Hyperplex-MRM: A Hybrid Multiple Reaction Monitoring Method Using

Article pubs.acs.org/jpr

Hyperplex-MRM: A Hybrid Multiple Reaction Monitoring Method Using mTRAQ/iTRAQ Labeling for Multiplex Absolute Quantification of Human Colorectal Cancer Biomarker Hong-Rui Yin,†,‡ Lei Zhang,† Li-Qi Xie,† Li-Yong Huang,† Ye Xu,† San-Jun Cai,† Peng-Yuan Yang,†,‡ and Hao-Jie Lu*,†,‡ †

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

‡

S Supporting Information *

ABSTRACT: Novel biomarker verification assays are urgently required to improve the efficiency of biomarker development. Benefitting from lower development costs, multiple reaction monitoring (MRM) has been used for biomarker verification as an alternative to immunoassay. However, in general MRM analysis, only one sample can be quantified in a single experiment, which restricts its application. Here, a HyperplexMRM quantification approach, which combined mTRAQ for absolute quantification and iTRAQ for relative quantification, was developed to increase the throughput of biomarker verification. In this strategy, equal amounts of internal standard peptides were labeled with mTRAQ reagents Δ0 and Δ8, respectively, as double references, while 4-plex iTRAQ reagents were used to label four different samples as an alternative to mTRAQ Δ4. From the MRM trace and MS/MS spectrum, total amounts and relative ratios of target proteins/peptides of four samples could be acquired simultaneously. Accordingly, absolute amounts of target proteins/peptides in four different samples could be achieved in a single run. In addition, double references were used to increase the reliability of the quantification results. Using this approach, three biomarker candidates, ademosylhomocysteinase (AHCY), cathepsin D (CTSD), and lysozyme C (LYZ), were successfully quantified in colorectal cancer (CRC) tissue specimens of different stages with high accuracy, sensitivity, and reproducibility. To summarize, we demonstrated a promising quantification method for high-throughput verification of biomarker candidates. KEYWORDS: MRM, iTRAQ, mTRAQ, quantification, biomarker verification

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tryptic digested plasma,9 45 proteins were quantified with great precision and reproducibility by combining MRM technique with stable isotope-labeled standard peptides. And up to 1000 yeast peptides were quantified in one 60 min LC-MS experiment using intelligent selected reaction monitoring (iSRM) on triple quadruple mass spectrometry.10 It is the most important that the significantly shorter lead-time and reduced costs of MRM assay make it a good alternative to immunoassay in verification of candidate biomarkers. In general MRM analysis, only one sample can be analyzed in a single experiment. However, a large number of samples are needed to be examined for biomarker validation, which calls for high throughput quantification techniques. Recently, mTRAQ in triplex format has become available, which was first designed in two chemically identical versions and designed to take advantage of MRM.11 The triplex mTRAQ provides a method of performing multiplex absolute quantification.12 mTRAQ is a triplex set (Δ0, Δ4, Δ8) of nonisobaric (differing mass) amine

INTRODUCTION The discovery of novel disease related biomarkers by quantitative proteomics techniques is an exciting prospect for improving patient healthcare.1,2 The large number of candidate proteins generated by quantitative proteomics analysis were subsequently needed to be verified before applying in clinical setting.3 Immunoassay has been successfully applied in protein biomarker validation to support preclinical and clinical studies. However, the lack of commercially available reagents, high costs, and labor intensive production procedures have rendered the large majority of candidates unevaluated.4 Therefore, the development of alternate methods for target protein quantification with high accuracy, reproducibility, and high throughput is needed to access clinical translation of approved biomarkers.5 Recently, multiple reaction monitoring (MRM) technique was used to verify candidate biomarkers by quantifying the absolute amount of proteins.6−8 In MRM analysis, only specific mass to charge (m/z) values of target analytes are monitored, which increases the probability of detecting low-abundance analytes in complex samples. In addition, multiple peptides can be quantified in a single MRM analysis. For example, in a study of © 2013 American Chemical Society

Received: February 5, 2013 Published: August 5, 2013 3912

dx.doi.org/10.1021/pr4005025 | J. Proteome Res. 2013, 12, 3912−3919

Journal of Proteome Research

Article

Table 1. Transitions Used for MRM Analysis protein

peptide sequence

CTSD

VGFAEAAR (PCTSD)

LYZ

AHCY

AWVAWR (PLYZ)

VADIGLAAWGR (PAHCY)

label type

Q1(m/z)

charge state (Q1)

Q3(m/z)

charge state (Q3)

CE (V)

Δ0

480.77

2+

Δ4

482.77

2+

Δ8

484.77

2+

317.19(y3) 644.34(b5) 317.19(y3) 648.35(b5) 317.19(y3) 652.35(b5)

1+ 1+ 1+ 1+ 1+ 1+

29 29 29 29 29 29

Δ0

464.76

2+

Δ4

466.76

2+

Δ8

468.77

2+

531.30(y4) 717.38(y5) 531.30(y4) 717.38(y5) 531.30(y4) 717.38(y5)

1+ 1+ 1+ 1+ 1+ 1+

22 22 22 22 22 22

Δ0

634.86

2+

Δ4

636.86

2+

Δ8

638.86

2+

730.40(y7) 958.51(y9) 730.40(y7) 958.51(y9) 730.40(y7) 958.51(y9)

1+ 1+ 1+ 1+ 1+ 1+

30 30 30 30 30 30

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labeling reagents, which have the same chemical structure as the 4-plex iTRAQ reagents. The mTRAQ Δ0 contains no isotopes and Δ8 has two 15N and six 13C isotope atoms, while Δ4 has one 15 N and three 13C isotope atoms which has identical structure and molecular weight as 4-plex iTRAQ reagents. The mass of both mTRAQ Δ4 and 4-plex iTRAQ is 145 Da. Here, to maximize the quantification throughput of MRM, we developed a Hyperplex-MRM quantitation strategy which combined mTRAQ for absolute quantification with iTRAQ for relative quantification. In our strategy, mTRAQ reagents Δ0 and Δ8 were used to label internal standard peptides. mTRAQ reagent Δ4 was replaced by 4-plex iTRAQ, which had identical structure and molecular weight, to label samples. From the MRM trace and MS/MS spectrum, total amounts and the relative ratios of target proteins/peptides of four samples could be acquired simultaneously. Thus, the absolute amount of target protein in four different samples could be obtained in a single run. Even if four iTRAQ labeled samples were combined, the complexity of mass spectra did not increase, meanwhile the quantification accuracy could benefit from much less background interference in MS/MS spectra.13 In addition, double references could make the quantification results more reliable. Colorectal cancer (CRC) is the third leading cause of solid organ cancer-related mortality in the world. Despite of continuous improvements in diagnosis and therapy for CRC, approximately 60% of CRC patients are resistant to be cured.14 The lack of suitable biomarkers and corresponding screening technologies is the key reason why many patients with CRC are intractable.15 Ademosylhomocysteinase (AHCY), cathepsin D (CTSD), and lysozyme C (LYZ) were reported to have correlation with tumorigensis and lymph node metastasis of CRC.16−19 The expression of CTSD was reported to be varied between different individuals.20 In order to fully understand their expression patterns in CRC progression, Hyperplex-MRM was used to quantify the three proteins in CRC tissue specimens covering all four stages. Our results demonstrated good accuracy and precision of quantification and also showed a feasibility of the strategy for candidate biomarker verification.

MATERIALS AND METHODS

Chemicals and Materials

Formic acid (FA) was provided by TEDIA (Fairfield, OH). mTRAQ and iTRAQ reagents were obtained from Applied Biosystems (Framingham, MA). All the water used in this experiment was prepared using a Mill-Q system (Millipore, Bedford, MA). The 3000 Da MWCO centrifugal filters were purchased from Millipore (Bedford, MA). Other chemicals used in this study were purchased from Sigma (St. Louis, MO). Three peptides chosen for this study were VGFAEAAR, named as PCTSD, VADIGLAAWGR, named as PAHCY, and AWVAWR, named as PLYZ, which belonged to CTSD, AHCY, and LYZ, respectively. These peptides were provided by Shanghai China Peptides Co., Ltd., at stated purities of more than 99%, and the inspection reports were also provided. Sample Preparation and Digestion

Colorectal tissue samples were provided by Shanghai Cancer Center of Fudan University, China. All tissue specimens were obtained from surgical resection. Tissues, which were used in the experiment, covered from stage I to IV. Every stage had 10 cases except for stage IV, which had 7 cases. Clinical features of these patients were shown in Table 6 in the Supporting Information. All candidates gave informed consent for the study, and samples were handled according to ethical and legal standards. All these tissues were lysed in the same buffer, consisting of 7 M urea, 2 M thiourea, 1 mM phenylmethanesulfonyl fluoride (PMSF), and protease inhibitor cocktail (Complete tablets; RocheDiagnostics). The proteins were extracted in lysis buffer by vortexing and sonication, and then the concentration was determined by Bradford assay. The proteins were reduced and the cysteine groups were blocked according the protocol of iTRAQ labeling provided by Applied Biosystems. The sample buffer was changed with iTRAQ dissolution buffer through 3000 Da MWCO centrifugal filters. The final concentration of the sample was quantified by Bradford assay, then trypsin was added to the solution with the ratio of trypsin to protein at 1:25, and the solution was incubated in 37 °C for 16 h. All the resulting peptide solutions were lyophilized for iTRAQ processing. 3913



Article

Peptides Labeled with iTRAQ/mTRAQ Reagents

recorded and integrated. Their ratios also were calibrated by work curves produced using synthetic peptides.

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Tryptic digestions were labeled with iTRAQ regents according to the manufacturer’s protocol. For each stage, equal amount digestions were suspended in labeling buffer and labeled with one of the 4-plex regents. The solutions were incubated at room temperature for 1 h, the reaction was quenched by adding water, and then the four labeled samples were mixed and lyophilized. Synthetic standard peptides were labeled with mTRAQ reagents, using the same labeling procedure. Equivalent amounts of peptides were labeled with light and heavy mTRAQ labels, respectively. After labeling, the light- and heavy-labeled peptides were mixed at 1:1 as double references, and then added to the iTRAQ-labeled protein digestions.

RESULTS AND DISCUSSION

Workflow of Hyperplex-MRM

Hyperplex-MRM achieved absolute quantification of multisamples by combining mTRAQ with iTRAQ. Absolute quantification and relative quantification were completed simultaneously because both the MRM trace and MS/MS spectrum could be acquired in MIDAS mode. As shown in Figure 1, CRC samples of four different stages were labeled with 4-plex

MRM Q1/Q3 Ion Pair Selection

The transitions and corresponding collision energies (CE) of the peptides were tuned to optimize the intensities of the fragments by direct injection of the mTRAQ-labeled synthetic peptides, using a 4000 QTRAP hybrid triple quadrupole/linear ion trap mass spectrometer (Applied Biosystems) equipped with a nanospray ionization source (Table 1). The best transitions were chosen from the MS/MS spectrum according to the intensity of MS2 peak. The best CE of every transition was obtained using MRMPilot 2.1 software (Applied Biosystems). In designing of MRM strategy, the method focused on “Find best CE”. The CE increment was set as 2 V, and five steps were used based on the theoretical CE provided by MRMPilot. LC-MRM/MS Analysis

All sample analyses were completed by using an 4000 QTRAP instrument with multiple reaction monitoring-initiated detection and sequencing (MIDAS)21,22 workflow controlled by Analyst 1.5.2 software (Applied Biosystems). The combined mTRAQ and iTRAQ sample was injected (5 μg) onto a C18 precolumn (0.5 × 2 mm, MICHROM Bioresources Inc., Auburn, CA) and washed with 100% phase C (0.1% FA) at a flow rate of 2 μL/min for 17 min. The peptides were separated on a homemade 75 μm i.d. × 150 mm reversed-phase capillary column packing with C18 particles (3 μm, 100 Å, Welcn Materious. Inc.) at a constant flow rate of 300 nL/min. The elution gradient was 5% phase B (98% acetonitrile in 0.1% FA) for 3 min, 5−10% B in 2 min, 10−25% B in 25 min, 25−40% B in 20 min, 40−80% B in 3 min, 80% B for 2 min, 80−5% B in 1 min, and 5% B for 14 min. The 4000 QTRAP instrument was operated in the positive ionization mode. The optimal acquisition parameters were as follows: curtain gas (25), ionspray voltage (2500 V), ion source gas (25), interface heater temperature (180 °C), collision gas (High), declustering potential (75), entrance potential (10), and collision cell exit potential (15). The resolution parameters of the first and the third quadrupole were set as “unit”. The target ions were transmitted with a narrow window (0.7 Da). The dwell time was 100 ms for every transition. The precursor whose intensity was more than 700 cps was selected for MS/MS analysis.

Figure 1. Experimental scheme of the Hyperplex-MRM quantification strategy for candidate biomarker quantification of CRC. CRC samples of different stages were labeled with 4-plex iTRAQ regents, respectively. Equivalent amounts of synthetic standard peptides were labeled with light (Δ0) or heavy (Δ8) mTRAQ regents as double references. Total amount of iTRAQ-labeled peptide (Δ4-like) can be calculated by the peak area compared with mTRAQ-labeled transitions from the MRM trace. Relative ratio of this target peptide among these four samples is calculated by comparing the peak intensities of reporter ions in MS/MS spectrum. From the total amount and the relative ratio of target peptide, the absolute amount of target peptide in every sample can be calculated. Thus, the target peptide from four different samples can be quantified simultaneously in a single run.

iTRAQ regents, respectively. Equivalent amounts of synthetic standard peptides were labeled with light (Δ0) and heavy (Δ8) mTRAQ regents, respectively, and then mixed as double references before adding to the iTRAQ-labeled samples for following MS analysis. Total amount of iTRAQ-labeled peptide (termed as Δ4-like) was calculated by the peak area compared with mTRAQ-labeled transitions (eq 1). Relative ratio of this target peptide among these four different samples was calculated by comparing the peak intensities of reporter ions in MS/MS spectrum (eq 2). From the total amount of target peptide and the relative ratio, the absolute amount of target peptide in every sample could be calculated using eq 3. And the amount of protein could be deduced from the amount of peptide.

Data Analysis

All data were processed using Analyst 1.5.2 software. For absolute quantification of unique peptides from target proteins, the peak area of every transition was integrated with MultiQuant 2.0.2 software (Applied Biosystems). To calculate the concentration of the endogenous target peptides, the synthetic peptides were used to establish calibration curves. Protein abundance was calculated from the unique peptide stoichiometry. For relative quantification, the intensities of peaks 114, 115, 116, 117 were 3914



Article

Figure 2. MRM trace and MS/MS spectrum of PCTSD (VGFAEAAR). In the MRM traces of transition y3 and b5, blue represents Δ0, green represents Δ8, and red represents Δ4-like. The right panel shows the spectrum of reporter ions representing different samples.

Figure 3. Calibration curves of six transitions of PCTSD, PAHCY, and PLYZ for absolute quantification. The average ratios were calculated and plotted with error bars of standard deviation based on three replications.

MΔ4 ‐ like = MΔ0

AΔ4 ‐ like /AΔ0 + AΔ4 ‐ like /AΔ8 2

I114: I115: I116: I117 = MI : MII : MIII : MIV

Mi = MΔ4 − like

(1)

Ij I114 + I115 + I116 + I117

(3)

MΔ4‑like is the total absolute amount of peptide in four stages of CRC. MΔ0 is the absolute amounts of Δ0-labeled peptides. AΔ0,

(2) 3915



Article

Figure 4. Calibration curves for relative quantification: (a) PCTSD, (b) PAHCY, and (c) PLYZ. The average ratios were calculated and plotted with error bars of standard deviation based on three replications.

AΔ4‑like, and AΔ8 are measured peak areas of Δ0-, Δ4-like-, and Δ8-labeled peptides, respectively. Ij (j = 114, 115, 116, or 117) is measured peak intensity of different reporter ion of iTRAQ regents. Mi (i = I, II, III, or IV) represents the absolute amount of peptide in every stage of CRC. The relationship is one-to-one between i and j, when i is I, j is 114, and so on. To verify the feasibility of the strategy, a pooled stage I CRC tissue sample was divided into four equal parts and labeled with iTRAQ regents (114, 115, 116 and 117, respectively) before mixing together. The light and heavy mTRAQ-labeled standard peptides (VGFAEAAR) were equally mixed, and added to the iTRAQ-labeled sample. In the MS analysis results, the ratios of Δ0/Δ8 from the MRM trace of the two transitions (y3, b5) were 1.02 and 1.20, respectively (closed to the expected value of 1), which indicated the reliability of the results (Figure 2). As for the iTRAQ reporter ions, the ratio of 1.00:1.07:1.03:1.16 (114:115:116:117) was also closed to the expected value 1:1:1:1. These results proved that the Hyperplex-MRM strategy could achieve accurate relative and absolute quantification simultaneously. If just one mTRAQ label was used as reference, the absolute amount of target proteins in up to five samples would be obtained in a single experiment. Here we developed a new quantification strategy to improve the throughput of MRM for candidate biomarker verification. In previous research,23 iTRAQ and mTRAQ based quantitation were compared extensively, and the results indicated that iTRAQ was superior to mTRAQ for quantitative global proteomics and phosphoproteomics. iTRAQ labeling had an additive effect on precursor intensities and could quantify more proteins compared to mTRAQ labeling. In our work, taking the advantage of mTRAQ in the application of MRM and the advantage of iTRAQ in increasing the precursor intensities and quantifying multiple samples, iTRAQ and mTRAQ labeling were integrated to analysis multiple samples in the same run. To our best knowledge, it is the first time to combine iTRAQ and mTRAQ in a single experiment to improve the throughput of analysis.

with three replicates. The calibration curves are shown in Figure 3; every transition exhibited a linear response in ratios from 0.1 to 10 with correlation coefficient r > 0.99. The relative standard deviations (RSDs) of every transition were less than 20% in most mixtures (Table 2, Supporting Information). For PAHCY, the RSDs were higher than those of the other two peptides, which might be interfered by high intensity coelution peptides from matrix sample. Double references were used for quantifying most transitions for absolute quantification. As mentioned by Song et al.,24 double references could increase the reliability of the quantitative results. For transitions with RSDs that were higher than 20%, we also calculated their RSDs using just one of the references, and the reference with higher RSDs was discarded to ensure the precision of the quantification results (Table 2, Supporting Information). In addition, the double references mixed at 1:1 were measured at ratio of 0.9−1.1 in most mixtures (Table 3, Supporting Information), which convinced us of the accuracy of the results. iTRAQ-labeling was used to quantify the relative amount of target peptides from four different samples. As mentioned above, the different iTRAQ-labeled standard peptides were mixed at the ratio of 1:2:3:4 (114:115:116:117) in every mixture. To ensure the accuracy of relative quantification results, the calibration curve of every peptide was set in a concentration range closed to its endogenous abundance. For the peptide PCTSD, the relative calibration curve was made at 0.0075−0.03 ng, at 0.03−0.12 ng for peptide PAHCY, and at 0.0125−0.05 ng for peptide PLYZ (Figure 4). Every peptide showed a good linear response with r > 0.97, which supported the reliability of relative quantification results. However, the experimental ratios were slightly lower than expected values which suggested that the dynamic range of iTRAQ-labeling was compressed, as mentioned by Sousa et al.11 Similarly, the reproducibility of the relative quantification was also evaluated. The RSDs (Table 4, Supporting Information) were a little higher than absolute quantification. Larger ratio might cause the relatively poor quantification reproducibility in MS analysis. For example, the RSDs of 117:114 mixed at ratio of 4:1 were 10−30% higher than 116:114 and 115:114. These results demonstrated that our Hyperplex-MRM strategy could achieve reliable and reproducible quantification not only in absolute quantification but also in relative quantification, which further confirmed the feasibility of the strategy. A 102 dynamic range for absolute quantification and 4fold dynamic range for relative quantification were achieved. Both absolute and relative quantification showed excellent linear response with r > 0.97.

Dynamic Range and Reproducibility of Hyperplex-MRM Quantification

In Hyperplex-MRM, mTRAQ-based absolute quantification and iTRAQ based relative quantification were completed simultaneously. Dynamic range and reproducibility of both absolute and relative quantification were assessed. First, in order to assess the dynamic range of absolute quantification based on isotopic peak area ratios, seven mixtures (for details, see Table 1 in the Supporting Information) of each peptide with known Δ0/Δ4like concentration ratios ranging from 0.1 to 10 were prepared. The amounts of Δ0 and Δ8 labeled peptides were equal and fixed. The four iTRAQ reagents labeled peptides were mixed at 1:2:3:4 (114:115:116:117) as Δ4-like. Pooled unlabeled CRC stage IV sample was added as blank matrix. Two transitions were selected for every peptide (Table 1). Every mixture was analyzed

Application of the Hyperplex-MRM Strategy for Protein Quantification in CRC Samples

Colorectal cancer is one of the major causes of morbidity and mortality worldwide.25,26 A patient’s prognosis is highly dependent on the stage of cancer at diagnosis.13 Using 3916



Article

Figure 5. Concentration distribution of CTSD, LYZ and AHCY in CRC tissues of different stage.

understand the regulation mechanism of CTSD, more samples are needed. AHCY is an enzyme that catalyzes the reversible hydrolysis of S-adenosylhomocysteine to homocysteine and adenosine.18 The up-regulation of AHCY has been described in CRC in cDNA, mRNA, and protein expression level compared with paracancerous tissues.16,33−38 In a recent iTRAQ study, the protein expression differences in different stages of CRC were compared, and AHCY expression in stage II and III was a little higher than that in stage I and IV.27 Consistently, AHCY protein expression in stage II was the highest among the four stages in our study. These demonstrated important roles of AHCY in the development of CRC. As an enzyme with potent antibacterial properties,17 several investigations have shown LYZ’s expression to be altered in gastric cancer.39,40 The up-regulation of LYZ was also described in CRC researches.17,27,28 As mentioned above, LYZ was found up-regulated in advanced stage CRC in our study, which was consistent with former studies. The result suggested that LYZ might play some roles in metastasis of CRC, though the mechanism was unclear and needed to be further investigated.

Hyperplex-MRM, three proteins, AHCY, CTSD, and LYZ, which were expressed differentially in various stages of colorectal cancer according to previous research,27,28 were examined. For each protein, one unique peptide was selected (Table 1), and 10 specimens of every CRC stage were used except stage IV. Seven cases of stage IV were pooled together as a control to indicate the accuracy of the quantification results. These specimens were subjected to 10 independent experiments, and every experiment was performed with three replicates. The absolute concentrations of the three proteins in every sample were calculated (Table 5, Supporting Information) and are plotted in Figure 5. The standard deviation values (SDs) of the measured concentrations of target proteins in stage IV were less than 9%, which indicated high reliability of the quantification results. We could indicate from Figure 5 that the individual variation of target proteins was greater in early stages than in advanced stages in CRC patients, which may be one of reasons that the finding of an appropriate biomarker for early diagnosis is more difficult. Table 2 shows average protein concentration of every protein in each Table 2. Concentration of CTSD, LYZ, and AHCY in CRC Tissues of Different Stage (ng/5μg protein)

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CONCLUSION In this study, a Hyperplex-MRM quantification strategy combining mTRAQ-labeling for absolute quantification and iTRAQ-labeling for relative quantification was developed for biomarker verification. The absolute amount of target protein in up to five samples could be calculated simultaneously, which greatly increased throughput of sample analysis. Double references were used to guarantee the reliability of the results. Calibration curves for absolute and relative quantification both showed excellent linear response. The method was also successfully applied in evaluating amounts of three candidate proteins in different stages of CRC with high accuracy, sensitivity, and reproducibility. In our study, the concentration of CTSD was not significantly changed in all stages, while the amounts of LYZ and AHCY were increased with progression of CRC, except for extremely high AHCY level in stage II CRC.

stages (ng/5 μg) protein

I

II

III

IV

CTSD LYZ AHCY

1.10 ± 0.30 0.59 ± 0.38 3.50 ± 2.30

1.10 ± 0.25 0.69 ± 0.28 4.10 ± 1.85

1.03 ± 0.28 0.78 ± 0.20 3.79 ± 1.31

1.06 ± 0.09 0.80 ± 0.07 3.89 ± 0.47

stage. The expression of CTSD was not significantly changed among the four stages, while the amounts of LYZ and AHCY were increased with the progression of CRC, except for extremely high AHCY level in stage II CRC. CTSD is a lysosonal acid proteinase, which can degrade structural and functional proteins, peptides, peptide precursors and hormones.29−32 It has been reported to correlate with progression and lymph node metastasis of CRC.18,19 The concentration of CTSD was reported to be significantly associated with tumor stage, and its expression was varied between different individuals.19,20 In the study of IacobuzioDonahue et al.,20 59 CRC tumors were analyzed. Normal CTSD expression was detected in some early stage tumors, and CTSD levels were increased in 1/3 but decreased in 2/3 of stage III and IV tumors. In our results, the average concentrations of CTSD in four stages were not changed significantly, however, the individual variation was great. The divergent expression pattern of CTSD suggests its complex function in CRC. In order to fully

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ASSOCIATED CONTENT

S Supporting Information *

Detailed components of the calibration mixtures; RSDs of the calibration mixtures for absolute quantification and relative quantification; ratios for preparing calibration curve for absolute quantification; absolute concentrations of AHCY, CTSD and LYZ in CRC tissue samples; clinical features of CRC patient 3917



Article

(11) DeSouza, L. V.; Taylor, A. M.; Li, W.; Minkoff, M. S.; Romaschin, A. D.; Colgan, T. J.; Siu, K. W. Multiple reaction monitoring of mTRAQlabeled peptides enables absolute quantification of endogenous levels of a potential cancer marker in cancerous and normal endometrial tissues. J. Proteome Res. 2008, 7 (8), 3525−3234. (12) Yoon, J. Y.; Yeom, J.; Lee, H.; Kim, K.; Na, S.; Park, K.; Paek, E.; Lee, C. High-throughput peptide quantification using mTRAQ reagent triplex. BMC Bioinf. 2011, 12 (Suppl1), S46. (13) Wilm, M. Quantitative proteomics in biological research. Proteomics 2009, 9 (20), 4590−4605. (14) Herrmann, K.; Walch, A.; Balluff, B.; Tanzer, M.; Hofler, H.; Krause, B. J.; Schwaiger, M.; Friess, H.; Schmid, R. M.; Ebert, M. P. Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers. Nat. Clin. Pract. Gastroenterol. Hepatol. 2009, 6 (3), 170−183. (15) Ludwig, J. A.; Weinstein, J. N. Biomarkers in cancer staging, prognosis and treatment selection. Nat. Rev. Cancer 2005, 5 (11), 845− 856. (16) Fan, J.; Yan, D.; Teng, M.; Tang, H.; Zhou, C.; Wang, X.; Li, D.; Qiu, G.; Peng, Z. Digital transcript profile analysis with aRNALongSAGE validates FERMT1 as a potential novel prognostic marker for colon cancer. Clin. Cancer Res. 2011, 17 (9), 2908−2918. (17) Rubio, C. A. Lysozyme-rich mucus metaplasia in duodenal crypts supersedes Paneth cells in celiac disease. Virchows Arch. 2011, 459 (3), 339−346. (18) Oh-e, H.; Tanaka, S.; Kitadai, Y.; Shimamoto, F.; Yoshihara, M.; Haruma, K. Cathepsin D expression as a possible predictor of lymph node metastasis in submucosal colorectal cancer. Eur. J. Cancer 2001, 37 (2), 180−188. (19) Leto, G.; Tumminello, F. M.; Crescimanno, M.; Flandina, C.; Gebbia, N. Cathepsin D expression levels in nongynecological solid tumors: clinical and therapeutic implications. Clin. Exp. Metastasis 2004, 21 (2), 91−106. (20) Iacobuzio-Donahue, C.; Shuja, S.; Cai, J.; Peng, P.; Willett, J.; Murnane, M. J. Cathepsin D protein levels in colorectal tumors: divergent expression patterns suggest complex regulation and function. Int. J. Oncol. 2004, 24 (3), 473−485. (21) Unwin, R. D.; Griffiths, J. R.; Leverentz, M. K.; Grallert, A.; Hagan, I. M.; Whetton, A. D. Multiple reaction monitoring to identify sites of protein phosphorylation with high sensitivity. Mol. Cell. Proteomics 2005, 4 (8), 1134−1144. (22) Unwin, R. D.; Griffiths, J. R.; Whetton, A. D. A sensitive mass spectrometric method for hypothesis-driven detection of peptide posttranslational modifications: multiple reaction monitoring-initiated detection and sequencing (MIDAS). Nat. Protoc. 2009, 4 (6), 870−877. (23) Mertins, P.; Udeshi, N. D.; Clauser, K. R.; Mani, D. R.; Patel, J.; Ong, S. E.; Jaffe, J. D.; Carr, S. A. iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics. Mol. Cell. Proteomics 2012, 11 (6), M111 014423. (24) Song, C.; Wang, F.; Ye, M.; Cheng, K.; Chen, R.; Zhu, J.; Tan, Y.; Wang, H.; Figeys, D.; Zou, H. Improvement of the quantification accuracy and throughput for phosphoproteome analysis by a pseudo triplex stable isotope dimethyl labeling approach. Anal. Chem. 2011, 83 (20), 7755−7762. (25) Burt, R. W. Colorectal cancer screening. Curr. Opin. Gastroenterol. 2010, 26 (5), 466−470. (26) Xing, X.; Lai, M.; Gartner, W.; Xu, E.; Huang, Q.; Li, H.; Chen, G. Identification of differentially expressed proteins in colorectal cancer by proteomics: down-regulation of secretagogin. Proteomics 2006, 6 (9), 2916−2923. (27) Besson, D.; Pavageau, A. H.; Valo, I.; Bourreau, A.; Belanger, A.; Eymerit-Morin, C.; Mouliere, A.; Chassevent, A.; Boisdron-Celle, M.; Morel, A.; Solassol, J.; Campone, M.; Gamelin, E.; Barre, B.; Coqueret, O.; Guette, C. A quantitative proteomic approach of the different stages of colorectal cancer establishes OLFM4 as a new nonmetastatic tumor marker. Mol. Cell. Proteomics 2011, 10 (12), M111 009712. (28) Xie, L. Q.; Zhao, C.; Cai, S. J.; Xu, Y.; Huang, L. Y.; Bian, J. S.; Shen, C. P.; Lu, H. J.; Yang, P. Y. Novel proteomic strategy reveal combined alpha1 antitrypsin and cathepsin D as biomarkers for

tissue samples. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +0086-021-54237618. Fax: +0086-021-54237961. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The work was supported by National Science and Technology Key Project of China (2012CB910602, 2012AA020203, and 2010CB912700), National Science Foundation of China (21025519, 31070732, and 21105015), Shanghai Projects (11XD1400800 and B109), and Eastern Scholar.

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ABBREVIATIONS MRM, multiple reaction monitoring; iSRM, intelligent selected reaction monitoring; iTRAQ, isobaric tags for relative and absolute quantification; CTSD, cathepsin D; AHCY, ademosylhomocysteinase; LYZ, lysozyme C; PCTSD, VGFAEAAR; PAHCY, VADIGLAAWGR; PLYZ, AWVAWR; CRC, colorectal cancer; MIDAS, multiple reaction monitoring-initiated detection and sequencing; CE, collision energies; EPI, enhanced product ion; FA, formic acid; PMSF, phenylmethanesulfonyl fluoride; SD, standard deviation; RSD, relative standard deviation

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REFERENCES

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