Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) is an accurate, rapid and inexpensive tool for routine identification of bacteria and yeasts. However, its role in the identification of filamentous fungi is less well defined. Here, we evaluated the utility of MALDI-TOF MS for the identification of 70 clinically relevant mould isolates.
Sixty-three Aspergillus (A. fumigatus, A. flavus, A. niger, A. terreus, A. nidulans, A. versicolor and A. sydowii) and seven Scedosporium (S. apiospermum and S. aurantiacum) isolates were studied. Fungal proteins were extracted directly from isolates grown on Sabouraud dextrose agar, incubated at 28ºC for 2 to 7 days, using the National Institutes of Health (NIH; Bethesda, MD) protocol. Spectra were acquired on the MALDI-TOF MicroFlex LT mass spectrometer (BrukerDaltonics) and analysed against the Bruker Filamentous Fungi Library (version 3.1). Each isolate was analysed in duplicate.
Overall, species- (score ≥2.0) and genus-level only (score ≤1.999 and ≥1.7) identification was achieved for 43 (61%) and 19 (27%) isolates, respectively. Amongst Aspergillus isolates, 43 (68%) were identified to species level and an additional 15 (24%) to genus level only. Four Scedosporium isolates identified to genus level only.
Despite database representation, five isolates of Aspergillus and one Scedosporium species, were not identified (score <1.7). Two S .aurantiacum isolates, which are not represented in the database, were misidentified as S. apiospermum.
MALDI-TOF MS is a promising tool for the clinical mycology laboratory. Our preliminary results indicate that the NIH extraction protocol works well with the Bruker database, providing faster identification (30 min) of filamentous fungi compared with the manufacturer’s liquid media extraction protocol (>25 h). Optimization techniques are being investigated and further validation on a wider variety of moulds is in progress. A collaborative evaluation of a more comprehensive database developed at the NIH will likely improve identification results to ~90%, as has been shown in several US studies.