Absolute Quantification of Uric Acid in Human Urine Using Surface Enhanced Raman Scattering with the Standard Addition Method.

High levels of uric acid in urine and serum can be indicative of hypertension and the pregnancy related condition, preeclampsia. We have developed a simple, cost-effective, portable surface enhanced Raman scattering (SERS) approach for the routine analysis of uric acid at clinically relevant levels in urine patient samples. This approach, combined with the standard addition method (SAM), allows for the absolute quantification of uric acid directly in a complex matrix such as that from human urine. Results are highly comparable and in very good agreement with HPLC results, with an average <9% difference in predictions between the two analytical approaches across all samples analyzed, with SERS demonstrating a 60-fold reduction in acquisition time compared with HPLC. For the first time, clinical prepreeclampsia patient samples have been used for quantitative uric acid detection using a simple, rapid colloidal SERS approach without the need for complex data analysis.

From Multistep Enzyme Monitoring to Whole-Cell Biotransformations: Development of Real-Time Ultraviolet Resonance Raman Spectroscopy.

Process analytical technologies (PAT) are used within industry to give real-time measurements of critical quality parameters, ultimately improving the quality by design (QbD) of the final product and reducing manufacturing costs. Spectroscopic and spectrophotometric methods are readily employed within PAT due to their ease of use, compatibility toward a range of sample types, robustness, and multiplexing capabilities. We have developed a UV resonance Raman (UVRR) spectroscopy approach to quantify industrially relevant biotransformations accurately, focusing on nitrile metabolizing enzymes: nitrile hydratase (NHase) and amidase versus nitrilase activity. Sensitive detection of the amide intermediate by UVRR spectroscopy enabled discrimination between the two nitrile-hydrolyzing pathways. Development of a flow-cell apparatus further exemplifies its suitability toward PAT measurements, incorporating in situ analysis within a closed system. Multivariate curve resolution-alternating least-squares (MCR-ALS) was applied to the UVRR spectra, as well as off-line HPLC measurements, to enable absolute quantification of substrate, intermediate, and product. Further application of hard modeling to MCR-ALS deconvolved concentration profiles enabled accurate kinetic determinations, thus removing the requirement for comparative off-line HPLC. Finally, successful quantitative measurements of in vivo activity using whole-cell biotransformations, where two Escherichia coli strains expressing either NHase (transforming benzonitrile to benzamide) or amidase (further conversion of benzamide to benzoic acid), illustrate the power, practicality, and sensitivity of this novel approach of multistep and, with further refinement, we believe, multiple micro-organism biotransformations.

Real-Time Monitoring of Enzyme-Catalysed Reactions using Deep UV Resonance Raman Spectroscopy.

For enzyme-catalysed biotransformations, continuous in situ detection methods minimise the need for sample manipulation, ultimately leading to more accurate real-time kinetic determinations of substrate(s) and product(s). We have established for the first time an on-line, real-time quantitative approach to monitor simultaneously multiple biotransformations based on UV resonance Raman (UVRR) spectroscopy. To exemplify the generality and versatility of this approach, multiple substrates and enzyme systems were used involving nitrile hydratase (NHase) and xanthine oxidase (XO), both of which are of industrial and biological significance, and incorporate multistep enzymatic conversions. Multivariate data analysis of the UVRR spectra, involving multivariate curve resolution-alternating least squares (MCR-ALS), was employed to effect absolute quantification of substrate(s) and product(s); repeated benchmarking of UVRR combined with MCR-ALS by HPLC confirmed excellent reproducibility.

Label-Free Surface Enhanced Raman Scattering Approach for High-Throughput Screening of Biocatalysts.

Biocatalyst discovery and directed evolution are central to many pharmaceutical research programs, yet the lack of robust high-throughput screening methods for large libraries of enzyme variants generated (typically 10(6)-10(8)) has hampered progress and slowed enzyme optimization. We have developed a label-free generally applicable approach based on Raman spectroscopy which results in significant reductions in acquisition times (>30-fold). Surface enhanced Raman scattering (SERS) is employed to monitor the enzyme-catalyzed conversion by xanthine oxidase of hypoxanthine to xanthine to uric acid. This approach measures the substrates and products directly and does not require chromogenic substrates or lengthy chromatography, was successfully benchmarked against HPLC, and shows high levels of accuracy and reproducibility. Furthermore, we demonstrate that this SERS approach has utility in monitoring enzyme inhibition illustrating additional medical significance to this high-throughput screening method.

Simultaneous quantification of the boar-taint compounds skatole and androstenone by surface-enhanced Raman scattering (SERS) and multivariate data analysis.

This study investigates the feasibility of using surface-enhanced Raman scattering (SERS) for the quantification of absolute levels of the boar-taint compounds skatole and androstenone in porcine fat. By investigation of different types of nanoparticles, pH and aggregating agents, an optimized environment that promotes SERS of the analytes was developed and tested with different multivariate spectral pre-processing techniques, and this was combined with variable selection on a series of analytical standards. The resulting method exhibited prediction errors (root mean square error of cross validation, RMSECV) of 2.4 × 10(-6) M skatole and 1.2 × 10(-7) M androstenone, with a limit of detection corresponding to approximately 2.1 × 10(-11) M for skatole and approximately 1.8 × 10(-10) for androstenone. The method was subsequently tested on porcine fat extract, leading to prediction errors (RMSECV) of 0.17 µg/g for skatole and 1.5 µg/g for androstenone. It is clear that this optimized SERS method, when combined with multivariate analysis, shows great potential for optimization into an on-line application, which will be the first of its kind, and opens up possibilities for simultaneous detection of other meat-quality metabolites or pathogen markers. Graphical abstract Artistic rendering of a laser-illuminated gold colloid sphere with skatole and androstenone adsorbed on the surface.

Achieving optimal SERS through enhanced experimental design.

One of the current limitations surrounding surface-enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal-based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.