Differential LC-MS-Based Proteomics of Surgical Human Cholangiocarcinoma Tissues Hiroshi Kawase,†,‡,§ Kiyonaga Fujii,*,†,§ Masaki Miyamoto,‡ Kanako C. Kubota,| Satoshi Hirano,‡ Satoshi Kondo,‡ and Fuyuhiko Inagaki§ Department of Surgical Oncology, Division of Surgery, Hokkaido University Graduate School of Medicine, N-15, W-7, Kita-ku, Sapporo 060-8638, Japan, Department of Structural Biology, Graduate School of Pharmaceutical Sciences, Hokkaido University, N-21, W-11, Kita-ku, Sapporo 001-0021, Japan, and Department of Surgical Pathology, Hokkaido University Hospital, N-14, W-5, Kita-ku, Sapporo 060-8648, Japan Received May 26, 2009
Cholangiocarcinoma is an intractable cancer for which there is no effective therapy other than surgical resection, and many patients are not candidates for this treatment. Even for patients who undergo surgical resection, the 5-year survival rate is low. One reason for this is that the disease is often detected in late stages. Thus, there is a clear need for better biomarkers to facilitate early diagnosis and prognostication. During the biomarker discovery phase of our study, we used LC-MS-based proteomics with spectral counting, a semiquantitative approach to differential expression profiling, in paired cancerous and normal bile duct tissue samples from two cases. In total, 38 proteins up-regulated in the cancer samples were identified. These were verified using a SILAC method for MS-based validation. The results led to the identification of well-characterized proteins and proteins of unknown function that are up-regulated in cholangiocarcinoma. We used immunoblot analysis to validate four candidate biomarkers, actinin-1, actinin-4, protein DJ-1 and cathepsin B, with the test case samples and four additional cholangiocarcinoma case samples. Each of the four candidate proteins was overexpressed in a subset of five of the six cases tested. By immunohistochemistry, we further confirmed that expression of these proteins was elevated in cancer cells as compared with normal bile duct cells. Thus, we successfully identified several proteins up-regulated in cholangiocarcinoma. These proteins are candidate biomarkers and may also help to provide new insights into our understanding of the disease. Keywords: cholangiocarcinoma • surgical tissue specimen • biomarker • LC-MS-based proteomics • spectral counting • SILAC • immunohistochemistry.
Introduction Cholangiocarcinoma is a fatal cancer of the biliary epithelium, and the overall survival rate is poor. Less than 5% of patients survive longer than 5 years, and the rate of survival has not changed significantly over the past 30 years.1 Despite advances in chemo-, immuno-, photodynamic-, and radiotherapy for treatment of cancers in general, complete surgical resection is currently the only cure for cholangiocarcinoma.2 However, more than two-thirds of patients with cholangiocarcinoma are not candidates for curative resection at the time of the initial diagnosis,3 which likely reflects the difficulty of detecting the disease in early stages. Indeed, most patients have developed jaundice at presentation and are already at an * To whom correspondence should be addressed: Kiyonaga Fujii, Ph.D., Department of Structural Biology, Graduate School of Pharmaceutical Sciences, Hokkaido University, N-21, W-11, Kita-ku, Sapporo 001-0021, Japan. Fax: +81-11-706-9012. E-mail:
[email protected]. † These authors contributed equally to this work. ‡ Hokkaido University Graduate School of Medicine. § Hokkaido University. | Hokkaido University Hospital.
4092 Journal of Proteome Research 2009, 8, 4092–4103 Published on Web 07/02/2009
advanced stage. One reason for this is that the anatomical relationships between hepatic hilar structure and modes of tumor extension are complicated. Hence, there is an immediate need for better disease markers that facilitate early diagnosis and prognostication, provide insights into pathogenesis, and point to potential new treatments.4 Recent advances in proteomics technologies provide tremendous opportunities for establishment of biomarker-related clinical applications. A variety of technologies have been applied to biomarker discovery, including two-dimensional gelelectrophoresis,5 liquid chromatography mass spectrometry (LC-MS), and protein- and antibody-based microarrays.6-8 LC-MS- or tandem MS (MS/MS)-based proteomics technologies offer highly sensitive analytical capabilities and a relatively large dynamic range of detection.9 Additionally, relatively highthroughput LC-MS analysis is amenable to clinical applications with human biofluids and tissues.10 In the area of gastrointestinal oncology, more than 130 exploratory studies using various proteomics technologies have identified candidate biomarkers in serum, gastrointestinal fluids, or cancer tissue.11 However, 10.1021/pr900468k CCC: $40.75
2009 American Chemical Society
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LC-MS-Based Tissue Proteomics of Cholangiocarcinoma Table 1. Clinical Characteristics of the Subjects with Cholangiocarcinoma pathology
stump of bile ductc patient no.
1 2 3 4 5 6
a
sex
age
lesion site
M M M M M F
67 75 47 61 70 64
Bmsc BmiscC Bim Bh Bim Bi
b
differentiation
moderate moderate moderate moderate moderate well
to well to poor to poor to poor
hepatic
duodenal
negative positive negative negative negative negative
negative negative negative negative negative negative
a Patient no. 1 and 2 samples were subjected to proteomic analysis. b Bh: Intrahepatic bile ducts, Bc: Hilar bile duct, Bs: Superior bile duct, Bm: Middle bile duct, Bi: Inferior bile duct, C: Cystic duct c The stump with bold changes were used as normal tissues samples.
only a few studies of cholangiocarcinoma using bile and related tissues have been done.4,12-15 We therefore undertook biomarker discovery for cholangiocarcinoma using a LC-MS-based proteomics approach. In the present study, we applied tissue proteomics to a pair of cancerous and normal bile duct tissue specimens from individual patients with cholangiocarcinoma. To do this, surgical tissue specimens from two cases were subjected to LC-MSbased proteomics. First, a semiquantitative proteomic analysis approach with spectral counting was used to explore protein profiles differentially expressed between cancerous and normal tissues. The label-free approach offers the benefit of facilitating high-throughput differential expression analyses and relatively easy data-processing of large proteomic data sets. To improve reproducibility and reliability of spectral counting of individual samples, LC-MS analysis was carried out in triplicate and the resulting spectral counts were estimated as the mean value. Next, we applied a method for highly accurate and precise relative quantitative analysis to verify the results of our semiquantitative approach. The approach is based on stable isotope labeling by amino acids in cell culture (SILAC) and uses labeled proteins as internal standards for quantitative tissue proteomics.16 Here, we used a HeLa cell line for SILAC labeling and changes in protein expression in the tissue were quantitatively estimated by comparing the calculated ratio of peptide peak areas between the labeled and tissue peptides as determined with LTQ-Orbitrap mass spectrometry. The results led to the identification of well-characterized proteins and proteins of unknown function that are up-regulated in cholangiocarcinoma. Finally, we focused on four proteins previously reported to be up-regulated in other cancers. These proteins were validated by immunoblotting and their different pathological features in cholangiocarcinoma were also confirmed by immunohistochemical analysis. We further validated these proteins by immunoblot analysis in four additional surgical tissue specimens. In summary, we describe the discovery of proteins that are up-regulated in cholangiocarcinoma, as identified by LC-MSbased differential expression analysis with label-free and labeling approaches that facilitate investigation of the tissue proteomics of primary human surgical specimens. Furthermore, a subset of candidate biomarkers was validated by immunoblot and immunohistochemical analyses.
Experimental Procedures Clinical Tissue Specimens. Pairs of cancerous and normal bile duct tissues diagnosed by a pathologist were collected from six patients with primary cholangiocarcinoma (intrahepatic, n ) 1; extrahepatic, n ) 3; hilar, n ) 2) who underwent radical
surgery at the Department of Surgical Oncology, Hokkaido University Hospital. Clinical characteristics of the subjects are summarized in Table 1. The operative procedure for cases 1 and 2 included subtotal stomach-preserving pancreatoduodenectomy such that there was no duodenal bile duct stump. For those cases, normal tissue samples were collected from the inferior bile duct distal to the main tumor. Fresh tissues were procured at the time of surgery and snap-frozen in liquid nitrogen after washing with saline to remove blood and bile. The tissues were stored at -80 °C until use. Cases 1 and 2 were used as the test set subjected to proteomics analysis. The remaining four cases were used for validation. The study was vetted by and received prior approval from an institutional review board at Hokkaido University and all tissues were collected only after receipt of informed consent from patients. Protein Extraction, Electrophoresis, and Tryptic Digestion. Frozen tissues were crushed with Shakeman2 (Bio Medical Science, Gunma, Japan) and immersed in 100 µL of PBS containing the protease inhibitors aprotinin and phenylmethanesulfonyl fluoride. Tissue samples were homogenized on ice for 2 min using a pestle (Scientific Specialties, Lodi, CA) followed by sonication on ice 10 times for 30 s each with an ultrasonic disintegrator (Bioruptor UCD-200, Cosmo Bio, Tokyo, Japan). Homogenates were then centrifuged at 15 000 rpm for 10 min at 4 °C, and the supernatants were stored at -80 °C until analysis. Protein concentrations were measured using the Bradford method with the Bio-Rad Protein Assay Kit (Bio-Rad, Hercules, CA). Equal amounts of protein extracts of each tissue samples (5 µg) were loaded on a 4-12% NuPAGE gel (Invitrogen, Carlsbad, CA) followed by silver staining (Silver Staining KANTO-III Kit, Kanto Chemical, Tokyo, Japan). For tryptic digestion, equal amounts of protein extracts of each tissue sample (20 µg) were brought to a total volume of 160 µL with 100 mM ammonium bicarbonate, and 10 µL of acetonitrile was then added. Each lysate was treated with 10 µL of a reducing agent, 10 mM tris(2-carboxyethyl)phosphine, at 37 °C for 45 min, and alkylated with 10 µL of cysteine blocking reagent, 50 mM iodoacetamide, at 24 °C for 1 h in the dark. Protein samples were digested with sequencing grade trypsin (Promega, Madison, WI; 1:50 w/w) for 14 h at 37 °C. The digestion reaction was stopped by addition of 200 µL of 1% trifluoroacetic acid. All of the reactions were carried out in methylpentene polymer tubes (Hitech Inc., Tokyo, Japan), and the solutions were interval-mixed for 10 s at 850 rpm using an Eppendorf Thermomixer Comfort (Eppendorf AG, Hamburg, Germany). Finally, the digested samples were frozen at -80 °C until further processing. Liquid Chromatography Tandem Mass Spectrometry. LCMS/MS analysis of the digested sample was carried out using Journal of Proteome Research • Vol. 8, No. 8, 2009 4093
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Figure 1. Strategy for differential LC-MS-based proteomics of surgical human cholangiocarcinoma tissues. Semiquantitative proteomic analysis with spectral counting was applied to identify differentially expressed protein profiles between cancerous and normal tissues. A relative quantitative analysis of proteins upregulated in cholangiocarcinoma was performed with a SILAC method using labeled HeLa cells to verify the semiquantitative evaluation. Putative up-regulated proteins were then validated biologically via immunoblot and immunohistochemical analyses.
reversed-phase liquid chromatography (RP-LC) interfaced with a LTQ-Orbitrap hybrid mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) using a nanoelectrospray device (AMR, Tokyo, Japan). The RP-LC system (Paradigm MS4B, Michrom BioResources, Auburn, CA) consisted of a peptide Cap-Trap cartridge (2.0 × 0.5 mm i.d.) and an analytical column (L-column Micro, 150 × 0.2 mm L-C18, 3 µm, 12 nm, CERI, Tokyo, Japan) fitted with an emitter tip (FortisTip, OmniSeparoTJ, Hyogo, Japan). Samples were loaded onto the trap cartridge and washed with mobile phase A (98% H2O with 2% acetonitrile and 0.1% formic acid) for concentration and desalting. Subsequently, peptides were eluted over 70 min from the analytical column via the trap cartridge using a linear gradient of 5-40% mobile phase B (10% H2O with 90% acetonitrile and 0.1% formic acid) at a flow-rate of 1 µL/min. The mass spectrometer was operated in the data-dependent mode to automatically switch between one high resolution MS survey scan (resolution, 30 000; scan range, m/z 400 to 1600) by the Orbitrap and up to three concurrent MS/MS scans in the LTQ for the three most intense peaks selected from each survey scan. Automatic gain control was set to 500 000 for Orbitrap survey scans and 10 000 for LTQ MS/MS scans. Survey scans were acquired in profile mode and MS/MS scans were acquired in centroid mode. General mass spectrometric conditions were as follows: electrospray voltage, 3.0 kV, no sheath and auxiliary gas flow; ion 4094
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Kawase et al. transfer tube temperature, 200 °C; collision energy, 35%; ion selection threshold, 1000 counts for MS/MS. An activation q-value of 0.25 and an activation time of 30 ms were applied for MS/MS acquisitions. Acquired MS/MS spectra were converted to single Dta files using Bioworks (version 3.3, Thermo Fisher Scientific) and merged into Mascot generic format files for database searching. Database Searching and Semiquantitation with Spectral Counting. Mascot software (version 2.1.1, Matrix Science, London, UK) was used for database search against Homo sapiens entries in the Swiss-Prot database (Release 56.6, 20413 entries). Peptide mass tolerance was 10 ppm, fragment mass tolerance was 0.8 Da, and trypsin specificity was applied with a maximum of two missed cleavages. Carbamidomethylation of cysteines and oxidation of methionines were allowed as fixed and variable modifications, respectively. To estimate the false positive rate for protein identification, a decoy database was created by reversing the protein sequences in the original database. Based on search results for the decoy database, the estimated false positive rate of peptide matches was 0.45% under p < 0.005 for protein score threshold conditions. For semiquantitative analysis, protein identification from individual Mascot search results was integrated in Scaffold software (version 2.02.03, Proteome Software, Portland, OR). A spectral counting method was used to determine the proteins that were differentially expressed between cancerous and normal tissue samples. The number of peptide spectra with high confidence (Mascot ion score, p < 0.005) was used as the spectral count value. All proteins with greater than two peptide spectra in a single LC-MS/MS analysis were considered for protein quantification using spectral counting. The averaged values based on the triplicate analyses were estimated as spectral count values of each protein for individual tissue samples to take into consideration statistical significance. The fold changes in the levels of differentially abundant proteins were calculated using the ratio of spectral counts. Stable Isotope Labeling of HeLa Cells and SILAC Quantitation. HeLa cells were cultured in arginine- and lysinedeficient SILAC/RPMI medium (Invitrogen) supplemented with dialyzed FBS, L-glutamine, glucose, phenol red solution and containing heavy [13C6] L-lysine and [13C6, 15N4] L-arginine according to the manufacturer’s instructions (Invitrogen). The cells were grown for at least six passages to allow full incorporation of labeled amino acids, and then harvested in PBS with protease inhibitors. Protein extraction and tryptic digestion were carried out in the same manner as described for tissue samples. After evaluation of the intensity of target peptides by LC-MS/MS analysis, half of the digested HeLa sample volumes were mixed with the above-mentioned individual tissue samples as internal standards for SILAC quantitation. The mixed samples were subjected to LC-MS/MS analysis under the following elution conditions: linear gradient of 5-45% mobile phase B over 160 min. SILAC quantitation was carried out using the Mascot Distiller Quantitation Tool (version 2.2.1). XIC peak areas of the heavy and light peptides were measured and the results were verified by manual inspection of MS spectra. SILAC ratios for the proteins were calculated by comparing the XIC peak areas of all matched light peptides with those of the heavy peptides. The fold change values of target proteins between cancerous and normal tissue samples were estimated using the SILAC ratios for a single common peptide. Immunoblot Analysis. The following antibodies were used for immunoblotting and immunohistochemistry: antibodies
LC-MS-Based Tissue Proteomics of Cholangiocarcinoma directed against alpha-actinin-1 (rabbit monoclonal), alphaactinin-4 (rabbit polyclonal), protein DJ-1 (mouse monoclonal), cathepsin B (rabbit polyclonal) and actin (mouse monoclonal) were purchased from Epitomics (Burlingame, CA), Alexis Biochemicals (San Diego, CA), MBL (Nagoya, Japan), Santa Cruz Biotechnology (Santa Cruz, CA) and Chemicon International (Temecula, CA), respectively. Two micrograms of protein extracts from tissue samples were subjected to SDS-PAGE followed by transfer to Hybond-ECL nitrocellulose membranes (GE Healthcare, Uppsala, Sweden). The membranes were blocked with 5% skim milk in Tris-buffered saline with 0.05% Tween 20 (TBS-T, pH 7.5) overnight at 4 °C and then incubated with primary antibody for 2 h at room temperature followed by washing and incubation with a 1:10,000 dilution of peroxidase-conjugated goat antimouse or antirabbit antibody (Jackson ImmunoResearch Laboratories, West Grove, PA) for 2 h at room temperature. Antibodies were detected with immunoreaction enhancer solutions (Can Get Signal, Toyobo, Osaka, Japan) and washing steps were carried out in TBS-T. The immunoreactive bands were visualized using ECL Plus Western Blotting Detection Reagents (GE Healthcare). Immunohistochemical Staining. Cancerous and normal tissue specimens were fixed in 10% formalin and embedded in paraffin wax. Serial 4-µm-thick sections of each specimen were examined by immunohistochemical analysis. The specimens were deparaffinised in xylene and then hydrated through a graded series from ethanol to water. For antigen retrieval, sections were floated on 10 mM citric acid buffer (pH 7.0, actinin-1 and -4; pH 6.0, protein DJ-1 and cathepsin B) and then heated in a domestic pressure cooker for 15 min after the cooker reached the maximum pressure. Once cooled, the heattreated sections were washed three times for 5 min each with PBS (pH 7.4). Before staining, endogenous peroxidase activity was eliminated by incubation for 30 min in 0.3% hydrogen peroxide in methanol. After washing in PBS, specimens were blocked with 10% normal goat serum (Nichirei, Tokyo, Japan) for 30 min and then incubated at 4 °C overnight with 1:100 anti actinin-1, 1:200 anti actinin-4, 1:50 anti protein DJ-1 and 1:50 anti cathepsin B in antibody diluent (Dako-Cytomation, Glostrup, Denmark). After washing with PBS, the sections were incubated for 30 min at room temperature with a biotinylated goat antibody to mouse and rabbit immunoglobulin (Histofine Simple Stain MAX PO [MULTI], Nichirei). After washing in PBS, immunohistochemical staining was developed by incubating the sections in freshly prepared 3,3′-diaminobenzidine tetrahydrochloride (Histofine Simple Stain DAB Solution, Nichirei) for approximately 5 min. The sections were washed in distilled water, counterstained with hematoxylin for 1 min, and mounted in Permount (Muto-Glass, Tokyo, Japan). Mouse IgG1 (DakoCytomation, Glostrup, Denmark) was used as a negative control primary antibody. Immunohistochemically stained sections were evaluated under a microscope (Olympus Optical Co. Ltd., Tokyo, Japan). The current study was performed in a retrospective manner, but all specimens were evaluated by two investigators blinded to the patients’ clinical information.
Results Experimental Strategy and Sample Preparation of Tissue Specimens. The aim of the present study was to identify proteins differentially overexpressed in cholangiocarcinoma and to validate these proteins as potential biomarkers. In the discovery phase, we collected cancerous and normal bile duct tissues from the same patients, who had been diagnosed with
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Figure 2. Comparison of total protein levels in cancerous versus normal bile samples. Silver stained SDS-PAGE of equal amounts of cancerous and normal bile duct tissue samples from cases 1 and 2; that is, the samples that were subjected to proteomic analysis. M, molecular weight markers; N, normal tissues; C, cancerous tissues.
cholangiocarcinoma, and a quantitative proteomic study was carried out as shown in Figure 1. First, we applied LC-MSbased semiquantitative proteomic analysis using spectral counting to identify proteins that may be differentially expressed in cancerous versus normal tissue. Next, a SILAC-label method was applied to verify the semiquantitative LC-MS approach. Selected up-regulated proteins were further validated by immunoblotting and different pathological features in cholangiocarcinoma were confirmed by immunohistochemical analysis. We used surgically removed tissue samples from two cases of cholangiocarcinoma (cases 1 and 2; Table 1) for the proteomic analysis and four additional cases were used in secondary studies. In tissue proteomics, the uniformity of protein distribution in surgical tissue specimens is an important issue for quantitative analysis. Because almost all surgical specimens are contaminated with blood, the protein distribution in tissue extracts is likely to include many blood components. In this study, we used surgical specimens washed with saline to remove bile and blood prior to freezing, and the protein distribution of the extracts was analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Samples were normalized based on protein concentration. Figure 2 shows a silver stained gel loaded with equal amounts of cancerous and normal bile duct tissue extracts from cases 1 and 2, the same samples subjected to proteomic analysis. There were no remarkable differences in the protein distribution of the cancerous versus normal tissue samples in terms of blood components such as albumin (70 kDa) and hemoglobin (15 kDa). The differential Journal of Proteome Research • Vol. 8, No. 8, 2009 4095
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Table 2. List of Up-Regulated Proteins Identified in Cholangiocarcinoma spectral counts
SILAC ratio
fold change in spectral counts
fold change from SILAC ratio
protein name
case 1
case 2
case 1
case 2
normal
cancer
normal
cancer
normal
cancer
normal
cancer
Mucin-5AC Versican core protein Serpin H1 Filamin-B Keratin, type II cytoskeletal 7 ATP-dependent DNA helicase 2 subunit 2 Ras GTPase-activating-like protein IQGAP1 14-3-3 protein sigma Proteasome subunit alpha type-7 Eukaryotic initiation factor 4A-I Olfactomedin-4 Cathepsin B Peptidyl-prolyl cis-trans isomerase B ADP-ribosylation factor 1 Neutral alpha-glucosidase AB Nucleoside diphosphate kinase B Isocitrate dehydrogenase [NADP] Protein SET Glucosidase 2 subunit beta Alpha-actinin-4 Moesin Prostagla-in E synthase 3 Elongation factor 1-beta Vinculin Protein DJ-1 Alpha-actinin-1 Cytosol aminopeptidase Ribonuclease inhibitor Transketolase Adenylyl cyclase-associated protein 1 Galectin-1 Calumenin Elongation factor 1-delta Annexin A1 Calmodulin Glutathione S-transferase P Peroxiredoxin-6 Ribosome-binding protein 1
-
-
3.2 4.0 10.9 1.9
4.5 2.7 2.4 5.0
-
5.3 5.7 3.0 3.0 3.0 2.3
-
7.0 4.7 4.0 2.3 1.0 1.3
0.09 0.06 0.02 0.13
0.28 0.22 0.25 0.26
0.07 0.04 0.07 0.03
0.33 0.11 0.16 0.14
-
-
7.1
2.8
-
2.0
-
1.7
0.05
0.35
0.02
0.07
8.0 -
10.0 2.0
7.0 1.8 2.7
12.1 1.8 1.6
0.3 -
0.7 2.3 2.0 2.3 2.7 3.0
0.3 0.7
2.3 0.3 0.7 3.3 1.3
0.02 0.06 0.10
0.17 0.10 0.26
0.02 0.06 0.11
0.26 0.11 0.19
6.0 4.0 3.0 6.0 4.0 7.0 3.1 3.0 6.0 2.0 5.0 3.0 3.5 3.7 2.8 3.0 2.1
6.0 12.0 3.0 6.0 2.0 6.0 20.0 2.0 6.0 2.5 4.0 3.0 2.0 3.0 2.3 4.5
1.8 1.6 9.1 1.5 1.3 4.7 1.7 2.6 7.8 2.1 1.7 -
2.1 2.2 2.6 1.9 2.0 1.4 1.3 1.0 1.2 -
0.3 1.0 1.0 0.7 0.7 0.3 3.0 4.3 0.3 0.3 0.7 0.7 0.7 1.0 1.3 1.7 2.3
2.0 4.0 3.0 4.0 2.7 2.3 9.3 13.0 2.0 0.7 3.3 2.0 2.3 3.7 3.7 5.0 5.0
0.3 0.3 0.3 0.3 0.3 2.0 0.3 0.3 0.3 0.7 0.7 1.3 0.7 0.7 1.0 0.7
2.0 1.3 4.0 1.0 2.0 0.7 12.0 6.7 0.7 2.0 1.7 2.7 4.0 1.3 2.0 2.3 3.0
0.42 0.16 0.24 0.22 0.50 0.05 0.35 0.47 0.04 0.46 0.08 -
0.76 0.24 2.23 0.32 0.66 0.25 0.59 1.21 0.28 0.99 0.32 2.73 0.14 -
0.11 0.10 0.27 0.04 0.18 0.40 0.02 0.33 0.03 0.05 -
0.22 0.22 0.11 0.34 0.78 0.03 0.44 0.03 2.11 0.06 -
3.0 2.7 3.0 2.2 2.2 2.7 2.0 2.0
2.0 2.3 2.0 3.2 3.0 2.0 2.3 2.1
2.1 2.4 7.4 1.9 2.6 1.4 2.1 1.4
1.2 1.4 1.6 2.4 1.3 1.7
1.3 1.0 0.7 4.0 2.0 2.3 2.0 3.0
4.0 2.7 2.0 8.7 4.3 6.3 4.0 6.0
1.3 1.0 0.7 1.7 0.7 3.0 1.0 3.0
2.7 2.3 1.3 5.3 2.0 6.0 2.3 6.3
0.36 0.17 0.03 0.47 1.31 0.55 0.26 1.81
0.76 0.42 0.25 0.89 3.40 0.77 0.55 2.51
0.34 0.17 0.03 0.15 0.41 1.57
0.43 0.23 0.05 0.37 1.53 0.53 2.73
case 1
case 2
case 1
case 2
Fold change values were calculated by dividing data from cancer samples into data from normal samples. The proteins with bold changes were applied to immuno blot and immunohistochemical analyses. “-”indicats “not detected” and/or “not calculated”.
expression analyses with LC-MS/MS were performed using equal amounts of protein extract after trypsin digestion. Mass Spectrometry and Semiquantitation for Differential Proteomic Profiling. To identify proteins differentially expressed between cancerous and normal tissue samples, we applied statistical analysis of the spectral counts, which is based on the idea that the frequency and number of peptides sequenced for a given protein in LC-MS/MS analysis provides an estimate of protein abundance. Spectral counting has become a widely used approach for measuring protein abundance in label-free LC-MS-based proteomics, and correlates linearly with protein abundance, even in a mixture of proteins, within 2 orders of magnitude.17 In this study, we carried out LC-MS/MS analysis in triplicate for each sample to assess reproducibility and reliability of both protein identification and spectral counting (Supplementary Figure 1, Supporting Information). The data generated by triplicate LC-MS/MS analysis of four samples (i.e., paired cancerous and normal tissue samples from cases 1 and 2) were searched against Homo 4096
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sapiens entries in the Swiss-Prot database using Mascot. Based on peptide spectral criteria with high confidence in Mascot (i.e., ion score, p < 0.005), proteins were identified and semiquantified by the spectral counts. In total, 482 proteins were identified when we limited the set to those with more than two unique tryptic peptides among all of the data from cases 1 and 2. The complete protein list can be found in Supplementary Table 1, Supporting Information. To identify differentially overexpressed proteins in cholangiocarcinoma, we studied the semiquantitative evaluation of protein concentration based on spectral counts between cancerous and normal tissue samples. In order to calculate fold changes, 309 proteins detected with at least two peptides in either single LC-MS/MS analysis were extracted from the set of 482 identified proteins (Supplementary Table 2, Supporting Information). Spectral count values for proteins in each sample were estimated as the mean value of three spectral count data sets from the triplicate LC-MS/MS analysis. We observed that 72 or 89 proteins were up-regulated (>2-fold change) and 31
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Figure 3. Representative mass spectra of tryptic peptides from overexpressed proteins. Shown, mass spectra of the tryptic peptides VGWEQLLTTIAR from actinin-4 (A) and VTVAGLAGKDPVQCSR from protein DJ-1 (B) in each of four cases as assayed by relative quantitative analysis using a SILAC-based method. Light peptides (L) are from tissue samples and heavy peptides (H) are from HeLa samples labeled with 13C6-lysine and 13C6,15N4-arginine as internal standards.
or 28 proteins were down-regulated (
