J Eukaryot Microbiol

Emerging methods based on mass spectrometry (MS) can be used in the rapid identification of microorganisms. Thus far, these practical and rapidly evolving methods have mainly been applied to characterize prokaryotes. We applied matrix-assisted laser-desorption-ionization-time-of-flight mass spectrometry MALDI-TOF MS in the analysis of whole cells of 18 N. fowleri isolates belonging to three genotypes. Fourteen originated from the cerebrospinal fluid or brain tissue of primary amoebic meningoencephalitis patients and four originated from water samples of hot springs, rivers, lakes or municipal water supplies. Whole Naegleria trophozoites grown in axenic cultures were washed and mixed with MALDI matrix. Mass spectra were acquired with a 4700 TOF-TOF instrument. MALDI-TOF MS yielded consistent patterns for all isolates examined. Using a combination of novel data processing methods for visual peak comparison, statistical analysis and proteomics database searching we were able to detect several biomarkers that can differentiate all species and isolates studied, along with common biomarkers for all N. fowleri isolates. Naegleria fowleri could be easily separated from other species within the genus Naegleria. A number of peaks detected were tentatively identified. MALDI-TOF MS fingerprinting is a rapid, reproducible, high-throughput alternative method for identifying Naegleria isolates. This method has potential for studying eukaryotic agents.CC999999/Intramural CDC HHS/United States2017-12-26T00:00:00Z25231600PMC574320

Proteomic fingerprint identification of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used as biomarkers for taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both principal component analysis (PCA) and linear discriminant analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens. Author Summary Hard ticks (Ixodidae) are external parasites that feed on the blood of almost every species of terrestrial vertebrate on earth, including humans. Due to a complete dependency on blood, both sexes and even immature stages, are capable of transmitting disease agents to their hosts, causing distress and sometimes death. Despite the public health significance of ixodid ticks, accurate species identification remains problematic. Vector species identification is core to developing effective vector control schemes. Herein, we provide the first report of MALDI identification of several species of field-collected Neotropical tick specimens preserved in ethanol for up to four years. Our methodology shows that identification does not depend on a commercial reference library of lab-reared samples, but with the help of machine learning it can rely on a self-curated reference library. In addition, our approach offers greater accuracy and lower cost per sample than conventional and modern identification approaches such as morphology and molecular barcoding.Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used as biomarkers for taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both principal component analysis (PCA) and linear discriminant analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens. Author Summary Hard ticks (Ixodidae) are external parasites that feed on the blood of almost every species of terrestrial vertebrate on earth, including humans. Due to a complete dependency on blood, both sexes and even immature stages, are capable of transmitting disease agents to their hosts, causing distress and sometimes death. Despite the public health significance of ixodid ticks, accurate species identification remains problematic. Vector species identification is core to developing effective vector control schemes. Herein, we provide the first report of MALDI identification of several species of field-collected Neotropical tick specimens preserved in ethanol for up to four years. Our methodology shows that identification does not depend on a commercial reference library of lab-reared samples, but with the help of machine learning it can rely on a self-curated reference library. In addition, our approach offers greater accuracy and lower cost per sample than conventional and modern identification approaches such as morphology and molecular barcoding

LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise

BACKGROUND: Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances. RESULTS: LIMPIC preprocessing proves to be superior than other classical preprocessing techniques, allowing for a reliable decomposition of the background noise and the baseline drift from the MALDI-TOF mass spectra. It provides lower coefficient of variation associated with the peak intensity, improving the reliability of the information that can be extracted from single spectra. Our results show that LIMPIC peak-picking is effective even in low protein concentration regimes. The analytical comparison with commercial and freeware peak-picking algorithms demonstrates its superior performances in terms of sensitivity and specificity, both on in-vitro purified protein samples and human plasma samples. CONCLUSION: The quantitative information on the peak intensity extracted with LIMPIC could be used for the recognition of significant protein profiles by means of advanced statistic tools: LIMPIC might be valuable in the perspective of biomarker discovery

IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

Mass spectrometry data mining for cancer detection

Early detection of cancer is crucial for successful intervention strategies. Mass spectrometry-based high throughput proteomics is recognized as a major breakthrough in cancer detection. Many machine learning methods have been used to construct classifiers based on mass spectrometry data for discriminating between cancer stages, yet, the classifiers so constructed generally lack biological interpretability. To better assist clinical uses, a key step is to discover ”biomarker signature profiles”, i.e. combinations of a small number of protein biomarkers strongly discriminating between cancer states. This dissertation introduces two innovative algorithms to automatically search for a signature and to construct a high-performance signature-based classifier for cancer discrimination tasks based on mass spectrometry data, such as data acquired by MALDI or SELDI techniques. Our first algorithm assumes that homogeneous groups of mass spectra can be modeled by (unknown) Gibbs distributions to generate an optimal signature and an associated signature-based classifier by robust log-likelihood analysis; our second algorithm uses a stochastic optimization algorithm to search for two lists of biomarkers, and then constructs a signature-based classifier. To support these two algorithms theoretically, this dissertation also studies the empirical probability distributions of mass spectrometry data and implements the actual fitting of Markov random fields to these high-dimensional distributions. We have validated our two signature discovery algorithms on several mass spectrometry datasets related to ovarian cancer and to colorectal cancer patients groups. For these cancer discrimination tasks, our algorithms have yielded better classification performances than existing machine learning algorithms and in addition,have generated more interpretable explicit signatures.Mathematics, Department o

Penalized regressions for variable selection model, single index model and an analysis of mass spectrometry data.

The focus of this dissertation is to develop statistical methods, under the framework of penalized regressions, to handle three different problems. The first research topic is to address missing data problem for variable selection models including elastic net (ENet) method and sparse partial least squares (SPLS). I proposed a multiple imputation (MI) based weighted ENet (MI-WENet) method based on the stacked MI data and a weighting scheme for each observation. Numerical simulations were implemented to examine the performance of the MIWENet method, and compare it with competing alternatives. I then applied the MI-WENet method to examine the predictors for the endothelial function characterized by median effective dose and maximum effect in an ex-vivo experiment. The second topic is to develop monotonic single-index models for assessing drug interactions. In single-index models, the link function f is unnecessary monotonic. However, in combination drug studies, it is desired to have a monotonic link function f . I proposed to estimate f by using penalized splines with I-spline basis. An algorithm for estimating f and the parameter a in the index was developed. Simulation studies were conducted to examine the performance of the proposed models in term of accuracy in estimating f and a. Moreover, I applied the proposed method to examine the drug interaction of two drugs in a real case study. The third topic was focused on the SPLS and ENet based accelerated failure time (AFT) models for predicting patient survival time with mass spectrometry (MS) data. A typical MS data set contains limited number of spectra, while each spectrum contains tens of thousands of intensity measurements representing an unknown number of peptide peaks as the key features of interest. Due to the high dimension and high correlations among features, traditional linear regression modeling is not applicable. Semi-parametric AFT model with an unspecified error distribution is a well-accepted approach in survival analysis. To reduce the bias caused in denoising step, we proposed a nonparametric imputation approach based on Kaplan-Meier estimator. Numerical simulations and a real case study were conducted under the proposed method