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[2602.16737] Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection
Summary
This article explores the use of MALDI-TOF mass spectrometry and antimicrobial resistance patterns as cost-effective alternatives to whole genome sequencing for detecting hospital outbreak clusters.
Why It Matters
Timely identification of hospital outbreaks is crucial for infection control. This research highlights innovative methods that could enhance outbreak surveillance, making it more accessible and efficient, which is vital in managing public health risks.
Key Takeaways
- MALDI-TOF mass spectrometry can provide rapid pathogen identification.
- Antimicrobial resistance patterns can complement traditional outbreak detection methods.
- A machine learning framework can improve the analysis of MALDI-TOF spectra and AR data.
- These methods may reduce reliance on costly whole genome sequencing.
- Multi-species analyses show promise for broader outbreak surveillance applications.
Quantitative Biology > Quantitative Methods arXiv:2602.16737 (q-bio) [Submitted on 17 Feb 2026] Title:Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection Authors:Chang Liu, Jieshi Chen, Alexander J. Sundermann, Kathleen Shutt, Marissa P. Griffith, Lora Lee Pless, Lee H. Harrison, Artur W. Dubrawski View a PDF of the paper titled Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection, by Chang Liu and 7 other authors View PDF Abstract:Accurate and timely identification of hospital outbreak clusters is crucial for preventing the spread of infections that have epidemic potential. While assessing pathogen similarity through whole genome sequencing (WGS) is considered the gold standard for outbreak detection, its high cost and lengthy turnaround time preclude routine implementation in clinical laboratories. We explore the utility of two rapid and cost-effective alternatives to WGS, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry and antimicrobial resistance (AR) patterns. We develop a machine learning framework that extracts informative representations from MALDI-TOF spectra and AR patterns for outbreak detection and explore their fusion. Through multi-species analyses, we demonstrate that in some cases MALDI-TOF and AR have the potential to reduce reliance on WGS, enabling more accessible and rapid outbreak surveillance...
