A comparison of depletion versus equalization for reducing high-abundance proteins in human serum

In this work three methods to diminish the content of most highly abundant proteins in human serum have been studied and compared. Protein depletion with ACN or DTT and protein equalization with the ProteoMiner^? (PM) have been assessed by 1‐D gel electrophoresis and MS. After treatment 5, 18 and 9 major proteins within the 20 most abundant proteins in serum were identified for the ACN, DTT and PM methods, respectively. The ACN method was efficient for depleting high molecular weight proteins, over 75 KDa, resulting in 10±4% (n=3) of the total protein content remaining in the depleted serum. In addition, 75% of the proteins belonging to the group of the 20 most abundant proteins were not detected, making this depletion strategy a cheap alternative to expensive commercial tools regularly used for removing high abundance proteins from serum. The ACN extract was found rich in apolipoproteins. The dithithreitol method promotes the precipitation of proteins rich in disulfide bonds, mainly albumin, with 73±7% (n=3) of the total protein content remaining in the depleted serum, which was found rich in immunoglobulins. The PM method compresses the dynamic range of the serum proteins, rendering an extract containing 16±2% (n=3) of the total initial protein content. The extract was found to be rich in both apolipoproteins and immunoglobulins. As a general rule the DTT and PM methods provide a compression of the dynamic range of serum protein concentrations while the ACN method allows an effective depletion of the protein fraction above 72 KDa.     

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).     

去除血浆中高丰度蛋白质的二维液相色谱体系的建立

血浆中高丰度蛋白质的存在严重干扰低丰度蛋白质的检测,是困扰血浆蛋白质组学研究的技术瓶颈之一。针对这一热点问题,建立了一种二维液相色谱(强阴离子交换色谱-反相高效液相色谱)分离系统,对血浆中的高丰度蛋白质进行了色谱定位并进行去除。选择TSKgel SuperQ-5PW为第一维色谱分离柱,第二维色谱分离采用Jupiter C4柱,对第一维的馏分进行进一步的分离。通过梯度优化,血浆样品经过二维系统得到充分分离。第二维分离过程中从紫外信号强度高(215 nm,大于20 mAU)的峰中选择10个峰,利用液相色谱-串联质谱鉴定出32种高丰度蛋白质,包括人血清白蛋白、免疫球蛋白G等高丰度蛋白质。该体系为血浆中更多高丰度蛋白质的去除以及血浆蛋白质组学的更深入研究提供了重要思路。     

Comparison of protein precipitation methods for various rat brain structures prior to proteomic analysis

Sample preparation is a fundamental step in proteomic methodologies. The quality of the results from a proteomic experiment is dependent on the nature of the sample and the properties of the proteins. In this study, various pre-treatment methods were compared by proteomic analysis; we analysed various rat brain structures after chloroform/methanol, acetone, TCA/acetone and TCA protein precipitation procedures. The protein content of the supernatant was also examined by 2-DE. We found that for four of the rat brain structures, precipitation with chloroform/methanol and acetone delivered the highest protein recovery for top-down proteomic analysis; however, TCA precipitation resulted in good protein separation and the highest number of protein spots in 2-DE. Moreover, TCA precipitation also gave high efficiency of protein recovery if prior sonication procedure was performed.     

Proteomic profiling of human sera for discovery of potential biomarkers to monitor abstinence from alcohol abuse

Although numerous biomarkers or biomarker candidates have been discovered to detect levels of drinking and intervals of time after last drinking episode, only a few biomarkers have been applied to monitor abstinence in a longer interval (≥6 wks) from alcohol abuse. Considering sample sources, sensitivity, and specificity, new biomarkers from blood with better accuracy are needed. To address this, serum proteomic profiles were compared between pre‐ and post‐ treatment samples from subjects seeking treatment for alcohol abuse and dependence in an intensive 6wk daily outpatient program using high‐abundance plasma protein immunodepletion and LC‐MS/MS techniques. Protein identification, quantification, candidate biomarker selection, and prioritization analyses were carried out. Among the 246 quantified serum proteins, abundance of 13 and 45 proteins in female and male subjects were significantly changed (p ≤ 0.05), respectively. Of these biomarker candidate proteins, 2 (female) and 8 (male) proteins were listed in category 1, with high area under the receiver operating characteristic curve, sensitivity, specificity, and fold change. In summary, several new biomarker candidates have been identified to monitor abstinence from alcohol abuse.     

Protein profiling for cancer biomarker discovery using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and infrared imaging: a review

Cancer biomarker refers to a substance or process that is indicative of the presence of cancer in the body. A biomarker might be either a molecule secreted by a tumor or it can be a specific response of the body to the presence of cancer. Cancer biomarker-based diagnostics have applications for establishing disease predisposition, early detection, cancer staging, therapy selection, identifying whether or not a cancer is metastatic, therapy monitoring, assessing prognosis, and advances in the adjuvant setting. Full adoption of cancer biomarkers in the clinic has to date been slow, and only a limited number of cancer biomarker products are currently in routine use.Among proteomic technologies, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) is a technique that has allowed rapid progress in cancer biology. Different further developed methods including e.g. SELDI (surface-enhanced laser desorption/ionization) and MELDI (material-enhanced laser desorption/ionization) are simple and high-throughput techniques that analyze with high sensitivity and specificity intact proteins expressed in complex biological mixtures, such as serum, urine, and tissues. The combination of mass spectrometry (MS) with infrared (IR) spectroscopic imaging is an attempt to combine different technologies in systems analytics. Both MALDI-TOF and infrared tissue imaging enable studying proteins distribution in tissue samples with a resolution down to 50 and 5 μm, respectively.In this review, we summarize recent applications and the synergistic combination of these new technologies to proteomic profiling for cancer biomarker discovery.     

Combinatorial peptide ligand library plasma treatment: Advantages for accessing low‐abundance proteins

Depletion of major blood proteins is one of the most promising approaches to accessing low‐abundance biomarkers. This study compared the use of combinatorial peptide ligand library (CPLL) and albumin and immunoglobulins (IgGs) depletion technology for accessing these low‐abundance proteins in plasma using 2‐DE in an acidic restricted pH range (4–7). Compared with native plasma, both techniques enlarge the visibility of other proteins than albumin and IgG, but there were marked differences in their composition. An increase of the number of spots was detected compared with native plasma (157 spots) with 427 and 557 spots, respectively, detected with albumin and IgG depletion, and CPLL treatment. We selected 70 spots to be identified by MALDI‐TOF related to their absence in the 2‐D gels from native or albumin and IgG‐depleted plasma. The 42 spots identified corresponded to 24 different proteins, with more than half of the proteins which did not belong to the major plasma proteins. CPLL treatment allowed the accessibility to proteolytic fragments obtained from major plasma proteins. We found a large superiority of the CPLL approach over the albumin and IgG depletion process. These findings show the utility of depleting major blood proteins to be able to access low‐abundance proteins and the potential of CPLL to select and identify candidate biomarkers in clinical studies.     

Verification of protein biomarker specificity for the identification of biological stains by quadrupole time‐of‐flight mass spectrometry

Advances in proteomics technology over the past decade offer forensic serologists a greatly improved opportunity to accurately characterize the tissue source from which a DNA profile has been developed. Such information can provide critical context to evidence and can help to prioritize downstream DNA analyses. Previous proteome studies compiled panels of “candidate biomarkers” specific to each of five body fluids (i.e ., peripheral blood, vaginal/menstrual fluid, seminal fluid, urine, and saliva). Here, a multiplex quadrupole time‐of‐flight mass spectrometry assay has been developed in order to verify the tissue/body fluid specificity the 23 protein biomarkers that comprise these panels and the consistency with which they can be detected across a sample population of 50 humans. Single‐source samples of these human body fluids were accurately identified by the detection of one or more high‐specificity biomarkers. Recovery of body fluid samples from a variety of substrates did not impede accurate characterization and, of the potential inhibitors assayed, only chewing tobacco juice appeared to preclude the identification of a target body fluid. Using a series of 2‐component mixtures of human body fluids, the multiplex assay accurately identified both components in a single‐pass. Only in the case of saliva and peripheral blood did matrix effects appear to impede the detection of salivary proteins.     

Comparison of protein precipitation methods from adipose tissue using difference gel electrophoresis

Proteomic methods have great potential to aid our understanding of the functional and pathological roles of adipose tissue. A critical initial step in the proteomic studies is the efficient isolation of proteins before conducting detailed analysis. In this study, three different methods were used for precipitating proteins; we analyzed samples from visceral adipose tissue, subcutaneous adipose tissue, and stromal visceral fraction extracts after chloroform/methanol, acetone, and trichloroacetic acid precipitation. The proteins recovered after the precipitation steps were examined by 2D‐DIGE. Statistical analyses were carried out using simple linear regression analyses and R2 values were calculated for the intra‐ and inter‐method comparisons. We found that all three precipitation methods provided highly reproducible protein spots that were recovered when run in duplicate using the same method of precipitation, irrespective of whether it was solvent (R2 = 0.85–0.98) or acid‐based (R2 = 0.80–0.96). A higher variation and poor correlation was noted for the recovered protein spots when comparisons were made between the methods (R2 = 0.40–0.88) and also when the same method was compared between different sample types. In this study, TCA‐precipitated samples were enriched in lower molecular mass proteins compared to the samples extracted by solvent‐based precipitation methods.     

Thawing as a critical pre-analytical step in the lipidomic profiling of plasma samples: New standardized protocol

Lipid profiling is a promising tool for the discovery and subsequent identification of biomarkers associated with various diseases. However, data quality is quite dependent on the pre-analytical methods employed. To date, potential confounding factors that may affect lipid metabolite levels after the thawing of plasma for biomarker exploration studies have not been thoroughly evaluated. In this study, by means of experimental design methodology, we performed the first in-depth examination of the ways in which thawing conditions affect lipid metabolite levels. After the optimization stage, we concluded that temperature, sample volume and the thawing method were the determining factors that had to be exhaustively controlled in the thawing process to ensure the quality of biomarker discovery. Best thawing conditions were found to be: 4 °C, with 0.25 mL of human plasma and ultrasound (US) thawing. The new US proposed thawing method was quicker than the other methods we studied, allowed more features to be identified and increased the signal of the lipids. In view of its speed, efficiency and detectability, the US thawing method appears to be a simple, economical method for the thawing of plasma samples, which could easily be applied in clinical laboratories before lipid profiling studies.     

Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in‐gel electrophoresis

Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor‐specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non‐cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in‐gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine‐rich α2 glycoprotein‐1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.     

Sparse canonical correlation analysis applied to ‐omics studies for integrative analysis and biomarker discovery

With the rapid development of new ‐omics measurement methods, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods such as canonical correlation analysis (CCA) have been proposed to analyze the intrinsic correlation relationship by integrating two data sets. However, because of the high dimensionality of data and the relative scarcity of samples, the ordinary CCA is usually faced with variable selection problems and thereby fails to obtain a satisfactory relationship. Here, we explored the potential of sparse CCA (SCCA) to find the correlative components in two sparse views. SCCA aims at finding sparse projection directions to well extract the correlation between two data sets. We applied this method to one simulation data and one real ‐omics data to illustrate the performance of SCCA. The results from two studies show that SCCA could effectively find the correlated patterns between two data sets, which are of high importance for understanding the relationship between two underlying chemical or biological processes. The corresponding variable subsets selected by sparse weight vectors can assist in a better interpretation of the chemical or biological process. The integrative analysis from two views by SCCA helps in improving the discriminative ability of classification models for various ‐omics studies. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison of protein precipitation methods for sample preparation prior to proteomic analysis

Protein samples should be free of salt and other disturbing agents and have an appropriate concentration to be suitable for two-dimensional (2D) electrophoresis, the principal step of proteomics. To find the most efficient method for sample preparation, we used human plasma and compared four widely applied precipitation methods, using trichloroacetic acid (TCA), acetone, chloroform/methanol and ammonium sulfate, as well as ultrafiltration. Precipitation with TCA and acetone and ultrafiltration resulted in an efficient sample concentration and desalting. We also found that ammonium sulfate fractionation can efficiently remove albumin, which represents more than 50% of plasma proteins.     

Fractionation of the human plasma proteome for monoclonal antibody proteomics‐based biomarker discovery 2: Antigen identification by dot‐blot array screening

Immunization with complex mixtures, like the human plasma resulted in the generation of cloned mAb libraries (PlasmaScan? and QuantiPlasma? libraries, with >1000 individual mAbs) reacting with a nonredundant set of antigenic epitopes. mAb proteomics refers to quasi‐hypothesis‐free profiling of plasma samples with nascent or cloned mAb libraries for the discovery of disease‐specific biomarkers. Once mAbs with biomarker potential have been identified, the next task is the determination of cognate antigens recognized by the respective mAbs. To determine the cognate protein antigen corresponding to each individual mAbs in the cloned mAb libraries, we have separated human plasma by consecutive steps of desalting and various chromatography procedures. The process resulted in 783 fractions, which we termed “Analyte Library” (AL). The AL represents the human plasma proteome in relatively low‐protein complexity fractions. Here, to determine the utility of the AL, we selected ten plasma proteins and checked for their presence in the fractions. Among the ten cases, the distribution of four selected plasma proteins matched expectations, as these proteins were present only in a few fractions corresponding to their physical, chemical, and biochemical properties. However, in six cases, we observed “smear” ‐like distribution or complete absence of the proteins, suggesting that protein–protein interactions or protein variants may alter the observed plasma distribution profiles. Nevertheless, we conclude that the AL is an efficient, high throughput tool to complement the mAb biomarker discovery process with cognate protein antigen identification for each mAbs.     

Selectivity of monolithic supports under overloading conditions and their use for separation of human plasma and isolation of low abundance proteins

Human serum albumin (HSA) and immunoglobulin G (IgG) represent over 75% of all proteins present in human plasma. These two proteins frequently interfere with detection, determination and purification of low abundance proteins that can be potential biomarkers and biomarker candidates for various diseases. Some low abundance plasma proteins such as clotting factors and inhibitors are also important therapeutic agents. In this paper, the characterization of ion-exchange monolithic supports under overloading conditions was performed by use of sample displacement chromatography (SDC). If these supports were used for separation of human plasma, the composition of bound and eluted proteins in both anion- and cation-exchange mode is dependent on column loading. Under overloading conditions, the weakly bound proteins such as HSA in anion-exchange and IgG in cation-exchange mode are displaced by stronger binding proteins, and this phenomenon was not dependent on column size. Consequently, small monolithic columns with a column volume of 100 and 200 μL are ideal supports for high-throughput screening in order to develop new methods for separation of complex mixtures, and for sample preparation in proteomic technology.     

Lysine‐directed staining of proteins for MS‐based analyses

Visualization of proteins and MS‐based analyses are elemental tasks in modern biochemistry. Nevertheless, reports about covalent protein dyes and their suitability for subsequent MS experiments remain scarce. In a recent work, we demonstrated that covalent prestaining of proteins with Uniblue A drastically speeds up proteomic workflows. The present study introduces dabsyl chloride as another truly MS‐compatible protein stain. Remarkably, although Uniblue A and dabsyl chloride employ different nucleophilic reaction mechanisms, both are highly specific for lysine residues. The predictable peptide modifications allow easy integration into state‐of‐the‐art bioinformatic workflows. Further, lysine‐directed derivatizations with hydrophobic reagents such as dabsyl chloride complement the cysteine‐directed ALiPHAT strategy for increasing the sensitivity of peptide identifications.     

Proteomics in cancer research: methods and application of array-based protein profiling technologies

With the human genome sequence now determined, the field of molecular medicine is moving beyond genomics to proteomics, the large-scale analysis of proteins.It is now possible to examine the expression of more than 1000 proteins using mass spectrometry technology coupled with various separation methods.Microarray technology is a new and efficient approach, for extracting relevant biomedical data and has a wide range of applications. It provides a versatile tool to study protein-protein, protein-nucleic acid, protein-lipid, enzyme-substrate and protein-drug interactions.This review paper will explore the key themes in proteomics and their application in clinical cancer research.     

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical tools to distinguish such compounds from those showing only random variation. Two univariate methods, autocorrelation and curve-fitting, are used either as stand-alone methods or in combination to discover unknown metabolites in data sets originating from target-compound analysis. Both techniques reduce the long list of detected compounds in the kinetic sample set to include only those having a pre-defined interesting time profile. Thus, new metabolites may be discovered within data structures that are usually only used for target-compound analysis.The new strategy is tested on a sample set obtained from a gut fermentation study of a polyphenol-rich diet. For this study, the initial list of over 9000 potentially interesting features was reduced to less than 150, thus significantly reducing the expensive and time-consuming manual examination.     

High‐sensitive LC‐MS/MS method for the simultaneous determination of mirodenafil and its major metabolite,SK‐3541, in human plasma: Application to microdose clinical trials of mirodenafil

A high‐sensitivity LC/MS/MS method was developed and validated for the simultaneous determination of mirodenafil and its major metabolite, SK‐3541, in human plasma. Mirodenafil, SK‐3541, and udenafil as an internal standard were extracted from plasma samples with methyl tert‐butyl ether. Chromatographic separation was performed on a Luna phenyl‐hexyl column (100 × 2.0 mm) with an isocratic mobile phase consisting of 5 mM ammonium formate and ACN (23:77, v/v) at a flow rate of 0.35 mL/min. Detection and quantification were performed using a mass spectrometer in selected reaction monitoring mode with positive ESI at m/z 532.3 → 296.1 for mirodenafil, m/z 488.1 → 296.1 for SK‐3541, and m/z 517.3 → 283.2 for udenafil. The calibration curves were linear over a concentration range of 2–500 pg/mL using 0.5 mL plasma for the microdose of mirodenafil (100 μg). Analytical method validation of the clinical dose (100 mg), with a calibration curve range of 2–500 ng/mL using 0.025‐mL plasma, was also conducted. The other LC‐MS/MS conditions were similar to those used for the microdosing. Each method was applied successfully to pharmacokinetic studies after a microdose or clinical dose of mirodenafil to six healthy Korean male volunteers.     

High‐throughput negative detection of SDS‐PAGE separated proteins and its application for proteomics

A negative detection method for proteins on SDS‐PAGE is described. In this method, Eosin Y (EY) was selectively precipitated in the gel background, which is absent from those zones where proteins are located through the formation of a stable water‐soluble protein–dye complex. Negative staining of proteins using EY, allows high‐sensitivity, low‐cost, and simple protocol. The new described method takes less than an hour to complete all the protocol, with a detection limit of 0.5 ng of single protein band. Comparing with imidazole‐zinc negative stain, EY dye provides broader linear dynamic range, higher sensitivity and reproducibility, and better obvious contrast between the protein bands or spots and background. Furthermore, the novel technique developed here presented a real practical method for simultaneous processing of multiple gels, which makes it possible to perform high‐throughput staining for proteome research. Additionally, we have also compared the influence of staining method on the quality of mass spectra by PMF.