 1
Figure 5. Comparison of different methods in quantifying proteins involved in focal adhesion pathway in quadruplicate LCM samples. (A) CVs ofpeptide and protein ratios quantified from replicate LCM samples (n = 4) with three different quantitative methods (only peptides quantified in ≥3measurements were considered). (B) Number of quantified peptides and proteins involved in focal adhesion pathway from quadruplicate LCMsamples using three different quantitative methods. (**: P < 0.01, ***: P < 0.001, ****: P < 0.0001, NS: P > 0.05).
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for WTL samples, mean ± SD) in shotgun measurements(Figure 5B and Figure S5B in the Supporting Information).Surprisingly, SILAC-AIMS measurements were not able toquantify more peptides (223 ± 8 for LCM samples and 215 ± 4for WTL samples, mean ± SD) and proteins (23 ± 1 for LCMsamples and 23 ± 1 for WTL samples, mean ± SD) from the“focal adhesion” pathway than shotgun measurements (Figure5B and Figure S5B in the Supporting Information). Similar tothe observation from global proteome profiling, LFQ morereproducibly quantified the same peptides and proteins thanSILAC methods (shotgun and AIMS approaches) betweendifferent measurements in replicate LCM samples and replicateWTL samples (Figure S6 in the Supporting Information).These results suggest that quantitative precision of LFQ isindeed close to SILAC in quantifying “focal adhesion” proteins.Furthermore, LFQ could reproducibly quantify more peptidesand proteins from this pathway than SILAC in our breastcancer tissues. However, the directed AIMS method did notshow better sensitivity than the shotgun method in quantifying“focal adhesion” proteins by nLC-LTQ-Orbitrap-MS/MSplatform in this study. Therefore, a more targeted MS approachshould be used to achieve better sensitivity for a subset ofproteins when SILAC is needed for precise quantification.
Development of SRM Assays for Proteins Involved in FocalAdhesion Pathway
Targeted SRM assay is known as a sensitive and reproduciblemethod for clinical application. As a proof-of-principle study,we developed SILAC-based SRM assays for 27 peptides derivedfrom 17 “focal adhesion” proteins (Table 2). Figure S7 in theSupporting Information shows an example of SRM assay for
peptide “K.SPFEVYVDK.S” from Filamin-A protein (P21333).Most of the measured peptides eluted at similar retention time,as predicted by Sequence Specific Retention Calculator33
(SSRCalculator) (Figure S7A in the Supporting Information).Both “light” (monoisotopic) and “heavy” (SILAC) versions ofpeptide “K.SPFEVYVDK.S” eluted at the same retention timeand with clean baseline. These observations suggested the SRMassays might correctly and precisely quantify the peptides ofinterest.Next, we compared SILAC ratios of these 27 peptides
quantified by SRM, shotgun, and directed approaches. Goodcorrelation of SILAC ratios was achieved between all threedifferent approaches in LCM samples (Spearman correlationcoefficients: 0.9036 to 0.9941) (Figure 6, left panel) and inWTL samples (Spearman correlation coefficients: 0.6078 to1.0000) (Figure 6, right panel). Good correlation suggestedsimilar quantitative results of SRM (quantification of MS/MSfragments), shotgun, and directed methods (quantification ofMS parent ions) in quantifying peptides from “focal adhesion”proteins.CVs of these quantified peptides varied between 4.60 and
58.50% (median CV = 14.94%) in quadruplicate LCM samplesand between 1.96 and 56.79% (median CV = 8.07%) in sixreplicate WTL samples, respectively (Table 3A). Five proteinsquantified with two or more peptides had CVs within a range of5.76 and 16.06% in LCM samples and a range of 2.41 and5.91% in WTL samples (Table 3B). Good quantitativeprecision was achieved using a targeted SRM assay to quantifymultiple peptides and proteins from the “focal adhesion”pathway, especially in WTL samples, suggesting that the
Table 2. Transition Used in SRM Assay of the Selected Peptides and Their SILAC Standardsa
protein id protein name peptide sequence Q1 (m/z) Q3 (m/z)
aAll parent ions (Q1) are doubly charged and their daughter ions (Q3) are singly charged. Transitions given in this Table derive from light peptides,and heavy peptides are labeled with R10K8 SILAC labels.
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approach may aid in developing specific assays to measureclinical valuable protein signatures in trace amount of tumortissues.
■ DISCUSSIONIn this study, we compared LFQ and SILAC quantification incombination with different MS strategies. Also, we described amethod for precise peptide and protein quantification in LCMbreast cancer tissues using a universal SILAC standard andtargeted SRM assays. This study provides insight into selectionof quantitative MS methods for biomarker discovery andvalidation studies and development of clinically relevant MS-based quantitative assays.LFQ method is frequently considered as a semiquantitative
method. This approach has been criticized due to poorquantitative precision for low abundant species.16 The variationis introduced by background interference and machineryvariation.34 The introduction of a universal SILAC standardgreatly circumvents quantitative variation because quantifiedproteins can be normalized on the same standard.23 However,
we previously described a label-free proteomics pipeline thatreproducibly and precisely quantified thousands of peptides andproteins from LCM samples.20 In the current study,comparison was performed between our previously describedtissue proteomics pipeline and the SILAC method. On peptidelevel, SILAC method outperformed LFQ by ∼2-fold decreasein median CVs in replicate measurements of LCM samples, inboth global proteome quantification and selected quantificationof “focal adhesion” proteins. On the protein level, the differencein quantitative precision remained significant for quantificationof the global proteome but not for selected quantification of“focal adhesion” proteins. Difference in median CVs of replicatemeasurements between LFQ and SILAC quantification becamesmaller because proteins quantified by LFQ were normalizedon their background proteome.27 Although SILAC quantifica-tion slightly outperformed LFQ in quantitative precision, bothmethods could confidently identify candidate proteins bycomparing with gene expression data. We showed that only 1/51 and 1/6 candidate proteins from LFQ and SILACquantification were expressed in a different direction than
Figure 6. Scatter plots showing linearity of SILAC quantification performed by SRM, shotgun, and AIMS method. All three methods showedsignificant correlation in quantifying the same sets of peptides (Spearman correlation). In LCM samples, correlation coefficients between each of thetwo methods are >0.90.
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Table
3.QuantitativePrecision
ofSelected
PeptidesandProteins
(A)Quantified
Peptides WTL-CTRL(n
=6)
LCM-CTRL(n
=4)
proteinID
proteinname
peptidesequence
peptideratio
(mean±
SD)
peptideratio
(CV)
peptideratio
(mean±
SD)
peptideratio
(CV)
P18206
vinculin
SLGEISA
LTSK
0.928±
0.065
6.99%
0.903±
0.123
13.65%
ALA
SQLQ
DSL
K0.963±
0.098
10.13%
0.952±
0.070
7.33%
SFLD
SGYR
1.063±
0.080
7.55%
1.036±
0.195
18.82%
O75369
filamin-b
SPFE
VSV
DK
0.806±
0.063
7.80%
1.110±
0.250
22.53%
VFG
PGVER
0.727±
0.022
3.01%
0.936±
0.107
11.41%
VVASG
PGLE
HGK
0.797±
0.061
7.66%
0.938±
0.140
14.94%
P05556
integrin
beta-1
SGEPQ
TFT
LK0.781±
0.130
16.70%
1.063±
0.182
17.12%
IGFG
SFVEK
0.817±
0.062
7.60%
0.480±
0.080
16.65%
WDTGEN
PIYK
0.798±
0.103
12.90%
P21333
filamin-A
AEA
GVPA
EFSIWTR
2.924±
0.194
6.65%
4.621±
0.330
7.15%
ENGVYLIDVK
2.643±
0.213
8.07%
3.931±
0.181
4.60%
LYSV
SYLL
K2.811±
0.109
3.86%
4.131±
0.282
6.83%
Q9Y
490
talin-1
ILAQATSD
LVNAIK
0.888±
0.157
17.65%
1.030±
0.111
10.75%
ALD
GAFT
EENR
1.073±
0.090
8.38%
0.662±
0.063
9.50%
VLV
QNAAGSQ
EK1.165±
0.116
9.94%
0.806±
0.149
18.47%
O43707
alpha-actin
in-4
EAILAIH
K0.688±
0.042
6.11%
1.161±
0.153
13.18%
P11047
laminin
subunitgamma-1
LSAED
LVLE
GAGLR
0.901±
0.093
10.36%
1.854±
0.347
18.69%
P12111
collagenalpha-3(VI)
chain
LSDAGITPL
FLTR
11.340
±2.410
21.25%
1.048±
0.613
58.50%
P17655
calpain-2catalytic
subunit
SDTFINLR
0.403±
0.054
13.33%
0.056±
0.004
7.29%
P21333
filamin-A
SPFE
VYVDK
2.476±
0.172
6.94%
3.561±
0.554
15.56%
O14950
myosinregulatory
light
chain12B
FTDEE
VDEL
YR
1.231±
0.075
6.06%
1.875±
0.264
14.10%
P02751
fibronectin
VFA
VSH
GR
0.829±
0.471
56.79%
P07996
thrombospondin-1
FVFG
TTPE
DILR
52.653
±8.025
15.24%
46.084
±12.817
27.81%
P23229
integrin
alpha-6
LIATFP
DTLT
YSA
YR
1.733±
0.457
26.36%
4.267±
0.314
7.36%
P62993
grow
thfactor
receptor-bound
protein2
ESESA
PGDFS
LSVK
0.695±
0.333
48.01%
0.473±
0.142
29.95%
P63000
Ras-related
C3botulinum
toxinsubstrate1
TVFD
EAIR
0.984±
0.019
1.96%
0.231±
0.037
16.01%
Q05397
focaladhesion
kinase
1NLL
DVID
QAR
1.163±
0.071
6.10%
1.034±
0.203
19.61%
(B)Quantified
Proteins
WTL-CTRL(n
=6)
LCM-CTRL(n
=4)
proteinID
proteinname
peptide/protein(W
TL-/LCM-CTRL)
proteinratio
(mean±
SD)
proteinratio
(CV)
proteinratio
(mean±
SD)
proteinratio
(CV)
P18206
vinculin
3/3
0.984±
0.046
4.64%
0.964±
0.127
13.16%
O75369
filamin-B
3/3
0.777±
0.031
3.94%
0.995±
0.110
11.06%
P05556
integrin
beta-1
3/2
0.799±
0.047
5.90%
0.772±
0.124
16.06%
P21333
filamin-A
3/3
2.793±
0.067
2.41%
4.228±
0.244
5.76%
Q9Y
490
talin-1
3/3
1.042±
0.039
3.74%
0.833±
0.048
5.80%
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gene expression data between ER+ and ER− samples,respectively. This discrepancy can be reasonably explained bypost-transcriptional events, such as miRNA interference orprotein modification. However, LFQ showed a dominantadvantage to the SILAC method in terms of the number ofpeptide and protein quantifications and reproducibility ofquantification. This observation can probably be explained bythe undersampling issue that is caused by increased complexitydue to the addition of SILAC standard into tissue samples.Therefore, less high-quality MS/MS spectra can be recorded forpeptide and protein identification. Improvements in chromato-graphic separation (longer columns, smaller particle sizes, andmultidimensional separation) or new types of MS instrumentssuch as Q-Exactive Orbitrap MS35 and Orbitrap Fusion TribridMS36 might provide solutions for this low identification issuebecause better LC separation, higher resolution of MS detector,and shorter MS and MS/MS cycle time can be achieved.Therefore, more MS and MS/MS spectra with high quality canbe recorded for identification. Thereby, these proteins failed tobe quantified by SILAC method. This problem may be solvedby carefully selecting reference cell lines or application ofabsolute SILAC method.37,38 Here we conclude that LFQreached a good balance between quantitative precision and thenumber of quantified features on our LTQ-Orbitrap-XL systemand therefore outperformed the SILAC method in quantifyingglobal proteome and “focal adhesion” proteins in both LCMand WTL breast cancer tissues.The shotgun MS approach is commonly used for discovery
studies, while directed and targeted MS approaches have beenapplied for verification and validation types of studies. Ingeneral, shotgun MS can reproducibly capture high abundantspecies but not their low abundant coeluents, which leaves ahigh percentage of missing data behind. Directed AIMS methodcan easily be applied to partially overcome this drawback. Inthis method, the MS device specifically selects masses ofinterest for fragmentation.13 Therefore, more reproducibleidentification is performed, which indirectly leads to morereproducible quantification of identified species. However,AIMS yielded less peptide and protein identification andquantification of “focal adhesion” pathway using directed AIMSmode compared with shotgun screening mode. A relativelywide m/z window (20 ppm) used may explain the reason forthis lower peptide and protein identification rate. On one hand,the use of a wide m/z window can avoid failure offragmentation of peptides of interest due to relative imprecisemeasurement of parent masses. On the other hand, interferingLC-MS features that have similar m/z (within 20 ppm window)and thus the coeluents could be isolated and fragmented priorto the LC-MS features in the inclusion list. Consequently,peptides of interest would remain not fragmented by LC-MSand associated MS/MS would not be recorded. In the shotgunapproach, this problem is not present because peptides areselected for fragmentation based on their abundance but notretention time. To solve this problem, more stringent m/ztolerance or restricted retention time of the selected massescould be applied to improve the sensitivity of AIMS method. Inaddition, a new generation of Orbitrap MS (Q-Exactive MS orOrbitrap Fusion Tribrid MS) can enhance mass accuracy andacquisition of MS and MS/MS spectra35 and therefore achievemore sensitive identification and reproducible quantification bydirected AIMS method.Targeted MS quantification using SRM assay is often applied
for biomarker validation.39,40 In this study, we described a
method to quantify selected “focal adhesion” proteins in LCMand WTL tissue samples using an SRM assay in the presence ofa universal SILAC standard. This method showed overall goodquantitative precision in LCM and WTL tissues. SILAC ratiosof quantified proteins were significantly correlated with thosequantified by shotgun and directed AIMS method. Only a fewpeptides quantified in WTL sample showed different ratiosbetween Orbitrap and SRM quantification. This is due to lowquality of MS spectra or lack of a good transition for thesepeptides. Extra effort is needed to obtain reliable quantificationby optimizing SRM assays for these peptides. However, we alsonoticed that quantitative precision of SRM method was notbetter than Orbitrap-based shotgun and directed AIMSmethods in LCM samples. This is possibly due to intertissuevariation of LCM technique in isolating tumor epithelial cellsfrom different tumor sections.20,21 In global approaches, thistechnical variation can be compensated by normalization onbackground proteome or universal SILAC standard, while nogood normalization method is available in targeted SRMapproach. A possible normalization method for targeted SRMassays is to select a few stable housekeeping proteins fornormalization,41 which should be further evaluated. Never-theless, this SILAC-based SRM assay is cost-efficient andprecise in quantifying selected biomarkers or pathway proteinsfrom tissue lysates (e.g., tumor biopsies), from which proteininput can be carefully controlled by protein assays. In AQUA-based SRM approach, quantification of proteins is typicallybased only on one or two peptides due to high costs of AQUApeptides, possibly resulting in biased quantitative accuracy.37
Therefore, this SILAC-based SRM assay could be potentiallymore accurate and precise to quantify proteins than AQUA-based method when absolute SILAC approach is applied.In summary, we systematically evaluated several approaches
for quantification of global proteome and selected “focaladhesion” proteins in LCM breast cancer tissues. We observedthat LFQ in combination with shotgun proteomics achieved thebest balance among precision for protein quantification,number of quantified proteins, and reproducibility of proteinquantification using our tissue proteomics pipeline. In addition,SILAC-based SRM assay showed great potential for thedevelopment of quantitative protein assays in clinical practice.Therefore, we recommend the use of a label-free proteomicpipeline for biomarker discovery and validation studies in LCMtumor samples. When biomarkers are biologically validated,targeted SILAC-based SRM assays can be developed for clinicalassays.
■ ASSOCIATED CONTENT
*S Supporting Information
Raw nLC-Orbitrap-MS data have been submitted toProteomeXchange (accession number: PXD000278), and rawnLC-SRM-MS data and transition lists have been deposited inPeptideAtlas (identifier: PASS00253). This material is availablefree of charge via the Internet at http://pubs.acs.org.
The authors declare no competing financial interest.
■ ACKNOWLEDGMENTS
This work was financially supported by the NetherlandsGenomics Initiative (NGI)/Netherlands Organization forScientific Research (NWO) and the Dutch Cancer Society(EMCR 2009-4319). We acknowledge Ms. Lona Zeneyedpourfor technical assistance on LC-MS measurements.
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