Proteomic profile analysis and biomarker discovery from mass spectra using independent component analysis combined with uncorrelated linear discriminant analysis

A strategy based on Independent Component Analysis (ICA) and Uncorrelated linear discriminant analysis (ULDA) was proposed for proteomic profile analysis and potential biomarker discovery from proteomic mass spectra of cancer and control samples. The method mainly includes 3 steps: (1) ICA decomposition for the mass spectra; (2) selection of discriminatory independent components (ICs) using nonparametric Mann-Whitney U-test; and (3) selection of special peaks (m/z locations) as potential biomarkers by executing of ULDA on a mass spectra data set which was reconstructed with the m/z locations that collected from the selected discriminatory ICs. A colorectal cancer data set and an ovarian cancer data set were analyzed with the proposed method. As results, 9 and 10 m/z locations were selected as potential biomarkers for the colorectal and ovarian cancer data set respectively. The classification results of ULDA using the selected potential biomarkers yielded better results than fisher discriminant analysis (FDA) and principal component analysis (PCA), and could distinguish the disease samples from healthy controls on the independent test sets with 100% of sensitivities and specificities for the colorectal cancer dataset and 100% of sensitivity and 96.77% of specificity for the ovarian cancer dataset.     

Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe: application to the analysis of acetaminophen metabolites in urine

Cryogenic cooling of the NMR radio frequency coils and electronics to give greatly enhanced sensitivity is arguably the most significant recent advance in NMR spectroscopy. Here we report the first cryogenic probe built in flow configuration and demonstrate the application to LC-NMR-MS studies. This probe provides superior sensitivity over conventional noncryogenic flow NMR probes, allowing the use of 100 microL of untreated urine (40% less material than previous studies that required preconcentration) and yet revealing drug metabolites hitherto undetected by LC-NMR-MS at 500 MHz. Besides the known sulfate and glucuronide metabolites, previously undetected metabolites of acetaminophen were directly observable in a 15-min on-flow experiment. Simultaneous MS data also provided knowledge on the NMR-silent functional moieties. Further, stop-flow LC-NMR-MS experiments were conducted for greater signal-to-noise ratios on minor metabolites. The cryoflow probe enables the NMR analysis of lower concentrations of metabolites than was previously possible for untreated biofluids. This strategy is generally applicable for samples containing mass-limited analytes, such as those from drug metabolism studies, biomarker and toxicity profiling, impurity analysis, and natural product analysis.     

Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.     

Practical approach for the identification and isomer elucidation of biomarkers detected in a metabonomic study for the discovery of individuals at risk for diabetes by integrating the chromatographic and mass spectrometric information

Sensitive and high-resolution chromatographic-driven metabonomomics studies experienced major growth with the aid of new analytical technologies and bioinformatics software packages. Hence, data collections by LC-MS and data analyses by multivariate statistical methods are by far the most straightforward steps, and the detection of biomarker candidates can easily be achieved. However, the unequivocal identification of the detected metabolite candidates, including isomer elucidation, is still a crux of current metabonomics studies. Here we present a comprehensive analytical strategy for the elucidation of the molecular structure of metabolite biomarkers detected in a metabonomics study, exemplified analyzing spot urine of a cohort of healthy, insulin sensitive subjects and clinically well characterized prediabetic, insulin resistant individuals. An integrated approach of LC-MS fingerprinting, multivariate statistic analysis, LC-MSn experiments, micro preparation, FTICR-MS, GC retention index, database search, and generation of an isotope labeled standard was applied. Overall, we could demonstrate the efficiency of our analytical approach by the unambiguous elucidation of the molecular structure of an isomeric biomarker candidate detected in a complex human biofluid. The proposed strategy is a powerful new analytical tool, which will allow the definite identification of physiologically important molecules in metabonomics studies from basic biochemistry to clinical biomarker discovery.     

Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies

Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz 1H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.     

Triple signal amplification of graphene film, polybead carried gold nanoparticles as tracing tag and silver deposition for ultrasensitive electrochemical immunosensing

A triple signal amplification strategy was designed for ultrasensitive immunosensing of cancer biomarker. This strategy was achieved using graphene to modify immunosensor surface for accelerating electron transfer, poly(styrene-co-acrylic acid) microbead (PSA) carried gold nanoparticles (AuNPs) as tracing tag to label signal antibody (Ab(2)) and AuNPs induced silver deposition for anodic stripping analysis. The immunosensor was constructed by covalently immobilizing capture antibody on chitosan/electrochemically reduced graphene oxide film modified glass carbon electrode. The in situ synthesis of AuNPs led to the loading of numerous AuNPs on PSA surface and convenient labeling of the tag to Ab(2). With a sandwich-type immunoreaction, the AuNPs/PSA labeled Ab(2) was captured on the surface of an immunosensor to further induce a silver deposition process. The electrochemical stripping signal of the deposited silver nanoparticles in KCl was used to monitor the immunoreaction. The triple signal amplification greatly enhanced the sensitivity for biomarker detection. The proposed method could detect carcinoembryonic antigen with a linear range of 0.5 pg mL(-1) to 0.5 ng mL(-1) and a detection limit down to 0.12 pg mL(-1). The immunosensor exhibited good stability and acceptable reproducibility and accuracy, indicating potential applications in clinical diagnostics.     

Cross-platform method for identifying candidate network biomarkers for prostate cancer

Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein?protein interactions (PPIs) to discover high confidence network biomarkers. Using this pipeline, a total of 474 molecules (genes and proteins) related to prostate cancer were identified and a prostate-cancer-related network (PCRN) was derived from the integrative information. Thus, a set of candidate network biomarkers were identified from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. The network biomarkers with PPIs can accurately distinguish the prostate patients from the normal ones, which potentially provide more reliable hits of biomarker candidates than conventional biomarker discovery methods.     

Sample classification based on Bayesian spectral decomposition of metabonomic NMR data sets

1H NMR spectra of biofluids provides a wealth of biochemical information on the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, the data have been shown to provide information on disease diagnosis and the beneficial and adverse effects of potential therapeutics. Here, a novel approach is described for identifying subsets of spectral patterns in databases of NMR spectra, and it is shown that the intensities of these spectral patterns can be related to the onset and recovery from a toxic lesion in both a time-related and dose-related fashion. These patterns form a new type of combination biomarker for the biological effect under study. The approach is illustrated with a study of liver toxicity in rats using NMR spectra of urine following administration of a model hepatotoxin hydrazine.     

Monitoring kidney-transplant patients using metabolomics and dynamic modeling

A kidney transplant provides the only hope for a normal life for patients with end-stage renal disease, i.e., kidney failure. Unfortunately, the lack of available organs leaves some patients on the waiting list for years. In addition, the post-transplant treatment is extremely important for the final outcome of the surgery, since immune responses, drug toxicity and other complications pose a real and present threat to the patient. In this article, we describe a novel strategy for monitoring kidney transplanted patients for immune responses and adverse drug effects in their early recovery. Nineteen patients were followed for two weeks after renal transplantation, two of them experienced problems related to kidney function, both of whom were correctly identified by means of nuclear magnetic resonance spectroscopic analysis of urine samples and multivariate data analysis.     

Monocrystalline Labeling Enables Stable Plasmonic Enhancement for Isolation-Free Extracellular Vesicle Analysis

Sensitive detection of extracellular vesicles (EVs) as emerging biomarkers has shown great promises for disease diagnosis. Plasmonic metal nanostructures conjugated with molecules that bind specific biomarker targets are widely used for EVs sensing but involve tradeoffs between particle-size-dependent signal intensity and conjugation efficiency. One solution to this problem would be to induce nucleation on nanoparticles that have successfully bound a target biomarker to permit in situ nanoparticle growth for signal amplification, but approaches that are evaluated to date require harsh conditions or lack nucleation specificity, prohibiting their effective use with most biological specimens. This study describes a one-step in situ strategy to induce monocrystalline copper shell growth on gold nanorod probes without decreasing signal by disrupting probe-target interactions or lipid bilayer integrity to enable EV biomarker detections. This approach increases the detected nanoparticle signal about two orders of magnitude after a 10 min copper nanoshell growth reaction. This has significant implications for improved disease detection, as indicated by the ability of a novel immunoassay using this approach to detect low abundance EVs carrying a pathogen-derived biomarker, after their direct capture from serum, to facilitate the diagnosis of tuberculosis cases in a diagnostically challenging pediatric cohort.     

Simultaneous determination of salsolinol enantiomers and dopamine in human plasma and cerebrospinal fluid by chemical derivatization coupled to chiral liquid chromatography/electrospray ionization-tandem mass spectrometry

A sensitive, specific, and robust method to simultaneously determine enantiomeric salsolinol (1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline, SAL), a potential biomarker implicated in alcohol-related neurotoxicity in a stereoselective manner, and its precursor dopamine (DA) has been developed using simple chemical derivatization and chiral separation coupled with electrospray ionization-tandem mass spectrometry (ESI-MS/MS). SAL enantiomers and DA were converted to stable pentafluorobenzyl (PFB) derivatives directly from aqueous media. Bulky PFB groups introduced into the SAL structure enabled baseline separation of SAL stereoisomers on a chiral column without cumbersome chiral derivatization to unstable SAL diastereomers. Subsequent analysis by ESI-MS/MS with multiple reaction monitoring (MRM) in the presence of deuterium-labeled internal standards allowed specific detection of both derivatives with a wide dynamic range (SAL, 0.5-5000 pg; DA, 0.02-20 ng). The limit of quantitation assayed in the plasma matrix was below 10 pg for each SAL enantiomer and 100 pg for DA. Both coefficient of variance and error for inter- and intraday measurements in the blank plasma were less than 10% for SAL and DA in the concentration range of 10-4000 pg/mL and 0.1-8 ng/mL, respectively. This strategy enabled routine and specific determination of both SAL enantiomers and DA from 0.5 mL of human plasma and cerebrospinal fluid, which has not been possible using existing methodologies.     

GC/MS method for positive detection of Bacillus anthracis endospores

A simple method was developed for detection of Bacillus anthracis (BA) endospores and for differentiation of them from other species in the Bacillus cereus group. Chemical profiles that include lipids (i.e., fatty acids), carbohydrates (i.e., sugars), and the spore-specific biomarker, dipicolinic acid, were generated by one-step thermochemolysis (TCM) at 140 °C in 5 min to provide specific biomarker signatures. Anthrose, which is a biomarker characteristic of the B. cereus group of bacteria, was determined from a fragment produced by TCM. Surprisingly, several virulent BA strains contained very low levels of anthrose, which confounded their detection. A statistical discrimination algorithm was constructed using a combination of biomarkers, which was robust against different growth conditions (medium and temperature). Fifteen endospore-forming Bacillus species were confirmed in a statistically designed test (~90%) using the algorithm, including six BA strains (four virulent isolates), five B. thuringiensis (BT) isolates, and one isolate each for B. cereus (BC), B. mycoides (BM), B. atrophaeus (BG), and B. subtilis (BS). The detection limit for B. anthracis was found to be 50,000 endospores, on the basis of the GC/MS detection limits for 3-methyl-2-butenoic acid methyl ester, which is the biomarker derived from TCM of anthrose.     

PICquant: a quantitative platform to measure differential peptide abundance using dual-isotopic labeling with 12C6- and 13C6-phenyl isocyanate

We have developed a complete system for the isotopic labeling, fractionation, and automated quantification of differentially expressed peptides that significantly facilitates candidate biomarker discovery. We describe a new stable mass tagging reagent pair, (12)C(6)- and (13)C(6)-phenyl isocyanate (PIC), that offers significant advantages over currently available tags. Peptides are labeled predominantly at their amino termini and exhibit elution profiles that are independent of label isotope. Importantly, PIC-labeled peptides have unique neutral-mass losses upon CID fragmentation that enable charge state and label isotope identification and, thereby, decouple the sequence identification from the quantification of candidate biomarkers. To exploit these properties, we have coupled peptide fractionation protocols with a Thermo LTQ-XL LC-MS(2) data acquisition strategy and a suite of automated spectrum analysis software that identifies quantitative differences between labeled samples. This approach, dubbed the PICquant platform, is independent of protein sequence identification and excludes unlabeled peptides that otherwise confound biomarker discovery. Application of the PICquant platform to a set of complex clinical samples showed that the system allows rapid identification of peptides that are differentially expressed between control and patient groups.     

Heteronuclear 1H-31P statistical total correlation NMR spectroscopy of intact liver for metabolic biomarker assignment: application to galactosamine-induced hepatotoxicity

As part of our ongoing development of methods for enhanced biomarker information recovery from spectroscopic data we present the first example of a new hetero-nuclear statistical total correlation spectroscopy (HET-STOCSY) approach applied to intact tissue samples collected as part of a toxicological study. One-dimensional 1H and 31P-{1H} magic angle spinning (MAS) NMR spectra of intact liver samples after galactosamine (galN) treatment to rats and after cotreatment of galN plus uridine were collected at 275 K. Individual samples were also followed by 1H and 31P-{1H} MAS NMR through time generating time dependent modulations in metabolite signatures relating to toxicity. High-resolution 1H NMR spectra of urine and plasma and clinical chemical data were also collected to establish a biological framework in which to place these novel statistical heterospectroscopic data. In HET-STOCSY, calculation of the covariance between the 31P-{1H} and 1H NMR signals of phosphorus containing metabolites allows their molecular connectivities to be established and the construction of virtual two-dimensional heteronuclear correlation spectra that connect all protons on the molecule to the heteroatom. We show how HET-STOCSY applied to MAS NMR spectra of liver samples can be used to augment biomarker detection. This approach is generic and can be applied to correlate the covarying signals for any spin-active nuclei where there is parallel or serial collection of data.     

Bayesian Network Resource for Meta‐Analysis: Cellular Toxicity of Quantum Dots

A web‐based resource for meta‐analysis of nanomaterials toxicity is developed whereby the utility of Bayesian networks (BNs) is illustrated for exploring the cellular toxicity of Cd‐containing quantum dots (QDs). BN models are developed based on a dataset compiled from 517 publications comprising 3028 cell viability data samples and 837 IC₅₀ values. BN QD toxicity (BN‐QDTox) models are developed using both continuous (i.e., numerical) and categorical attributes. Using these models, the most relevant attributes identified for correlating IC₅₀ are: QD diameter, exposure time, surface ligand, shell, assay type, surface modification, and surface charge, with the addition of QD concentration for the cell viability analysis. Data exploration via BN models further enables identification of possible association rules for QDs cellular toxicity. The BN models as web‐based applications can be used for rapid intelligent query of the available body of evidence for a given nanomaterial and can be readily updated as the body of knowledge expands.     

Fast monitoring of species-specific peptide biomarkers using high-intensity-focused-ultrasound-assisted tryptic digestion and selected MS/MS ion monitoring

A new strategy for the fast monitoring of peptide biomarkers is described. It is based on the use of accelerated in-solution trypsin digestions under an ultrasonic field provided by high-intensity focused ultrasound (HIFU) and the monitoring of several peptides by selected MS/MS ion monitoring in a linear ion trap mass spectrometer. The performance of the method was established for the unequivocal identification of all commercial fish species belonging to the Merlucciidae family. Using a particular combination of only 11 peptides, resulting from the HIFU-assisted tryptic digestion of the thermostable proteins parvalbumins, the workflow allowed the unequivocal identification of these closely related fish species in any seafood product, including processed and precooked products, in less than 2 h. The present strategy constitutes the fastest method for peptide biomarker monitoring. Its application for food quality control provides to the authorities an effective and rapid method of food authentication and traceability to guarantee the quality and safety to the consumers.     

Microfluidic liquid chromatography system for proteomic applications and biomarker screening

A microfluidic liquid chromatography (LC) system for proteomic investigations that integrates all the necessary components for stand-alone operation, i.e., pump, valve, separation column, and electrospray interface, is described in this paper. The overall size of the LC device is small enough to enable the integration of two fully functional separation systems on a 3 in. x 1 in. glass microchip. A multichannel architecture that uses electroosmotic pumping principles provides the necessary functionality for eluent propulsion and sample valving. The flow rates generated within these chips are fully consistent with the requirements of nano-LC platforms that are routinely used in proteomic applications. The microfluidic device was evaluated for the analysis of a protein digest obtained from the MCF7 breast cancer cell line. The cytosolic protein extract was processed according to a shotgun protocol, and after tryptic digestion and prefractionation using strong cation exchange chromatography (SCX), selected sample subfractions were analyzed with conventional and microfluidic LC platforms. Using similar experimental conditions, the performance of the microchip LC was comparable to that obtained with benchtop instrumentation, providing an overlap of 75% in proteins that were identified by more than two unique peptides. The microfluidic LC analysis of a protein-rich SCX fraction enabled the confident identification of 77 proteins by using conventional data filtering parameters, of 39 proteins with p < 0.001, and of 5 proteins that are known to be cancer-specific biomarkers, demonstrating thus the potential applicability of these chips for future high-throughput biomarker screening applications.     

Simultaneous analysis of glycosylated and sialylated prostate-specific antigen revealing differential distribution of glycosylated prostate-specific antigen isoforms in prostate cancer tissues

Aberrant protein glycosylation has been shown to be associated with disease progression and can be potentially useful as a biomarker if disease-specific glycosylation can be identified. However, high-throughput quantitative analysis of protein glycosylation derived from clinical specimens presents technical challenges due to the typically high complexity of biological samples. In this study, a mass spectrometry-based analytical method was developed to measure different glycosylated forms of glycoproteins from complex biological samples by coupling glycopeptide extraction strategy for specific glycosylation with selected reaction monitoring (SRM). Using this method, we monitored glycosylated and sialylated prostate-specific antigen (PSA) in prostate cancer and noncancer tissues. Results of this study demonstrated that the relative abundance of glycosylated PSA isoforms were not correlated with total PSA protein levels measured in the same prostate cancer tissue samples by clinical immunoassay. Furthermore, the sialylated PSA was differentially distributed in cancer and noncancer tissues. These data suggest that differently glycosylated isoforms of glycoproteins can be quantitatively analyzed and may provide unique information for clinically relevant studies.     

High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses

In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.