The identification of filamentous fungi by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) relies mainly on a robust and extensive database of reference spectra. To this end, a large in-house library containing 760 strains and representing 472 species was built and evaluated on 390 clinical isolates by comparing MALDI-TOF MS with the classical identification method based on morphological observations. The use of MALDI-TOF MS resulted in the correct identification of 95.4% of the isolates at species level, without considering LogScore values. Taking into account the Brukers’ cutoff value for reliability (LogScore >1.70), 85.6% of the isolates were correctly identified. For a number of isolates, microscopic identification was limited to the genus, resulting in only 61.5% of the isolates correctly identified at species level while the correctness reached 94.6% at genus level. Using this extended in-house database, MALDI-TOF MS thus appears superior to morphology in order to obtain a robust and accurate identification of filamentous fungi. A continuous extension of the library is however necessary to further improve its reliability. Indeed, 15 isolates were still not represented while an additional three isolates were not recognized, probably because of a lack of intraspecific variability of the corresponding species in the database.

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.

Several Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry protocols, which differ in identification criteria, have been developed for mold and dermatophyte identification. Currently, the most widely used approach is Bruker technology, although no consensus concerning the log(score) threshold has been established. Furthermore, it remains unknown how far increasing the number of spots to compare results might improve identification performance. In this study, we used in-house and Bruker reference databases as well as a panel of 422 isolates belonging to 126 species to test various thresholds. Ten distinct identification algorithms requiring one to four spots were tested. Our findings indicate that optimal results were obtained by applying a decisional algorithm in which only the highest score of four spots was taken into account with a 1.7 log(score) threshold. Testing the entire panel enabled identification of 87.41% (in-house database) and 35.15% (Bruker database) of isolates, with a positive predictive value (PPV) of 1 at the genus level for both databases as well as 0.89 PPV (in-house database) and 0.72 PPV (Bruker database) at the species level. Applying the same rules to the isolates for which the species were represented by at least three strains in the database enabled identification of 92.1% (in-house database) and 46.6% (Bruker database) of isolates, with 1 PPV at the genus level for both databases as well as 0.95 PPV (in-house database) and 0.93 PPV (Bruker database) at the species level. Depositing four spots per extract and lowering the threshold to 1.7, a threshold which is notably lower than that recommended for bacterial identification, decreased the number of unidentified specimens without altering the reliability of the accepted results. Nevertheless, regardless of the criteria used for mold and dermatophyte identification, commercial databases require optimization.

The clinical diagnosis of mould infections currently involves complex species identification based on morphological criteria, which is often prone to error. Employing an extensive mould species reference spectral library (up to 2832 reference spectra, corresponding to 708 strains from 347 species), we assessed the extent to which matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) enhanced the accuracy of species identification. MALDI-TOF MS data were validated against morphology-based and DNA sequence-based results with 262 clinical isolates collected over a 4-month period in 2013. The implementation of MALDI-TOF MS resulted in a dramatic improvement in mould identification at the species level (from 78.2% to 98.1%) and a marked reduction in the misidentification rate (from 9.8% to 1.2%). We then compared the mould identification results obtained before (i.e. 2011) and after (i.e. 2013) the implementation of MALDI-TOF MS in routine identification procedures, which showed an improvement from 64.57% to 100%. Reassessment of a set of isolates from 2011 with this procedure, including MALDI-TOF MS, yielded an increase in species diversity from 16 to 42 species. Finally, application of this procedure during a 16-month period (2012-2013) enabled the identification of 1094 of 1107 (98.8%) clinical mould isolates corresponding to 107 distinct species. MALDI-TOF MS-based mould species identification may soon challenge traditional techniques in the clinical laboratory, as patient prognosis is largely contingent on rapid and accurate diagnosis.