Comparative performance of Filamentous Fungi 4.0 and Mass Spectrometry Identification 2.0 databases for Aspergillus spp.: identification using a simplified protein extraction method
DOI:
https://doi.org/10.1590/Keywords:
Aspergillus identification, MALDI-TOF MS, Spectral databases, Medical mycologyAbstract
Aspergillus spp. are major agents of pulmonary aspergillosis, posing diagnostic challenges in clinical laboratories due to morphological similarity among species. This study compared the performance of two spectral databases, Filamentous Fungi v4.0 (Bruker Daltonics) and Mass Spectrometry Identification v2.0 (MSI 2.0), for Aspergillus species identification by MALDI-TOF MS, and evaluated the applicability of the simplified protein extraction method from solid culture medium for routine use in hospital laboratories. Overall, 46 clinical isolates from patients with pulmonary aspergillosis were cultured on Sabouraud Dextrose Agar (SDA) at 37 °C for 48 h. Proteins were extracted directly from colonies and analysed using the Bruker MALDI Biotyper system. Spectra were compared against FF_4.0 and MSI_2.0 databases, and results were correlated with molecular identification by benA, cmd5, and ITS gene sequencing. The extraction method yielded high-quality spectra with peaks between 2–15 kDa. The FF_4.0 database identified 52.17% of isolates at the section level and none at the species level, reflecting limited spectral compatibility and taxonomic coverage. Conversely, MSI_2.0 correctly identified 82.6% of isolates at the species level, 15.23% at the section level, and only 2.17% were not identified. Concordance between MSI_2.0 and sequencing reached 44.7%, without statistical significance (p = 0.627). The simplified solid-medium extraction method combined with MSI 2.0 proved efficient, reproducible, and suitable for clinical routine, offering faster identification of Aspergillus species compared to conventional methods. In contrast, FF_4.0 showed limited applicability for hospital workflows.
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Copyright (c) 2026 Gustavo Giacon Damiani, Vivian Caso Coelho, Juliana Possato Fernandes Takahashi, Ingrid Gonçalves Costa Leite, Marcia Regina von Zeska Kress, Marcello Mihailenko Chaves Magri, Valdes Roberto Bollela, Roberto Martínez, Gil Benard, Tiago Alexandre Cocio

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Funding data
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Fundação de Amparo à Pesquisa do Estado de São Paulo
Grant numbers 2022/14747 -
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Grant numbers 306612/2022-4;150639/2022-8