Related Experiment Video
Updated: Apr 13, 2026

Subtyping of Campylobacter jejuni ssp. doylei Isolates Using Mass Spectrometry-based PhyloProteomics MSPP
Published on: October 30, 2016
Development of a novel matrix-assisted laser desorption/ionization time-of-flight mass spectrum (MALDI-TOF-MS)-based
O Ueda1, S Tanaka2, Z Nagasawa3
1Microbiology Group, MM Business Unit, Siemens Healthcare Diagnostics KK, Tokyo, Japan; Department of Pathology and Microbiology, Faculty of Medicine, Saga University, Saga, Japan.
Background:
Mass spectrum analysis enables species- and subspecies-level identification, and can be used as an epidemiological tool in outbreak management. However, its reliability at clonal level has yet to be established.
Aim:
To establish a matrix-assisted laser desorption/ionization time-of-flight mass-spectrum-based method that enables bacterial clone identification with accuracy equivalent to pulsed-field gel electrophoresis/phage open-reading frame typing (PFGE/POT).
Methods:
Meticillin-resistant Staphylococcus aureus (MRSA) was used in this study. Mass spectra were obtained from a standard strain of S. aureus (ATCC29213) and 57 clinically isolated strains, categorized according to POT. Peaks associated with MRSA clone identification (N = 67) were extracted. Based on this peak information, the feasibility of MRSA clone identification was examined by cluster analysis.
Findings:
In addition to the 58 strains used for peak extraction, mass spectrum analysis of 24 clinically isolated outbreak strains revealed that peak data could be used for successful identification of clones. These typing results were fully consistent with the PFGE and POT results.
Conclusion:
This novel method enables simple and rapid typing with accuracy equivalent to PFGE/POT. This method would be suited to rapid outbreak analysis, offering accurate information to combat infectious diseases.
Related Concept Videos
Modern Molecular Taxonomy
MALDI-TOF Mass Spectrometry
Matrix-Assisted Laser Desorption Ionization (MALDI)
Clinical Significance of Antibiotic Resistance
Methods of Classification and Identification
Automated Microbial Diagnostics

