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PeerJ. 2018 Nov 7;6:e5784. doi: 10.7717/peerj.5784. eCollection 2018.

Application of a MALDI-TOF analysis platform (ClinProTools) for rapid and preliminary report of MRSA sequence types in Taiwan.

Wang HY#1,2, Lien F#1, Liu TP1, Chen CH3, Chen CJ4,5, Lu JJ1,6,7.

Author information

Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Tauyuan, Taiwan.
Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan.
Department of Information Management, Chang Gung University, Taoyuan, Taiwan.
Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan.
Proteomics Core Laboratory, China Medical University Hospital, Taichung, Taiwan.
Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan.
School of Medicine, Chang Gung University, Taoyuan, Taiwan.
Contributed equally



The accurate and rapid preliminarily identification of the types of methicillin-resistant Staphylococcus aureus (MRSA) is crucial for infection control. Currently, however, expensive, time-consuming, and labor-intensive methods are used for MRSA typing. By contrast, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a potential tool for preliminary lineage typing. The approach has not been standardized, and its performance has not been analyzed in some regions with geographic barriers (e.g., Taiwan Island).


The mass spectra of 306 MRSA isolates were obtained from multiple reference hospitals in Taiwan. The multilocus sequence types (MLST) of the isolates were determined. The spectra were analyzed for the selection of characteristic peaks by using the ClinProTools software. Furthermore, various machine learning (ML) algorithms were used to generate binary and multiclass models for classifying the major MLST types (ST5, ST59, and ST239) of MRSA.


A total of 10 peaks with the highest discriminatory power (m/z range: 2,082-6,594) were identified and evaluated. All the single peaks revealed significant discriminatory power during MLST typing. Moreover, the binary and multiclass ML models achieved sufficient accuracy (82.80-94.40% for binary models and >81.00% for multiclass models) in classifying the major MLST types.


A combination of MALDI-TOF MS analysis and ML models is a potentially accurate, objective, and efficient tool for infection control and outbreak investigation.


ClinProTools; Lineage typing; MALDI-TOF; ML; MLST; MRSA

Conflict of interest statement

The authors declare that they have no competing interests.

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