Raman spectroscopy detects phenotypic differences among Escherichia coli enriched for 1‐butanol tolerance using a metagenomic DNA library

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy

Cholangiocarcinoma (CCA) is highly prevalent in the northeastern region of Thailand. Current diagnostic methods for CCA are often expensive, time-consuming, and require medical professionals. Thus, there is a need for a simple and low-cost CCA screening method. This work developed a rapid label-free technique by Raman spectroscopy combined with the multivariate statistical methods of principal component analysis and linear discriminant analysis (PCA-LDA), aiming to analyze and classify between CCA (n = 30) and healthy (n = 30) serum specimens. The model's classification performance was validated using k-fold cross validation (k = 5). Serum levels of cholesterol (548, 700 cm-1), tryptophan (878 cm-1), and amide III (1248,1265 cm-1) were found to be statistically significantly higher in the CCA patients, whereas serum beta-carotene (1158, 1524 cm-1) levels were significantly lower. The peak heights of these identified Raman marker bands were input into an LDA model, achieving a cross-validated diagnostic sensitivity and specificity of 71.33% and 90.00% in distinguishing the CCA from healthy specimens. The PCA-LDA technique provided a higher cross-validated sensitivity and specificity of 86.67% and 96.67%. To conclude, this work demonstrated the feasibility of using Raman spectroscopy combined with PCA-LDA as a helpful tool for cholangiocarcinoma serum-based screening.

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes of Synechocystis PCC6803 with Raman spectroscopy

Background

Synechocystis sp. PCC6803 is a model cyanobacterium that has been studied widely and is considered for metabolic engineering applications. Here, Raman spectroscopy and Raman chemometrics (Rametrix™) were used to (i) study broad phenotypic changes in response to growth conditions, (ii) identify phenotypic changes associated with its circadian rhythm, and (iii) correlate individual Raman bands with biomolecules and verify these with more accepted analytical methods.

Methods

Synechocystis cultures were grown under various conditions, exploring dependencies on light and/or external carbon and nitrogen sources. The Rametrix™ LITE Toolbox for MATLAB® was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix™ PRO Toolbox was used to validate these models through leave-one-out routines that classified a Raman spectrum when growth conditions were withheld from the model. Performance was measured by classification accuracy, sensitivity, and specificity. Raman spectra were also subjected to statistical tests (ANOVA and pairwise comparisons) to identify statistically relevant changes in Synechocystis phenotypes. Finally, experimental methods, including widely used analytical and spectroscopic assays were used to quantify the levels of glycogen, fatty acids, amino acids, and chlorophyll a for correlations with Raman data.

Results

PCA and DAPC models produced distinct clustering of Raman spectra, representing multiple Synechocystis phenotypes, based on (i) growth in the presence of 5 mM glucose, (ii) illumination (dark, light/dark [12 h/12 h], and continuous light at 20 µE), (iii) nitrogen deprivation (0–100% NaNO₃ of native BG-11 medium in continuous light), and (iv) throughout a 24 h light/dark (12 h/12 h) circadian rhythm growth cycle. Rametrix™ PRO was successful in identifying glucose-induced phenotypes with 95.3% accuracy, 93.4% sensitivity, and 96.9% specificity. Prediction accuracy was above random chance values for all other studies. Circadian rhythm analysis showed a return to the initial phenotype after 24 hours for cultures grown in light/dark (12 h/12 h) cycles; this did not occur for cultures grown in the dark. Finally, correlation coefficients (R > 0.7) were found for glycogen, all amino acids, and chlorophyll a when comparing specific Raman bands to other experimental results.

Characterizing metabolic stress-induced phenotypes of Synechocystis PCC6803 with Raman spectroscopy

BACKGROUND

During their long evolution, Synechocystis sp. PCC6803 developed a remarkable capacity to acclimate to diverse environmental conditions. In this study, Raman spectroscopy and Raman chemometrics tools (RametrixTM) were employed to investigate the phenotypic changes in response to external stressors and correlate specific Raman bands with their corresponding biomolecules determined with widely used analytical methods.

METHODS

Synechocystis cells were grown in the presence of (i) acetate (7.5-30 mM), (ii) NaCl (50-150 mM) and (iii) limiting levels of MgSO4 (0-62.5 mM) in BG-11 media. Principal component analysis (PCA) and discriminant analysis of PCs (DAPC) were performed with the RametrixTM LITE Toolbox for MATLABⓇ. Next, validation of these models was realized via RametrixTM PRO Toolbox where prediction of accuracy, sensitivity, and specificity for an unknown Raman spectrum was calculated. These analyses were coupled with statistical tests (ANOVA and pairwise comparison) to determine statistically significant changes in the phenotypic responses. Finally, amino acid and fatty acid levels were measured with well-established analytical methods. The obtained data were correlated with previously established Raman bands assigned to these biomolecules.

RESULTS

Distinguishable clusters representative of phenotypic responses were observed based on the external stimuli (i.e., acetate, NaCl, MgSO4, and controls grown on BG-11 medium) or its concentration when analyzing separately. For all these cases, RametrixTM PRO was able to predict efficiently the corresponding concentration in the culture media for an unknown Raman spectra with accuracy, sensitivity and specificity exceeding random chance. Finally, correlations (R > 0.7) were observed for all amino acids and fatty acids between well-established analytical methods and Raman bands.

Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix)

Raman Chemometric Urinalysis (Rametrix^TM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. Rametrix^TM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing “unknown” specimens, Rametrix^TM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. Rametrix^TM was able to identify “unknown” urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.

Taking the technical microbiome into the next decade

The 'microbiome' has become a buzzword. Multiple new technologies allow to gather information about microbial communities as they evolve under stable and variable environmental conditions. The challenge of the next decade will be to develop strategies to compose and manage microbiomes. Here, key aspects are considered that will be of crucial importance for future microbial technological developments. First, the need to deal not only with genotypes but also particularly with phenotypes is addressed. Microbial technologies are often highly dependent on specific core organisms to obtain the desired process outcome. Hence, it is essential to combine omics data with phenotypic information to invoke and control specific phenotypes in the microbiome. Second, the development and application of synthetic microbiomes is evaluated. The central importance of the core species is a no-brainer, but the implementation of proper satellite species is an important route to explore. Overall, for the next decade, microbiome research should no longer almost exclusively focus on its capacity to degrade and dissipate but rather on its remarkable capability to capture disordered components and upgrade them into high-value microbial products. These products can become valuable commodities in the cyclic economy, as reflected in the case of 'reversed sanitation', which is introduced here.

Characterization of an evolved carotenoids hyper-producer of Saccharomyces cerevisiae through bioreactor parameter optimization and Raman spectroscopy

An evolutionary engineering approach for enhancing heterologous carotenoids production in an engineered Saccharomyces cerevisiae strain was used previously to isolate several carotenoids hyper-producers from the evolved populations. β-Carotene production was characterized in the parental and one of the evolved carotenoids hyper-producers (SM14) using bench-top bioreactors to assess the impact of pH, aeration, and media composition on β-carotene production levels. The results show that with maintaining a low pH and increasing the carbon-to-nitrogen ratio (C:N) from 8.8 to 50 in standard YNB medium, a higher β-carotene production level at 25.52 ± 2.15 mg β-carotene g−1 (dry cell weight) in the carotenoids hyper-producer was obtained. The increase in C:N ratio also significantly increased carotenoids production in the parental strain by 298 % [from 5.68 ± 1.24 to 22.58 ± 0.11 mg β-carotene g−1 (dcw)]. In this study, it was shown that Raman spectroscopy is capable of monitoring β-carotene production in these cultures. Raman spectroscopy is adaptable to large-scale fermentations and can give results in near real-time. Furthermore, we found that Raman spectroscopy was also able to measure the relative lipid compositions and protein content of the parental and SM14 strains at two different C:N ratios in the bioreactor. The Raman analysis showed a higher total fatty acid content in the SM14 compared with the parental strain and that an increased C:N ratio resulted in significant increase in total fatty acid content of both strains. The data suggest a positive correlation between the yield of β-carotene per biomass and total fatty acid content of the cell.