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.

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.

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.

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.

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.

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.

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.

An improved simple method for the identification of Mycobacteria by MALDI-TOF MS (Matrix-Assisted Laser Desorption- Ionization mass spectrometry).

The aim of this study was to establish a simple method for the rapid identification of Mycobacteria species by MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass spectrometry) using the Bruker MALDI-TOF Biotyper system (Bruker Daltonik, Bremen, Germany). A multicentre, prospective, and single blind study was performed in three European Hospitals, two Spanish and one UK hospital from May to August 2018. The BD BACTEC MGIT (Becton Dickinson, Berks, UK) liquid culture system was used in all three centres for the growth of Mycobacteria. When signal positive, tubes were removed from the analyser and in addition to standard laboratory procedures were subcultured on blood agar plates for MALDI-TOF analysis. Plates were incubated aerobically for 1 to 7 days at 37 °C and inspected every day. Once any growth was visible, it was transferred to the steel target plate, overlaid with 1 µl of neat formic acid and 1 µl HCCA matrix (alpha hydroxyl 4 cinnamic acid), and analysed in a Bruker Biotyper MALDI-TOF. Results given by MALDI-TOF were compared with the reference methods used for identification in the different centres. At two Spanish hospitals, identification by MALDI-TOF was only attempted on presumptive non-tuberculosis mycobacteria (NTM) and the results were initially compared with the results obtained by a commercial reverse hybridisation assay, GenoType CM/AS (Hain Lifescience, Tübingen, Germany). At the UK Hospital, identification of any presumptive mycobacteria was attempted and compared with the results obtained by whole genome sequencing (WGS). Overall in 142/167 (85%) of cases the identifications obtained were concordant; all Mycobacterium tuberculosis (MTB) isolates 43/43 (100%), 57/76 (75%) of the rapid growing nontuberculous mycobacteria (NTM), and 42/48 (85%) slow growing NTM tested were identified correctly. We report a new, easy, cheap and quick method for isolation and identification of Mycobacterium spp. without the need for additional steps or equipment and this method is in routine used in all three centres.

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.

Use of physiological information based on grayscale images to improve mass spectrometry imaging data analysis from biological tissues.

The characterization of cancer tissues by matrix-assisted laser desorption ionization-mass spectrometry images (MALDI-MSI) is of great interest because of the power of MALDI-MS to understand the composition of biological samples and the imaging side that allows for setting spatial boundaries among tissues of different nature based on their compositional differences. In tissue-based cancer research, information on the spatial location of necrotic/tumoral cell populations can be approximately known from grayscale images of the scanned tissue slices. This study proposes as a major novelty the introduction of this physiologically-based information to help in the performance of unmixing methods, oriented to extract the MS signatures and distribution maps of the different tissues present in biological samples. Specifically, the information gathered from grayscale images will be used as a local rank constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the analysis of MALDI-MSI of cancer tissues. The use of this constraint, setting absence of certain kind of tissues only in clear zones of the image, will help to improve the performance of MCR-ALS and to provide a more reliable definition of the chemical MS fingerprint and location of the tissues of interest. The general strategy to address the analysis of MALDI-MSI of cancer tissues will involve the study of the MCR-ALS results and the posterior use of MCR-ALS scores as dimensionality reduction for image segmentation based on K-means clustering. The resolution method will provide the MS signatures and their distribution maps for each tissue in the sample. Then, the resolved distribution maps for each biological component (MCR scores) will be submitted as initial information to K-means clustering for image segmentation to obtain information on the boundaries of the different tissular regions in the samples studied. MCR-ALS prior to K-means not only provides the desired dimensionality reduction, but additionally resolved non-biological signal contributions are not used and the weight given to the different biological components in the segmentation process can be modulated by suitable preprocessing methods.

The clinical impact of patients with bloodstream infection with different groups of Viridans group streptococci by using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).

The accuracy of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) for identifying viridans group streptococcus (VGS) was improving. However, the clinical impact of identifying VGS had not been well recognized. Our study had comprehensively studied the clinical manifestations and outcome of VGS blood stream infection by using MALDI-TOF MS for identification.This retrospective study enrolled 312 adult patients with a monomicrobial blood culture positive for VGS. Blood culture was examined through MALDI-TOF MS.The most common VGS species were the Streptococcus anginosus group (38.8%) and Streptococcus mitis group (22.8%). Most species showed resistance to erythromycin (35.6%), followed by clindamycin (25.3%) and penicillin (12.5%). Skin and soft tissue infection and biliary tract infection were significantly related to S. anginosus group bacteremia (P = .001 and P = .005, respectively). S. mitis group bacteremia was related to infective endocarditis and bacteremia with febrile neutropenia (P = .005 and P < .001, respectively). Infective endocarditis was also more likely associated with S. sanguinis group bacteremia (P = .009). S. anginosus group had less resistance rate to ampicillin, erythromycin, clindamycin, and ceftriaxone (P = .019, <.001, .001, and .046, respectively). A more staying in intensive care unit, underlying solid organ malignancy, and a shorter treatment duration were independent risk factors for 30-day mortality. This study comprehensively evaluated different VGS group and their clinical manifestations, infection sources, concomitant diseases, treatments, and outcomes. Categorizing VGS into different groups by MALDI-TOF MS could help clinical physicians well understand their clinical presentations.

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.

Rapid and robust analytical protocol for E. coli STEC bacteria subspecies differentiation using whole cell MALDI mass spectrometry.

Whole cell MALDI is regularly used for the identification of bacteria to species level in clinical Microbiology laboratories. However, there remains a need to rapidly characterize and differentiate isolates below the species level to support outbreak management. We describe the implementation of a modified preparative approach for MALDI-MS combined with a custom analytical computational pipeline as a rapid procedure for subtyping Shigatoxigenic E. coli (STEC) and accurately identifying strain-specifying biomarkers. The technique was able to differentiate E. coli O157:H7 from other STEC. Within O157 serotype O157:H7 isolates were readily distinguishable from Sorbitol Fermenting O157 isolates. Overall, nine homogeneous groups of isolates were distinguished, each exhibiting distinct profiles of defining mass spectra features. This offers a robust analytical tool useable in reference/diagnostic public health scenarios.

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.

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.

Proteomic-Biostatistic Integrated Approach for Finding the Underlying Molecular Determinants of Hypertension in Human Plasma.

Despite advancements in lowering blood pressure, the best approach to lower it remains controversial because of the lack of information on the molecular basis of hypertension. We, therefore, performed plasma proteomics of plasma from patients with hypertension to identify molecular determinants detectable in these subjects but not in controls and vice versa. Plasma samples from hypertensive subjects (cases; n=118) and controls (n=85) from the InGenious HyperCare cohort were used for this study and performed mass spectrometric analysis. Using biostatistical methods, plasma peptides specific for hypertension were identified, and a model was developed using least absolute shrinkage and selection operator logistic regression. The underlying peptides were identified and sequenced off-line using matrix-assisted laser desorption ionization orbitrap mass spectrometry. By comparison of the molecular composition of the plasma samples, 27 molecular determinants were identified differently expressed in cases from controls. Seventy percent of the molecular determinants selected were found to occur less likely in hypertensive patients. In cross-validation, the overall R2 was 0.434, and the area under the curve was 0.891 with 95% confidence interval 0.8482 to 0.9349, P<0.0001. The mean values of the cross-validated proteomic score of normotensive and hypertensive patients were found to be -2.007±0.3568 and 3.383±0.2643, respectively, P<0.0001. The molecular determinants were successfully identified, and the proteomic model developed shows an excellent discriminatory ability between hypertensives and normotensives. The identified molecular determinants may be the starting point for further studies to clarify the molecular causes of hypertension.

A highly sensitive carbapenemase assay using laser desorption/ionization mass spectrometry based on a parylene-matrix chip.

A quantitative carbapenemase assay was developed using laser desorption/ionization mass spectrometry (LDI-MS) based on a parylene-matrix chip. As a first step, the reproducibility (spot-to-spot, shot-to-shot, and day-to-day) of LDI-MS based on a parylene-matrix chip and the quantification ranges for four carbapenem antibiotics (doripenem, ertapenem, imipenem, and meropenem) were determined. A carbapenem-susceptibility test was performed using the four carbapenems and 51 bacterial strains that displayed (1) carbapenem resistance with carbapenemase, (2) carbapenem resistance without carbapenemase, or (3) carbapenem susceptibility. The susceptibility test results showed that LDI-MS based on a parylene-matrix chip was more sensitive and selective for detecting the carbapenemase reaction than conventional MALDI-TOF MS based on a 2,5-dihydroxybenzoic acid matrix.

MALDI-TOF MS Profiling-Advances in Species Identification of Pests, Parasites, and Vectors.

Invertebrate pests and parasites of humans, animals, and plants continue to cause serious diseases and remain as a high treat to agricultural productivity and storage. The rapid and accurate species identification of the pests and parasites are needed for understanding epidemiology, monitoring outbreaks, and designing control measures. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling has emerged as a rapid, cost effective, and high throughput technique of microbial species identification in modern diagnostic laboratories. The development of soft ionization techniques and the release of commercial pattern matching software platforms has resulted in the exponential growth of applications in higher organisms including parasitology. The present review discusses the proof-of-principle experiments and various methods of MALDI MS profiling in rapid species identification of both laboratory and field isolates of pests, parasites and vectors.

Pharmacists' Familiarity with and Institutional Utilization of Rapid Diagnostic Technologies for Antimicrobial Stewardship.

Rapid diagnostic technologies (RDTs) significantly reduce organism identification time and can augment antimicrobial stewardship program (ASP) activities. An electronic survey quantified familiarity with and utilization of RDTs by clinical pharmacists participating in ASPs. Familiarity was highest with polymerase chain reaction (PCR). Formal infectious diseases training was the only significant factor influencing RDT familiarity. Infect Control Hosp Epidemiol 2017;38:863-866.