Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model

Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.     

Rapid identification using pyrolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs

Curie-point pyrolysis mass spectra were obtained from reference Propionibacterium strains and canine isolates. Artificial neural networks (ANNs) were trained by supervised learning (with the back-propagation algorithm) to recognize these strains from their pyrolysis mass spectra; all the strains isolated from dogs were identified as human wild type P. acnes. This is an important nosological discovery, and demonstrates that the combination of pyrolysis mass spectrometry and ANNs provides an objective, rapid and accurate identification technique. Bacteria isolated from different biopsy specimens from the same dog were found to be separate strains of P. acnes , demonstrating a within-animal variation in microflora. The classification of the canine isolates by Kohonen artificial neural networks (KANNs) was compared with the classical multivariate techniques of canonical variates analysis and hierarchical cluster analysis, and found to give similar results. This is the first demonstration, within microbiology, of KANNs as an unsupervised clustering technique which has the potential to group pyrolysis mass spectra both automatically and relatively objectively.     

Application of artificial neural networks for yeast cells classification

The present paper describes a method for automatic classification of yeast cells in four groups: active with oval form, budding, weakened and dead. This method can be used in the previously developed structural mathematical model of the yeast cultivation process described in [1].     

Rapid authentication of animal cell lines using pyrolysis mass spectrometry and auto-associative artificial neural networks

Summary Pyrolysis mass spectrometry (PyMS) was used to produce biochemical fingerprints from replicate frozen cell cultures of mouse macrophage hybridoma 2C11-12, human leukaemia K562, baby hamster kidney BHK 21/C13, and mouse tumour BW-O, and a fresh culture of Chinese hamster ovary CHO cells. The dimensionality of these data was reduced by the unsupervised feature extraction pattern recognition technique of auto-associative neural networks. The clusters observed were compared with the groups obtained from the more conventional statistical approaches of hierarchical cluster analysis. It was observed that frozen and fresh cell line cultures gave very different pyrolysis mass spectra. When only the frozen animal cells were analysed by PyMS, auto-associative artificial neural networks (ANNs) were employed to discriminate between them successfully. Furthermore, very similar classifications were observed when the same spectral data were analysed using hierarchical cluster analysis. We demonstrate that this approach can detect the contamination of cell lines with low numbers of bacteria and fungi; this approach could plausibly be extended for the rapid detection of mycoplasma infection in animal cell lines. The major advantages that PyMS offers over more conventional methods used to type cell lines and to screen for microbial infection, such as DNA fingerprinting, are its speed, sensitivity and the ability to analyse hundreds of samples per day. We conclude that the combination of PyMS and ANNs can provide a rapid and accurate discriminatory technique for the authentication of animal cell line cultures.     

Strategy for purification and mass spectrometry identification of SELDI peaks corresponding to low-abundance plasma and serum proteins

Analysis by SELDI-TOF-MS of low abundance proteins makes it possible to select peaks as candidate biomarkers. Our aim was to define a purification strategy to optimise identification by MS of peaks detected by SELDI-TOF-MS from plasma or serum, regardless of any treatment by a combinatorial peptide ligand library (CPLL). We describe 2 principal steps in purification. First, choosing the appropriate sample containing the selected peak requires setting up a databank that records all the m/z peaks detected from samples in different conditions. Second, the specific purification process must be chosen: separation was achieved with either chromatographic columns or liquid-phase isoelectric focusing, both combined when appropriate with reverse-phase chromatography. After purification, peaks were separated by gel electrophoresis and the candidate proteins were analyzed by nano-liquid-chromatography-MS/MS. We chose 4m/z peaks (9400, 13,571, 13,800 and 15,557) selected for their differential expression between two conditions, as examples to explain the different strategies of purification, and we successfully identified 3 of them. Despite some limitations, our strategy to purify and identify peaks selected from SELDI-TOF-MS analysis was effective.     

Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA in vitro and 13 PKA phosphorylation sites were identified by mass spectrometry. NetPhosK was 100% sensitive and 41% specific in predicting PKA sites in the four proteins. These results demonstrate the potential of using integrated computational and experimental methods for detailed investigations of the phosphoproteome.     

Identification of the SELDI ProteinChip human serum retentate by microcapillary liquid chromatography-tandem mass spectrometry

Surface-enhanced laser desorption/ionization (SELDI) time-of-flight (TOF) mass spectrometry (MS) has been widely applied for conducting biomarker research with the goal of discovering patterns of proteins and/or peptides from biological samples that reflect disease status. Many diseases, ranging from cancers of the colon, breast, and prostate to Alzheimer's disease, have been studied through serum protein profiling using SELDI-based methods. Although the results from SELDI-based diagnostic studies have generated a great deal of excitement and skepticism alike, the basis of the molecular identities of the features that underpin the diagnostic potential of the mass spectra is still largely unexplored. A detailed investigation has been undertaken to identify the compliment of serum proteins that bind to the commonly used weak cation exchange (WCX-2) SELDI protein chip. Following incubation and washing of a standard serum sample on the WCX-2 sorbent, proteins were harvested, digested with trypsin, fractionated by strong cation exchange liquid chromatography (LC), and subsequently analyzed by microcapillary reversed-phase LC coupled online with an ion-trap mass spectrometer. This analysis resulted in the identification of 383 unique proteins in the WCX-2 serum retentate. Among the proteins identified, 50 (13%) are documented clinical biomarkers with 36 of these (72%) identified from multiple peptides.     

Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates

Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 images from eight different macroinvertebrate taxa and the aim is to examine the suitability of artificial neural networks (ANNs) for automated taxa identification of macroinvertebrates. More specifically, the focus is drawn on different training algorithms of Multi-Layer Perceptron (MLP), probabilistic neural network (PNN) and Radial Basis Function network (RBFN). We performed thorough experimental tests and we tested altogether 13 training algorithms for MLPs. The best classification accuracy of MLPs, 95.3%, was obtained by two conjugate gradient backpropagation variations and scaled conjugate gradient backpropagation. For PNN 92.8% and for RBFN 95.7% accuracies were achieved. The results show how important a proper choice of ANN is in order to obtain high accuracy in the automated taxa identification of macroinvertebrates and the obtained model can outperform the level of identification which is made by a taxonomist.     

Rapid screening for metabolite overproduction in fermentor broths, using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks

Binary mixtures of model systems consisting of the antibiotic ampicillin with either Escherichia coli or Staphylococcus auresu were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra, so as to obtain quantitative information on the concentration of ampicilin in the mixtures, partial least squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard backpropagation algorithm, and the nodes used a sigmoidal squsahing funciton. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of ampicillin in samples on which they had not been trained. Furthermore, ANNs trained to predict the amount of ampicilin in E. coli were able to generalise so as to predict the concentration of ampicillin in a S. aureus background, illustrating the robustness of ANNs to rather substantial variations in the biological background. The PyMS of the complex mixture of ampicilin in bacteria could not be expressed simply in terms of additive combinations of the spectra describing the pure components of the mixtures and their relative concentrations. Intermolecular reactions took place in the pyrolysate, leading to a lack of superposition of the spectral components and to a dependence of the normalized mass spectrum on sample size. Samples from fermentations of a single organism in a complex production medium were also analyzed quantitatively for a drug of commercial interest. The drug could also be quantified in a variety of mutant-producing strains cultivated in the same medium. The combination of PyMS and ANNs constitutes a novel, rapid, and convenient method for exploitation in strain improvement screening programs. (c) 1994 John Wiley & Sons, Inc.     

Evaluation and amelioration of computer-aided diagnosis with artificial neural networks utilizing small-sized sample sets

Concerns about the specificity and reliability of artificial neural networks (ANNs) impede further application of ANNs in medicine. This is particularly true when developing computer-aided diagnosis (CAD) tools using ANNs for orphan diseases and emerging research areas where only a small-sized sample set is available. It is unreasonable to claim one ANN's performance as better than another simply on the basis of a single output without considering possible output variability due to factors including data noise and ANN training protocols. In this paper, a bootstrap resampling method is proposed to quantitatively analyze ANN output reliability and changing performance as the sample data and training protocols are varied. The method is tested in the area of feature classification for analysis of masses detected on mammograms. Our experiments show that ANNs performance, measured in terms of the area under the receiver operating characteristic (ROC) curve, is not a fixed value, but follows a distribution function sensitive to many factors. We demonstrate that our approach to determining the bootstrap estimates of confidence intervals (CIs) and prediction intervals (PIs) can be used to assure optimal performance in terms of ANN model configuration. We also show that the unintentional inclusion of data noise, which biases ANN results in small task-specific databases, can be accurately detected via the bootstrap estimates.     

Online coupling of capillary electrophoresis with mass spectrometry for the identification of biomarkers for clinical diagnosis

Proteomic screening of complex biologic samples is of increasing importance in clinical research and diagnosis. In the postgenomic area it is evident that changes of the composition of body fluids, as well as post-translational modifications of proteins and peptides, provide more information than genetic typing. The study of these changes allows the state of health or disease of particular organs, and consequently, the whole organism, to be described. This review describes the application of capillary electrophoresis coupled online to an electrospray ionization time-of-flight mass spectrometer to the analysis of body fluids obtained from patients for the identification of biomarkers for diagnostic purposes.     

Online coupling of capillary electrophoresis with mass spectrometry for the identification of biomarkers for clinical diagnosis

Proteomic screening of complex biologic samples is of increasing importance in clinical research and diagnosis. In the postgenomic area it is evident that changes of the composition of body fluids, as well as post-translational modifications of proteins and peptides, provide more information than genetic typing. The study of these changes allows the state of health or disease of particular organs, and consequently, the whole organism, to be described. This review describes the application of capillary electrophoresis coupled online to an electrospray ionization time-of-flight mass spectrometer to the analysis of body fluids obtained from patients for the identification of biomarkers for diagnostic purposes.     

An hierarchical artificial neural network system for the classification of transmembrane proteins.

This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF's) identified in complete genomes and, especially, those ORF's that correspond to proteins with unknown function. The network described here has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF's of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2. A WWW server running the PRED-TMR2 software is available at http://o2.db.uoa.gr/PRED-TMR2     

Ribosomal proteins as biomarkers for bacterial identification by mass spectrometry in the clinical microbiology laboratory

Whole-cell matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) is a rapid method for identification of microorganisms that is increasingly used in microbiology laboratories. This identification is based on the comparison of the tested isolate mass spectrum with reference databases. Using Neisseria meningitidis as a model organism, we showed that in one of the available databases, the Andromas database, 10 of the 13 species-specific biomarkers correspond to ribosomal proteins. Remarkably, one biomarker, ribosomal protein L32, was subject to inter-strain variability. The analysis of the ribosomal protein patterns of 100 isolates for which whole genome sequences were available, confirmed the presence of inter-strain variability in the molecular weight of 29 ribosomal proteins, thus establishing a correlation between the sequence type (ST) and/or clonal complex (CC) of each strain and its ribosomal protein pattern. Since the molecular weight of three of the variable ribosomal proteins (L30, L31 and L32) was included in the spectral window observed by MALDI-TOF MS in clinical microbiology, i.e., 3640–12000 m/z, we were able by analyzing the molecular weight of these three ribosomal proteins to classify each strain in one of six subgroups, each of these subgroups corresponding to specific STs and/or CCs. Their detection by MALDI-TOF allows therefore a quick typing of N. meningitidis isolates.