Meso-Raman approach for rapid yeast cells identification

Raman spectromics method for fast and label-free genotype screening

It is now understood that genes and their various mutations are associated with the onset and progression of diseases. However, routine genetic testing techniques are limited by their high cost, time consumption, susceptibility to contamination, complex operation, and data analysis difficulties, rendering them unsuitable for genotype screening in many cases. Therefore, there is an urgent need to develop a rapid, sensitive, user-friendly, and cost-effective method for genotype screening and analysis. In this study, we propose and investigate a Raman spectroscopic method for achieving fast and label-free genotype screening. The method was validated using spontaneous Raman measurements of wild-type Cryptococcus neoformans and its six mutants. An accurate identification of different genotypes was achieved by employing a one-dimensional convolutional neural network (1D-CNN), and significant correlations between metabolic changes and genotypic variations were revealed. Genotype-specific regions of interest were also localized and visualized using a gradient-weighted class activation mapping (Grad-CAM)-based spectral interpretable analysis method. Furthermore, the contribution of each metabolite to the final genotypic decision-making was quantified. The proposed Raman spectroscopic method demonstrated huge potential for fast and label-free genotype screening and analysis of conditioned pathogens.

Rapid identification and drug resistance screening of respiratory pathogens based on single-cell Raman spectroscopy

Respiratory infections rank fourth in the global economic burden of disease. Lower respiratory tract infections are the leading cause of death in low-income countries. The rapid identification of pathogens causing lower respiratory tract infections to help guide the use of antibiotics can reduce the mortality of patients with lower respiratory tract infections. Single-cell Raman spectroscopy is a "whole biological fingerprint" technique that can be used to identify microbial samples. It has the advantages of no marking and fast and non-destructive testing. In this study, single-cell Raman spectroscopy was used to collect spectral data of six respiratory tract pathogen isolates. The T-distributed stochastic neighbor embedding (t-SNE) isolation analysis algorithm was used to compare the differences between the six respiratory tract pathogens. The eXtreme Gradient Boosting (XGBoost) algorithm was used to establish a Raman phenotype database model. The classification accuracy of the isolated samples was 93-100%, and the classification accuracy of the clinical samples was more than 80%. Combined with heavy water labeling technology, the drug resistance of respiratory tract pathogens was determined. The study showed that single-cell Raman spectroscopy-D₂O (SCRS-D₂O) labeling could rapidly identify the drug resistance of respiratory tract pathogens within 2 h.

Brillouin and Raman Micro-Spectroscopy: A Tool for Micro-Mechanical and Structural Characterization of Cortical and Trabecular Bone Tissues

Human bone is a specialized tissue with unique material properties, providing mechanical support and resistance to the skeleton and simultaneously assuring capability of adaptation and remodelling. Knowing the properties of such a structure down to the micro-scale is of utmost importance, not only for the design of effective biomimetic materials but also to be able to detect pathological alterations in material properties, such as micro-fractures or abnormal tissue remodelling. The Brillouin and Raman micro-spectroscopic (BRmS) approach has the potential to become a first-choice technique, as it is capable of simultaneously investigating samples’ mechanical and structural properties in a non-destructive and label-free way. Here, we perform a mapping of cortical and trabecular bone sections of a femoral epiphysis, demonstrating the capability of the technique for discovering the morpho-mechanics of cells, the extracellular matrix, and marrow constituents. Moreover, the interpretation of Brillouin and Raman spectra merged with an approach of data mining is used to compare the mechanical alterations in specimens excised from distinct anatomical areas and subjected to different sample processing. The results disclose in both cases specific alterations in the morphology and/or in the tissue chemical make-up, which strongly affects bone mechanical properties, providing a method potentially extendable to other important biomedical issues.