Yeast cell wall – Silver nanoparticles interaction: A synergistic approach between surface-enhanced Raman scattering and computational spectroscopy tools

Gherman, Ana Maria Raluca; Dina, Nicoleta Elena; Chiş, Vasile; Haisch, Christoph

doi:10.1016/j.saa.2019.117223

Cited by 21 publications

(18 citation statements)

References 36 publications

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“…After spectral pre-processing, a principal component analysis (PCA) model was combined with a support vector machine (SVM) model in order to reduce the high dimensionality of Raman spectra and to differentiate between the pathogenic and non-pathogenic bacteria. In this context, different chemometric techniques can be also implemented for feature selection and pathogenicity identification, e.g., biomolecular component analysis [ 16 ] and the combination of fuzzy principal component analysis or principal component analysis with linear discriminant analysis [ 17 , 18 ]. In our work, the PCA-SVM model was trained on the 14 cultivated E. coli strains while the pathogenicity of the E. coli isolates was predicted using the trained model.…”

Section: Methodsmentioning

confidence: 99%

Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy

et al. 2020

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Bacteria can be harmless commensals, beneficial probiotics, or harmful pathogens. Therefore, mankind is challenged to detect and identify bacteria in order to prevent or treat bacterial infections. Examples are identification of species for treatment of infection in clinics and E. coli cell counting for water quality monitoring. Finally, in some instances, the pathogenicity of a species is of interest. The main strategies to investigate pathogenicity are detection of target genes which encode virulence factors. Another strategy could be based on phenotypic identification. Raman spectroscopy is a promising phenotypic method, which offers high sensitivities and specificities for the identification of bacteria species. In this study, we evaluated whether Raman microspectroscopy could be used to determine the pathogenicity of E. coli strains. We used Raman spectra of seven non-pathogenic and seven pathogenic E. coli strains to train a PCA-SVM model. Then, the obtained model was tested by identifying the pathogenicity of three additional E. coli strains. The pathogenicity of these three strains could be correctly identified with a mean sensitivity of 77%, which is suitable for a fast screening of pathogenicity of single bacterial cells.

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Section: Methodsmentioning

confidence: 99%

Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy

et al. 2020

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“…This rich spectroscopic data is interpreted by using chemometrics, classical machine learning, and deep learning methods [18][19][20][21][22][23][24]. Although most of the studies involving filamentous fungi and yeasts have been conducted by Fourier transform infrared (FTIR) spectroscopy, as of late, Raman spectroscopy has been applied to study fungi, in particular regarding pigments [12,[25][26][27][28][29][30][31][32], lipids [10,29,[33][34][35][36], and cell wall composition [37,38]. Compared to FTIR spectroscopy, Raman spectroscopy is based on a fundamentally different principle.…”

Section: Introductionmentioning

confidence: 99%

“…In Raman spectroscopy, molecular vibrations originate from the interaction of the sample and the excitation radiation, typically from a laser in the ultraviolet, visible, or near-infrared region of the electromagnetic spectrum. In the case of biological samples, the resulting Raman spectrum usually displays a broad range of signals related to various types of cellular analytes, such as lipids, proteins, pigments, and carbohydrates [27,35,37,39]. Raman spectroscopy is very suitable for biotechnology applications since it does not require special sample pretreatment, it is non-destructive, and it is fast.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Biotechnologically Important Filamentous Fungal Biomass by Fourier Transform Raman Spectroscopy

Dzurendová

Shapaval

Tafintseva

et al. 2021

IJMS

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Oleaginous filamentous fungi can accumulate large amount of cellular lipids and biopolymers and pigments and potentially serve as a major source of biochemicals for food, feed, chemical, pharmaceutical, and transport industries. We assessed suitability of Fourier transform (FT) Raman spectroscopy for screening and process monitoring of filamentous fungi in biotechnology. Six Mucoromycota strains were cultivated in microbioreactors under six growth conditions (three phosphate concentrations in the presence and absence of calcium). FT-Raman and FT-infrared (FTIR) spectroscopic data was assessed in respect to reference analyses of lipids, phosphorus, and carotenoids by using principal component analysis (PCA), multiblock or consensus PCA, partial least square regression (PLSR), and analysis of spectral variation due to different design factors by an ANOVA model. All main chemical biomass constituents were detected by FT-Raman spectroscopy, including lipids, proteins, cell wall carbohydrates, and polyphosphates, and carotenoids. FT-Raman spectra clearly show the effect of growth conditions on fungal biomass. PLSR models with high coefficients of determination (0.83–0.94) and low error (approximately 8%) for quantitative determination of total lipids, phosphates, and carotenoids were established. FT-Raman spectroscopy showed great potential for chemical analysis of biomass of oleaginous filamentous fungi. The study demonstrates that FT-Raman and FTIR spectroscopies provide complementary information on main fungal biomass constituents.

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“…SERS offers qualitative and quantitative detection of the analyte because it has high specificity 12 . The SERS spectral bands are narrow with high resolution of spectral peaks, thus minimizing fluorescence background interference 13 . The characteristic spectra facilitate simultaneous detection of multiple components.…”

Section: Introductionmentioning

confidence: 99%

A novel method for identifying and distinguishing Cryptococcus neoformans and Cryptococcus gattii by surface-enhanced Raman scattering using positively charged silver nanoparticles

Hu¹,

Chen

et al. 2020

Sci Rep

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There are approximately 1 million cryptococcal infections per year among HIV+ individuals, resulting in nearly 625,000 deaths. Cryptococcus neoformans and Cryptococcus gattii are the two most common species that cause human cryptococcosis. These two species of Cryptococcus have differences in pathogenicity, diagnosis, and treatment. Cryptococcal infections are usually difficult to identify because of their slow growth in vitro. In addition, the long detection cycle of Cryptococcus in clinical specimens makes the diagnosis of Cryptococcal infections difficult. Here, we used positively charged silver nanoparticles (AgNPs + ) as a substrate to distinguish between C. neoformans and C. gattii in clinical specimens directly via surface-enhanced Raman scattering (SERS) and spectral analysis. The AgNPs + self-assembled on the surface of the fungal cell wall via electrostatic aggregation, leading to enhanced SERS signals that were better than the standard substrate negatively charged silver nanoparticles (AgNPs). The SERS spectra could also be used as a sample database in the multivariate analysis via orthogonal partial least-squares discriminant analysis. This novel SERS detection method can clearly distinguish between the two Cryptococcus species using principal component analysis. The accuracy of the training data and test data was 100% after a tenfold crossover validation.

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Yeast cell wall – Silver nanoparticles interaction: A synergistic approach between surface-enhanced Raman scattering and computational spectroscopy tools

Cited by 21 publications

References 36 publications

Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy

Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy

Assessment of Biotechnologically Important Filamentous Fungal Biomass by Fourier Transform Raman Spectroscopy

A novel method for identifying and distinguishing Cryptococcus neoformans and Cryptococcus gattii by surface-enhanced Raman scattering using positively charged silver nanoparticles

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