Proteomic Analysis of Human Plasma Proteins by Two-Dimensional Gel Electrophoresis and by Antibody Arrays Following Depletion of High-Abundance Proteins

Desrosiers, Richard R.; Beaulieu, Édith; Buchanan, Marguerite; Béliveau, Richard

doi:10.1007/s12013-007-0048-z

Cited by 25 publications

(22 citation statements)

References 29 publications

(61 reference statements)

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“…Some literature has mentioned that the removal of abundant proteins may decrease the number or influence the pattern of less abundant proteins on 2-DE gels images[7]. This was not observed in this study.…”

Section: Discussioncontrasting

confidence: 53%

“…The identification of potential less abundant disease biomarkers may be complicated or masked by these highly abundant proteins [7]. As a result, finding an efficient method that can remove these abundant proteins and not jeopardizing the presentation of less abundant proteins on 2-DE gel images is important in disease biomarker research.…”

Section: Discussionmentioning

confidence: 99%

“…A practical thing to do will be to remove the unwanted and uninformative high abundant proteins, thus enhancing the disease related low abundant proteins. However, some studies have mentioned that the removal of abundant proteins may also remove some of the less abundant proteins[7]. …”

Section: Introductionmentioning

confidence: 99%

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Optimizing Human Synovial Fluid Preparation for Two-Dimensional Gel Electrophoresis

et al. 2011

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BackgroundProteome analysis is frequently applied in identifying the proteins or biomarkers in knee synovial fluids (SF) that are associated with osteoarthritis and other arthritic disorders. The 2-dimensional gel electrophoresis (2-DE) is the technique of choice in these studies. Disease biomarkers usually appear in low concentrations and may be masked by high abundant proteins. Therefore, the main aim of this study was to find the most suitable sample preparation method that can optimize the expression of proteins on 2-DE gels that can be used to develop a reference proteome picture for non-osteoarthritic knee synovial fluid samples. Proteome pictures obtained from osteoarthritic knee synovial fluids can then be compared with the reference proteome pictures obtained in this study to assist us in identifying the disease biomarkers more correctly.ResultsThe proteomic tool of 2-DE with immobilized pH gradients was applied in this study. A total of 12 2-DE gel images were constructed from SF samples that were free of osteoarthritis. In these samples, 3 were not treated with any sample preparation methods, 3 were treated with acetone, 3 were treated with 2-DE Clean-Up Kit, and 3 were treated with the combination of acetone and 2-D Clean-Up Kit prior to 2-DE analysis. Gel images were analyzed using the PDQuest Basic 8.0.1 Analytical software. Protein spots that were of interest were excised from the gels and sent for identification by mass spectrometry. Total SF total protein concentration was calculated to be 21.98 ± 0.86 mg/mL. The untreated SF samples were detected to have 456 ± 33 protein spots on 2-DE gel images. Acetone treated SF samples were detected to have 320 ± 28 protein spots, 2-D Clean-Up Kit treated SF samples were detected to have 413 ± 31 protein spots, and the combined treatment method of acetone and 2-D Clean-Up Kit was detected to have 278 ± 26 protein spots 2-DE gel images. SF samples treated with 2-D Clean-Up Kit revealed clearer presentation of the isoforms and increased intensities of the less abundant proteins of haptoglobin, apolipoprotein A-IV, prostaglandin-D synthase, alpha-1B-glycoprotein, and alpha-2-HS-glycoprotein on 2-DE gel images as compared with untreated SF samples and SF samples treated with acetone.ConclusionsThe acetone precipitation method and the combined treatment effect of acetone and 2-DE Clean-Up Kit are not preferred in preparing SF samples for 2-DE analysis as both protein intensities and numbers decrease significantly. On the other hand, 2-D Clean-Up Kit treated SF samples revealed clearer isoforms and higher intensities for the less abundant proteins of haptoglobin, apolipoprotein A-IV, prostaglandin-D synthase, alpha-1B-glycoprotein, and alpha-2-HS-glycoprotein on 2-DE gels. As a result, it is recommended that SF samples should be treated with protein clean up products such as 2-D Clean-Up Kit first before conducting proteomic research in searching for the relevant biomarkers associated with knee osteoarthritis.

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Section: Discussioncontrasting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

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Optimizing Human Synovial Fluid Preparation for Two-Dimensional Gel Electrophoresis

et al. 2011

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“…These abundant proteins limit the ionization efficiency during mass spectrometric analysis, preventing the identification of low abundance proteins. Although various techniques have been used for albumin and immunoglobulin depletion (24,25), we chose to perform size exclusion chromatography and centrifugal ultrafiltration to fractionate ascites fluid on the basis of molecular mass. Because the top 20 most abundant serum proteins have molecular masses greater than 30 kDa, we arbitrarily chose 30 kDa as the approximate molecular mass cutoff for the identification of the ascites fluid subproteome.…”

Section: Resultsmentioning

confidence: 99%

Mining the Ovarian Cancer Ascites Proteome for Potential Ovarian Cancer Biomarkers

Kuk

Kulasingam

Gunawardana

et al. 2009

Molecular & Cellular Proteomics

114

111

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Current ovarian cancer biomarkers are inadequate because of their relatively low diagnostic sensitivity and specificity. There is a need to discover and validate novel ovarian cancer biomarkers that are suitable for early diagnosis, monitoring, and prediction of therapeutic response. We performed an in-depth proteomics analysis of ovarian cancer ascites fluid. Size exclusion chromatography and ultrafiltration were used to remove high abundance proteins with molecular mass >30 kDa. After trypsin digestion, the subproteome (<30 kDa) of ascites fluid was determined by two-dimensional liquid chromatography-tandem mass spectrometry. Filtering criteria were used to select potential ovarian cancer biomarker candidates. By combining data from different size exclusion and ultrafiltration fractionation protocols, we identified 445 proteins from the soluble ascites fraction using a two-dimensional linear ion trap mass spectrometer. Among these were 25 proteins previously identified as ovarian cancer biomarkers. After applying a set of filtering criteria to reduce the number of potential biomarker candidates, we identified 52 proteins for which further clinical validation is warranted. Our proteomics approach for discovering novel ovarian cancer biomarkers appears to be highly efficient because it was able to identify 25 known biomarkers and 52 new candidate biomarkers that warrant further validation. Molecular & Cellular Proteomics 8:661-669, 2009.Accounting for ϳ3% of all new cancer cases in 2008 (1) with a 1 in 59 (1.7%) lifetime probability of developing the disease, ovarian cancer is the most lethal gynecological malignancy deeming 5-6% of all cancer deaths (1). Hidden deep within the pelvis, ovarian cancer is relatively asymptomatic in early stages, and because of the lack of adequate screening, ovarian cancer has resulted in the majority of cases being presented with late stage disease in association with a low 5-year survival rate of 25-40%. When presented at an early stage, the 5-year survival rate exceeds 90% and most patients are cured by surgery alone (2). Although the most widely used serum marker for ovarian cancer is carbohydrate antigen 125 (CA125), 1 its utility as a screening marker is limited because of its high false positive rates and elevation in other malignancies such as uterine, fallopian, colon, and gastric cancer (3, 4) as well as in non-malignant conditions such as pregnancy and endometriosis (5). These reasons alone demonstrate the need and immediate benefit in using biomarkers with increased sensitivity and specificity for early diagnosis, prognosis, or monitoring of ovarian cancer.Many advanced stage ovarian cancer patients exhibit rapid growth of intraperitoneal tumors along with abdominal distention as a result of accumulation of ascites fluid in the peritoneal cavity. Mechanistically ascites formation occurs as malignant cells secrete proteins, growth factors, and cytokines that cause neovascularization, angiogenesis, increased fluid filtration, and/or lymphatic obstruction (6 -8) resulting...

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“…First, depletion strategies are subtractive methods that use affinity methods, usually based on antigen∶antibody interaction for selective removal of abundant proteins from a sample [6], [7]. Almost without exception, this approach has been applied to human blood plasma [8]–[10] and has the goal of removal of major protein species that contribute the most intense ions in an LC-MS/MS analysis. Whilst such approaches can deplete abundant proteins (provided appropriate high specificity antibodies are available) they are not capable of enriching trace proteins, and under certain conditions can be non-specific in removing selected high abundance proteins [11].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Proteome Equalization of the Skeletal Muscle Proteome Using a Combinatorial Hexapeptide Library

et al. 2011

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Immobilized combinatorial peptide libraries have been advocated as a strategy for equalization of the dynamic range of a typical proteome. The technology has been applied predominantly to blood plasma and other biological fluids such as urine, but has not been used extensively to address the issue of dynamic range in tissue samples. Here, we have applied the combinatorial library approach to the equalization of a tissue where there is also a dramatic asymmetry in the range of abundances of proteins; namely, the soluble fraction of skeletal muscle. We have applied QconCAT and label-free methodology to the quantification of the proteins that bind to the beads as the loading is progressively increased. Although some equalization is achieved, and the most abundant proteins no longer dominate the proteome analysis, at high protein loadings a new asymmetry of protein expression is reached, consistent with the formation of complex assembles of heat shock proteins, cytoskeletal elements and other proteins on the beads. Loading at different ionic strength values leads to capture of different subpopulations of proteins, but does not completely eliminate the bias in protein accumulation. These assemblies may impair the broader utility of combinatorial library approaches to the equalization of tissue proteomes. However, the asymmetry in equalization is manifest at either low and high ionic strength values but manipulation of the solvent conditions may extend the capacity of the method.

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Proteomic Analysis of Human Plasma Proteins by Two-Dimensional Gel Electrophoresis and by Antibody Arrays Following Depletion of High-Abundance Proteins

Cited by 25 publications

References 29 publications

Optimizing Human Synovial Fluid Preparation for Two-Dimensional Gel Electrophoresis

Optimizing Human Synovial Fluid Preparation for Two-Dimensional Gel Electrophoresis

Mining the Ovarian Cancer Ascites Proteome for Potential Ovarian Cancer Biomarkers

Asymmetric Proteome Equalization of the Skeletal Muscle Proteome Using a Combinatorial Hexapeptide Library

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