MALDI‐TOF MS: from Microbiology to Drug Discovery

Walker, Ruth H.; Dueñas, María Emilia; Ward, Alan C.; Emami, Kaveh

doi:10.1002/9781119814085.ch13

Cited by 2 publications

(1 citation statement)

References 120 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) has emerged as a powerful technique widely employed for microbial analysis. − It allows for the rapid and accurate identification of microorganisms based on their unique mass spectral fingerprints. In seconds, mass spectra can be acquired for bacterial or fungal colonies, providing a characteristic pattern of peaks for the classification of microbial species.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted MALDI Mass Spectrometry for Rapid Antimicrobial Resistance Prediction in Clinicals

Gao,

Li,

Yang

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

Antimicrobial susceptibility testing (AST) plays a critical role in assessing the resistance of individual microbial isolates and determining appropriate antimicrobial therapeutics in a timely manner. However, conventional AST normally takes up to 72 h for obtaining the results. In healthcare facilities, the global distribution of vancomycin-resistant Enterococcus fecium (VRE) infections underscores the importance of rapidly determining VRE isolates. Here, we developed an integrated antimicrobial resistance (AMR) screening strategy by combining matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) with machine learning to rapidly predict VRE from clinical samples. Over 400 VRE and vancomycin-susceptible E. faecium (VSE) isolates were analyzed using MALDI-MS at different culture times, and a comprehensive dataset comprising 2388 mass spectra was generated. Algorithms including the support vector machine (SVM), SVM with L1-norm, logistic regression, and multilayer perceptron (MLP) were utilized to train the classification model. Validation on a panel of clinical samples (external patients) resulted in a prediction accuracy of 78.07%, 80.26%, 78.95%, and 80.54% for each algorithm, respectively, all with an AUROC above 0.80. Furthermore, a total of 33 mass regions were recognized as influential features and elucidated, contributing to the differences between VRE and VSE through the Shapley value and accuracy, while tandem mass spectrometry was employed to identify the specific peaks among them. Certain ribosomal proteins, such as A0A133N352 and R2Q455, were tentatively identified. Overall, the integration of machine learning with MALDI-MS has enabled the rapid determination of bacterial antibiotic resistance, greatly expediting the usage of appropriate antibiotics.

show abstract

Section: Introductionmentioning

confidence: 99%