Analysis Of MALDI-TOF Mass Spectra Research Articles

Study of biofilms by V. cholerae strains on the surfaces of biotic and abiotic substrates using mass spectrometry

Objective: identification and comparative study of protein spectra of Vibrio cholerae biofilms by MALDI-ToF mass spectrometry.Materials and methods: V. cholerae O1 strains isolated from different sources were studied. Methods: bacteriological, mass spectrometry.Results: mass spectrometric analysis showed that it was impossible to identify strains from “plankton” samples and from chitin and plastic plates. After culturing the biofilm and plankton strains, all of them were assigned to the V. cholerae species with a Score above 2,300. A сomparative analysis of MALDI-ToF mass spectra of different variants of V. cholera cultures revealed differences, while general peaks with different intensity were maintained.Conclusions: all samples of V. cholerae cultures taken for analysis aft er culture on a nutrient medium were reliably identified with a high Score. As a result of comparative analysis of protein mass spectra by the m/z value and relative intensity of peaks, no proteins characteristic only for plankton cultures or biofilms of V. cholerae were detected. The stability of the phenotypic properties of V. cholerae regardless of its form of existence and the high reliability of the MALDI-ToF mass spectrometry method for identifying both planktonic and biofilm forms of V. cholerae with the preservation of the research protocol and bacteriological culturation.

Accurate differentiation of Mycobacterium chimaera from Mycobacterium intracellulare by MALDI-TOF MS analysis.

The increasing number of infections caused by nontuberculous mycobacteria (NTM) has prompted the need for rapid and precise identification methods of these pathogens. Several studies report the applicability of MALDI-TOF mass spectrometry (MS) for identification of NTM. However, some closely related species have very similar spectral mass fingerprints, and until recently, Mycobacterium chimaera and M. intracellulare could not be separated from each other by MALDI-TOF MS. The conventional identification methods used in routine diagnostics have similar limitations. Recently, the differentiation of these two species within the Mycobacterium avium complex has become increasingly important due to reports of M. chimaera infections related to open heart surgery in Europe and in the USA. In this report, a method for the distinct differentiation of M. chimaera and M. intracellulare using a more detailed analysis of MALDI-TOF mass spectra is presented. Species-specific peaks could be identified and it was possible to assign all isolates (100 %) from reference strain collections as well as clinical isolates to the correct species. We have developed a model for the accurate identification of M. chimaera and M. intracellulare by MALDI-TOF MS. This approach has the potential for routine use in microbiology laboratories, as the model itself can be easily implemented into the software of the currently available systems by MALDI-TOF MS manufacturers.

Multivariate classification of disease phenotypes of esophageal adenocarcinoma by pattern recognition analysis of MALDI-TOF mass spectra of serum N-linked glycans

The development of a novel two-step data analysis methodology to uncover signatures of potential cancer biomarkers in matrix assisted laser desorption ionization (MALDI) time of flight (TOF) mass spectra of large serum peptidomes is described. First, raw spectral data are processed using QceAlign, which exploits Bayesian and maximum entropy methods for peak identification and calibration. The raw spectral data are baseline corrected and normalized. Peak identification is based on a Bayesian second derivative of the baseline-corrected and normalized raw data, with peak S/N statistics provided by a maximum entropy smoothing function. A reference MALDI-TOF reference spectrum is created from the data and each spectrum is slid by n data points to the right or left along the x axis of the reference file. At each relative position n, the Shannon entropy of the sum of the two files is computed. Optimal alignment is associated with the shift that produces the minimum Shannon entropy. Second, a genetic algorithm (GA) for pattern recognition analysis is applied to the peak matched data. The pattern recognition GA selects features that optimize the separation of the sample classes in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the spectral features chosen by the pattern recognition GA convey information primarily about the differences between classes in the data. In addition, the algorithm focuses on those classes and or samples that are difficult to classify as it trains by boosting the sample and class weights. Samples that consistently classify correctly are not as heavily weighted as those samples that are difficult to classify. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a “smart” one -pass procedure for features selection, classification, and prediction in a single step.

Trace labeling of proteins with stable isotopes to identify fragments in complex mixtures.

We describe a novel nonradioactive protein-labeling technique that permits mass spectrometric identification of fragments of labeled proteins. Proteins are labeled by modulating their content of carbon-13 and labeled fragments identified from the distinctive isotope pattern observed on MALDI-TOF mass spectrometry. We show that carbon-13 enrichment to just 2.3% of total carbon (about twice the natural abundance of 1.1%) is sufficient for all fragments to be distinguishable from fragments of natural carbon-13-content proteins. Distinguishing labeled fragments is easily accomplished by visual inspection of spectra, but importantly, we show that labeled fragments can also be identified by computer analysis of spectra using novel parameters we have derived. The technique is demonstrated for identification of fragments of carbon-13-enriched glutathione transferase within a complex mixture of unlabeled peptides by visual and computer analysis of MALDI-TOF mass spectra, but it could be developed to mass spectrometrically identify and characterize fragments of labeled proteins recovered from biological systems.