Screening of Missing Proteins in the Human Liver Proteome by

Mar 5, 2014 - To completely annotate the human genome, the task of identifying and characterizing proteins that currently lack mass spectrometry (MS) ...
0 downloads 9 Views 3MB Size
Download PDF
Article pubs.acs.org/jpr

Screening of Missing Proteins in the Human Liver Proteome by Improved MRM-Approach-Based Targeted Proteomics Chen Chen,†,‡ Xiaohui Liu,†,‡ Weimin Zheng,† Lei Zhang,‡ Jun Yao,‡ and Pengyuan Yang*,†,‡ †

Department of Chemistry, Fudan University, Shanghai 200032, P. R. China Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, P. R. China

‡

S Supporting Information *

ABSTRACT: To completely annotate the human genome, the task of identifying and characterizing proteins that currently lack mass spectrometry (MS) evidence is inevitable and urgent. In this study, as the first effort to screen missing proteins in large scale, we developed an approach based on SDS-PAGE followed by liquid chromatography−multiple reaction monitoring (LC-MRM), for screening of those missing proteins with only a single peptide hit in the previous liver proteome data set. Proteins extracted from normal human liver were separated in SDS-PAGE and digested in split gel slice, and the resulting digests were then subjected to LC-schedule MRM analysis. The MRM assays were developed through synthesized crude peptides for target peptides. In total, the expressions of 57 target proteins were confirmed from 185 MRM assays in normal human liver tissues. Among the proved 57 one-hit wonders, 50 proteins are of the minimally redundant set in the PeptideAtlas database, 7 proteins even have none MS-based information previously in various biological processes. We conclude that our SDS-PAGE-MRM workflow can be a powerful approach to screen missing or poorly characterized proteins in different samples and to provide their quantity if detected. The MRM raw data have been uploaded to ISB/SRM Atlas/PASSEL (PXD000648). KEYWORDS: one-hit wonder, MRM, target proteomics, mass spectrometry

■

INTRODUCTION One of the primary tasks after the sequencing of any genome is the annotation of protein-coding genes. This reverse process of genome annotation from proteins to genes possesses great promise for increasing the accuracy of a predicted gene structure. So far, annotation of genomes using mass spectrometry-based proteomics data is complementary to other gene prediction methods.1 There are two effective approaches to annotate the protein-coding genes: by the deep sequence approach for those proteins currently lacking high quality mass spectrometry (MS) evidence including lowabundance proteins, followed by possible antibody-based detection in selected tissue and cell lines; and by a targeted proteomics approach using MS-based validation for uncharacterized missing proteins. Recent advances in shot-gun proteomics enable the identification and quantitation of thousands of proteins in various biological samples. It has been reported that a well designed experiment could reach more than 10,000 protein identifications.1 To discover more proteins, numerous samples from different sources (e.g., the liver,2,3 brain,4 and kidney5) have been deep sequenced. In total, 14,000 proteins are declared to be identified by an extensive discovery proteomics experiment; however, more than 5,000 proteins still lack MS experimental evidence in the neXtProt database.6 Since 2010, an international Chromosome-Centric Human Proteome Project (C-HPP) was launched to map and annotate all proteins encoded by the genes on each human chromosome. © 2014 American Chemical Society

To realize the mentioned objective above, identifying and characterizing missing proteins is inevitable and crucial. There are some reasons that weaken experimental evidence for protein identification. First, these missing proteins might be lost during the sample preparation and fractionation procedures.7 Numerous sample processing methods were developed, like filter-aided sample preparation (FASP),8 tubegel digestion,9 stable isotope standards with capture by antipeptide antibodies (SISCAPA),10 and so on. Different separation techniques, singly or in combination, were used prior to LC−MS analysis.11−13 Second, the deficient sensitivity of mass spectrometric analysis will cause a failure of detection. One of the most bothersome challenges for genome-wide protein characterization is the wide dynamic range of protein abundances, from a few copies per cell to ten million copies per cell, was far beyond the dynamic range of MS instrumentation performance. In addition, in shot-gun proteomics workflow, protein identities were reconstructed based upon peptide assignments which were inferred from the fragment MS/MS spectrum. The inherent complexity of proteome and uncertainty of mass spectrometric data14,15 bring about spurious peptide-spectrum matches and thus lead to incorrect or missing protein identifications. Fortunately, bioinformatics approaches have been developed to estimate the error rate of peptide-spectrum matches and protein identification, giving Received: November 7, 2013 Published: March 5, 2014 1969

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

PMSF) was added to approximately 1 g of tissue to extract the total proteins by sonication for 5 s × 10 times on ice. After centrifugation at 25 000g for 30 min at 4 °C, the supernatant was collected and stored at −80 °C. The total protein concentration was measured using PlusOne 2-D Quant Kit (GE, Amersham Biosciences). The different lysis buffers used were buffer A, 40 mM Tris-HCl, pH 8.5; buffer B, 100 mM DTT, 4% SDS, and 100 mM Tris-HCl, pH 7.6; buffer C, 8 M urea and 2 M thiourea; buffer D, 7 M urea, 2 M thiourea, and 2% (w/v) CHAPS; buffer E, 7 M urea, 2 M thiourea, 2% (w/v) CHAPS, 0.1% DTT, and 2% (w/v) SB3-10; buffer F, 50 mM HEPES, 5% glycerol, and 15 mM DTT; buffer G, 8 M urea, 4% (w/v) CHAPS, and 40 mM Tris-HCl; and buffer H, (RIPA): 0,1%, SDS, 1% NP-40, and 0.5% sodium deoxycholate.

relatively more reliable peptide and protein identifications. All of these factors leading to the possible failure of protein identification, should be taken into account when verifying missing proteins. However, for missing-protein validation by a targeted proteomics approach, multiple reaction monitoring (MRM) has emerged as an attractive method in recent years. It monitors specific proteins based on their proteolytic peptides and does not need high quality antibodies. MRM analyses can achieve high sensitivity and high specificity by the two consecutive mass filtering stages and is able to multiplex the protein assays. Thus, the MRM approach can obtain accurate quantitative linear range in more than 4 orders of magnitude, which makes this MS-based approach particularly effective compared to enzyme-linked immune sorbent assays (ELISAs), the current gold standard in clinical laboratories.16,17 On the basis of these features, the MRM approach will be well suited for detecting those missing proteins. Therefore, in standard guidelines for the Chromosome-Centric Human Proteome Project (CHPP),6 MRM was put forward as an alternative approach in the case where a protein cannot be detected by MS measurement of the enzymatic digest. Over the past decade, there has been much argument regarding the criteria for protein identification. For example, one debate is about the two-peptide rule. Although rigid guidelines ensure high quality of the reported identifications and avoid the inflation of identification lists with erroneous entries, the exclusion of single hit wonders may result in a loss of potentially valuable meta-data. In fact, recent studies show evidence that retaining single hit wonders instead could be advantageous because these single hit wonders still comprise many correct identifications.14,15,18 The single-peptide approaches in conjunction with the control of the FDR in several protein inference strategies (such as Proteinphophet19 and Barista20) are widely used to maximize the number of identifications. In fact, it would be more acceptable that one protein could be represented by its unique peptide(s) in targeted and quantitative proteomics. Therefore, retrial of those proteins with minimal evidence of one-hit wonders in a large scale data set would replenish our knowledge in gene-centric proteomics. In this study, we have developed modified MRM-approach based targeted proteomics and investigated its performance to screen weakly characterized missing proteins. As an example, we utilized the developed MRM assays to analyze 185 one-hit proteins in the first edition of the CNHLPP data set2 and screened those target proteins in normal human liver tissues. We have developed a sample preparation method including protein extraction, prefractionation for enrichment of targets, and investigated optimal MRM conditions and proper MS instrument status at state of the art for the detection of missing proteins. In total, we have successfully detected 57 targeted proteins to be expressed in normal human liver tissues.

■

SDS-PAGE/In-Gel Trypsin Digestion

Two hundred micrograms of proteins per lane were loaded into 10-well 10% SDS-PAGE (17 cm ×17 cm) and separated at 40 mA until the tracking dye had migrated nearly to the edge. Gels were stained with Commassie Blue. Subsequently, each lane was equally sliced into 24 slices according to quantity (Figure S3, Supporting Information). The gel pieces were destained using a solution of 50% acetonitrile and 25 mM ammonium bicarbonate buffer and then dried with 100% acetonitrile. Reduction was carried out by incubating the dehydrated gels with 10 mM DTT in 25 mM ABC buffer for 60 min at 37 °C. The reduction solution was then replaced with 25 mM of iodoacetamide in 25 mM ABC, and the sample was incubated for 45 min in the dark. The gel samples were then washed twice in 50 mM ABC and dehydrated with 100% acetonitrile. Modified sequencing-grade trypsin (Promega) at a concentration of 10 μg/mL in 25 mM ABC buffer was added, and digestion was carried out overnight at 37 °C and terminated by the addition of TFA to a final concentration of 0.1% v/v. The peptides were extracted from the gel twice with 200 μL of 50% ACN and 0.1% TFA for 30 min. The extracted solution of each gel band was pooled, lyophilized, and stored at −80 °C until use. Generation of the Peptide Spectral Library

We have selected 185 targeted proteins from the one-hit wonders in the HLP data set and used just one unique peptide of such one-hit wonders for each targeted protein in order to reduce cost in the MRM assay. One hundred eighty-five peptides were synthesized using solid phase peptide synthesis technology, lyophilized in a 96-well plate (∼1 μmol of peptide material per well; ChinaPeptides), and used in an unpurified form. Crude peptide mixtures were resuspended in 5% acetonitrile and 0.1% formic acid, and analyzed by nano LC− MS/MS on the LTQ-Orbitrap XL mass spectrometer. The separation of the peptides was performed in a Symmetry C18, 5 μm, 180 μm i.d. × 2 cm trap-column and a BEH300 C18, 1.7 μm, 75 μm i.d. × 15 cm reverse phase column (Waters Corporation, Milford, MA). The mobile phases were 5% ACN with 0.1% formic acid (phase A and the loading phase) and 95% ACN with 0.1% formic acid (phase B). A total 60-min linear gradient was employed. The flow rate of the mobile phase was set at 300 nL/min. Raw data were searched against a homemade database which combined the Swiss-Prot yeast database and the synthetic peptides sequences using the SEQUEST algorithm. Peptide probabilities were computed using the tools of the TransProteomic Pipeline. A minimum PeptideProphet probability score (P) filter of 0.9 was elected as the threshold to remove low-probability peptides. MS/MS

EXPERIMENTAL SECTION

Human Liver Tissue Samples

The normal liver tissue was the same batch collected by CHLPP (Chinese Human Liver Proteome Project).2 Frozen liver tissues were cut into small pieces and washed with ice-cold saline to remove blood. Then, the tissues were ground to powder with liquid nitrogen in a mortar. Three milliliter lysis buffer (buffer A-H containing a protease inhibitor cocktail and 1970

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

spectra for each identified peptide with P ≥ 0.9 were extracted to build a targeted peptide spectral library using SpectraST.21

Chromatography was performed with solvent A (Milli-Q [Millipore, Billerica, MA] water with 2% acetonitrile and 0.1% formic acid) and solvent B (90% acetonitrile with 0.1% formic acid). Peptides were eluted at 300 nL/min for 5% B for 5 min, 5−40% B over 65 min, 40−80% B over 1 min, and 80% B for 5 min before returning to 5% B over 20 min. An inclusion list consisted of M/Z, and Z for targeted peptides was loaded in the mass spectrometry instrument method. The peptide assumed charges of z = 2 and z = 3. The m/z of the peptide at a given charge is between 300 and 1500. Carbamidomethylation of cysteine was set as static modification. A single Orbitrap MS scan from m/z 350 to 1800 at a resolution 60,000 was followed by up to 10 ion trap MS/MS scans at the normal scan rate. The top 10 most abundant precursors from the inclusion list (if present) were targeted for MS/MS spectrum acquisition over the course of a 90 min experiment. The m/z tolerance around targeted precursors was ±20 ppm. Dynamic exclusion was also enabled with a repeat count of 2 with a repeat duration of 10 s and an exclusion duration of 500 s. The intensity threshold for triggering of a detected peak was set to 100, and collision energy was specified at 30% for all list members. Raw data was searched against the Swiss-Prot human protein database (version 20130720) using SEQUEST, and the result was filtered using TPP. The PeptideProphet probability score (P) was limited to 0.9, and the ProteinProphet probability score (P) was limited to 0.95.

MRM Assay Development and Optimization

For each peptide, the most intense nine peaks in the MS/MS spectrum were picked manually as preliminary MRM transitions. If more than one charge state was detected, each of the nine transitions for both charge states was selected. MRM methods were generated by Skyline.22,23 The declustering potential was 70, dwell time was 15 ms, and Q1/Q3 quadrupoles were set at unit resolution (0.7 fwhm). Collision energy optimization parameters were set to use five steps on either side of the value predicted by the default equation, with the step size set to 1 V. In total, 11 collision energy voltage values were considered for each fragment ion. Skyline, Microsoft Access, Excel macros, and in-house-written Perl scripts were used to calculate the area of each transition chromatogram and pick out the six or five most intense transitions for each peptide. Then the peptides mixture was tested three times with the optimized transitions and collision energy. Average retention time for each peptide was used for the next scheduled MRM experiment. Only b and y series ions were accepted. Fragments with m/z values close to the precusor ion m/z (|m/zQ1 − m/zQ3|≤ 5 Th) were discarded, as such transitions result in high noise levels. Scheduled MRM Analysis

■

MRM experiments were performed on a 4000 QTRAP hybrid triple quadrupole/linear ion trap mass spectrometer (AB Sciex, CA) interfaced with a Eksigent nano 1D plus system (Waters, Milford, MA). Ten microliters of tryptic digests were injected using the partial loop injection mode onto a UPLC symmetry trap column (180 μm i.d. × 2 cm packed with 5 μm C18 resin; Waters) and then separated by RP-HPLC on a column (75 μm i.d.) packed in-house with 15 cm of Magic C18 3 μm reversedphase resin (Michrom Bioresources, Auburn, CA). Chromatography was performed with solvent A (Milli-Q [Millipore, Billerica, MA] water with 2% acetonitrile and 0.1% formic acid) and solvent B (90% acetonitrile with 0.1% formic acid). Peptides were eluted at 300 nL/min for 5% B for 3 min, 5− 10% B over 5 min, 10−15% B over 27 min, 15−40% B over 60 min, 40−80% B over 1 min, and 80% B for 5 min before returning to 5% B over 20 min. Ten fmol/μL of 15 heavy isotope-labeled peptides (Pierce Retention Time Calibration Mixture, USA) with elution times spanning the whole solvent gradient were spiked into the sample to evaluate the performance of HPLC, the reverse-phase column, and mass spectrometer, and facilitate the correlation of relative retention times between real sample run and standard peptides runs. MRM data were acquired with an ion spray voltage of 2,500 V and curtain gas of 25 p.s.i. The declustering potential value was predicted by Skyline. The pause between MS was 5 ms. MRM transitions were monitored using unit resolution in both Q1 and Q3 quadrupoles to maximize specificity. The scheduled MRM was performed with 5-min retention time windows and an instrument cycle time of 10 s. Data analyses were performed using Skyline (version 1.1). The MRM data has been deposited in the PeptideAtlas (PASSEL component) with the data set identifier PXD000648.

RESULTS AND DISCUSSION

Development of MRM Assays

Here, we developed a general MRM workflow, as shown in Figure 1, to detect and screen missing proteins in tissue samples. The targeted missing proteins being validated in this study were selected from the human liver proteome (HLP) data set2 accomplished by the Chinese Human Liver Proteome Project (CNHLPP). In the HLP data set, 6788 proteins

Figure 1. Targeted proteome workflow for the screening of missing proteins. Missing proteins were selected from the one-hit proteins in the CNHLPP data set. Crude peptides were ordered to generate the standard peptide spectrum library and develop MRM assays. Proteins were extracted from liver tissue samples in lysis A-H and separated in SDS-PAGE. Proteins in lysis A-H with the same molecular weight were mixed together. The mixed sample and lysis D sample were digested in gel and then subjected to LC-MRM analysis.

Accurate Inclusion Mass Screening for Targeted Peptides

AIMS were performed on a ThermoFisher Scientific Orbitrap mass spectrometer coupled to an Eksigent nano 1D plus fitted with a 15 cm × 75 μm column (C18, 3 μm, Thermo Scientific). 1971

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

Figure 2. Optimization of the CE value and LC gradient for good sensitivity. Panels A−C show an example of CE optimization for the peptide LTPVSAQFQDIEGK; each colored line plots the total ion current for the sum of nine transitions under CE value (A); the histograms show the peak area in different CE values for the product ion of y4+ (B) and y122+ (C). (D) Plot of the growth percentage of the peak area after CE optimization against the total number of parent ions. (E) Plot of the number of concurrent transitions that are scheduled to be observed for the same time windows for the ±1 (black curve) and ±5 min (red) detection windows within a 120 min LC gradient (blue).

to their weak signals of 185 targets. This low successful rate in AIMS suggests that even if these missing compounds were indeed expressed in the human liver, they could be at relatively low abundance. Considering the extremely low abundance, we used synthetic crude peptides to develop MRM assays.25 In the present work, 185 targeted peptides were ordered in unpurified form and analyzed with Orbitrap. We identified eventually all of the peptides with probability ≥0.9. On the basis of these standard MS/MS spectra, we selected the transitions to optimize the MRM methods for all of the 185 targeted peptides. As a result, we can conclude that the synthetic crude peptides will be likely more suitable to succeed in extreme conditions, such as low abundant proteins or missing proteins, although the use of MS/MS spectra in a real sample like AIMS does not need standard peptides if signal intensities were fairly good. To yield high sensitivity for the detection of these missing proteins, transitions and collision energy (CE) should be optimized.26 Most software designed for MRM experiments such as Skyline22 will give a predicted optimal CE for each parent ion. In Skyline, the default collision energies used for the 4000 QTRAP instrument were calculated according to the formulas CE = 0.057 × (precursor m/z) − 4.265 and CE = 0.031 × (precursor m/z) + 7.082, for doubly and triply charged precursor ions, respectively. Nine to 18 transitions for each peptide (1104 transitions in total) were measured in 11 different CE values (five steps on either side of the value predicted by the default formulas, with the step size set to 1 V). Finally, the most intense 6 or 5 transitions with corresponding optimal CE were selected for each peptide. A single set of measurements for the CE optimization chromatogram acquired for the doubly charged peptide LTPVSAQFQDIEGK is shown in Figure 2A−C. Each colored line represents the total ion current for the sum of nine transitions selected for 11 different voltages of collision energy. The red line represents the default

corresponding to the concentration range of 0.1 fmol−1 nmol proteins per mg liver sample were reported as the core data set, with at least two peptide matches at 95% confidence. Besides, the 6163 one-hit wonders seemed as a weak or untrustworthy identification, among which 3105 one-hit peptides (http:// hlpic.hupo.org.cn/dblep/main.jsf) were unique to one protein only among the IPI Human v3.07 database. We selected targeted proteins from these 3105 one-hit wonders according to the following criteria: the hit peptides have a length of 6−20 amino acids, no glutamin on the N-terminal, and are unique to one gene checked by the manual blast in Uniprot. Peptides meeting the above criteria are sorted in descending order according to their number of counts. On the list, the top 185 peptides corresponding to 185 genes were taken as our final targets (transcriptome information for these 185 targeted proteins are list in Table S5, Supporting Information). Here for practical cost reasons, no more peptides other than the hit peptides were selected as targeted peptides. Crude peptides for these 185 targets were ordered (ChinaPeptides Co.) and analyzed with LC-Orbitrap. Finally, the well-matched MS/MS spectra were obtained for 185 target peptides. The standard spectra library for these target peptides was generated by Spectra ST.21 We employed two methods at the start point to develop MRM assays: accurate inclusion mass screening of the liver sample and synthetic crude peptide library. The development of the MRM method usually begins with well-matched MS/MS spectra of the target peptide which can be obtained from the identification spectrum in the real sample24 or standard peptide.25 Because we were lacking high-quality spectra of targeted peptides in the real sample, accurate inclusion mass screening (AIMS) of the liver sample on Obitrap was likely employed to generate MS/MS data. Unfortunately, 14 of 185 targeted peptides were identified in the SDS-PAGE-LC/AIMS experiment (Table S3 in Supporting Information), due perhaps 1972

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

multiple product ions for a given peptide has been attempted to minimize the incidents of m/z redundancy. LC-retention time determined from synthetic standards or predicted would also be useful in the improvement of specificity.30 The concept of unique ion signatures (UIS), which means that they do not occur in any other peptides all at once, was introduced into the MRM-assay design recently.29,31 UIS of three transitions could suffice to conclusively identify 85% of all human peptides by theoretical calculation.31 A computational tool, mProphet,32 could provide an automated system that computes accurate error rates for MRM data based on the statistical model.30 In mProphet, multiple dimensions of information have been integrated in a probabilistic scoring model, like the coelution, peak shape similarity, intensity, and correlation of relative intensities between peak groups in MRM data. Combining all of these relevant features in MRM data could maximize the specificity and sensitivity. In this work, we manually checked the MRM result data and would accept data only when the MRM chromatogram fulfilled all of the following conditions: (a) at least five out of six monitored transitions have a S/N > 3; (b) different transition peaks should be coeluting; (c) the shape and relative intensity of transitions should be similar to those of the synthetic peptide; and (d) the coeluting transitions should contain at least one UIS (calculated by MRM Collider29 human tryptic background with one missed cleavage). Figure 3 shows two examples of interfering signals. Five of the six predefined transitions for peptides GMVVNSKEISDAVGQSK were coeluted in the preset time window. However, the relative intensities of the five coeluted transitions were not similar to those of the synthetic peptide at all. Besides, the 5 transitions did not contain any UIS. For the peptides GAGVNGVTAGAGGR, all of the six predefined transitions were detected but without expected relative intensities and shapes. Two coeluted transitions (572.3 → 688.4 (y8) and 572.3 → 417.2 (y5)) are even UIS. Both of them partially meet the above requirement and seem to be false detections. These interfering signals show the great complexity of the human liver sample. We believe the results passing the rigorous requirements will give a strong evidence for the expression of these one-hit proteins in the human liver.

CE (39.4 V) predicted by the equation. The histograms (Figure 2B−C) clearly tell that the optimal CE is different for different fragment ions produced from the same parent ion. The optimal CE is 35.4 V for the transition 766.9 (m/z for MS1) > 659.8 (m/z for MS2) and 44.4 V for the transition 766.9 (m/z for MS1) > 446.3 (m/z for MS2). Figure 2D shows the frequency of maximum intensity gain. Most of the parent ions display 10%−40% increments in peak area, while 27 parent ions get less than 10% increasing peak area. The results of these analyses suggest that optimization of CE is advisable, especially when high sensitivity is needed. In total, 1110 transitions (Table S1, Supporting Information) with optimal CE for 185 targeted peptides were selected. Dwell time for each transition was another factor which affects significantly the sensitivity of MRM assays. The dwell time is defined as a particular time assigned to monitor each transition within one duty cycle. To achieve high sensitivity, the dwell time needs to be long enough to accumulate a sufficient signal.17,27,28 Practical dwell-time settings range between 10 ms for good sensitivity and 100 ms for excellent sensitivity.17 WolfYadlin et al. reported to apply a dwell time of 100 ms for a limit of quantification of below 3 amol peptide loaded on column.27 However, the longer the dwell time, the less the transition can be monitored at the same time under a fixed MS speed. In other words, the achievement of high sensitivity by a long dwell time occurs at the cost of throughput. To balance throughput against sensitivity, we employed scheduled MRM method, in which the targeted peptide was monitored at the predetermined retention time within a tested ±1 or ±5 min window. In our experiment, the peak width of most peptides was approximately 60−90 s at baseline under a flow rate of 300 nL/min and in a 120 min LC gradient. Cycle time was set at 10 s to acquire at least six data points across one chromatographic elution profile. Under this condition, the LC gradient was optimized using synthetic crude peptides to ensure that the number of transitions in one cycle was less than 100 for the ±5 min window and less than 20 for the ±1 min window. Then at either the ±5 or ±1 min window, more than 100 ms of the dwell time could be obtained for each transition (Figure 2E). In the following investigation, we used the ±5 min wide window to alleviate the possible chance for escaped targeted peptides. Finally, the 185 targeted peptides were split into 4 scheduled MRM experiments. All of these efforts guaranteed an excellent sensitivity and a moderate throughput for the next MRM experiment of the real sample.

MRM Analysis of the Liver Sample

Considering those missing proteins are inferred at low abundance, here SDS-PAGE fractionation prior to LC-MRM analysis was performed to enrich relatively the targeted proteins according to the molecular weight. MRM definitely has advantages in sensitivity over other MS-based techniques by scanning targeted m/z signals of parent ions and product ions. However, current MRM techniques are still challenged by detection and quantification of low abundant proteins (e.g., the proteins which present at 10 ng/mL or lower levels in blood plasma16). There are usually two strategies to improve the limit of detection: advancing the MS instrumentation or interface technologies33,34 to improve the performance of the MS instrument and different sample processing strategies to reduce relatively the complexity of sample. In a complex biological sample such as plasma or CSF, depletion of highly and moderately abundant proteins is one of the most commonly used methods to enhance the detection sensitivity of lowabundant proteins.35,36 Stable isotope standards and capture by antipeptide antibodies (SISCAPA) employs immune affinity enrichment of targeted peptides prior to MRM analysis.10,37

Specificity of MRM Assays

We consider that the MRM specificity should be strictly important if no previous verification data were given to those targeted missing proteins selected. The two consecutive mass filters on the triple quadrupole can filter most of the matrix and therefore bring about high selectivity and specificity for the MRM assay. When designing MRM assays, unique peptides selected for targeted proteins would appear to be inherently specific. However, because the triple quadrupole filters signals according to the predefined precursor and product ion m/z values, peptides with interfering transitions similar to those of the targeted peptides may cause ambiguities in MRM results. Redundancy of the precursor and product ion m/z values in the matrix will cause ambiguity29 so that the phenomenon of interference will be prevalent in MRM experiments especially in the context of a complex mixture. There have been several studies to find how to reduce the interference. Targeting 1973

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

M urea, 2 M thiourea, 2% (w/v) CHAPS). Seven proteins (O43315, Q92621, Q8IU81, Q13325, P15559, O15372, and Q8N0Z2) were only detected in the mixture sample (Table S2, Supporting Information). The results indicated that the protein might have a preference for different lysis buffers. We mapped these targeted proteins to the latest protein identification data set of the human liver (Chinese Chromosome Proteome Data set (CCPD)1.0),43 which included 9346 proteins published in the earlier special issue of Chromosome-Centric Human Proteome Project. As shown in Figure 4B, 21 targeted proteins were identified in the latest

Figure 3. Interference of MRM analysis. (A,) MRM signals for the targeted peptide GMVVNSKEISDAVGQSK (A) and GAGVNGVTAGAGGR (B). Reference represents MRM signals obtained from a synthetic peptide. Sample represents MRM signals obtained from a liver sample. Although the predetermined transitions were detected and coeluted in the MRM analysis of the real sample, the inconsistence of the shape and relative intensity with the reference MRM spectrum excluded the identification of these two targeted peptides. (C) MRM signals for the targeted peptide DFIIQTGDPTGTGR, an example of successful MRM detection. The MRM chromatogram in the real sample was similar to the reference chromatogram.

Other than these immune affinity methods, combined multidimensional fractionation techniques will dramatically reduce sample complexity and improve the limit of detection, and will be economical in large scale targeted proteomics studies. However, one more dimension of fractionation will consume the exponential increase of the amount of consuming samples and machine hour at the same time. Meanwhile, MRM has a relative low throughput compared with traditional shot-gun technologies. Therefore, only one more dimensional fractionation is advisable before LC-MRM, such as SCX,38 1DSDSPAGE,12,39−42 and IEF. Furthermore, considering that the different lysis buffers used to extract proteins will impact the result of observation, proteins were lysed by buffers A−H (Figure 1, protein in lysis A named as sample A and so on). After electrophoresis, the gel was sliced, and proteins in samples A−H that have the same molecular weight were mixed and digested together. Sample D (which was used in HLPP experiments) independently underwent the paralleled separation and in-gel digestion. Then both the mixed sample and sample D were subjected to LC/MRM analysis. In all, 57 peptides were detected (corresponding to 57 proteins) out of 185 targeted peptides (corresponding to 185 proteins). The MRM chromatogram of the real sample and standard peptides for these 57 detected peptides are shown in Supporting Information (Figure S1). Fifty proteins were detected in the sample lysed by buffer D (7

Figure 4. General description of MRM analysis results. (A) Fifty-seven targeted proteins were detected in total, 7 proteins were only detected in the mixture of protein lysis, and 16 proteins were only detected in the lysis D sample. (B) Map of the targeted proteins in the latest HPP data set. Twenty-one targeted proteins have been identified in the latest HPP data set, and all of them were confirmed with MRM in this work. The rest 164 targeted proteins have not been identified in human liver, and 36 of them were confirmed with MRM for the first time in human liver. (C) Standard linear response curves for MRM label free quantitative analysis.

HLPP data set, and all of them were confirmed with MRM in this work. The other 164 targeted proteins had no information in this newest HPP data set, among which we detected 36 proteins with MRM. All these results demonstrate that an MRM-based targeted proteomics approach could confirm and complement the shot-gun data. The results from AIMS and MRM have been compared. Twelve of 14 targeted peptides detected in AIMS were also identified in MRM. In other words, 12 peptides could be detected by both AIMS and MRM. We manually checked the MS1 intensity at the peak of the chromatogram for these 14 detected peptides in AIMS. Their absolute intensity was between 1 × 104 and 8 × 106 for the MS1 signal and between 1 1974

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

Table 1. 57 Targeted Missing Proteins Detected in SDS-PAGE-MRM Experimentsa

a

Uniprot

peptide

PeptideAtlas _presence level

ProteinAtlas_ protein evidence

ProteinAtlas_ protein evidence_liver

chromosome

Q13325 Q9Y6K0 P10301 P41227 Q15363 Q7Z3E5 Q15388 Q9H019 P52594 O14957 Q14061 O14907 Q9NP77 P61803 Q05519 A6NEN9 Q8IVP5 Q7Z570 Q9H173 P15559 P49207 Q8IU81 Q9Y5U9 O43315 Q96G23 P13945 Q92562 P59768 P18077 Q9P0S9 Q8N0U8 Q9NPP6 Q8IUR0 O75348 P09669 Q8N0Z2 Q6P4F2 O60262 Q6DKI1 Q5TGS1 O15127 Q96IX5 O14561 O15372 Q8WUA2 O14896 Q6GMV3 P61758 Q71UM5 Q8WUH6 Q9NZ01 P61513 Q92979 Q9BQ15 Q92621 Q8NBP5 Q6ZTJ7

IDPENAEFLTALCELR LFQLPTPPLSR LNVDEAFEQLVR DLSEVSETTESTDVK LEEMINELAVAMTAVK ELFQDSWTPELK IVSAQSLAEDDVE TTELQDELSHLR APVGSVVSVPSQSSASSDK LILDWVPYINGK PGLVDSNPAPPESQEK VSEGGPAEIAGLQIGDK VYDQVVEDLNSR FLEEYLSSTPQR ALIVVPYAEGVIPDEAK TNTIDLCEQTGK AAPEINNLIEEATEFIK ENTDYPVK LDINTNTYTSQDLK FGLSVGHHLGK AFLIEEQK LPLPSPALEYTLGSR NIGWGTDQGIGGFGEEPGIK LFTALAGWGFEVFR SRPLANGHPILNNNHR YLAVTNPLRYGALVTK YVWNGELLDIIK EDPLLTPVPASENPFR DETEFYLGK FMPAGLIAGASLLMVAK GFGLLGSIFGK VEDTAVYYCAR EPLINTYISVPK EEAQAEIEQYR AGIFQSVK LPEGHGDGQSSEK LGCQIVLTPELEGAEFTLPK NDPLLVGVPASENPFK FGVICLEDLIHEIAFPGK GAEQPSGFRSCLPGVSQLLR TGASFQQAQEEFSQGIFSSR YFNSYTLTGR LMCPQEIVDYIADK LFMAQALQEYNN DFIIQTGDPTGTGR YQEGVDDPDPAK VVLEAPDETTLK NLDSLEEDLDFLR DLLHPSLEEEK GGVSAVAGGVTAVGSAVVNK HYEVEILDAK TVAGGAWTYNTTSAVTVK FCGLMVQLLHK NLNLIFIVLETGR VHNLITDFLALMPMK TGTEAEAADSGAVGAR ILFVNNLL

canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical not observed canonical not observed canonical canonical canonical canonical canonical not observed canonical not observed canonical canonical canonical canonical canonical canonical canonical canonical canonical not observed canonical canonical canonical not observed canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical canonical not observed

medium medium medium high high high high high high RNA high medium medium medium high RNA medium medium high high medium high medium RNA high RNA medium high medium medium medium n/a medium medium high low medium medium medium RNA high medium medium high high medium medium high medium medium high medium n/a high medium RNA n/a

RNA RNA low RNA medium low medium low low RNA high RNA RNA medium medium RNA medium high medium not detected medium medium high RNA medium n/a medium RNA RNA RNA RNA n/a RNA low high high low high low medium high RNA RNA medium low RNA RNA medium RNA RNA medium RNA n/a RNA RNA medium n/a

10 1 19 X 12 2 1 1 2 19 3 17 1 14 1 X X 2 5 16 4 19 18 15 1 8 6 14 3 6 7 n/a 19 9 8 8 19 19 6 1 15 10 16 8 6 1 2 X 15 12 19 2 12 12 7 2 n/a

quantitation (pmol/μg)

235

5 75 30

2

The absolute quantity of five representative targeted proteins was calculated according to the linear response curves in MRM label free experiments.

× 102 and 7 × 103 for MS/MS. The MRM signal for these peptides detected by both was between 300 and 7 × 104 (Table

S3, Supporting Information). As for the rest of the 171 unidentified targeted peptides in AIMS, all of them have been 1975

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

Figure 5. Result of MRM analysis. (A) The proteolytic peptides for protein O14957 and their predicted suitability score given by PeptideAtlas. (B) MRM chromatogram for peptides LILDWVPYINGK. (C−I) MRM chromatograms for the seven detected proteins which had no MS evidence before in PeptideAtlas.

work, the unique peptide for zinc finger protein 804A was detected for the first time, and the absolute concentration was calculated as 75 pmol per microgram total protein. The developed MRM assay should be used for the future study of zinc finger protein 804A. Afterward, we should ask why some missing proteins, if expressed, have only one-hit peptide detected? One possible reason for one-hit wonders is that the protein has minimal proteotypic peptides, experimentally observable peptides that uniquely identify a specific protein or protein isoform.44 As shown in Figure 5A,B, O14957 (cytochrome b-c1 complex subunit 10) has only two proteolytic peptides (NWVPTAYTWGAVGAVGLVWATDWR and LILDWVPYINGK). The predicted suitability score for peptide NWVPTAYTWGAVGAVGLVWATDWR is much lower than the peptide LILDWVPYINGK. Thus, this protein is much more likely to have single peptide LILDWVPYINGK to be identified. For another possible reason, these single-hit proteins are at low abundance, thus having a greater randomness during the sample preparation and the subsequent mass spectrometry analysis. On the basis of the MRM label free data, we can know that the abundance for these five missing was between pmol/μg to subfmol/μg. We also queried all of these 57 detected proteins in PeptideAtlas (Table S4, Supporting Information). Fifty proteins have MS information and are of the minimally redundant set in the newest PeptideAtlas Build (Human 201207, noted as canonical). The rest of the 7 proteins have no

observed in MS1 in AIMS, with no convincing MS/MS data. The MS/MS spectra in AIMS and the MRM chromatogram (if have) were compared for each peptide in Figure S2 (Supporting Information). The MRM chromatogram clearly has less interference and high S/N ratio, which may be attributed to two consecutive mass filters. The identified MS/ MS spectrum was noisy and uncertain by contrast. Therefore, we suggest that MRM will be better for screening and verification of missing proteins Label Free MRM Quantification

We next estimated the expression level of these missing proteins in general and picked out 5 representative proteins. Pure peptides were used to plot the linear response curves (Figure 4C). The absolute amount of the peptides was estimated according to these standard curves (Table 1). Since these standard curves have no background matrix, these protein amounts were just approximate values due to matrix effects and ion suppression. The lowest MRM signal detected was corresponding to pmol per microgram total proteins. Take the zinc finger protein 804A (Q7Z570) as an example; it is indistinguishable from A4D1E1 in PepetideAtlas. There are two identified peptides both mapped to zinc finger protein 804A (Q7Z570) and zinc finger protein 804B (A4D1E1) in PepetideAtlas, but none of the unique peptides mapped to zinc finger protein 804A. Zinc finger protein 804A is a wellestablished genome-wide significant marker for schizophrenia. In all of the previous studies of zinc finger protein 804A, only the genomic method and antibody have been used. In our 1976

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

■

previous MS-based information (Figure 5C−H). From the number of observations given by PeptideAtlas, we can infer that other than the immunoglobulin heavy chain variant (Q9NPP6), all of the detected proteins are at low abundance. Q9NPP6 belongs to the IgG family and shares many peptides with other high abundant IgG proteins. We checked manually the unique peptide in Q9NPP6 and found them at low abundance (e.g., VEDTAVYYCAR n.obs = 647). As the proteins of the IgG family mainly secreted outside of the cell, it did give low intensity signals in the liver tissue sample. However, we cannot exclude the other possibility that IgGs was detected from the blood rather than the liver tissue. These results were consistent with the supposition that these missing proteins are at low abundance. Moreover, according to the IPA database, which is the largest expertly curated repository of biological and functional information, upregulation of human AQP9 mRNA is found to be associated with rheumatoid arthritis, renal-cell carcinoma, and psoriasis. ADRB3 protein has been reported to be associated with many diseases. It is the target for many clinical trial drugs and approved drugs. For example, Olanzapine, an antagonist of human ADRB3 protein, is in phase IV clinical trials for the treatment of Alzheimer’s disease in humans. Both AQP9 and ADRB3 were detected using a mass-spectrometry-based method for the first time. Finally, we consulted the Human Protein Atlas (HPA),45 which is the largest protein database based on antibody information, for some verification data from other techniques. In the HPA, 53 detected proteins have RNA expression in the human liver. On the basis of the immunoassay, eight proteins have low protein evidence, 15 proteins have medium protein evidence, and 7 proteins have high protein evidence in the human liver (Table 1). In general, most of our detected proteins have had expression evidence based on immunoassays.

■

CONCLUSION

■

ASSOCIATED CONTENT

Article

AUTHOR INFORMATION

Corresponding Author

*Tel: 086-021-65642009. Fax: 086-021-54237961. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was supported by S973/863 projects of MOST (2010CB912700, 2012CB910602, and 2012AA020203), NSF of China (21025519, 31070732, and 21105015), and Shanghai Projects (11XD1400800, Eastern Scholar and B109).

■

REFERENCES

(1) Nilsson, T.; Mann, M.; Aebersold, R.; Yates, J. R.; Bairoch, A.; Bergeron, J. J. M. Mass spectrometry in high-throughput proteomics: ready for the big time. Nat. Methods 2010, 7 (9), 681−685. (2) Chinese Human Liver Proteome Profiling Consortium. First insight into the human liver proteome from PROTEOMESKYLIVERHu 1.0, a publicly available database. J. Proteome Res. 2009, 9 (1), 79−94. (3) Zhang, Y.; Yan, G.; Zhai, L.; Xu, S.; Shen, H.; Yao, J.; Wu, F.; Xie, L.; Tang, H.; Yu, H.; Liu, M.; Yang, P.; Xu, P.; Zhang, C.; Li, L.; Chang, C.; Li, N.; Wu, S.; Zhu, Y.; Wang, Q.; Wen, B.; Lin, L.; Wang, Y.; Zheng, G.; Zhou, L.; Lu, H.; Liu, S.; He, F.; Zhong, F. Proteome atlas of human chromosome 8 and its multiple 8p deficiencies in tumorigenesis of the stomach, colon, and liver. J. Proteome Res. 2012, 12 (1), 81−88. (4) Kwon, K.-H.; Kim, J. Y.; Kim, S.-Y.; Min, H. K.; Lee, H.-J.; Ji, I. J.; Kang, T.; Park, G. W.; An, H. J.; Lee, B.; Ravid, R.; Ferrer, I.; Chung, C. K.; Paik, Y.-K.; Hancock, W. S.; Park, Y. M.; Yoo, J. S. Chromosome 11-centric human proteome analysis of human brain hippocampus tissue. J. Proteome Res. 2013, 12 (1), 97−105. (5) Yamamoto, T.; Nakayama, K.; Hirano, H.; Tomonaga, T.; Ishihama, Y.; Yamada, T.; Kondo, T.; Kodera, Y.; Sato, Y.; Araki, N.; Mamitsuka, H.; Goshima, N. Integrated view of the human chromosome x-centric proteome project. J. Proteome Res. 2012, 12 (1), 58−61. (6) Paik, Y.-K.; Omenn, G. S.; Uhlen, M.; Hanash, S.; Marko-Varga, G.; Aebersold, R.; Bairoch, A.; Yamamoto, T.; Legrain, P.; Lee, H.-J.; Na, K.; Jeong, S.-K.; He, F.; Binz, P.-A.; Nishimura, T.; Keown, P.; Baker, M. S.; Yoo, J. S.; Garin, J.; Archakov, A.; Bergeron, J.; Salekdeh, G. H.; Hancock, W. S. Standard guidelines for the ChromosomeCentric Human Proteome Project. J. Proteome Res. 2012, 11 (4), 2005−2013. (7) Shiromizu, T.; Adachi, J.; Watanabe, S.; Murakami, T.; Kuga, T.; Muraoka, S.; Tomonaga, T. Identification of missing proteins in the neXtProt database and unregistered phosphopeptides in the PhosphoSitePlus database as part of the Chromosome-Centric Human Proteome Project. J. Proteome Res. 2013, 12 (6), 2414−2421. (8) Stergachis, A. B.; MacLean, B.; Lee, K.; Stamatoyannopoulos, J. A.; MacCoss, M. J. Rapid empirical discovery of optimal peptides for targeted proteomics. Nat. Methods 2011, 8 (12), 1041−1043. (9) Boersema, P.; Mohammed, S.; Heck, A. R. Hydrophilic interaction liquid chromatography (HILIC) in proteomics. Anal. Bioanal. Chem. 2008, 391 (1), 151−159. (10) Leigh Anderson, N.; Anderson, N. G.; Haines, L. R.; Hardie, D. B.; Olafson, R. W.; Pearson, T. W. Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies. J. Proteome Res. 2004, 3 (2), 235−244. (11) Schäfer, A.; von Toerne, C.; Becker, S.; Sarioglu, H.; Neschen, S.; Kahle, M.; Hauck, S. M.; Ueffing, M. Two-dimensional peptide separation improving sensitivity of selected reaction monitoring-based quantitative proteomics in mouse liver tissue: comparing off-gel electrophoresis and strong cation exchange chromatography. Anal. Chem. 2012, 84 (20), 8853−8862.

Although numerous studies have identified thousands of proteins in various samples, few of them have paid much attention to the rest of the weakly identified or missing proteins, due to the unavailability of antibodies with high quality or the lack of other effective methods. Since C-HPP was initiated in 2012, looking for these missing or weakly characterized proteins has been on the agenda across the world. However, like the marginal utility in economics, when the number of protein identification is increasing to near the margin, discovery of new proteins will be much more laborious. As the first effort, this study showed that the MRM-based targeted proteomics strategy can contribute to gene-centric proteomics. In the future, we would apply this method for validating the missing proteins in the chromosome proteome.

S Supporting Information *

MRM chromatogram of the real sample and standard peptides for these 57 detected targeted peptides; comparison of MS/MS spectra and MRM chromatogram for 12 targeted peptides; and schematic to show how the gel was excised exactly. This material is available free of charge via the Internet at http:// pubs.acs.org. 1977

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978

Journal of Proteome Research

Article

(12) Ang, C.-S.; Nice, E. C. Targeted in-gel mrm: a hypothesis driven approach for colorectal cancer biomarker discovery in human feces. J. Proteome Res. 2010, 9 (9), 4346−4355. (13) Krisp, C.; McKay, M. J.; Wolters, D. A.; Molloy, M. P. Multidimensional protein identification technology-selected reaction monitoring improving detection and quantification for protein biomarker studies. Anal. Chem. 2012, 84 (3), 1592−1600. (14) Claassen, M. Inference and validation of protein identifications. Mol. Cell. Proteomics 2012, 11 (11), 1097−1104. (15) Claassen, M.; Reiter, L.; Hengartner, M. O.; Buhmann, J. M.; Aebersold, R. Generic comparison of protein inference engines. Mol. Cell. Proteomics 2012, 11 (4), O110.007088. (16) Pan, S.; Aebersold, R.; Chen, R.; Rush, J.; Goodlett, D. R.; McIntosh, M. W.; Zhang, J.; Brentnall, T. A. Mass spectrometry based targeted protein quantification: methods and applications. J. Proteome Res. 2009, 8 (2), 787−797. (17) Lange, V.; Picotti, P.; Domon, B.; Aebersold, R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 2008, 4, 222. (18) Gupta, N.; Pevzner, P. A. False discovery rates of protein identifications: a strike against the two-peptide rule. J. Proteome Res. 2009, 8 (9), 4173−4181. (19) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. A Statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75 (17), 4646−4658. (20) Spivak, M.; Tomazela, D.; Weston, J.; MacCoss, M. J.; Noble, W. S. Direct maximization of protein identifications from tandem mass spectra. Mol. Cell. Proteomics 2012, 11 (2), M111.012161. (21) Lam, H.; Deutsch, E. W.; Eddes, J. S.; Eng, J. K.; King, N.; Stein, S. E.; Aebersold, R. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 2007, 7 (5), 655−667. (22) MacLean, B.; Tomazela, D. M.; Shulman, N.; Chambers, M.; Finney, G. L.; Frewen, B.; Kern, R.; Tabb, D. L.; Liebler, D. C.; MacCoss, M. J. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26 (7), 966−968. (23) Bereman, M. S.; MacLean, B.; Tomazela, D. M.; Liebler, D. C.; MacCoss, M. J. The development of selected reaction monitoring methods for targeted proteomics via empirical refinement. Proteomics 2012, 12 (8), 1134−1141. (24) Jaffe, J. D.; Keshishian, H.; Chang, B.; Addona, T. A.; Gillette, M. A.; Carr, S. A. Accurate inclusion mass screening. Mol. Cell. Proteomics 2008, 7 (10), 1952−1962. (25) Picotti, P.; Rinner, O.; Stallmach, R.; Dautel, F.; Farrah, T.; Domon, B.; Wenschuh, H.; Aebersold, R. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 2010, 7 (1), 43−46. (26) MacLean, B.; Tomazela, D. M.; Abbatiello, S. E.; Zhang, S.; Whiteaker, J. R.; Paulovich, A. G.; Carr, S. A.; MacCoss, M. J. Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry. Anal. Chem. 2010, 82 (24), 10116−10124. (27) Wolf-Yadlin, A.; Hautaniemi, S.; Lauffenburger, D. A.; White, F. M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl. Acad. Sci. U.S.A. 2007, 104 (14), 5860−5865. (28) Kiyonami, R.; Schoen, A.; Prakash, A.; Peterman, S.; Zabrouskov, V.; Picotti, P.; Aebersold, R.; Huhmer, A.; Domon, B. Increased selectivity, analytical precision, and throughput in targeted proteomics. Mol. Cell. Proteomics 2010, 10 (2), M110.002931. (29) Rost, H. L.; Malmstrom, L.; Aebersold, R. A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol. Cell. Proteomics 2012, 11 (8), 540−9. (30) Sherman, J.; McKay, M. J.; Ashman, K.; Molloy, M. P. How specific is my SRM?: The issue of precursor and product ion redundancy. Proteomics 2009, 9 (5), 1120−1123.

(31) Sherman, J.; McKay, M. J.; Ashman, K.; Molloy, M. P. Unique ion signature mass spectrometry, a deterministic method to assign peptide identity. Mol. Cell. Proteomics 2009, 8 (9), 2051−2062. (32) Reiter, L.; Rinner, O.; Picotti, P.; Huttenhain, R.; Beck, M.; Brusniak, M.-Y.; Hengartner, M. O.; Aebersold, R. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 2011, 8, 430−435. (33) Enjalbert, Q.; Simon, R.; Salvador, A.; Antoine, R.; Redon, S.; Ayhan, M. M.; Darbour, F.; Chambert, S.; Bretonnière, Y.; Dugourd, P.; Lemoine, J. Photo-SRM: laser-induced dissociation improves detection selectivity of selected reaction monitoring mode. Rapid Commun. Mass. Spectrom. 2011, 25 (22), 3375−3381. (34) Hossain, M.; Kaleta, D. T.; Robinson, E. W.; Liu, T.; Zhao, R.; Page, J. S.; Kelly, R. T.; Moore, R. J.; Tang, K.; Camp, D. G.; Qian, W.J.; Smith, R. D. Enhanced sensitivity for selected reaction monitoring mass spectrometry-based targeted proteomics using a dual stage electrodynamic ion funnel interface. Mol. Cell. Proteomics 2011, 10 (2), M000062-MCP20. (35) Tang, H.-Y.; Beer, L. A.; Chang-Wong, T.; Hammond, R.; Gimotty, P.; Coukos, G.; Speicher, D. W. A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer. J. Proteome Res. 2012, 11 (2), 678−691. (36) Wehr, A. Y.; Hwang, W.-T.; Blair, I. A.; Yu, K. H. Relative quantification of serum proteins from pancreatic ductal adenocarcinoma patients by stable isotope dilution liquid chromatography-mass spectrometry. J. Proteome Res. 2012, 11 (3), 1749−1758. (37) Whiteaker, J. R.; Zhao, L.; Anderson, L.; Paulovich, A. G. An automated and multiplexed method for high throughput peptide immunoaffinity enrichment and multiple reaction monitoring mass spectrometry-based quantification of protein biomarkers. Mol. Cell. Proteomics 2010, 9 (1), 184−196. (38) Muraoka, S.; Kume, H.; Watanabe, S.; Adachi, J.; Kuwano, M.; Sato, M.; Kawasaki, N.; Kodera, Y.; Ishitobi, M.; Inaji, H.; Miyamoto, Y.; Kato, K.; Tomonaga, T. Strategy for SRM-based verification of biomarker candidates discovered by iTRAQ method in limited breast cancer tissue samples. J. Proteome Res. 2012, 11 (8), 4201−4210. (39) Xiang, Y.; Remily-Wood, E. R.; Oliveira, V.; Yarde, D.; He, L.; Cheng, J. Q.; Mathews, L.; Boucher, K.; Cubitt, C.; Perez, L.; Gauthier, T. J.; Eschrich, S. A.; Shain, K. H.; Dalton, W. S.; Hazlehurst, L.; Koomen, J. M. Monitoring a nuclear factor-κB signature of drug resistance in multiple myeloma. Mol. Cell. Proteomics 2011, 8 (16), M110.005520. (40) Shevchenko, A.; Tomas, H.; Havlis, J.; Olsen, J. V.; Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protocols 2007, 1 (6), 2856−2860. (41) Barnidge, D. R.; Goodmanson, M. K.; Klee, G. G.; Muddiman, D. C. Absolute quantification of the model biomarker prostate-specific antigen in serum by LC−MS/MS using protein cleavage and isotope dilution mass spectrometry. J. Proteome Res. 2004, 3 (3), 644−652. (42) Kirkpatrick, D. S.; Gerber, S. A.; Gygi, S. P. The absolute quantification strategy: a general procedure for the quantification of proteins and post-translational modifications. Methods 2005, 35, 265− 273. (43) Wu, S.; Li, N.; Ma, J.; Shen, H.; Jiang, D.; Chang, C.; Zhang, C.; Li, L.; Zhang, H.; Jiang, J.; Xu, Z.; Ping, L.; Chen, T.; Zhang, W.; Zhang, T.; Xing, X.; Yi, T.; Li, Y.; Fan, F.; Li, X.; Zhong, F.; Wang, Q.; Zhang, Y.; Wen, B.; Yan, G.; Lin, L.; Yao, J.; Lin, Z.; Wu, F.; Xie, L.; Yu, H.; Liu, M.; Lu, H.; Mu, H.; Li, D.; Zhu, W.; Zhen, B.; Qian, X.; Qin, J.; Liu, S.; Yang, P.; Zhu, Y.; Xu, P.; He, F. First proteomic exploration of protein-encoding genes on chromosome 1 in human liver, stomach, and colon. J. Proteome Res. 2012, 12 (1), 67−80. (44) Kuster, B.; Schirle, M.; Mallick, P.; Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell. Biol. 2005, 6 (7), 577−583. (45) Uhlen, M.; Oksvold, P.; Fagerberg, L.; Lundberg, E.; Jonasson, K.; Forsberg, M.; Zwahlen, M.; Kampf, C.; Wester, K.; Hober, S.; Wernerus, H.; Bjorling, L.; Ponten, F. Towards a knowledge-based human protein atlas. Nat. Biotechnol. 2010, 28 (12), 1248−1250. 1978

dx.doi.org/10.1021/pr4010986 | J. Proteome Res. 2014, 13, 1969−1978