Matrix-assisted laser desorption/ionisation-time of flight mass spectrometry: Protocol standardisation, comparison and database expansion for faster and reliable identification of dermatophytes.

BACKGROUND: Accurate and early identification of dermatophytes enables prompt antifungal therapy. However, phenotypic and molecular identification methods are time-consuming. MALDI-TOF MS-based identification is rapid, but an optimum protocol is not available. OBJECTIVES: To develop and validate an optimum protein extraction protocol for the efficient and accurate identification of dermatophytes by MALDI-TOF MS. MATERIALS/METHODS: Trichophyton mentagrophytes complex (n = 4), T. rubrum (n = 4) and Microsporum gypseum (n = 4) were used for the optimisation of protein extraction protocols. Thirteen different methods were evaluated. A total of 125 DNA sequence confirmed clinical isolates of dermatophytes were used to create and expand the existing database. The accuracy of the created database was checked by visual inspection of MALDI spectra, MSP dendrogram and composite correlation index matrix analysis. The protocol was validated further using 234 isolates. RESULT: Among 13 protein extraction methods, six correctly identified dermatophytes but with a low log score (≤1.0). The modified extraction protocol developed provided an elevated log score of 1.6. Significant log score difference was observed between the modified protocol and other existing protocols (T. mentagrophytes complex: 1.6 vs. 0.2-1.0, p < .001; T. rubrum: 1.6 vs. 0.4-1.0, p < .001; M. gypseum:1.6 vs. 0.2-1.0, p < .001). Expansion of the database enabled the identification of all 234 isolates (73.5% with log score ≥2.0 and 26.4% with log scores range: 1.75-1.99). The results were comparable to DNA sequence-based identification. CONCLUSION: MALDI-TOF MS with an updated database and efficient protein extraction protocol developed in this study can identify dermatophytes accurately and also reduce the time for identifying them.

Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.

MALDI-TOF MS has shown great utility for rapidly identifying microbial species. It can be used to successfully type bacteria and fungi from a variety of sources more rapidly and cost-effectively than traditional methods. One area where improvements are necessary is in the typing of highly similar samples, such as those samples from the same genus but different species or samples from within a single species but from different strains. One promising way to address this current limitation is by using advanced machine learning techniques. In this work, we adapt a newly developed machine learning tool, the Aristotle Classifier, to bacterial classification of MALDI-TOF MS data. This tool was originally developed for classifying glycomics and glycoproteomics data, so we modified it to be well-suited for assigning mass spectral data from bacterial proteins. The classifier exceeds existing benchmarks in classifying bacteria, and it shows particularly strong performance when the samples to be identified are highly similar. The combination of mass spectrometry data and tools like the Aristotle Classifier could ameliorate the ambiguities associated with challenging bacterial classification problems.

Direct identification of fish species by surface molecular transferring.

With the expansion of the aquatic market and the large quantity of seafood consumption, the issues on safety, traceability and authenticity of seafood are becoming more and more important. Herein, a mass spectrometric method by direct analysis of fish samples was developed for fish authentication. A high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS) technique was applied to analyze the substances on the fish skin, or the surface molecularly imprinted substances on the surface of muscle tissues using a MALDI-target plate. A multivariate analysis was executed on the obtained mass spectra, and plots of principal component analysis (PCA) for different fish samples were differently clustered in a 95% confidence level. The developed strategy was capable of classifying and identifying fish species. The molecular imprinting method was found with good analytical reproducibility. The strategy enables the distinguishment of fish samples in a quick, efficient and easy mode. It is promising to apply the presently developed strategy for the authentication of seafood and extend the protocol for the detection of other protein food products.

Rapid Visualization of Chemically Related Compounds Using Kendrick Mass Defect As a Filter in Mass Spectrometry Imaging.

Kendrick mass defect (KMD) analysis is widely used for helping the detection and identification of chemically related compounds based on exact mass measurements. We report here the use of KMD as a criterion for filtering complex mass spectrometry data set. The method allow automated, easy and efficient data processing, enabling the reconstruction of 2D distributions of families of homologous compounds from MSI images. We show that KMD filtering, based on in-house software, is suitable and robust for high resolution (full width at half-maximum, fwhm, at m/z 410 of 20 000) and very high-resolution (fwhm, at m/z 410 of 160 000) MSI data. This method has been successfully applied to two different types of samples, bacteria cocultures, and brain tissue sections.

Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications.

Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2013-2014.

This review is the eighth update of the original article published in 1999 on the application of Matrix-assisted laser desorption/ionization mass spectrometry (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2014. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, and arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly- saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Much of this material is presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 37:353-491, 2018.

Principal component analysis of normalized full spectrum mass spectrometry data in multiMS-toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains.

RATIONALE: Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen. METHODS: Two software utilities, the ms-alone, a python-based utility for mass spectrometry data preprocessing and peak extraction, and the multiMS-toolbox, an R software tool for advanced peak registration and detailed explorative statistical analysis, were implemented. The bacterial culture of Cronobacter sakazakii was cultivated on Enterobacter sakazakii Isolation Agar, Blood Agar Base and Tryptone Soya Agar for 24 h and 48 h and applied by the smear method on an Autoflex speed MALDI-TOF mass spectrometer. RESULTS: For three tested cultivation media only two different metabolic patterns of Cronobacter sakazakii were identified using PCA applied on data normalized by two different normalization techniques. Results from matched peak data and subsequent detailed full spectrum analysis identified only two different metabolic patterns - a cultivation on Enterobacter sakazakii Isolation Agar showed significant differences to the cultivation on the other two tested media. The metabolic patterns for all tested cultivation media also proved the dependence on cultivation time. CONCLUSIONS: Both whole spectrum based normalization techniques together with the full spectrum PCA allow identification of important discriminative factors in experiments with several variable condition factors avoiding any problems with improper identification of peaks or emphasis on bellow threshold peak data. The amounts of processed data remain still manageable. Both implemented software utilities are available free of charge from http://uprt.vscht.cz/ms.

Machine learning approaches in MALDI-MSI: clinical applications.

INTRODUCTION: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging. AREAS COVERED: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing. Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.

MALDI-TOF MS in clinical parasitology: applications, constraints and prospects.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is currently being used for rapid and reproducible identification of bacteria, viruses and fungi in clinical microbiological laboratories. However, some studies have also reported the use of MALDI-TOF MS for identification of parasites, like Leishmania, Giardia, Cryptosporidium, Entamoeba, ticks and fleas. The present review collates all the information available on the use of this technique for parasites, in an effort to assess its applicability and the constraints for identification/diagnosis of parasites and diseases caused by them. Though MALDI-TOF MS-based identification of parasites is currently done by reference laboratories only, in future, this promising technology might surely replace/augment molecular methods in clinical parasitology laboratories.

Comparison of information-dependent acquisition, SWATH, and MS(All) techniques in metabolite identification study employing ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.

Sensitive and selective liquid chromatography-mass spectrometry (LC-MS) analysis is a powerful and essential tool for metabolite identification in drug discovery and development. An MS(2) (or tandem, MS/MS) mass spectrum is acquired from the fragmentation of a precursor ion by multiple methods including information-dependent acquisition (IDA), SWATH (sequential window acquisition of all theoretical fragment-ion spectra), and MS(All) (also called MS(E)) techniques. We compared these three techniques in their capabilities to produce comprehensive MS(2) data by assessing both metabolite MS(2) acquisition hit rate and the quality of MS(2) spectra. Rat liver microsomal incubations from eight test compounds were analyzed with four methods (IDA, MMDF (multiple mass defect filters)-IDA, SWATH, or MS(All)) using an ultrahigh-performance liquid chromatography-qudrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) platform. A combined total of 227 drug-related materials (DRM) were detected from all eight test article incubations, and among those, 5% and 4% of DRM were not triggered for MS(2) acquisition with IDA and MMDF-IDA methods, respectively. When the same samples were spiked to an equal volume of blank rat urine (urine sample), the DRM without MS(2) acquisition increased to 29% and 18%, correspondingly. In contrast, 100% of DRM in both matrixes were subjected to MS(2) acquisition with either the SWATH or MS(All) method. However, the quality of the acquired MS(2) spectra decreased in the order of IDA, SWATH, and MS(All) methods. An average of 10, 9, and 6 out of 10 most abundant ions in MS(2) spectra were the real product ions of DRM detected in microsomal samples from IDA, SWATH, and MS(All) methods, respectively. The corresponding numbers declined to 9, 6, and 3 in the urine samples. Overall, IDA-based methods acquired qualitatively better MS(2) spectra but with a lower MS(2) acquisition hit rate than the other two methods. SWATH outperformed the MS(All) method given its better quality of MS(2) spectra with an identical MS(2) acquisition hit rate.

[Special application of matrix-assisted laser desorption ionization time-of-flight mass spectrometry in clinical microbiological diagnostics]. / Mátrix-asszisztált lézer deszorpciós, ionizációs, repülési ido mérésén alapuló tömegspektrometria speciális alkalmazása a klinikai mikrobiológiai diagnosztika területén.

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry as a new possibility for rapid identification of bacteria and fungi revolutionized the clinical microbiological diagnostics. It has an extreme importance in the routine microbiological laboratories, as identification of the pathogenic species rapidly will influence antibiotic selection before the final determination of antibiotic resistance of the isolate. The classical methods for identification of bacteria or fungi, based on biochemical tests, are influenced by many environmental factors. The matrix-assisted laser desorption ionization time-of-flight mass spectrometry is a rapid method which is able to identify a great variety of the isolated bacteria and fungi based on the composition of conserved ribosomal proteins. Recently several other applications of the method have also been investigated such as direct identification of pathogens from the positive blood cultures. There are possibilities to identify bacteria from the urine samples in urinary tract infection or from other sterile body fluids. Using selective enrichment broth Salmonella sp from the stool samples can be identified more rapidly, too. The extended spectrum beta-lactamase or carbapenemase production of the isolated bacteria can be also detected by this method helping the antibiotic selection in some cases. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry based methods are suitable to investigate changes in deoxyribonucleic acid or ribonucleic acid, to carry out rapid antibiotic resistance determination or other proteomic analysis. The aim of this paper is to give an overview about present possibilities of using this technique in the clinical microbiological routine procedures.

MALDI imaging mass spectrometry: statistical data analysis and current computational challenges.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.

Classification of proanthocyanidin profiles using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectra data combined with multivariate analysis.

Proanthocyanidin (PAC) profiles of apples (a-PAC), cranberries (c-PAC), and peanut skins (p-PAC) were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Deconvolution of overlapping isotopic patterns indicated that in apples, only 5% of the PAC oligomers contain one or more A-type bonds, whereas in cranberries and peanut skins, 96% of the PAC oligomers contain one or more A-type bonds. MALDI-TOF MS data combined with multivariate analysis, such as principal component analysis (PCA) and linear discriminant analysis (LDA), were used to differentiate and discriminate a-PAC, c-PAC, and p-PAC from one another. Mixtures of c-PAC with either a-PAC or p-PAC at different w/w ratios were evaluated by LDA modeling. The LDA model classified the training, testing, and validation sets with 99.4%, 100%, and 94.2% accuracy. Results suggest that MALDI-TOF MS and multivariate analysis are useful in determining authenticity of PAC from different sources and mixtures of PAC sources.

Quantitative analysis of blended oils by matrix-assisted laser desorption/ionization mass spectrometry and partial least squares regression.

Quantitative labeling of oil compositions has become a trend to ensure the quality and safety of blended oils in the market. However, methods for rapid and reliable quantitation of blended oils are still not available. In this study, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was used to profile triacylglycerols in blended oils, and partial least squares regression (PLS-R) was applied to establish quantitative models based on the acquired MALDI-MS spectra. We demonstrated that this new method allowed simultaneous quantitation of multiple compositions, and provided good quantitative results of binary, ternary and quaternary blended oils, enabling good limits of detection (e.g., detectability of 1.5% olive oil in sunflower seed oil). Compared with the conventional GC-FID method, this new method could allow direct analysis of blended oils, analysis of one blended oil sample within minutes, and accurate quantitation of low-abundance oil compositions and blended oils with similar fatty acid contents.

An insight into high-resolution mass-spectrometry data.

Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical preprocessing steps that are taken in order to interpret the data and use it in downstream statistical analyses. Because of the complexity of data acquisition, statistical methods developed for gene expression microarray data are not directly applicable to proteomic data. Areas in need of statistical research for proteomic data include alignment, experimental design, abundance normalization, and statistical analysis.

Approaching MALDI molecular imaging for clinical proteomic research: current state and fields of application.

MALDI imaging mass spectrometry ('MALDI imaging') is an increasingly recognized technique for biomarker research. After years of method development in the scientific community, the technique is now increasingly applied in clinical research. In this article, we discuss the use of MALDI imaging in clinical proteomics and put it in context with classical proteomics techniques. We also highlight a number of upcoming challenges for personalized medicine, development of targeted therapies and diagnostic molecular pathology where MALDI imaging could help.

Protein identification using receptor arrays and mass spectrometry.

Mass spectrometry is one of the main tools for protein identification in complex mixtures. When the sequence of the protein is known, we can check to see if the known mass distribution of peptides for a given protein is present in the recorded mass distribution of the mixture being analyzed. Unfortunately, this general approach suffers from high false-positive rates, since in a complex mixture, the likelihood that we will observe any particular mass distribution is high, whether or not the protein of interest is in the mixture. In this paper, we propose a scoring methodology and algorithm for protein identification that make use of a new experimental technique, which we call receptor arrays, for separating a mixture based on another differentiating property of peptides called isoelectric point (pI). We perform extensive simulation experiments on several genomes and show that additional information about peptides can achieve an average 30% reduction in false-positive rates over existing methods, while achieving very high true-positive identification rates.

Uncovering matrix effects on lipid analyses in MALDI imaging mass spectrometry experiments.

The specific matrix used in matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) can have an effect on the molecules ionized from a tissue sample. The sensitivity for distinct classes of biomolecules can vary when employing different MALDI matrices. Here, we compare the intensities of various lipid subclasses measured by Fourier transform ion cyclotron resonance (FT-ICR) IMS of murine liver tissue when using 9-aminoacridine (9AA), 5-chloro-2-mercaptobenzothiazole (CMBT), 1,5-diaminonaphthalene (DAN), 2,5-Dihydroxyacetophenone (DHA), and 2,5-dihydroxybenzoic acid (DHB). Principal component analysis and receiver operating characteristic curve analysis revealed significant matrix effects on the relative signal intensities observed for different lipid subclasses and adducts. Comparison of spectral profiles and quantitative assessment of the number and intensity of species from each lipid subclass showed that each matrix produces unique lipid signals. In positive ion mode, matrix application methods played a role in the MALDI analysis for different cationic species. Comparisons of different methods for the application of DHA showed a significant increase in the intensity of sodiated and potassiated analytes when using an aerosol sprayer. In negative ion mode, lipid profiles generated using DAN were significantly different than all other matrices tested. This difference was found to be driven by modification of phosphatidylcholines during ionization that enables them to be detected in negative ion mode. These modified phosphatidylcholines are isomeric with common phosphatidylethanolamines confounding MALDI IMS analysis when using DAN. These results show an experimental basis of MALDI analyses when analyzing lipids from tissue and allow for more informed selection of MALDI matrices when performing lipid IMS experiments.

Machine Learning in Mass Spectrometry: A MALDI-TOF MS Approach to Phenotypic Antibacterial Screening.

Machine learning techniques can be applied to MALDI-TOF mass spectral data of drug-treated cells to obtain classification models which assign the mechanism of action of drugs. Here, we present an example application of this concept to the screening of antibacterial drugs that act at the major bacterial target sites such as the ribosome, penicillin-binding proteins, and topoisomerases in a pharmacologically relevant phenotypic setting. We show that antibacterial effects can be identified and classified in a label-free, high-throughput manner using wild-type Escherichia coli and Staphylococcus aureus cells at variable levels of target engagement. This phenotypic approach, which combines mass spectrometry and machine learning, therefore denoted as PhenoMS-ML, may prove useful for the identification and development of novel antibacterial compounds and other pharmacological agents.

Detection of SARS-CoV-2 in nasal swabs using MALDI-MS.

Detection of SARS-CoV-2 using RT-PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT-PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.