Matrix-assisted laser desorption–ionization/time of flight mass spectrometry (MALDI-TOF MS) has been widely implemented for the rapid identification of microorganisms. Although most bacteria, yeasts and filamentous fungi can be accurately identified with this method, some closely related species still represent a challenge for MALDI-TOF MS. In this study, two MALDI-TOF-based approaches were applied for discrimination at the species-level of isolates belonging to the Cryptococcus neoformans complex, previously characterized by Amplified Fragment Length Polymorphism (AFLP) and sequencing of the ITS1-5.8S-ITS2 region: (i) an expanded database was built with 26 isolates from the main Cryptococcus species found in our setting (C. neoformans, C. deneoformans and AFLP3 interspecies hybrids) and (ii) peak analysis and data modeling were applied to the protein spectra of the analyzed Cryptococcus isolates. The implementation of the in-house database did not allow for the discrimination of the interspecies hybrids. However, the performance of peak analysis with the application of supervised classifiers (partial least squares-discriminant analysis and support vector machine) in a two-step analysis allowed for the 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. In addition, PCA analysis prior to support vector machine (SVM) provided 98.45% correct discrimination of the three analyzed species in a one-step analysis. This novel method is cost-efficient, rapid and user-friendly. The procedure can also be automatized for an optimized implementation in the laboratory routine.

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.

Background: Differentiation of the species within the Cryptococcus neoformans complex ( C. deneoformans, C. neoformans and C. neoformans interspecies hybrids – C. deneoformans x C. neoformans -) is important to define the epidemiology of the infection. Objectives: In this study we attempted the discrimination of three C. neoformans species using MALDI-TOF MS coupled with an in-house library. Methods: All Cryptococcus spp. isolates were identified by AFLP markers. An in-house database was constructed 26 well characterized C. deneoformans, C. neoformans and interspecies hybrids. Forty-four Cryptococcus spp. isolates were blindly identified using MALDI-TOF MS (Bruker Daltonics) and the expanded library. Their protein spectra were also submitted to hierarchical clustering and the resulting species were verified via Partial Least Squares Differential Analysis (PLS-DA) and Support-Vector Machine (SVM). Results: MALDI-TOF MS coupled with the in-house library allowed 100% correct identification of C. deneoformans and C. neoformans but misidentified the interspecies hybrids. The same level of discrimination among C. deneoformans and C. neoformans was achieved applying SVM. The application of the PLS-DA and SVM algorithms in a two-step analysis allowed 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. Besides, PCA analysis prior to SVM provided 98.45% correct discrimination of the 3 species analysed in a one-step analysis. Conclusions: Our results indicate that MALDI-TOF MS could be a rapid and reliable tool for the correct discrimination of C. deneoformans and C. neoformans . The correct identification of the interspecies hybrids could only be achieved by hierarchical clustering with other protein spectra from the same species.