Identification of toxic mold species through Raman spectroscopy of fungal conidia

Limit of Detection of Raman Spectroscopy Using Polystyrene Particles from 25 to 1000 nm in Aqueous Suspensions

Raman spectroscopy is an analytical method capable of detecting various microorganisms and small particles. Here, we used 25-1000 nm polystyrene particles in aqueous suspensions, which are comparable in size to viral particles and viral aggregates, to determine the limit of detection (LOD) of a confocal Raman microscope. We collected Raman spectra using a 785 nm wavelength laser with a power of 300 mW and a 10 s exposure time with a 5× objective lens. We detected the most prominent peak of the polystyrene particles at 1001 cm-1, corresponding to the ring breathing mode. We established the minimum and maximum LOD (LODmin and LODmax) using a Kernel partial least-squares model. The LOD of the smallest size of 50 nm was identified as 1.80 × 1012-8.31 × 1012 particle/mL, and for the largest size of 1000 nm, 5.11 × 108-2.53 × 109 particle/mL. We demonstrated that Raman spectroscopy was nondestructive under these conditions by comparing the particle size before and after collecting Raman spectra using dynamic light scattering. Due to their size similarity to viral particles and viral aggregates, this systematic characterization of polystyrene particles provides detailed information on their Raman spectral signatures in aqueous suspensions. These findings establish a foundation for using Raman spectroscopy for the detection of small particles in aqueous suspensions and highlight its potential as a tool for real-time monitoring in vaccine manufacturing.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Determination of aflatoxin B1 (AFB1) in maize based on a portable Raman spectroscopy system and multivariate analysis

Aflatoxin B₁ (AFB₁) is the most widely distributed, most toxic, and most harmful, and it is widely present in moldy grains. This study proposes a new method for quantitative and rapid determination of the AFB₁ content in maize based on Raman spectroscopy. The Raman spectra of maize samples with different mildew degrees were collected by a portable laser Raman spectroscopy system. Three different spectral selection methods, which were bootstrapping soft shrinkage (BOSS), variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS), were applied to optimize the characteristic wavelength variables of the pretreated Raman spectra. The support vector machine (SVM) detection models based on different optimized characteristic wavelength variables were established, and the results of each detection model were compared. The results obtained showed that the performance of the SVM models established by optimized features was significantly better than the performance of the SVM model built by full-spectrum data. Among them, the SVM model based on the characteristic wavelength variables optimized by the CARS method had the best performance, and its root mean square error of prediction (RMSEP) was 3.5377 μg∙kg^-1, the determination coefficient of prediction (R_P²) was 0.9715, and the relative prediction deviation (RPD) was 5.8258. The overall results reveal that the rapid quantitative detection of the AFB₁ in maize by Raman spectroscopy has a promising application prospect. In addition, the implementation of the characteristic wavelength optimization of Raman spectra in the model calibration process can effectively improve the detection accuracy of chemometric models.

Raman Characterization of Fungal DHN and DOPA Melanin Biosynthesis Pathways

Fungal melanins represent a resource for important breakthroughs in industry and medicine, but the characterization of their composition, synthesis, and structure is not well understood. Raman spectroscopy is a powerful tool for the elucidation of molecular composition and structure. In this work, we characterize the Raman spectra of wild-type Aspergillus fumigatus and Cryptococcus neoformans and their melanin biosynthetic mutants and provide a rough “map” of the DHN (A. fumigatus) and DOPA (C. neoformans) melanin biosynthetic pathways. We compare this map to the Raman spectral data of Aspergillus nidulans wild-type and melanin biosynthetic mutants obtained from a previous study. We find that the fully polymerized A. nidulans melanin cannot be classified according to the DOPA pathway; nor can it be solely classified according to the DHN pathway, consistent with mutational analysis and chemical inhibition studies. Our approach points the way forward for an increased understanding of, and methodology for, investigating fungal melanins.