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• W2036174939 abstract "Esophageal adenocarcinoma, currently the seventh leading cause of cancer-related death, has been associated with the presence of Barrett metaplasia. The malignant potential of Barrett metaplasia is evidenced by ultimate progression of this condition to invasive adenocarcinoma. We utilized liquid phase separation of proteins with chromatofocusing in the first dimension and nonporous reverse phase HPLC in the second dimension followed by ESI-TOF mass spectrometry to identify proteins differentially expressed in six Barrett metaplasia samples as compared with six esophageal adenocarcinoma samples; all six Barrett samples were obtained from the identical six patients from whom we obtained the esophageal adenocarcinoma tissue. Approximately 300 protein bands were detected by mass mappings, and 38 differentially expressed proteins were identified by μLC-MS/MS. The false positive rates of the peptide identifications were evaluated by reversed database searching. Among the proteins that were identified, Rho GDP dissociation inhibitor 2, α-enolase, Lamin A/C, and nucleoside-diphosphate kinase A were demonstrated to be up-regulated in both mRNA and protein expression in esophageal adenocarcinomas relative to Barrett metaplasia. Candidate proteins were examined at the mRNA level using high density oligonucleotide microarrays. The cellular expression patterns were verified in both esophageal adenocarcinomas and in Barrett metaplasia by immunohistochemistry. These differentially expressed proteins may have utility as useful candidate markers of esophageal adenocarcinoma. Esophageal adenocarcinoma, currently the seventh leading cause of cancer-related death, has been associated with the presence of Barrett metaplasia. The malignant potential of Barrett metaplasia is evidenced by ultimate progression of this condition to invasive adenocarcinoma. We utilized liquid phase separation of proteins with chromatofocusing in the first dimension and nonporous reverse phase HPLC in the second dimension followed by ESI-TOF mass spectrometry to identify proteins differentially expressed in six Barrett metaplasia samples as compared with six esophageal adenocarcinoma samples; all six Barrett samples were obtained from the identical six patients from whom we obtained the esophageal adenocarcinoma tissue. Approximately 300 protein bands were detected by mass mappings, and 38 differentially expressed proteins were identified by μLC-MS/MS. The false positive rates of the peptide identifications were evaluated by reversed database searching. Among the proteins that were identified, Rho GDP dissociation inhibitor 2, α-enolase, Lamin A/C, and nucleoside-diphosphate kinase A were demonstrated to be up-regulated in both mRNA and protein expression in esophageal adenocarcinomas relative to Barrett metaplasia. Candidate proteins were examined at the mRNA level using high density oligonucleotide microarrays. The cellular expression patterns were verified in both esophageal adenocarcinomas and in Barrett metaplasia by immunohistochemistry. These differentially expressed proteins may have utility as useful candidate markers of esophageal adenocarcinoma. Esophageal adenocarcinoma is increasing rapidly in Western countries and is currently the seventh leading cause of cancer-related death (1Jemal A. Thomas A. Murray T. Thun M. Cancer statistics, 2002.CA Cancer J. Clin. 2002; 52: 23-47Crossref PubMed Scopus (2931) Google Scholar). Esophageal adenocarcinoma has been associated with the presence of Barrett metaplasia, a condition in which the normal squamous epithelium of the esophagus is replaced by columnar epithelium. The malignant potential of this condition is evidenced by the progression of Barrett metaplasia to low grade dysplasia, high grade dysplasia, and ultimately to invasive adenocarcinoma. The risk of developing adenocarcinoma is 30–125 times higher in people who have Barrett metaplasia than people who do not. The prognosis of patients with esophageal adenocarcinoma remains poor with overall 5-year survival rates of only 5–15% (1Jemal A. Thomas A. Murray T. Thun M. Cancer statistics, 2002.CA Cancer J. Clin. 2002; 52: 23-47Crossref PubMed Scopus (2931) Google Scholar). Unfortunately patients often present with regionally advanced disease (2Jemal A. Siegel R. Ward E. Murray T. Xu J. Smigal C. Thun M.J. Cancer statistics, 2006.CA Cancer J. Clin. 2006; 56: 106-130Crossref PubMed Scopus (5521) Google Scholar). Given the poor prognosis associated with esophageal adenocarcinoma, it is imperative to improve our understanding of the tumorigenesis and the factors associated with increased risk. It is possible that therapeutic targets or protein markers can be identified that will ultimately facilitate improved patient survival. Proteomics technologies have been used for the identification of candidate markers for early cancer detection (3Tyers M. Mann M. From genomics to proteomics.Nature. 2003; 422: 193-197Crossref PubMed Scopus (786) Google Scholar). The global analysis of protein expression complements genomics analyses. For example, proteomics analysis may provide further insight into post-translational modifications affecting cellular function that otherwise could not be identified by genomics analysis. It is important to identify changes in global protein expression to identify specific proteins that are involved in cancer-related processes. We and others have demonstrated that two-dimensional (2-D) 1The abbreviations used are: 2-D, two-dimensional; NPS-RP, nonporous reverse phase; CF, chromatofocusing; OG, n-octyl β-d-glucopyranoside; bis-Tris, 2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diol; RhoGDI, Rho GDP dissociation inhibitor. liquid mass mapping can be used for quantitative and comparative proteomics analyses (4Lubman D.M. Kachman M.T. Wang H. Gong S. Yan F. Hamler R.L. O'Neil K.A. Zhu K. Buchanan N.S. Barder T.J. Two-dimensional liquid separations-mass mapping of proteins from human cancer cell lysates.J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2002; 782: 183-196Crossref PubMed Scopus (115) Google Scholar, 5Hamler R.L. Zhu K. Buchanan N.S. Kreunin P. Kachman M.T. Miller F.R. Lubman D.M. A two-dimensional liquid-phase separation method coupled with mass spectrometry for proteomic studies of breast cancer and biomarker identification.Proteomics. 2004; 4: 562-577Crossref PubMed Scopus (84) Google Scholar, 6Zhao J. Zhu K. Lubman D.M. Miller F.R. Shekhar M.P. Gerard B. Barder T.J. Proteomic analysis of estrogen response of premalignant human breast cells using a 2-D liquid separation/mass mapping technique.Proteomics. 2006; 6: 3847-3861Crossref PubMed Scopus (18) Google Scholar). Ion intensity-based quantitative approaches have progressively gained more popularity as mass spectrometry performance has improved significantly. 2-D fractionation techniques before mass detection simplify the complex proteome. Because mass is a unique tag for intact proteins, this method avoids the problem in quantitation induced by incomplete separation. In addition, liquid phase analysis allows easy interface to mass spectrometry analysis. In this study, we evaluated protein expression differences to identify markers of disease progression of Barrett metaplasia to esophageal cancer to gain further insight into potential mechanisms underlying these changes. Protein lysates were prepared from both high grade dysplasia and esophageal adenocarcinoma tissue, both obtained from the same six patients. These lysates were resolved by 2-D LC using chromatofocusing in the first dimension and nonporous silica reverse phase (NPS-RP) HPLC in the second dimension. Separated proteins in liquid phase were ionized by ESI-TOF, and the intact molecular weights were obtained after deconvolution of multiple charged peaks. Proteins were quantified based on individual molecular weight intensity and assembly of a protein mass map. Hierarchical clustering of the mass maps correctly segregated each of the 12 samples by histological type into Barrett metaplasia or esophageal adenocarcinoma. Differentially expressed proteins were identified by μLC-MS/MS after protease digestion. Candidate proteins were examined at the mRNA level using high density oligonucleotide microarrays as well as at the protein level using immunohistochemistry to verify cellular expression patterns. The 2-D mass maps and the corresponding protein identification have utility for analysis of cellular protein expression changes that are associated with progression of Barrett metaplasia to esophageal adenocarcinoma. Patients seen by the Section of General Thoracic Surgery at the University of Michigan Hospital between May 1994 and July 2004 for resection of esophageal adenocarcinoma were evaluated for inclusion in this study. Patients receiving previous chemotherapy or radiation treatment were excluded. Consent was obtained from all patients, and the project was approved by the local Institutional Review Board. Medical records were reviewed, and data were coded to protect patient confidentiality. Tumors and adjacent Barrett metaplasia tissue were collected immediately at the time of surgery and transported on ice to the laboratory in Dulbecco's modified Eagle's medium (Invitrogen). All tissue samples were stored at −80 °C. Hematoxylin-stained 5-μm frozen sections were reviewed by a board-certified pathologist (T. J. Giordano) with expertise in gastrointestinal pathology for tumor cellularity (adenocarcinomas) or mucosa (Barrett metaplasia). Specimens were excluded if tumor cellularity was less than 80% or if extensive lymphocytic infiltration or fibrosis was present. Barrett metaplasia or adenocarcinoma tissue samples were quickly thawed and immediately lysed with 2 ml of lysis buffer consisting of 7.5 m urea, 2.5 m thiourea, 4% n-octyl β-d-glucopyranoside (OG), 10 mm Tris(2-carboxyethyl)phosphine, 10% (v/v) glycerol, 50 mm Tris, and 40 μl of protease inhibitor solution (10-mg tablet in 1 ml of PBS buffer; Roche Applied Science). Samples were homogenized mechanically, vortexed frequently over a period of 1 h at room temperature, and then centrifuged at 30,000 rpm for 70 min at 4 °C. After the supernatant was collected, buffer exchange was conducted against the chromatofocusing start buffer using a PD-10 G-25 column (Amersham Biosciences). Protein concentration in each sample was quantified by the Bradford assay. Chromatofocusing (CF) separation was performed on an HPCF-1D column (250 × 2.1 mm) (Beckman-Coulter, Fullerton, CA) using a Beckman HPLC pump. The column was equilibrated with start buffer containing 25 mm bis-Tris propane, 6 m urea, and 1% OG adjusted to pH 7.4 with saturated iminodiacetic acid solution. After equilibration, 4.5 mg of total protein was loaded onto the CF column. Elution was achieved at pH 4.0 with elution buffer consisting of 10% (v/v) Polybuffer 74 (Amersham Biosciences), 6 m urea, and 1% OG at a flow rate of 0.2 ml/min. A linear pH gradient was generated at the outlet of the column that resulted in proteins eluting off the column according to their pI. Separation was monitored at 280 nm using a UV detector (Beckman-Coulter). The pH of the eluent was measured on line by a postdetector pH electrode/cell (Lazar Research Laboratories, Los Angeles, CA) with low dead volume. Fractions were collected at every 0.2 pH unit from pH 7.0 to 4.0 and split for further analysis by NPS-RP HPLC/ESI-TOF-MS and LC-MS/MS protein identification. Fractions obtained from CF were subjected to NPS-RP HPLC separation using an ODSIII-E (4.6 × 33-mm) column (Beckman-Coulter) packed with 1.5-μm non-porous silica. The reverse phase separation was performed at 0.5 ml/min, and a postcolumn splitter was used so that the 200 μl/min flow was directed into an orthogonal acceleration ESI-TOF MS system (LCT; Micromass/Waters, Milford, MA). Formic acid (0.5%) was added after the splitter through a T-connector using a syringe pump. The other 0.3 ml/min flow was monitored at 214 nm using a 166 Model UV detector (Beckman-Coulter), and peaks were collected by an automated fraction collector (Model SC 100; Beckman-Coulter) controlled by in-house designed DOS-based software. To enhance the speed, resolution, and reproducibility of the separation, the reverse phase column was heated to 60 °C using a column heater (Model 7971; Jones Chromatography). Both mobile phase A (water) and mobile phase B (ACN) contained 0.1% (v/v) TFA. The gradient profile was as follows: 5–15% B in 1 min, 15–25% B in 2 min, 25–31% B in 3 min, 31–41% B in 10 min, 41–47% B in 3 min, 47–67% B in 4 min, and 67–100% B in 1 min. The capillary voltage for electrospray was set at 3200 V, the sample cone was set at 35 V, the extraction cone was set at 3 V, and the reflection lens was set at 750 V. Desolvation was accelerated by maintaining the desolvation temperature at 330 °C and the source temperature at 130 °C. The desolvation gas flow was 650–800 liters/h. 1 μg of bovine insulin was introduced to each sample as an internal standard. The intact molecular weight was obtained by deconvoluting the combined ESI spectra with MaxEnt1 software. A mass map was generated by integrating the pI, Mr, and protein intensity of five CF fractions ranging from pH 4.6 to 5.6 into one single image using DeltaVue software (7Yan F. Sreekumar A. Laxman B. Chinnaiyan A.M. Lubman D.M. Barder T.J. Protein microarrays using liquid phase fractionation of cell lysates.Proteomics. 2003; 3: 1228-1235Crossref PubMed Scopus (57) Google Scholar). Maps with normalized protein value for Barrett tissue and the corresponding esophageal adenocarcinoma tissue from the same patient are shown at either side with a differential map of these two samples shown in the middle for comparison. The significance of protein expression differences was determined by χ2 analysis. After the mass maps of the pH 4.6–5.6 fractions from all 12 tissue samples were normalized based on insulin intensity and actin levels, the correlation of the mass maps was then visualized as a dendrogram by average-linked hierarchical clustering, which utilizes the mean distance between all possible pairs of entities of the two clusters. Pairs of samples are joined sooner if they have greater correlation. The length and the subdivision of the dendrogram branches reflect the relatedness of the 12 tissue samples based upon the expression of the individual proteins. Protein fractions of interest from NPS-RP HPLC were concentrated down to ∼20 μl with a SpeedVac concentrator (Labconco Corp., Kansas City, MO) operating at 60 °C. 20 μl of 100 mm ammonium bicarbonate with 5 mm DTT (Sigma-Aldrich) was then mixed with each concentrated sample to obtain a pH value of about 7.8. 0.5 μl of tosylphenylalanyl chloromethyl ketone-modified sequencing grade porcine trypsin (Promega, Madison, WI) was added and briefly vortexed prior to agitation for 20 h at 37 °C. Digested peptide mixtures were separated by a capillary RP column (C18, 0.3 × 50 mm) (Michrom Bioresources, Auburn, CA) on Paradigm MG4 micropumps (Michrom Bioresources) with a flow rate of 5 μl/min. Both solvent A (water) and solvent B (ACN) contained 0.3% formic acid. The gradient was initiated at 5% solvent B, ramped to 60% solvent B in 25 min, and then finally ramped to 90% in 5 min. The resolved peptides were analyzed on an LTQ mass spectrometer with an ESI ion source (Thermo, San Jose, CA). The capillary temperature was 175 °C, spray voltage was 4.2 kV, and capillary voltage was 30 V. The normalized collision energy was set at 35% for MS/MS. MS/MS spectra were searched using SEQUEST against the Swiss-Prot (8Boeckmann B. Bairoch A. Apweiler R. Blatter M.C. Estreicher A. Gasteiger E. Martin M.J. Michoud K. O'Donovan C. Phan I. Pilbout S. Schneider M. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003.Nucleic Acids Res. 2003; 31: 365-370Crossref PubMed Scopus (2796) Google Scholar) human protein database. One miscleavage was allowed. Methionine oxidation was considered as a variable modification during the SEQUEST analysis. Peptide tolerance was set at 1.5 Da for DTA file search. The search results were filtered according to specific criteria: the Xcorr cutoffs were set as 1.9, 2.5, and 3.5 for 1+, 2+, and 3+ charged peptides, respectively, and the ΔCn cutoff was set as 0.1. Positive protein identification was validated by the Trans-Proteomics Pipeline. This software includes both the PeptideProphet and ProteinProphet programs that were developed by Keller et al. (9Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.Anal Chem. 2002; 74: 5383-5392Crossref PubMed Scopus (3897) Google Scholar). The MS/MS data from all the UV peak fractions collected from reverse phase separation were searched independently against the normal and reversed human International Protein Index (IPI) protein database (41,216 protein entries) (ncrr.pnl.gov/data/) using the SEQUEST algorithm (Thermo Finnigan, San Jose, CA) for evaluation of the false positive rate (10Qian W.J. Liu T. Monroe M.E. Strittmatter E.F. Jacobs J.M. Kangas L.J. Petritis K. Camp III, D.G. Smith R.D. Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome.J Proteome Res. 2005; 4: 53-62Crossref PubMed Scopus (296) Google Scholar). The reversed protein database was created by reversing the order of amino acid sequences for each protein (the carboxyl terminus becomes the amino terminus and vice versa). The searching parameters were the same as above. The searching results were filtered according to specific criteria: the Xcorr cutoffs were set as 1.9, 2.5, and 3.5 for 1+, 2+, and 3+ charged peptides, respectively, and the ΔCn cutoff was set as 0.1. For correlation of protein and RNA gene expression patterns, a range of non-dysplastic and dysplastic Barrett samples and esophageal adenocarcinomas were examined by high density oligonucleotide microarrays as described previously (11Lin J. Raoof D.A. Thomas D.G. Greenson J.K. Giordano T.J. Robinson G.S. Bourner M.J. Bauer C.T. Orringer M.B. Beer D.G. l-type amino acid transporter-1 overexpression and melphalan sensitivity in Barrett's adenocarcinoma.Neoplasia. 2004; 6: 74-84Crossref PubMed Scopus (0) Google Scholar, 12Miller C.T. Lin L. Casper A.M. Lim J. Thomas D.G. Orringer M.B. Chang A.C. Chambers A.F. Giordano T.J. Glover T.W. Beer D.G. Genomic amplification of MET with boundaries within fragile site FRA7G and upregulation of MET pathways in esophageal adenocarcinoma.Oncogene. 2006; 25: 409-418Crossref PubMed Scopus (133) Google Scholar). We compared mRNA expression of 46 samples, including nine Barrett metaplasia, seven samples with a mixture of Barrett metaplasia and low grade dysplasia, eight low grade dysplasia, seven high grade dysplasia, and 15 esophageal adenocarcinomas. Total RNA was isolated from 50 esophageal samples using TRIzol (Invitrogen) and purified with RNeasy spin columns (Qiagen, Valencia, CA) according to the manufacturers' instructions. RNA quality was confirmed by 1% agarose gel electrophoresis and A260/A280 ratios. RNA quality was reassessed with the Agilent Bioanalyzer (Agilent Technologies, Palo Alto, CA) at intermediate steps after double-stranded cDNA and cRNA synthesis. Four samples were excluded due to insufficient quantity of RNA (<10 μg). cDNA synthesis, cRNA amplification, hybridization, and washing of the HG-U133A GeneChips (Affymetrix, Santa Clara, CA) were performed by the University of Michigan Cancer Center Microarray Core according to the manufacturer's instructions. To normalize the microarray data, a summary statistic was calculated using the 11 probe pairs for each gene and the robust multichip average method (13Irizarry R.A. Bolstad B.M. Collin F. Cope L.M. Hobbs B. Speed T.P. Summaries of Affymetrix GeneChip probe level data.Nucleic Acids Res. 2003; 31: e15Crossref PubMed Scopus (4024) Google Scholar) as implemented in the Affymetrix library of Bioconductor (version 1.3), which provides background adjustment, quantile normalization, and summarization. mRNA expression values for each sample were then compared with the mean expression value for the nine Barrett metaplasia samples. -Fold change greater than 2.0 was considered significant (14Risinger J.I. Maxwell G.L. Chandramouli G.V. Jazaeri A. Aprelikova O. Patterson T. Berchuck A. Barrett J.C. Microarray analysis reveals distinct gene expression profiles among different histologic types of endometrial cancer.Cancer Res. 2003; 63: 6-11PubMed Google Scholar). A tissue microarray was created, as described previously (15Kononen J. Bubendorf L. Kallioniemi A. Barlund M. Schraml P. Leighton S. Torhorst J. Mihatsch M.J. Sauter G. Kallioniemi O.P. Tissue microarrays for high-throughput molecular profiling of tumor specimens.Nat. Med. 1998; 4: 844-847Crossref PubMed Scopus (3536) Google Scholar), with formalin-fixed, paraffin-embedded tissues from 70 patients including 64 tumors, eight lymph node metastases, 11 dysplastic Barrett mucosa, 11 non-dysplastic Barrett metaplasia samples, and normal esophagus. 4-μm sections were transferred to poly(l-lysine)-coated slides, deparaffinized with xylene, rehydrated with a graded series of alcohols, and then rinsed with PBS. Antigen retrieval was performed using microwave pretreatment for 15 min in 0.01 m citrate buffer, pH 6.0. The slides were then blocked with 5% normal goat serum in PBS for 20 min, and endogenous peroxidase activity was quenched with 0.5% hydrogen peroxide in PBS. The slides were incubated with either a mouse anti-ENO1 antibody at a 1:1000 dilution (Abnova Corp., Niehu, Taiwan), rabbit anti-ARHGD1B antibody at a 1:50 dilution (Abnova Corp.), or anti-Lamin A/C (Chemicon International, Temecula, CA) at a 1:50 dilution for 1 h in 1.5% normal goat serum. After washing with PBS, the slides were incubated with a 1:1000 dilution of biotinylated goat anti-mouse antibody (Southern Biotechnology Associates, Birmingham, AL) or anti-rabbit antibody (Vector Laboratories Inc., Burlingame, CA) for 1 h and visualized using an avidin-biotin complex detection kit (VECTASTAIN ABC-GO kit; Vector Laboratories Inc.). A 0.1% solution of 3,3′-diaminobenzidine (Vector Laboratories Inc.) served as the chromagen. The slides were then counterstained with hematoxylin. Each sample was scored using a scale of 0, 1, 2, or 3 corresponding to absent, light, moderate, or intense staining. Samples without tumor or esophageal mucosa were excluded from analysis. Tissue lysates containing 4.5 mg of protein each from the six premalignant Barrett metaplasia tissues and six esophageal adenocarcinomas (both from the same set of six patients to eliminate confounding genetic polymorphisms) were resolved individually in the first dimension using CF, which combines the high capacity of ion-exchange chromatography with the high resolution of IEF. The proteins elute off the column sequentially according to their pI. Fig. 1a shows the CF chromatogram of a representative esophageal adenocarcinoma extract measured at 280 nm. Approximately 60 min was required for protein to elute off the column linearly from pH 7 to 4. Fractions were collected at 0.2 pH intervals with most protein eluting out between pH 5.8 and 4.4. The fractions of interest (pH 4.6–5.6) were further separated by NPS-RP HPLC at a flow rate of 0.5 ml/min. A postcolumn splitter was used so that 0.2 ml/min flow was directed into an orthogonal acceleration ESI-TOF MS instrument. The other 0.3 ml/min flow was directed through a UV detector at 214 nm, and the fractions were collected by UV peak for protein identification. Fig. 1b shows the UV chromatogram of an esophageal adenocarcinoma CF fraction (pH 5.2–5.0) at 214 nm. On-line ESI-TOF MS was used to obtain intact protein molecular weights. As shown in Fig. 1c, a series of multiple charged ion peaks in the combined spectrum having the same retention time as the circled UV peak in Fig. 1b were deconvoluted, and an intact mass was obtained (Fig. 1d). The theoretical mass of the protein, nucleoside-diphosphate kinase A, is 17,149 Da, and the experimental mass is 17,150 Da, which suggests a mass accuracy of 58 ppm. The abundance of each protein is indicated by peak area of the deconvoluted peak of the intact protein. Combined spectra of a selected ion chromatogram were generated for each protein to obtain a composite of that present in each CF fraction. 1 μg of pure insulin was also added to each CF fraction as shown in Fig. 1b, and the deconvoluted peak area was used as internal standard to normalize experimental variation. A mass map was created by integrating the Mr, pI, and the relative abundance of all proteins in all CF fractions into one single image using DeltaVue software. As shown in Fig. 1e, a mass map was generated by integrating the pI, Mr, and abundance of five CF fractions from pH 4.6 to 5.6. The map for Barrett tissue is shown on the left, and the map for esophageal adenocarcinomas from the same patient is shown on the right. The y axis represents intact Mr, and the x axis indicates the pH range of the CF fractions. The abundance of each protein is indicated by the intensity of each band on this map. To test the dynamic range and ion abundance variance of our approach, different amounts of bovine pancreatic Ribonuclease A were spiked into 30 μg of whole cell lysate of an ovarian cancer cell line. The theoretical mass of Ribonuclease A is 13,690 Da with the actual mass detected as 13,686 Da. The correlation of protein abundance and its corresponding molecular weight ion intensity is shown in Fig. 2. As indicated, the Mr ion intensities increased linearly with the protein abundance at 1:1 ratio when the protein abundance fell in the range of 0.001–2.0 μg. Above 2 μg, the ions were saturated; thus the intensity will not increase linearly. No ion signals were detected when the protein level fell below 0.001 μg. This might be due to limitations in instrument sensitivity and/or data processing software. From the ion intensities, the individual protein abundance in a CF fraction usually fell in the 0.01–1 level. This result indicates that the dynamic range is ∼2000, thereby facilitating our ability to semiquantify protein abundance in complex protein mixtures. As shown in Table I, the standard deviation of total ion intensities of the six individual samples of esophageal tissues is 15.3% of the averaged value. For specific ions, the variance percentage in the six individual samples is 13.2% for the band at 26,867 with an averaged intensity of 2277. For proteins with higher abundance, the variance percentage is 8.2% for the band at 32,716 and 5.7% for the band at 54,318 with an averaged intensity of 8301 and 48,638, respectively. As indicated, lower ion variance was achieved for the higher abundant proteins. We also examined the ion variance by repeating the analysis of the same amount of whole cell lysate from the same ovarian cancer cells (OVCA 432 cell line) five times using LC-ESI-TOF. As shown in Table II, the averaged ion variance is 5.1% for the Mr ions with intensities higher than 10,000 and 9.2% for intensities lower than 10,000. For the total ion intensities, the variance is 5.8%. The ion variance for the cell line sample was lower than that of the tissue samples because it was not affected by individual variance. The mass maps of three individual experiments are shown in Supplemental Fig. 1. Very similar patterns were observed especially with the high intensity Mr bands, thus demonstrating good reproducibility of the approach.Table IMr ion variance of six individual samples from normal esophageal tissuesIntensityAVEaThe average value of the target Mr ion intensities from different runs.S.D.%bThe percentage of S.D. in the average ion intensity.Mr = 26,867Low2,277300.113.2Mr = 32,716Medium8,301678.78.2Mr = 54,318High48,6382,793.95.7Total ion intensityAll Mr ions3,031,398462,890.315.3a The average value of the target Mr ion intensities from different runs.b The percentage of S.D. in the average ion intensity. Open table in a new tab Table IIMr ion variance of five runs of an OVCA432 cell line sampleIntensityAVEaThe average value of the target Mr ion intensities from different runs.S.D.%b% is the percentage of S.D. in the average ion intensity.High intensity ions>10,000cAll the Mr ions with intensity higher or lower than 10,000.2,026,708102,754.35.1Low intensities ions<10,000cAll the Mr ions with intensity higher or lower than 10,000.741,55267,980.69.2Total ion intensitiesAll Mr ions2,782,918160,156.65.8a The average value of the target Mr ion intensities from different runs.b % is the percentage of S.D. in the average ion intensity.c All the Mr ions with intensity higher or lower than 10,000. Open table in a new tab There are ∼300 bands present for each sample with the dynamic range of observable protein banding patterns being approximately 3 orders of magnitude. A differential map is shown in the middle of Fig. 1e that indicates the differentially expressed proteins between the Barrett metaplasia and the corresponding adenocarcinoma sample from the same patient. Both bovine insulin and actin were used for normalization. Insulin was injected with each sample as an internal control and thus to correct for experimental variation. The total intensity of the actin isoforms, which is shown in the Mr range of 41,600–41,900, was then used for normalization of sample amounts and for the protein assay to load similar amounts of protein from each sample. The mass maps of all pH 4.6–5.6 fractions from the 12 tissue samples were normalized based on insulin intensity and actin levels. Correlation of the mass maps was assessed using hierarchical clustering. Fig. 3 illustrates the" @default.
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• W2036174939 date "2007-06-01" @default.
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• W2036174939 title "Comparative Proteomics Analysis of Barrett Metaplasia and Esophageal Adenocarcinoma Using Two-dimensional Liquid Mass Mapping" @default.
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• W2036174939 doi "https://doi.org/10.1074/mcp.m600175-mcp200" @default.
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