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

PeerJ

article

Hsin-Yao Wang¹  Frank Lien¹  Tsui-Ping Liu¹  Chun-Hsien Chen³  Chao-Jung Chen⁴  Jang-Jih Lu¹ 

[1] Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou;Ph.D. Program in Biomedical Engineering, Chang Gung University;Department of Information Management, Chang Gung University;Graduate Institute of Integrated Medicine, China Medical University;Proteomics Core Laboratory, China Medical University Hospital;Department of Medical Biotechnology and Laboratory Science, Chang Gung University;School of Medicine, Chang Gung University

关键词: MALDI-TOF;    Lineage typing;    MLST;    ClinProTools;    ML;    MRSA;   

DOI  :  10.7717/peerj.5784

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background 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). Methods 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. Results A total of 10 peaks with the highest discriminatory power (m/z81.00% for multiclass models) in classifying the major MLST types. Conclusions A combination of MALDI-TOF MS analysis and ML models is a potentially accurate, objective, and efficient tool for infection control and outbreak investigation.

【 授权许可】

CC BY