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Related Experiment Video

Updated: Apr 7, 2026

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Predicting Staphylococcus aureus spa types using matrix-assisted laser desorption/ionization time-of-flight mass

Mariana Fonseca1, Jean-Philippe Roy2, Anitza Fragas Quintero3

  • 1Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe J2S 2M2, QC, Canada; Regroupement FRQNT Op+lait, Saint-Hyacinthe J2S 2M2, QC, Canada.

Journal of Dairy Science
|April 5, 2026
PubMed
Summary
This summary is machine-generated.

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) can predict certain Staphylococcus aureus spa types in dairy herds. This cost-effective method aids in tracking bovine mastitis transmission and understanding virulence factors.

Keywords:
Canadadairy cattlemastitis

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Area of Science:

  • Veterinary Microbiology
  • Bacterial Genomics
  • Machine Learning in Diagnostics

Background:

  • Staphylococcus aureus is a primary cause of intramammary infections (IMI) in Canadian dairy cattle, leading to significant economic losses.
  • Persistent IMI and cow-to-cow transmission underscore the need for effective epidemiological tracking of S. aureus strains.
  • Spa typing is a valuable tool for tracking S. aureus transmission and associating strains with virulence and clinical outcomes.

Purpose of the Study:

  • To evaluate the efficacy of machine learning algorithms in predicting common Staphylococcus aureus spa types using MALDI-TOF MS spectra.
  • To assess the potential of MALDI-TOF MS as a cost-effective alternative for bacterial typing in epidemiological studies.

Main Methods:

  • Utilized 373 S. aureus isolates previously spa typed, subjecting them to MALDI-TOF MS analysis.
  • Pre-processed spectra and identified 132 reference peaks, generating feature matrices for machine learning models.
  • Employed Principal Component Analysis (PCA) for data visualization and Random Forest (RF) models for spa type prediction.

Main Results:

  • PCA indicated clear distinguishability among certain S. aureus spa types.
  • Random Forest models achieved high accuracy for predicting spa types t529 (89.0%), t605 (99.4%), and t13401 (97.1%).
  • Model performance was lower for spa types t359, t267, and t2445, with AUCs of 0.53, 0.68, and 0.62, respectively.

Conclusions:

  • MALDI-TOF MS, coupled with machine learning, can accurately predict specific Staphylococcus aureus spa types.
  • This approach offers a cost-effective and accessible alternative to traditional molecular typing methods for epidemiological surveillance of bovine mastitis.
  • Further refinement of models may improve prediction accuracy for all prevalent S. aureus spa types in dairy herds.