Discriminating Glioblastoma from Peritumoral Tissue by a Nanohole Array-Based Optical and Label-Free Biosensor.

In glioblastoma (GBM) patients, maximal safe resection remains a challenge today due to its invasiveness and diffuse parenchymal infiltration. In this context, plasmonic biosensors could potentially help to discriminate tumor tissue from peritumoral parenchyma based on differences in their optical properties. A nanostructured gold biosensor was used ex vivo to identify tumor tissue in a prospective series of 35 GBM patients who underwent surgical treatment. For each patient, two paired samples, tumor and peritumoral tissue, were extracted. Then, the imprint left by each sample on the surface of the biosensor was individually analyzed, obtaining the difference between their refractive indices. The tumor and non-tumor origins of each tissue were assessed by histopathological analysis. The refractive index (RI) values obtained by analyzing the imprint of the tissue were significantly lower (p = 0.0047) in the peritumoral samples (1.341, Interquartile Range (IQR) 1.339-1.349) compared with the tumor samples (1.350, IQR 1.344-1.363). The ROC (receiver operating characteristic) curve showed the capacity of the biosensor to discriminate between both tissues (area under the curve, 0.8779, p < 0.0001). The Youden index provided an optimal RI cut-off point of 0.003. The sensitivity and specificity of the biosensor were 81% and 80%, respectively. Overall, the plasmonic-based nanostructured biosensor is a label-free system with the potential to be used for real-time intraoperative discrimination between tumor and peritumoral tissue in patients with GBM.

Validation of an expanded, in-house library and an optimized preparation method for the identification of fungal isolates using MALDI-TOF mass spectrometry.

The goal of this study was to validate an optimized sample preparation method for filamentous fungal isolates coupled with the use of an in-house library for the identification of moulds using Matrix Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) in a multicenter context. For that purpose, three Spanish microbiology laboratories participated in the identification of 97 fungal isolates using MALDI-TOF MS coupled with the Filamentous Fungi library 3.0 (Bruker Daltonics) and an in-house library containing 314 unique fungal references. The isolates analyzed belonged to 25 species from the genus Aspergillus, Fusarium, Scedosporium/Lomentospora, the Mucorales order and the Dermatophytes group. MALDI-TOF MS identification was carried out from hyphae resuspended in water and ethanol. After a high-speed centrifugation step, the supernatant was discarded and the pellet submitted to a standard protein extraction step. The protein extract was analyzed with the MBT Smart MALDI Biotyper system (Bruker Daltonics). The rate of accurate, species-level identification obtained ranged between 84.5% and 94.8% and the score values were 1.8 for 72.2-94.9% of the cases. Two laboratories failed to identify only one isolate of Syncephalastrum sp. and Trichophyton rubrum, respectively and three isolates could not be identified in the third center (F. proliferatum, n = 1; T.interdigitale, n = 2). In conclusion, the availability of an effective sample preparation method and an extended database allowed high rates of correct identification of fungal species using MALDI-TOF MS. Some species, such as Trichophyton spp. are still difficult to identify. Although further improvements are still required, the developed methodology allowed the reliable identification of most fungal species.

MALDI-TOF mass spectrometry has been improved as a diagnostic method for the rapid and reliable identification of filamentous fungi by means of the creation of an expanded database containing reference protein spectra of the most clinically impacting fungal species.

Identification of Nocardia and non-tuberculous Mycobacterium species by MALDI-TOF MS using the VITEK MS coupled to IVD and RUO databases.

Identification of Nocardia and Mycobacterium species by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is still a challenging task that requires both suitable protein extraction procedures and extensive databases. This study aimed to evaluate the VITEK MS Plus system coupled with updated RUO (v4.17) and IVD (v3.2) databases for the identification of Nocardia spp. and Mycobacterium spp. clinical isolates. Sample preparation was carried out using the VITEK MS Mycobacterium/Nocardia kit for protein extraction. From 90 Nocardia spp. isolates analysed, 86 (95.6%) were correctly identified at species or complex level using IVD and 78 (86.7%) using RUO. Only two strains were misidentified as other species pertaining to the same complex. Among the 106 non-tuberculous Mycobacterium clinical isolates tested from a liquid culture medium, VITEK MS identified correctly at species or complex level 96 (90.6%) isolates in the IVD mode and 89 (84.0%) isolates in the RUO mode. No misidentifications were detected. Although the IVD mode was unable to differentiate members of the M. fortuitum complex, the RUO mode correctly discriminated M. peregrinum and M. septicum. The robustness and accuracy showed by this system allow its implementation for routine identification of these microorganisms in clinical laboratories.

Evaluation of the Vitek Ms system for the identification of filamentous fungi.

The implementation of matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) for the identification of fungal isolates remains challenging and has been limited to experienced laboratories in sample preparation and in-house libraries construction. However, the development of commercial kits for standardized fungal sample preparation and updated reference libraries can fill this gap. This study aimed to evaluate the performance of the commercial VITEK MS Mould Kit (bioMérieux, Marcy l'Etoile, France) and the VITEK MS system (bioMérieux) for identification using a panel of fungal species of clinical interest. Overall, 200 isolates belonging to 13 genera and 43 fungal species were analyzed with the VITEK MS system equipped with the v3.2 IVD database. Overall, 89.0% of the isolates were correctly identified, 41.5 and 43.5% at species and complex level, respectively. For an additional 4.0% of the identifications, correlation at the genus level was reported. The remaining 21 isolates (10.5%) could not be identified among which 85.0% (18/21) were species not claimed in the database. One Syncephalastrum isolate was misidentified as Rhizopus microsporus complex. Specifically, 100% of the Scedosporium/Lomentospora, 97.1% of the Fusarium, 65.7% of the Mucorales and 86.4% of the Aspergillus isolates were correctly identified at the species and complex level. The methodology described allows for an easy implementation of MALDI-TOF MS for routine identification of fungal species in a fast and reliable manner. Although further improvement in the databases is still required, an important number of fungal species can be correctly identified at the species level using this method. LAY SUMMARY: The use of MALDI-TOF for fungal identification remains a challenge. In this study, using a commercial protein extraction kit and updated database, VITEK MS system was able to identify up to 89.0% of a diverse collection of 200 filamentous fungi representing 43 fungal species.

Detection of azole resistance in Aspergillus fumigatus complex isolates using MALDI-TOF mass spectrometry.

OBJECTIVES: The main goal of this study was to accurately detect azole resistance in species of the Aspergillus fumigatus complex by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). METHODS: Identification of isolates (n = 868) was done with MALDI-TOF MS using both commercial and in-house libraries. To determine azole susceptibility, the EUCAST E.Def. 9.3.2 method was applied as the reference standard. Identification of resistant isolates was confirmed by DNA sequence analysis. Protein spectra obtained by MALDI-TOF MS were analysed to differentiate species within the A. fumigatus complex and to detect azole-resistant A. fumigatus sensu stricto isolates. RESULTS: Correct discrimination of A. fumigatus sensu stricto from cryptic species was accomplished in 100% of the cases applying principal component analysis (PCA) to protein spectra generated by MALDI-TOF MS. Furthermore, a specific peak (4586 m/z) was found to be present only in cryptic species. The application of partial least squares (PLS) discriminant analysis allowed 98.43% (±0.038) discrimination between susceptible and azole-resistant A. fumigatus sensu stricto isolates. Finally, based on PLS and SVM, A. fumigatus sensu stricto isolates with different cyp51A gene mutations were correctly clustered in 91.5% of the cases. CONCLUSIONS: MALDI-TOF MS combined with peak analysis is a novel tool that allows the differentiation of A. fumigatus sensu stricto from other species within the A. fumigatus complex, as well as the detection of azole-resistant A. fumigatus sensu stricto. Although further studies are still needed, the results reported here show the great potential of MALDI-TOF and machine learning for the rapid detection of azole-resistant Aspergillus fumigatus isolates from clinical origins.