Oxidized derivatives of omega-3 fatty acids: identification of IPF3 alpha-VI in human urine

Isoprostanes (iPs) are prostaglandin-like molecules derived from autoxidation of polyunsaturated fatty acids (PUFAs). Urinary iP levels have been used as indices of in vivo lipid peroxidation. Thus far, it has only been possible to measure iPs derived from arachidonic acid in urine, because levels of iPs/neuroprostanes (nPs) derived from omega 3-PUFAs have been found to be below detection limits of available assays. Because of the interest in omega3-PUFA dietary supplementation, we developed specific methods to measure nPF4 alpha-VI and iPF3 alpha-VI [derived from 4,7,10,13,16,19-docosahexaenoic acid (DHA) and 5,8,11,14,17-eicosapentaenoic acid (EPA)] using a combination of chemical synthesis, gas chromatography/mass spectrometry (GC/MS), and liquid chromatography tandem mass spectrometry (LC/MS/MS). Although nPF4 alpha-VI was below the detection limit of the assay, we conclusively identified iPF3 alpha-VI in human urine by GC/MS and LC/MS/MS. The mean levels in 26 subjects were approximately 300 pg/mg creatinine. Our failure to detect nPF4 alpha-VI may have been due to its rapid metabolism by beta-oxidation to iPF3 alpha-VI, which we showed to occur in rat liver homogenates. In contrast, iPF3 alpha-VI is highly resistant to beta-oxidation in vitro. Thus iPF3 alpha-VI can be formed by two mechanisms: i) direct autoxidation of EPA, and ii) beta-oxidation of nPF4 alpha-VI, formed by autoxidation of DHA. This iP may therefore serve as an excellent marker for the combined in vivo peroxidation of EPA and DHA.     

LC-MS/MS methods for the detection of isoprostanes (iPF2alpha-III and 8,12-iso-iPF2alpha-VI) as biomarkers of CCl4-induced oxidative damage to hepatic tissue

Isoprostanes are formed after peroxidation of arachidonic acid and are promising biomarkers for reactive oxygen species. A LC-MS/MS based method was developed for the quantitation of two isoprostanes (iPF2alpha-III and 8,12-iso-iPF2alpha-VI) in hepatocytes, tissue and urine samples of rats. A column switching method was used to reduce sample preparation to a minimum. Precision was 9.4% and accuracy was between 96 and 114% for free iPF2alpha-III in tissue at concentrations from 1.9 to 6.1 ng/g. Treatment of rats with CCl4 to induce oxidative stress resulted in a dose-dependent increase (two- to three-fold) of iPF2alpha-III and 8,12-iso-iPF2alpha-VI in liver and kidney. For both isoprostanes an increase of four- to five-fold was observed in CCl4 treated hepatocytes and six- to eight-fold in CCl4 treated and glutathione depleted hepatocytes. In conclusion, the presented method is sensitive, specific and precise to be applied for the quantitation of iPF2alpha-III and 8,12-iso-iPF2alpha-VI which are shown to increase by CCl4 treatment in vitro and in vivo.     

Control of matrix effects in the analysis of urinary F2-isoprostanes using novel multidimensional solid-phase extraction and LC-MS/MS

F(2)-isoprostanes (F(2)-iPs), established markers of oxidative stress, exist as four sets of regioisomers. Simultaneous and specific determination of F(2)-iPs can be achieved by liquid chromatography-tandem mass spectrometry (LC-MS/MS). We developed novel methods for urine sample preparation and HPLC to control matrix-related ion suppression effects in the LC-MS/MS analysis of F(2)-iPs. A selective solid-phase extraction (SPE) wash protocol was developed with an Oasis HLB (hydrophilic-lipophilic balance) SPE cartridge using an elution profile of [(3)H]8-iso-prostaglandin (PG)F(2alpha) (iPF(2alpha)-III) when the methanol concentration was increased under acidic, neutral, and base wash conditions. A multidimensional (MD)-SPE method that incorporated size exclusion chromatography [corrected] reverse-phase chromatography, and normal-phase chromatography was developed using an Oasis HLB SPE cartridge and an HLB microElution SPE plate. Average extraction recoveries of the deuterated internal standards of iPF(2alpha)-III and iPF(2alpha)-VI were 62 +/- 8% and 60 +/- 10%. A buffer-free HPLC method for the separation of F(2)-iP isomers was developed on base-deactivated C8 columns. Average matrix effects for iPF(2alpha)-III and iPF(2alpha)-VI were 95 +/- 6% and 103 +/- 5%. The clean extraction of urine F(2)-iPs using MD-SPE and the separation of F(2)-iP isomers using a novel HPLC method did not cause apparent ion suppression in the analysis of iPF(2alpha)-III and iPF(2alpha)-VI using LC-MS/MS. These findings should be useful for establishing a routine LC-MS/MS method for the analysis of F(2)-iPs.     

Effects of 2 years of caloric restriction on oxidative status assessed by urinary F2‐isoprostanes: The CALERIE 2 randomized clinical trial

Calorie restriction (CR) without malnutrition slows aging in animal models. Oxidative stress reduction was proposed to mediate CR effects. CR effect on urinary F2‐isoprostanes, validated oxidative stress markers, was assessed in CALERIE, a two‐year randomized controlled trial. Healthy volunteers (n = 218) were randomized to prescribed 25% CR (n = 143) or ad libitum control (AL, n = 75) stratifying the randomization schedule by site, sex, and BMI. F2‐isoprostanes were quantified using LC‐MS/MS in morning, fasted urine specimens at baseline, at 12 and 24 months. The primary measure of oxidative status was creatinine‐adjusted 2,3‐dinor‐iPF(2α)‐III concentration, additional measured included iPF(2α)‐III, iPF2a‐VI, and 8,12‐iso‐iPF2a‐VI. Intention‐to‐treat analyses assessed change in 2,3‐dinor‐iPF(2α)‐III using mixed models assessing treatment, time, and treatment‐by‐time interaction effects, adjusted for blocking variables and baseline F2‐isoprostane value. Exploratory analyses examined changes in iPF(2α)‐III, iPF(2α)‐VI, and 8,12‐iso‐iPF(2α)‐VI. A factor analysis used aggregate information on F2‐isoprostane values. In CR group, 2,3‐dinor‐iPF(2α)‐III concentrations were reduced from baseline by 17% and 13% at 12 and 24 months, respectively; these changes were significantly different from AL group (p < .01). CR reduced iPF(2α)‐III concentrations by 20% and 27% at 12 and 24 months, respectively (p < .05). The effects were weaker on the VI‐species. CR caused statistically significant reduction in isoprostane factor at both time points, and mean (se) changes were ?0.36 (0.06) and ?0.31 (0.06). No significant changes in isoprostane factor were at either time point in AL group (p < .01 between‐group difference). We conclude that two‐year CR intervention in healthy, nonobese men and women reduced whole body oxidative stress as assessed by urinary concentrations of F2‐isoprostanes.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein–protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

A comprehensive peptidome profiling technology for the identification of early detection biomarkers for lung adenocarcinoma

The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000-5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p<0.01 and fold change >5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRM-based relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273-283, FIBA 5-16, and LBN 306-313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens.     

Practical 4'-phosphopantetheine active site discovery from proteomic samples

Polyketide and nonribosomal peptides constitute important classes of small molecule natural products. Due to the proven biological activities of these compounds, novel methods for discovery and study of the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) enzymes responsible for their production remains an area of intense interest, and proteomic approaches represent a relatively unexplored avenue. While these enzymes may be distinguished from the proteomic milieu by their use of the 4'-phosphopantetheine (PPant) post-translational modification, proteomic detection of PPant peptides is hindered by their low abundance and labile nature which leaves them unassigned using traditional database searching. Here we address key experimental and computational challenges to facilitate practical discovery of this important post-translational modification during shotgun proteomics analysis using low-resolution ion-trap mass spectrometers. Activity-based enrichment maximizes MS input of PKS/NRPS peptides, while targeted fragmentation detects putative PPant active sites. An improved data analysis pipeline allows experimental identification and validation of these PPant peptides directly from MS2 data. Finally, a machine learning approach is developed to directly detect PPant peptides from only MS2 fragmentation data. By providing new methods for analysis of an often cryptic post-translational modification, these methods represent a first step toward the study of natural product biosynthesis in proteomic settings.     

Specific analysis in plasma and urine of 2,3-dinor-5, 6-dihydro-isoprostane F(2alpha)-III, a metabolite of isoprostane F(2alpha)-III and an oxidation product of gamma-linolenic acid

F(2)-isoprostanes (iPs) are free radical-catalyzed isomers of prostaglandin F(2alpha). Circulating and urinary iPs have been used as indices of lipid peroxidation in vivo. Utilizing an (18)O-labeled homologous internal standard, we developed a gas chromatography/mass spectrometry assay for the 2,3-dinor-5,6-dihydro (dinor-dihydro) metabolite of iPF(2alpha)-III. Although urinary excretion of iPF(2alpha)-III reflects systemic lipid peroxidation, the metabolite is more abundant (median of 877 (range of 351-1831) versus 174 (range of 56-321) pg/mg of creatinine; p < 0.01) than the parent iP in urine and can be measured in plasma. Metabolite analysis may be preferable in plasma due to the abundance of arachidonic acid as a source of ex vivo lipid peroxidation. Also, iPF(2alpha)-III may be formed in blood samples in a cyclooxygenase-dependent manner by platelets ex vivo. By contrast, the metabolite is not formed by aggregated platelets (0.71 +/- 0.08 versus 0.65 +/- 0.09 pg/ml). Although the metabolite/parent ratio is altered in cirrhosis, urinary dinor-dihydro-iPF(2alpha)-III is elevated and increases further during reperfusion following orthoptic liver transplantation. In addition to its formation as an iPF(2) metabolite, analysis of gamma-linolenic acid autooxidation products and the compound present in freeze-thawed plasma suggests that gamma-linolenic acid may also be an important source of dinor-dihydro-iPF(2alpha)-III.     

QCMAP: An Interactive Web‐Tool for Performance Diagnosis and Prediction of LC‐MS Systems

The increasing role played by liquid chromatography‐mass spectrometry (LC‐MS)‐based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC‐MS systems. While numerous quality control tools have been developed to track the performance of LC‐MS systems based on a pre‐defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web‐based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC‐MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi‐user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC‐MS systems. It is demonstrated that the learned model can be applied to predict LC‐MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web‐application, QCMAP allows users to benchmark the performance of their LC‐MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from: http://shiny.maths.usyd.edu.au/QCMAP/ .     

Quantitation of isoprostane isomers in human urine from smokers and nonsmokers by LC-MS/MS

A simple, rapid liquid chromatography-tandem mass spectrometry method was developed to identify and quantitate in human urine the isoprostanes iPF(2 alpha)-III, 15-epi-iPF(2 alpha)-III, iPF(2 alpha)-VI, and 8,12-iso-iPF(2 alpha)-VI along with the prostaglandin PGF(2 alpha) and 2,3-dinor-iPF(2 alpha)-III, a metabolite of iPF(2 alpha)-III. Assay specificity, linearity, precision, and accuracy met the required criteria for most analytes. The urine sample storage stability and standard solution stability were also tested. The methodology was applied to analyze 24 h urine samples collected from smokers and nonsmokers on controlled diets. The results for iPF(2 alpha)-III obtained by our method were significantly correlated with results by an ELISA, although an approximately 2-fold high bias was observed for the ELISA data. For iPF(2 alpha)-III and its metabolite 2,3-dinor-iPF(2 alpha)-III, smokers had significantly higher concentrations than nonsmokers (513 +/- 275 vs. 294 +/- 104 pg/mg creatinine; 3,030 +/- 1,546 vs. 2,046 +/- 836 pg/mg creatinine, respectively). The concentration of iPF(2 alpha)-VI tended to be higher in smokers than in nonsmokers; however, the increase was not statistically significant in this sample set. Concentrations of the other three isoprostane isomers showed no trends toward differences between smokers and nonsmokers. Among smokers, the daily output of two type VI isoprostanes showed a weak correlation with the amount of tobacco smoke exposure, as determined by urinary excretion of total nicotine equivalents.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

Nanotechnologies in Glycoproteomics

Protein glycosylation, as an important post-translational modification, is implicated in a number of ailments. Applying proteomic approaches, including mass spectrometry (MS) analyses that have played a significant role in biomarker detection and early diagnosis of diseases, to the study of glycoproteins or glycopeptides will facilitate a deeper understanding of many physiological functions and biological pathways involved in cancer, inflammatory and degenerative diseases. The abundance of glycopeptides and their ionization potential are relatively lower compared to those of non-glycopeptides; therefore, sample enrichment is necessary for glycopeptides prior to MS analysis. The application of nanotechnology in the past decade has been rapidly penetrating into many diverse scientific research disciplines. Particularly in what we now refer to as the “glycoproteomics area”, nanotechnologies have enabled enhanced sensitivity and specificity of glycopeptide detection in complex biological fluids, which are critical for disease diagnosis and monitoring. In this review, we highlight some recent studies that combine the capabilities of specific nanotechnologies with the comprehensive features of glycoproteomics. In particular, we focus on the ways in which nanotechnology has facilitated the detection of glycopeptides in complex biological samples and enhanced their characterization by MS, in terms of intensity and resolution. These studies reveal an increasingly important role for nanotechnology in helping to overcome certain technical challenges in biomarker discovery, in general, and glycoproteomics research, in particular.     

Antibody‐based proteomics and biomarker research—Current status and limitations

Antibody‐based proteomics play a very important role in biomarker discovery and validation, facilitating the high‐throughput evaluation of candidate markers. Most proteomics‐driven discovery is nowadays based on the use of MS. MS has many advantages, including its suitability for hypothesis‐free biomarker discovery, since information on protein content of a sample is not required prior to analysis. However, MS presents one main caveat which is the limited sensitivity in complex samples, especially for body fluids, where protein expression covers a huge dynamic range. Antibody‐based technologies remain the main solution to address this challenge since they reach higher sensitivity. In this article, we review the benefits and limitations of antibody‐based proteomics in preclinical and clinical biomarker research for discovery and validation in body fluids and tissue. The combination of antibodies and MS, utilizing the best of both worlds, opens new avenues in biomarker research.     

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.     

Separation of kallikrein 6 glycoprotein subpopulations in biological fluids by anion-exchange chromatography coupled to ELISA and identification by mass spectrometry

Kallikrein 6 (KLK6) has been shown to be aberrantly glycosylated in ovarian cancer. Here, we report a novel HPLC anion exchange method, coupled to a KLK6-specific ELISA, capable of differentiating KLK6 glycoform subgroups in biological fluids. Biological fluids were fractionated using anion exchange and resulting fractions were analyzed for KLK6 content by ELISA producing a four-peak elution profile. Using this assay, the KLK6 elution profile and distribution across peaks of a set (n = 7) of ovarian cancer patient matched serum and ascites fluid samples was found to be different than the profile of serum and cerebrospinal fluid (CSF) of normal individuals (n = 7). Glycosylation patterns of recombinant KLK6 (rKLK6) were characterized using tandem mass spectrometry (MS/MS), and found to consist of a highly heterogeneous KLK6 population. This protein was found to contain all of the four diagnostic KLK6 peaks present in the previously assayed biological fluids. The rKLK6 glycoform composition of each peak was assessed by lectin affinity and MS/MS based glycopeptide quantification by product ion monitoring. The combined results showed an increase in terminal alpha 2-6 linked sialic acid in the N-glycans found on KLK6 from ovarian cancer serum and ascites, as opposed to CSF and serum of normal individuals.     

Synthesis,identification, and biological activity of metabolites of two novel selective S1P1 agonists

SYL927 and SYL930 are selective S1P₁ agonists under preclinical development. However, during their pharmacokinetic studies we detected two metabolites in rat blood that were tentatively identified as monohydroxylated metabolites of SYL927 and SYL930 based on LC–MS/MS data. In this study, we designed and synthesized possible monohydroxylated products 6a–e and used them as references to confirm the structures of the two metabolites detected by LC–MS/MS. We also evaluated the in vitro and in vivo biological activities of these two metabolites.     

Use of narrow‐range peptide IEF to improve detection of lung adenocarcinoma markers in plasma and pleural effusion

In this study we applied narrow‐range peptide IEF to plasma or pleural effusion prior to LC/MS/MS. Two methods for narrow‐range IEF were run; IPG strips and free‐flow electrophoresis. Data from this study was compared with cell line data to evaluate the method performance in body fluids. To test the methods potential in quantitative biomarker discovery studies, plasma and pleural effusion from patients with lung adenocarcinoma (n=3) were compared with inflammatory pleuritis (n=3) using iTRAQ quantification. Using narrow‐range IEF on the peptide level we were able to identify and quantify 282 proteins in plasma and 300 proteins in pleural effusion. These body fluid proteomes demonstrated high degree of overlap; however, more proteins significantly differently altered levels related to adenenocarcinoma were found in pleural effusion compared with plasma, suggesting enrichment of lung tissue‐related proteins in pleural effusion. Nine proteins were chosen for initial validation with Western blot, and one protein (NPC2) was chosen for further validation using imunohistochemistry. Overall, the quantitative results from IEF/LC/MS/MS showed good correlation with the results from Western blot and imunohistochemistry, showing the potential of this methodology in quantitative biomarker discovery studies.     

The knowledge-integrated network biomarkers discovery for major adverse cardiac events

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.     

Assessment of Blood Contamination in Biological Fluids Using MALDI-TOF MS

Biological fluid sample collection often includes the risk of blood contamination that may alter the proteomic profile of biological fluid. In proteomics studies, exclusion of contaminated samples is usually based on visual inspection and counting of red blood cells in the sample; analysis of specific blood derived proteins is less used. To fill the gap, we developed a fast and sensitive method for ascertainment of blood contamination in crude biological fluids, based on specific blood-derived protein, hemoglobin detection by MALDI-TOF MS. The MALDI-TOF MS based method allows detection of trace hemoglobin with the detection limit of 0.12 nM. UV-spectrometry, which was used as reference method, was found to be less sensitive. The main advantages of the presented method are that it is fast, effective, sensitive, requires very small sample amount and can be applied for detection of blood contamination in various biological fluids collected for proteomics studies. Method applicability was tested on human cerebrospinal and follicular fluid, which proteomes generally do not contain hemoglobin, however, which possess high risk for blood contamination. Present method successfully detected the blood contamination in 12 % of cerebrospinal fluid and 24 % of follicular fluid samples. High percentage of contaminated samples accentuates the need for initial inspection of proteomic samples to avoid incorrect results from blood proteome overlap.