Routine use of MALDI-TOF MS for anaerobic bacterial identification in clinical microbiology

Anaerobe ◽  
2018 ◽  
Vol 54 ◽  
pp. 191-196 ◽  
Author(s):  
Samantha Shannon ◽  
Daniel Kronemann ◽  
Robin Patel ◽  
Audrey N. Schuetz
2019 ◽  
Vol 16 (8) ◽  
pp. 695-710 ◽  
Author(s):  
Martin Welker ◽  
Alex Van Belkum ◽  
Victoria Girard ◽  
Jean-Philippe Charrier ◽  
David Pincus

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Kivanc Bilecen ◽  
Gorkem Yaman ◽  
Ugur Ciftci ◽  
Yahya Rauf Laleli

In clinical microbiology laboratories, routine microbial identification is mostly performed using culture based methodologies requiring 24 to 72 hours from culturing to identification. Matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technology has been established as a cost effective, reliable, and faster alternative identification platform. In this study, we evaluated the reliability of the two available MALDI-TOF MS systems for their routine clinical level identification accuracy and efficiency in a clinical microbiology laboratory setting. A total of 1,341 routine phenotypically identified clinical bacterial and fungal isolates were selected and simultaneously analyzed using VITEK MS (bioMérieux, France) and Microflex LT (Bruker Diagnostics, Germany) MALDI-TOF MS systems. For any isolate that could not be identified with either of the systems and for any discordant result, 16S rDNA gene or ITS1/ITS2 sequencing was used. VITEK MS and Microflex LT correctly identified 1,303 (97.17%) and 1,298 (96.79%) isolates to the species level, respectively. In 114 (8.50%) isolates initial phenotypic identification was inaccurate. Both systems showed a similar identification efficiency and workflow robustness, and they were twice as more accurate compared to routine phenotypic identification in our sample pool. MALDITOF systems with their accuracy and robustness offer a good identification platform for routine clinical microbiology laboratories.


Anaerobe ◽  
2018 ◽  
Vol 54 ◽  
pp. 151-158 ◽  
Author(s):  
Peivern Fong ◽  
Michelle J. Francis ◽  
John F. Hamblin ◽  
Tony M. Korman ◽  
Maryza Graham

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246002
Author(s):  
Kazuyuki Sogawa ◽  
Shigetsugu Takano ◽  
Takayuki Ishige ◽  
Hideyuki Yoshitomi ◽  
Shingo Kagawa ◽  
...  

Surgical site infections (SSIs) are significant and frequent perioperative complications, occurring due to the contamination of the surgical site. The late detection of SSIs, especially organ/space SSIs which are the more difficult to treat, often leads to severe complications. An effective method that can identify bacteria with a high accuracy, leading to the early detection of organ/space SSIs, is needed. Ninety-eight drainage fluid samples obtained from 22 patients with hepatobiliary pancreatic disease were analyzed to identify microorganisms using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) with a new membrane filtration protocol and rapid BACpro® pretreatment compared to sole rapid BACpro® pretreatment. The levels of detail of rapid BACpro® pretreatment with or without filtration were also evaluated for the accuracy of bacterial identification. We found that reliable scores for E. coli and E. faecalis were obtained by inoculation with 1.0 × 104 CFU/ml after preparation of the membrane filter with rapid BACpro®, indicating approximately 10-folds more sensitive compared to sole rapid BACpro® pretreatment in drainage fluid specimens. Among 60 bacterial positive colonies in drainage fluid specimens, the MALDI-TOF MS and the membrane filtration with rapid BACpro® identified 53 isolates (88.3%) with a significantly higher accuracy, compared to 25 isolates in the rapid BACpro® pretreatment group (41.7%) (p < 0.001). Among the 78 strains, 14 enteric Gram-negative bacteria (93.0%) and 55 Gram-positive cocci (87.3%) were correctly identified by the membrane filtration with rapid BACpro® with a high reliability. This novel protocol could identify bacterial species within 30 min, at $2-$3 per sample, thus leading to cost and time savings. MALDI-TOF MS with membrane filter and rapid BACpro® is a quick and reliable method for bacterial identification in drainage fluids. The shortened analysis time will enable earlier selection of suitable antibiotics for treatment of organ/space SSIs to improve patients’ outcomes.


Author(s):  
Arzu Akşit İlki ◽  
Sevim Özsoy ◽  
Gulşen Gelmez ◽  
Burak Aksu ◽  
Güner Söyletir

AbstractUrinary tract infections are one of the most common bacterial infections and rapid diagnosis of the infection is essential for appropriate antibiotic therapy. The goal of our study was to identify urinary pathogens directly by MALDI-TOF MS and to perform antibiotic susceptibility tests in order to shorten the period spent for culturing.Urine samples submitted for culture to the Clinical Microbiology Laboratory were enrolled in this study. Urine samples were screened for leukocyte and bacteria amount by flow cytometry. Samples with bacterial load of 106–107/mL were tested directly by MALDI-TOF MS and antibiotic susceptibility tests (AST) were performed.In total, 538 positive urine samples were evaluated in our study. MALDI-TOF MS identified the microorganism directly from the urine sample in 91.8% of these samples and the concordance rate of conventional identification and direct detection was 95.8% for Gram-negatives at the genus and species level. Escherichia coli (n:401) was the most frequently isolated microorganism, followed by Klebsiella pneumoniae (n:57). AST results were generated for 111 of these urine samples and the concordance was 90% and 87% for E. coli and K. pneumoniae, respectively.Our results showed that screening of urine samples with flow cytometry to detect positive samples and identification of uropathogens directly by MALDI-TOF MS with an accuracy of over 90% can be a suitable method particularly for Gram-negative bacteria in clinical microbiology laboratories.


2015 ◽  
Vol 99 (13) ◽  
pp. 5547-5562 ◽  
Author(s):  
Dominik Ziegler ◽  
Joël F. Pothier ◽  
Julie Ardley ◽  
Romain Kouakou Fossou ◽  
Valentin Pflüger ◽  
...  

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