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      Optimization of MALDI-ToF mass spectrometry for yeast identification: a multicenter study

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          Abstract

          Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) is routinely used in mycology laboratories to rapidly identify pathogenic yeasts. Various methods have been proposed to perform routine MS-based identification of clinically relevant species. In this study, we focused on Bruker technology and assessed the identification performance of three protocols: two pretreatment methods (rapid formic acid extraction directly performed on targets and full extraction using formic acid/acetonitrile in tubes) and a direct deposit protocol that omits the extraction step. We also examined identification performance using three target types (ground-steel, polished-steel, and biotargets) and two databases (Bruker and online MSI [biological-mass-spectrometry-identification application]) in a multicenter manner. Ten European centers participated in the study, in which a total of 1511 yeast isolates were analyzed. The 10 centers prospectively performed the three protocols on approximately 150 yeast isolates each, and the corresponding spectra were then assessed against two reference spectra databases (MSI and Bruker), with appropriate thresholds. Three centers evaluated the impact of the targets. Scores were compared between the various combinations, and identification accuracy was assessed. The protocol omitting the extraction step was inappropriate for yeast identification, while the full extraction method yielded far better results. Rapid formic acid extraction yielded variable results depending on the target, database and threshold. Selecting the optimal extraction method in combination with the appropriate target, database and threshold may enable simple and accurate identification of clinically relevant yeast samples. Concerning the widely used polished-steel targets, the full extraction method still ensured better scores and better identification rates.

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          Performance of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of bacterial strains routinely isolated in a clinical microbiology laboratory.

          Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently been introduced in diagnostic microbiology laboratories for the identification of bacterial and yeast strains isolated from clinical samples. In the present study, we prospectively compared MALDI-TOF MS to the conventional phenotypic method for the identification of routine isolates. Colonies were analyzed by MALDI-TOF MS either by direct deposition on the target plate or after a formic acid-acetonitrile extraction step if no valid result was initially obtained. Among 1,371 isolates identified by conventional methods, 1,278 (93.2%) were putatively identified to the species level by MALDI-TOF MS and 73 (5.3%) were identified to the genus level, but no reliable identification was obtained for 20 (1.5%). Among the 1,278 isolates identified to the species level by MALDI-TOF MS, 63 (4.9%) discordant results were initially identified. Most discordant results (42/63) were due to systematic database-related taxonomical differences, 14 were explained by poor discrimination of the MALDI-TOF MS spectra obtained, and 7 were due to errors in the initial conventional identification. An extraction step was required to obtain a valid MALDI-TOF MS identification for 25.6% of the 1,278 valid isolates. In conclusion, our results show that MALDI-TOF MS is a fast and reliable technique which has the potential to replace conventional phenotypic identification for most bacterial strains routinely isolated in clinical microbiology laboratories.
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            Validation of a New Web Application for Identification of Fungi by Use of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry

            Matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry has emerged as a reliable technique to identify molds involved in human diseases, including dermatophytes, provided that exhaustive reference databases are available. This study assessed an online identification application based on original algorithms and an extensive in-house reference database comprising 11,851 spectra (938 fungal species and 246 fungal genera). Validation criteria were established using an initial panel of 422 molds, including dermatophytes, previously identified via DNA sequencing (126 species). The application was further assessed using a separate panel of 501 cultured clinical isolates (88 mold taxa including dermatophytes) derived from five hospital laboratories. A total of 438 (87.35%) isolates were correctly identified at the species level, while 26 (5.22%) were assigned to the correct genus but the wrong species and 37 (7.43%) were not identified, since the defined threshold of 20 was not reached. The use of the Bruker Daltonics database included in the MALDI Biotyper software resulted in a much higher rate of unidentified isolates (39.76 and 74.30% using the score thresholds 1.7 and 2.0, respectively). Moreover, the identification delay of the online application remained compatible with real-time online queries (0.15 s per spectrum), and the application was faster than identifications using the MALDI Biotyper software. This is the first study to assess an online identification system based on MALDI-TOF spectrum analysis. We have successfully applied this approach to identify molds, including dermatophytes, for which diversity is insufficiently represented in commercial databases. This free-access application is available to medical mycologists to improve fungal identification.
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              Matrix-assisted laser desorption ionization time-of-flight mass spectrometry for the rapid identification of yeasts causing bloodstream infections.

              Few studies have systematically standardised and evaluated matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) for identification of yeasts from bloodstream infections. This is rapidly becoming pertinent for early identification of yeasts and appropriate antifungal therapy. We used 354 yeast strains identified by polymerase chain reaction (PCR) sequencing for standardisation and 367 blind clinical strains for validation of our MALDI-TOF MS protocols. We also evaluated different sample preparation methods and found the on-plate formic acid extraction method as most cost- and time-efficient. The MALDI-TOF assay correctly identified 98.9% of PCR-sequenced yeasts. Novel main spectrum projections (MSP) were developed for Candida auris, C. viswanathii and Kodamaea ohmeri, which were missing from the Bruker MALDI-TOF MS database. Spectral cut-offs computed by receiver operating characteristics (ROC) analysis showed 99.4% to 100% accuracy at a log score of ≥ 1.70 for C. tropicalis, C. parapsilosis, C. pelliculosa, C. orthopsilosis, C. albicans, C. rugosa, C. guilliermondii, C. lipolytica, C. metapsilosis, C. nivariensis. The differences in the species-specific scores of our standardisation and blind validation strains were not statistically significant, implying the optimal performance of our test protocol. The MSPs of the three new species also were validated. We conclude that MALDI-TOF MS is a rapid, accurate and reliable tool for identification of bloodstream yeasts. With proper standardisation, validation and regular database expansion, its efficiency can be further enhanced.
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                Author and article information

                Contributors
                Journal
                Medical Mycology
                Oxford University Press (OUP)
                1369-3786
                1460-2709
                July 2020
                July 01 2020
                October 03 2019
                July 2020
                July 01 2020
                October 03 2019
                : 58
                : 5
                : 639-649
                Affiliations
                [1 ]Laboratoire de Parasitologie-Mycologie, de Parasitologie-Mycologie Hôpital Pitié Salpêtrière, 75013 Paris, France
                [2 ]Mycologie, CHU de Bordeaux, Groupe Hospitalier Pellegrin, place Amélie Raba-Léon, 33000 Bordeaux, France
                [3 ]Bacteriology Laboratory, Service of Laboratory Medicine, Department of Genetics, Laboratory Medicine and Pathology, Geneva University Hospitals, 4 rue Gabrielle-Perret-Gentil, 1205 Geneva, Switzerland
                [4 ]Aix Marseille University, IRD, AP-HM, SSA, VITROME, IHU Méditerranée Infection, 13006 Marseille, France
                [5 ]CHU de Montpellier, 34090 Montpellier, France
                [6 ]EA 7510, ESCAPE, Laboratoire de Parasitologie-Mycologie, Université de Reims Champagne-Ardenne, 51100 Reims, France
                [7 ]Laboratoire de Parasitologie Mycologie, CHU de Reims Hôpital Maison Blanche, 51100 Reims, France
                [8 ]Service de Parasitologie-Mycologie, Hôpital Purpan, 31059 Toulouse, France
                [9 ]Department Microbiology and Infection Prevention, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Laarbeeklaan 101, 1090 Brussels, Belgium
                [10 ]Department of Medical Microbiology and Infectious Diseases, Erasmus MC, ’s-Gravendijkwal 230, 3015 CE Rotterdam, The Netherlands
                [11 ]Department of Clinical Microbiology, Hospital Clinic, 08036 Barcelona, Spain
                [12 ]Sciensano, BCCM/IHEM collection, Mycology and Aerobiology Unit, 1050 Brussels, Belgium
                [13 ]Sorbonne Université, INSERM, Institut Pierre-Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France
                Article
                10.1093/mmy/myz098
                18db330b-01cd-4c36-b1c3-f22302a84c6a
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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