A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Comprehensive MALDI-TOF biotyping of the non-redundant Harvard Pseudomonas aeruginosa PA14 transposon insertion mutant library

BACKGROUND

Pseudomonas aeruginosa is a gram-negative bacterium that is ubiquitously present in the aerobic biosphere. As an antibiotic-resistant facultative pathogen, it is a major cause of hospital-acquired infections. Its rapid and accurate identification is crucial in clinical and therapeutic environments.

METHODS

In a large-scale MALDI-TOF mass spectrometry-based screen of the Harvard transposon insertion mutant library of P. aeruginosa strain PA14, intact-cell proteome profile spectra of 5547 PA14 transposon mutants exhibiting a plethora of different phenotypes were acquired and analyzed.

RESULTS

Of all P. aeruginosa PA14 mutant profiles 99.7% were correctly identified as P. aeruginosa with the Biotyper software on the species level. On the strain level, 99.99% of the profiles were mapped to five different individual P. aeruginosa Biotyper database entries. A principal component analysis-based approach was used to determine the most important discriminatory mass features between these Biotyper groups. Although technical replicas were consistently categorized to specific Biotyper groups in 94.2% of the mutant profiles, biological replicas were not, indicating that the distinct proteotypes are affected by growth conditions.

CONCLUSIONS

The PA14 mutant profile collection presented here constitutes the largest coherent P. aeruginosa MALDI-TOF spectral dataset publicly available today. Transposon insertions in thousands of different P. aeruginosa genes did not affect species identification from MALDI-TOF mass spectra, clearly demonstrating the robustness of the approach. However, the assignment of the individual spectra to sub-groups proved to be non-consistent in biological replicas, indicating that the differentiation between biotyper groups in this nosocomial pathogen is unassured.

Current status and future prospects of mass spectrometry imaging of small molecules

In the field of small-molecule studies, vast efforts have been put forth in order to comprehensively characterize and quantify metabolites formed from complex mechanistic pathways within biochemical and biological organisms. Many technologies and methodologies have been developed to aid understanding of the inherent complexities within biological metabolomes. Specifically, mass spectroscopy imaging (MSI) has emerged as a foundational technique in gaining insight into the molecular entities within cells, tissues, and whole-body samples. In this chapter we provide a brief overview of major technical components involved in MSI, including topics such as sample preparation, analyte ionization, ion detection, and data analysis. Emerging applications are briefly summarized as well, but details will be presented in the following chapters.

Early diagnosis of Irkut virus infection using magnetic bead-based serum peptide profiling by MALDI-TOF MS in a mouse model

Early diagnosis is important for the prompt post-exposure prophylaxis of lyssavirus infections. To diagnose Irkut virus (IRKV) infection during incubation in mice, a novel method using magnetic bead-based serum peptide profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been established. For this test, serum peptides were concentrated by adsorption to and elution from the magnetic bead-based weak cation ion exchanger. Mass spectrograms obtained by MALDI-TOF MS were analyzed using ClinProTools bioinformatics software. Construction of the diagnostic model was performed using serum samples from mice infected with IRKV and rabies virus (RABV) BD06, Flury-LEP, and SRV₉ (as controls). The method accurately diagnosed sera 2, 4 and 8 days after IRKV and RABV infections. The sensitivity, specificity, and total accuracy of diagnosis were 86.7%, 95.2%, and 92.9%, respectively. However, IRKV could not be differentiated from RABV 1 day after infection. The results of the present study indicate that serum peptide profiling by MALDI-TOF MS is a promising technique for the early clinical diagnosis of lyssavirus infections and needs to be further tested in humans and farm animals.