Title |
Development of a rapid MALDI-TOF MS based epidemiological screening method using MRSA as a model organism
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Published in |
European Journal of Clinical Microbiology & Infectious Diseases, September 2017
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DOI | 10.1007/s10096-017-3101-x |
Pubmed ID | |
Authors |
Åsa Lindgren, Nahid Karami, Roger Karlsson, Christina Åhrén, Martin Welker, Edward R. B. Moore, Liselott Svensson Stadler |
Abstract |
In this study we present a method using whole cell MALDI-TOF MS and VITEK MS RUO/SARAMIS as a rapid epidemiological screening tool. MRSA was used as a model organism for setting up the screening strategy. A collection of well-characterised MRSA strains representing the 19 most common Pulsed-Field Gel Electrophoresis (PFGE)-types in the region of South-West Sweden for the past 20 years was analysed with MALDI-TOF MS. A total of 111 MRSA strains were used for creating 19 PFGE-specific Superspectra using VITEK MS RUO/SARAMIS. Prior to performing the final analysis, the 19 Superspectra were combined into ten groups displaying similar peak patterns, hereafter named "MALDI-types". Two-hundred fifty-five MRSA strains were analysed to test the constructed Superspectra/MALDI-type database. Matches to the Superspectra above a threshold of 65% (corresponding to the number of matched peaks in the Superspectrum) were considered as positive assignment of a strain to a MALDI-type. The median peak matching value for correct assignment of a strain to a MALDI-type was 78% (range 65.3-100%). In total, 172 strains (67.4%) were assigned to the correct MALDI-type and only 5.5% of the strains were incorrectly assigned to another MALDI-type than the expected based on the PFGE-type of the strain. We envision this methodology as a cost-efficient step to be used as a first screening strategy in the typing scheme of MRSA isolates, to exclude epidemiological relatedness of isolates or to identify the need for further typing. |
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Researcher | 6 | 16% |
Student > Doctoral Student | 3 | 8% |
Other | 3 | 8% |
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Other | 9 | 24% |
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Computer Science | 2 | 5% |
Other | 10 | 26% |
Unknown | 10 | 26% |