Label-free quantitative proteomics of CD133-positive liver cancer stem cells
© Tsai et al.; licensee BioMed Central Ltd. 2012
Received: 25 June 2012
Accepted: 2 November 2012
Published: 21 November 2012
CD133-positive liver cancer stem cells, which are characterized by their resistance to conventional chemotherapy and their tumor initiation ability at limited dilutions, have been recognized as a critical target in liver cancer therapeutics. In the current work, we developed a label-free quantitative method to investigate the proteome of CD133-positive liver cancer stem cells for the purpose of identifying unique biomarkers that can be utilized for targeting liver cancer stem cells. Label-free quantitation was performed in combination with ID-based Elution time Alignment by Linear regression Quantitation (IDEAL-Q) and MaxQuant.
Initially, IDEAL-Q analysis revealed that 151 proteins were differentially expressed in the CD133-positive hepatoma cells when compared with CD133-negative cells. We then analyzed these 151 differentially expressed proteins by MaxQuant software and identified 10 significantly up-regulated proteins. The results were further validated by RT-PCR, western blot, flow cytometry or immunofluorescent staining which revealed that prominin-1, annexin A1, annexin A3, transgelin, creatine kinase B, vimentin, and EpCAM were indeed highly expressed in the CD133-positive hepatoma cells.
These findings confirmed that mass spectrometry-based label-free quantitative proteomics can be used to gain insights into liver cancer stem cells.
KeywordsHepatocellular carcinoma LC-MS/MS Liver cancer stem cells Label-free quantitation
Numerous studies have identified a subpopulation of cells in a wide variety of tumors that possess stem cell characteristics, including the ability to self-renew and differentiate into heterogeneous tumor cells [1, 2]. These cells are called “cancer stem cells” (CSCs) or “tumor-initiating cells” (TICs) . CSCs were first discovered in leukemia [4, 5] and were subsequently identified in various solid tumors, including melanoma , breast cancer , brain tumors [8, 9], prostate cancer , head and neck cancer , lung cancer , colon cancer [13, 14], pancreatic adenocarcinoma , ovarian cancer , and hepatocellular carcinoma (HCC) [17, 18]. There is accumulating evidence that CSCs display drug resistance to many conventional therapies which therefore leads to cancer recurrence, suggesting that new cancer therapeutics may be required to target and eliminate the cancer stem cells. Therefore, a better understanding of the mechanisms that control the aspects of self-renewal and survival in cancer stem cells is very important. Furthermore, the identification of unique biomarkers could also facilitate the development of therapeutics that target CSCs.
The CD133 antigen (prominin-1) is a cell-surface glycoprotein that contains 865 amino acids, 5 trans-membrane domains, and 2 glycosylated extracellular loops . The glycosylated CD133 antigen that is recognized by AC133 monoclonal antibodies is a cell surface marker of hematopoietic stem cells and possibly hepatic stem cells [20, 21]. Recent findings have also revealed that the glycosylated CD133 antigen is also a potential marker for the isolation of CSCs from a wide variety of tumor tissues, including glioblastomas, lung cancer, pancreatic adenocarcinomas, prostate cancer, colon cancer and hepatocellular carcinomas [8, 10, 12–14, 22–24]. Recent reports suggest a subpopulation of liver cancer cells expressed the glycosylated CD133 antigen are with characteristics of CSCs and can lead to cancer progression and relapse [17, 23–25]. More importantly, CD133 is a prognostic marker that has either been linked to poor survival for liver cancer patients  or has been correlated with higher grade tumors . Therefore, CD133-positive liver cancer cells have been recognized as a critical target for liver cancer therapies.
The mass spectrometry (MS)-based proteomics technology has been shown as a powerful tool for large-scale protein identification and quantitation. To investigate the differentially expressed proteins in a complex biological system, strategies for reproducible and accurate quantification are required. Several quantitative proteomics methods have been developed, including stable isotope labeling and label-free methods . Although isotope labeling techniques have been widely used in quantitative proteomics research, researchers are increasingly turning to label-free shotgun proteomics techniques. Although both methods have certain strengths and weaknesses, MS-based label-free quantitative proteomics may be adequate for application in complex biological systems [29, 30]. Over the past few years, a limited number of groups have applied proteomics to investigate CSCs. For example, Ma et al., and Lee et al. had utilized two-dimension electrophoresis (2-DE) to determine the differently expressed proteins in CD133+ liver cancer cells and identified ALDH1A1 and transgelin were highly expressed in CD133+ cells compared with CD133- cells [31, 32]. Nevertheless, the 2-DE method is less efficient and time-consuming. Recently, Van Houdt et al. applied LC-MS/MS in combination with spectral counting for label-free quantitation to analyze colorectal cancer stem cells and identified BIRC6 as a key mediator of drug resistance against cisplatin and oxaliplatin . However, label-free quantitative proteomics encounters the problem of less accuracy although the method is rather simpler and cheaper.
Characterization of the CD133-positive subpopulation of the Huh7 hepatoma cells
Determination of the proteomes of the CD133+ and CD133- subpopulations of Huh7 cells and normal hepatocytes
Identification of the significantly up-regulated and down-regulated proteins in the CD133+ subpopulations of the Huh7 cells
Significantly up-regulated proteins in the CD133 + Huh7 cells
CD133 - Huh7 area
CD133 + Huh7 area
IDEAL-Q CD133 + /CD133 -
IDEAL-Q CD133 + /Hepatocyte
MaxQuant CD133 + /CD133 -b
Plasma membrane c
0.21 ± 0.05
0.13 ± 0.06
2.11 ± 0.30
0.01 ± 0.01
0.05 ± 0.01
0.69 ± 0.09
0.38 ± 0.03
0.82 ± 0.06
6.77 ± 0.47
0.02 ± 0.002
0.04 ± 0.01
0.33 ± 0.03
Collagen alpha-1(II) chain
0.01 ± 0.01
0.15 ± 0.05
0.94 ± 0.06
0.15 ± 0.003
0.44 ± 0.07
2.69 ± 0.48
0.05 ± 0.02
0.28 ± 0.04
1.29 ± 0.13
Tumor-associated calcium signal transducer 1 (EpCAM)
0.01 ± 0.004
0.12 ± 0.01
0.31 ± 0.02
Gastric-associated differentially-expressed protein YA61P
0.01 ± 0.01
0.42 ± 0.04
0.97 ± 0.07
Creatine kinase B-type
0.86 ± 0.15
6.39 ± 0.58
13.50 ± 2.43
In addition to the significantly up-regulated proteins, we also found 60 proteins that were down-regulated in the CD133+ subpopulations when compared to the CD133- subpopulations. The detailed list of the up- and down-regulated proteins is listed in Additional file 4: Table S4. Furthermore, in order to investigate the protein locations of the identified proteins, the Gene Ontology cellular component (GOCC) terms were annotated for the identified proteins. Twenty-one plasma membrane proteins had higher expression levels in the CD133+ Huh7 cells than in the CD133- Huh7 cells, six of them which has been listed in Table 1 had higher expression levels (>10-fold) in the CD133+ Huh7 cells when compared with the hepatocytes. We therefore proposed that these up-regulated plasma membrane proteins can possibly be used as markers for the enriched isolation of liver cancer stem cells.
Signaling molecules identified in the CD133+ Huh7 cells
In the current study, we not only identified the proteomes of human hepatocytes and CD133+/− Huh7 cells, but we also quantified the abundance of each protein and presented them in parts per million (ppm), which was then used to determine the protein concentration and to classify the individual protein as either a major or minor component. In addition, the protein ratio was used to determine the relative abundance in the CD133+/− subpopulation. We found that there were 2,590 proteins (77%) detected in both the CD133+ and CD133- Huh7 cells, but only 1,251 proteins (32%) were detected in all three cell types, suggesting the CD133+ cells and CD133- Huh7 cells have similar characteristics. Some of the calculated protein ratios were found to be different between the IDEA-Q results and the MaxQuant results. Both IDEA-Q and MaxQuant utilized the peptide intensity, retention time and mass to charge, but IDEAL-Q calculated the top three most abundant peptides and determined the protein ratio by dividing the average value. In contrast, the MaxQuant platform calculated the protein ratios as the median of all the ratios for the common peptides. The same method was also applied on a second liver cancer cell line, PLC/PRF/5 that expressed the hepatitis B surface antigen (HBsAg). We confirmed that annexin A1, annexin A3, creatine kinase B, and transgelin were also up-regulated in CD133+ PLC/PRF/5 cells. The proteomics results of PLC/PRF/5 cells are listed in Additional file 5: Table S5). We presume that this difference could be due to heterogeneity and the different causes of liver cancer. The identified proteins were searched against the GOCC, and we classified 409 IDs as plasma membrane proteins, including hepatic stem cell markers (CD133, CK19, EpCAM, and vimentin). These up-regulated plasma membrane proteins can be used as new surface targets to enrich or investigate liver CSCs. Interestingly, annexin A1 and annexin A3 were both up-regulated in the CD133+ Huh7 cells (7.8- and 6.1-fold, respectively). We found that annexin A2 was also slightly up-regulated (1.9-fold). These three proteins all belong to the same subgroup of the annexin family. Annexin A1 binds acidic phospholipids with a high affinity in the presence of calcium ions. It is also an endogenous anti-inflammatory protein that has roles in many diverse cellular functions, such as membrane aggregation, inflammation, phagocytosis, proliferation, and apoptosis . In a previous study, primary hepatocytes isolated from normal mice did not express annexin A1, but it was strongly expressed in the liver cells of transgenic mice during the development of hepatocarcinomas. Annexin A1 can be expressed by stimulating epidermal growth factor (EGF) or hepatic growth factor (HGF) . In addition, the up-regulation of annexin A1 in hepatic carcinogenesis has been correlated with the activation of the EGF pathway . In this study, we also observed more protein expression of EGF in the CD133+ Huh7 cells. Therefore, annexin A1 could be a potential marker for targeting liver CSCs. Annexin A3 had an approximately six-fold higher expression level in the CD133+ Huh7 cells compared with the CD133- Huh7 cells. The role of annexin A3 in cancer is still not fully understood, however, some proteomic studies have shown that annexin A3 is a biomarker in lung adenocarcinomas , and an overexpression of annexin A3 was detected in platinum-resistant human ovarian cancer cells [49, 50]. In the human prostate cancer xenograft model, annexin A3 was up-regulated and drug-resistant both in vivo and in the xenograft . In a previous study, annexin A3 was activated by a hepatocyte growth factor pathway and played an important role in rat liver regeneration . This implies that annexin A3 may be activated by a hepatocyte growth factor in the CD133+ Huh7 cells and is involved in liver tumor growth. In this study, EpCAM was highly expressed in the CD133+ Huh7 cells, and it was found to interact with CD133. EpCAM is a biomarker for hepatic stem cells, and it is also expressed in embryonic stem cells [53–56]. In 2009, EpCAM+ HCC cells were identified as possible liver cancer stem cells, and the expression of EpCAM is regulated by Wnt/β-catenin signaling . Currently, several antibody-based therapeutic approaches targeting EpCAM are being developed [57, 58]. These reports suggest that EpCAM is not only a biomarker of liver cancer stem cells but also may be a therapeutic target. Metastasis is the main cause of lethality in cancer patients. Cancer stem cells are responsible for both tumor invasion and metastasis [7, 18]. In the metastasis process, epithelial-mesenchymal transition (EMT) is a transient and reversible switch from an epithelial to a mesenchymal cellular phenotype, to become highly motile and invasive. EMT is regulated by the Wnt/β-catenin, TGFβ, and Notch pathways. In this study, we found several proteins that are involved in EMT and that were also up-regulated in the CD133+ Huh7 cells, such as transgelin, vimentin and collagen. Transgelin is a target of TGFβ signaling that regulates migration and invasion . In addition, it is also co-expressed with several EMT-associated genes, including N-cadherin, vimentin, Snail, and Twist . Vimentin is a mesenchymal marker that was also up-regulated in the CD133+ Huh7 cells. Vimentin was also found to be over-expressed in the HCC tissues, and it is involved in the metastasis of HCC . In addition, vimentin has been found to be expressed in multipotent progenitor cells from human fetal livers . Therefore, we identified higher levels of vimentin in CD133+ Huh7 cells, which may imply that the expression of vimentin is an important characteristic of liver CSCs. Translationally controlled tumor protein (TCTP) is a highly conserved, hydrophilic nuclear protein. TCTP is involved in many cellular processes [61, 62]. For example, TCTP interacts with BCL-XL to protect cells against apoptosis [63, 64]. More importantly, a recent report has shown that TCTP is a transcription factor that regulates the pluripotent gene oct4 , which is the key factor in regulating the pluripotency of embryonic stem cells. Thus, the higher levels of TCTP identified in the CD133+ Huh7 cells may imply that TCTP plays a role in regulating the stemness properties of liver CSCs.
With the assistance of the label-free quantitation analysis tool IDEAL-Q and MaxQuant, the current work identified several candidate proteins of liver cancer stem cells, which can possibly be used in the future for targeting liver cancer stem cells. Moreover, the proteome analysis utilized in the present work has provided a comprehensive characterization of CD133+ liver cancer stem cells, CD133- liver cancer cells, and human hepatocytes. We believe the proteome established in the current work can aid in the understanding of the molecular basis of the tumorigenic liver CSC subpopulations.
The hepatoma cell lines Huh7 and PLC/PRF/5  were maintained in Dulbecco’s modified Eagle’s medium (Hyclone, South Logan, UT, USA) supplemented with 10% fetal bovine serum (FBS) (Invitrogen, Carlsbad, CA, USA), 100 units/ml of penicillin, and 100 μg/ml of streptomycin (Invitrogen) at 37°C in a humidified atmosphere containing 5% CO2. Human hepatocytes, purchased from PromoCell, were maintained in Hepatocyte Growth Medium (PromoCell, HD, GRE) at 37°C in a humidified atmosphere containing 5% CO2.
Fluorescence-activated cell sorting
To isolate the CD133+ and CD133- fractions, Huh7 cells were re-suspended in Hank’s balanced saline solution (HBSS; Invitrogen) containing 2% FBS and 10 mM HEPES. The cell density was adjusted to 1 × 107/ml, and the Huh7 cells were stained with phycoerythrin (PE)-conjugated anti-human CD133 antibody (Miltenyi Biotec Inc., Cambridge, MA, USA). After staining, the Huh7 cells were re-suspended in HBSS containing 2% FBS and 1 mM HEPES and filtered through a 40 μm mesh filter, and the CD133+ and CD133- Huh7 fractions were sorted by a FACSAria™ (Becton Dickinson, San Jose, CA, USA).
Sphere forming assay
Freshly FACS-sorted CD133+ and CD133- Huh7 were plated into ultra-low attachment 6-well plates (Costar, Coining Inc) and cultured in DMEM/F12 medium supplemented with 1X B27 supplement, 1% penicillin-streptomycin, 0.4% bovine serum albumin, 200 ng/mL epidermal growth factor (EGF), 10 ng/mL basic fibroblast growth factor (bFGF), 5 μg/mL insulin at 37°C in a humidified atmosphere containing 5% CO2. Spheres were formed after 24 hours. The number of spheres that had diameter over 100 μM was count at Day 5.
RNA extraction and RT-PCR
To isolate the total RNA, 1 × 106 cells of both the CD133+ and CD133- Huh7 cell fractions were collected in microcentrifuge tubes containing 1 ml TRI Reagent® (Applied Biosystems, Foster City, CA, USA). The RNA was extracted according to the manufacturer’s protocol and treated with RQ-1 DNase (Promega, Madison, WI, USA) to remove contaminating genomic DNA. The purity and concentration of the RNA were determined with a NanoDrop® (NanoDrop Technologies, Wilmington, DE, USA). First strand complementary DNA was synthesized using SuperScript III reverse transcriptase (Invitrogen). The PCR reactions were performed with annealing temperatures of 57-58°C for either 23 or 35 cycles. The primer sequences used are listed in Additional file 6: Table S6).
The Huh7 cells were either cultured on non-coated glass coverslips or cytospined onto slides at 250 × g for 5 minutes. The cells were fixed with 4% paraformaldehyde in PBS for 30 minutes, permeabilized with 0.1% (v/v) Triton X-100 in PBS for 30 minutes, and incubated in 2% blocking buffer (Roche, Indianapolis, IN, USA) before sequential incubation with the primary and secondary antibodies. The antibodies were obtained as follows: mouse anti-human β-catenin was obtained from Becton Dickinson, rabbit anti-human α-fetoprotein was obtained from Dako, mouse anti-cytokeratin 19 and mouse anti-vimentin were purchased from Sigma, and the annexin A1 antibody was purchased from Abnova (Taipei City, Taiwan).
The cell extracts were prepared by lysing unsorted, CD133+, or CD133- Huh7 cells with RIPA buffer containing 150 mM NaCl, 50 mM Tris–HCl (pH 8), 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, protease inhibitors, and phosphatase inhibitors (Sigma). The cell extracts were run on an 8-10% SDS-PAGE gel and transferred onto a Hybond-P membrane (GE Healthcare, Buckinghamshire, NA, England), which was then probed with the primary antibodies. The antibodies used in the experiments are listed in Additional file 7: Table S7). The signal was detected with an enhanced chemiluminescence kit (GE Healthcare).
TOPFlash reporter (containing seven TCF/LEF binding sites) and FOPFlash reporter (containing six mutant TCF/LEF binding sites which was used as a negative control.) plasmids were originally obtained from Addgene (Dr. Randall Moon). Huh7 cells plated in 6-well plates at the density of 3 × 105 cells/well were grown for 24 hours. Either TOPFlash or FOPFlash reporter plasmids (1 μg/well) was transfected together with Renilla control luciferase plasmid (100 ng/well) using Fugene HD (Roche) according to the manufacturer’s instructions. 48 hours after transfection, Huh7 cells were harvested and stained with PE conjugated anti-human CD133 antibody. The CD133+ and CD133- Huh7 fractions were sorted by a FACSAria. After sorting, CD133+ and CD133- Huh7 cells were lysed, and then firefly and Renilla luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega).
Drug resistance assay
Huh7 cells were seeded at the density of 7,500 cells/well in 96-well plates and were treated with a serial dilution of 5-FU or Sorafenib for 48 hours. The cytotoxicity of the drug was determined using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltertrazolium bromide (MTT) colorimetric assay (Sigma).
The cell lysates were separated on a 12% polyacrylamide gel (10 × 10 cm, 0.75 mm thick), and the protein patterns were observed by Coomassie blue staining. Each lane was separated into 16 sections, and each section was cut into 0.75 mm3 cubes. The gel pieces were de-stained with a 25 mM NH4HCO3/50% acetonitrile (ACN) solution. After reduction and alkylation reactions using 10 mM DTT and 55 mM iodoacetamide, respectively, the proteins were digested overnight with modified trypsin (Promega, Madison, WI, USA) at 37°C. The tryptic peptides were extracted with 1% trifluoroacetic acid (TFA)/60% ACN. After lyophilization, the extracted peptides were re-dissolved in a 0.1% TFA solution.
The mass analysis experiments were performed using a 7-Tesla LTQ-FT-ICR (Linear quadrupole ion trap Fourier transform ion cyclotron resonance) mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a nanoelectrospray ion source (New Objective Inc., Woburn, MA, USA), an Agilent 1100 Series binary high-performance liquid chromatography pump (Agilent Technologies, Palo Alto, CA, USA), and a FAMOS autosampler (LC Packings, San Francisco, CA, USA). The peptide mixtures were injected (5 μl) at a flow rate of 10 μl/min onto a self-packed pre-column (150 μm I.D. × 15 mm, 5 μm, 100 Å). The chromatographic separation was performed on a self-packed reversed phase C18 nano-column (75 μm I.D. × 300 mm, 5 μm, 100 Å) at 300 nl/min with a 60 min gradient of 0 to 32% acetonitrile in 0.1% formic acid. The electrospray voltage was maintained at 2.5 kV, and the capillary temperature was set at 200°C. The full-scan MS spectra (m/z 300–1800) were acquired with the FT-ICR with a mass resolution of 100,000 at m/z 400. After the completion of the full-scan survey, the 10 most abundant ions detected in the full-MS scan were selected for peptide sequence determinations using MS/MS experiments. Each sample was analyzed in duplicate.
The peak-list files were obtained from the MS/MS data using Extract_msn 4.0 software (Thermo), which included the mass values and the intensity and charge of the precursor ions (parent ions with +2 or +3 charges in this study). For protein identification, the MS/MS data were submitted to the International Protein Index (IPI) human protein database (release 3.43, 72,340 sequences) using the MASCOT 2.2.2 search engine (Matrix Science, Boston, MA, USA) with the following settings: trypsin cleavage; a peptide mass matching error tolerance of 5 ppm; a fragment mass tolerance of 0.5 Da; fixed modification of carbamidomethylation of cysteine; variable modifications of deamidation of asparagine and glutamine, oxidation of methionine; a maximum of two missed cleavages; an ion score cutoff of 33 (p < 0.003); and a false discovery rate of < 1.1%. The false discovery rate was calculated by decoy database searching. The results were imported into Microsoft Excel for further analysis.
Quantitative data analysis
The IDEAL-Q method, which was developed previously , was utilized to process the LC-MS/MS data, and the Mascot search engine was used to extract the quantitative information. In brief, the raw data files were first converted into the mzXML data format by the ReAdW program (http://tools.proteomecenter.org/wiki/index.php?title=Software:ReAdW). The resulting mzXML files and the Mascot search results for the peptide and protein identification were used as the input for the quantitative tool. To process a peptide of an LC-MS run, we extracted the LC-MS data within the range of ± 1.5 minutes of its elution time and ± 3.5 Da of the precursor m/z value for quantitative analysis. We used the extracted ion chromatogram (XIC) to determine the peptide abundance in an LC-MS run. After determining the peptide abundance for each LC-MS run, we calculated the fraction’s peptide abundance by averaging the peptide abundances of the valid peptides in all of the LC-MS runs for the fraction. To determine the peptide abundance in a sample, we summed all of the peptide fraction abundances in the sample, and the peptide ratio between two samples was also calculated. To calculate the protein ratio, we selected the non-degenerate peptides and performed the Dixon’s test to eliminate the outliers for each protein. We then averaged the abundances of the top three most abundant peptides, and the protein ratio was determined by dividing the average value for each of the different cell types. The up-regulated or down-regulated proteins were defined as having a protein ratio with a significant change (p < 0.01). Label-free quantitation was also performed in MaxQuant [35, 36] version 18.104.22.168. The significance B and p-values were calculated by Perseus software.
Cancer stem cells
ID-based Elution time alignment by linear regression quantitation
Extracted ion chromatogram.
This work was supported by the Genomics Research Center, Academia Sinica, the National Science Council of Taiwan (NSC 99-2113-M-001-002-MY3), and the National Health Research Institute in Taiwan (NHRI-EX101-9803EI). The authors would like to acknowledge Miss Chiao-Yun Chien for assistance with immunofluorescent staining and Ms. K. J. Cherry Lin for help with manuscript preparation. Dr. P. R. Sudhir and Ms. Y. C. Sara Hu are acknowledged for their critical reading of the manuscript. This work was supported by the Genomics Research Center, Academia Sinica, and the National Health Research Institute.
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