Evaluating the interaction of human serum albumin (HSA) and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) liposomes in different aqueous environments using anisotropy resolved multi-dimensional emission spectroscopy (ARMES).

Studying the interaction between plasma proteins and liposomes is critical, particularly for their use as drug delivery systems. Here, the efficacy of anisotropy resolved multidimensional emission spectroscopy (ARMES) for investigating the interaction of human serum albumin (HSA) with liposomes was explored and compared to conventional spectroscopic techniques. Dynamic Light Scattering (DLS) and absorbance spectroscopy (with Multivariate Curve Resolution (MCR) modeling) indicated that the highest degree of liposome rupturing, and aggregation occurred in water, with less in ammonium bicarbonate buffer (ABC) and phosphate buffered saline (PBS). Fluorescence emission spectra of HSA-liposome mixtures revealed significant hypsochromic shifts for water and ABC, but much less in PBS, where the data suggests a non-penetrating protein layer was formed. Average fluorescence lifetimes decreased upon liposome interaction in water (6.2→5.2 ns) and ABC buffer (6.3→5.6 ns) but increased slightly for PBS (5.6→5.8 ns). ARMES using polarized Total Synchronous Fluorescence Scan measurements with parallel factor (PARAFAC) analysis resolved intrinsic HSA fluorescence into two components for interactions in water and ABC buffer, but only one component for PBS. These components, in water and ABC buffer, corresponded to two different HSA populations, one blue-shifted and penetrating the liposomes (λex/em = ~ 280/320 nm) and a second, similar to free HSA in solution (λex/em = ~ 282/356 nm). PARAFAC scores for water and ABC buffer suggested that a large proportion of HSA interacted in an end on configuration. ARMES provides a new way for investigating protein-liposome interactions that exploits the full intrinsic emission space of the protein and thus avoids the use of extrinsic labels. The use of multivariate data analysis provided a comprehensive and structured framework to extract a variety of useful information (resolving different fluorescent species, quantifying their signal contribution, and extracting light scatter signals) all of which can be used to discriminate between interaction mechanisms.

Advanced Spectroscopy and APBS Modeling for Determination of the Role of His190 and Trp103 in Mouse Thymidylate Synthase Interaction with Selected dUMP Analogues.

A homo-dimeric enzyme, thymidylate synthase (TS), has been a long-standing molecular target in chemotherapy. To further elucidate properties and interactions with ligands of wild-type mouse thymidylate synthase (mTS) and its two single mutants, H190A and W103G, spectroscopic and theoretical investigations have been employed. In these mutants, histidine at position 190 and tryptophan at position 103 are substituted with alanine and glycine, respectively. Several emission-based spectroscopy methods used in the paper demonstrate an especially important role for Trp 103 in TS ligands binding. In addition, the Advanced Poisson-Boltzmann Solver (APBS) results show considerable differences in the distribution of electrostatic potential around Trp 103, as compared to distributions observed for all remaining Trp residues in the mTS family of structures. Together, spectroscopic and APBS results reveal a possible interplay between Trp 103 and His190, which contributes to a reduction in enzymatic activity in the case of H190A mutation. Comparison of electrostatic potential for mTS complexes, and their mutants, with the substrate, dUMP, and inhibitors, FdUMP and N4-OH-dCMP, suggests its weaker influence on the enzyme-ligand interactions in N4OH-dCMP-mTS compared to dUMP-mTS and FdUMP-mTS complexes. This difference may be crucial for the explanation of the "abortive reaction" inhibitory mechanism of N4OH-dCMP towards TS. In addition, based on structural analyses and the H190A mutant capacity to form a denaturation-resistant complex with N4-OH-dCMP in the mTHF-dependent reaction, His190 is apparently responsible for a strong preference of the enzyme active center for the anti rotamer of the imino inhibitor form.

Quantitative analysis of weakly bound insulin oligomers in solution using polarized multidimensional fluorescence spectroscopy.

Being able to measure the size and distribution of oligomers in solution is a critical issue in the manufacture and stability of insulin and other protein formulations. Measuring oligomers reliably can however be complicated, due to their fragile self-assembled structures, which are held together by weak forces. This can cause issues in chromatographic based methods, where dissociation or re-equilibration of oligomer populations can occur e.g. upon dilution in a different eluting buffer, but also for light scattering based methods like dynamic light scattering (DLS) where the size difference involved (often less than a factor 3) does not allow mixtures of oligomers to be resolved. Intrinsic fluorescence offers an attractive alternative as it is non-invasive, sensitive but also because it contains scattered light when implemented via excitation emission matrix (EEM) measurements, that is sensitive to changes in particle size. Here, using insulin at formulation level concentrations, we show for the first time how EEM can both discriminate and quantify the proportion of oligomeric states in solution. This was achieved by using the Rayleigh scatter (RS) band and the fluorescence signal contained in EEM. After validating size changes with DLS, we show in particular how the volume under the RS band correlated linearly with protein/oligomer molecular weight, in agreement with the Debye-Zimm relationship. This was true for the RS data from both EEM and polarized EEM (pEEM) measurements, the latter providing a stronger scatter signal, more sensitive to particle size changes. The fluorescence signal was then used with multivariate curve resolution (MCR) to quantify more precisely the soluble oligomer composition of insulin solutions. In conditions that promoted the formation of mainly one type of oligomer (monomer, dimer, or hexamer), pEEM-MCR helped identify the presence of small amounts of other oligomeric forms, while in conditions that were previously said to favour the insulin tetramer, we show that in the presence of zinc, these insulin samples were instead a heterogenous mixture composed of mostly dimers and hexamers. These MCR results correlated in all cases with the observed discrimination by principal component analysis (PCA), and deviations observed in the RS data. In conclusion, using pEEM scatter and emission components with chemometric data analysis provides a unique analytical method for characterising and monitoring changes in the soluble oligomeric state of proteins.

Accurate anisotropy recovery from fluorophore mixtures using Multivariate Curve Resolution (MCR).

Anisotropy resolved multidimensional emission spectroscopy (ARMES) provides valuable insights into multi-fluorophore systems like proteins that have complex overlapping emission bands. The method combines multidimensional fluorescence, anisotropy, and chemometrics to facilitate the differentiation of fluorophores with very similar emission properties. Here, we address the critical issue of standardizing the chemometric methods required to accurately extract spectral and anisotropy information from fluorophore mixtures using two standard sample sets: perylene in glycerol, and a mixture of Erythrosin B and Phloxine B with overlapping emission but different anisotropies. We show for the first time how to accurately model component anisotropy using Multivariate Curve Resolution (MCR) from data collected using total synchronous fluorescence scan (TSFS) and Excitation Emission Matrix (EEM) measurement methods. These datasets were selected to avoid the presence of inner filter effects (IFE) or Förster resonance energy transfer (FRET) that would depolarize fluorescence emission or reduce data tri-linearity. This allowed the non-trilinear TSFS data to yield accurate component anisotropy data once modelled using the correct data augmentation strategy, however, the EEM data proved to be more accurate once optimal constraints (non-negativity and correspondence among species) were employed. For perylene (S2) and Phloxine B which both have very weak anisotropy (<0.06), while the spectral recovery was excellent, the modelled anisotropy values were reasonably accurate (±20% of the real value) because of large relative noise contributions. However, for perylene (S1) and Erythrosin B which have large (>0.2) anisotropies, bilinear and trilinear EEM models built using a total tri-linearity constraint, yielded solutions without any rotational ambiguities and very accurate (±4% of real value) anisotropy values. These sample systems thus provide simple and robust test systems for validating the spectral measurement and chemometric data analysis elements of ARMES.

Extended wavelength anisotropy resolved multidimensional emission spectroscopy (ARMES) measurements: better filters, validation standards, and Rayleigh scatter removal methods.

Anisotropy resolved multidimensional emission spectroscopy (ARMES) provides valuable insights into multi-fluorophore proteins (Groza et al 2015 Anal. Chim. Acta 886 133-42). Fluorescence anisotropy adds to the multidimensional fluorescence dataset information about the physical size of the fluorophores and/or the rigidity of the surrounding micro-environment. The first ARMES studies used standard thin film polarizers (TFP) that had negligible transmission between 250 and 290 nm, preventing accurate measurement of intrinsic protein fluorescence from tyrosine and tryptophan. Replacing TFP with pairs of broadband wire grid polarizers enabled standard fluorescence spectrometers to accurately measure anisotropies between 250 and 300 nm, which was validated with solutions of perylene in the UV and Erythrosin B and Phloxine B in the visible. In all cases, anisotropies were accurate to better than ±1% when compared to literature measurements made with Glan Thompson or TFP polarizers. Better dual wire grid polarizer UV transmittance and the use of excitation-emission matrix measurements for ARMES required complete Rayleigh scatter elimination. This was achieved by chemometric modelling rather than classical interpolation, which enabled the acquisition of pure anisotropy patterns over wider spectral ranges. In combination, these three improvements permit the accurate implementation of ARMES for studying intrinsic protein fluorescence.

Kernel principal component analysis residual diagnosis (KPCARD): An automated method for cosmic ray artifact removal in Raman spectra.

A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present. CRA correction was achieved by spectral replacement where, the nearest neighbor (NN) spectrum, most spectroscopically similar to the CRA contaminated spectrum and principal components (PCs) obtained by kPCA were both used to generate a robust, best curve fit to the CRA contaminated spectrum. This best fit spectrum then replaced the CRA contaminated spectrum in the dataset. KPCARD efficacy was demonstrated by using simulated data and real Raman spectra collected from solid-state materials. The results showed that KPCARD was fast (<1 min per 8400 spectra), accurate, precise, and suitable for the automated correction of very large (>1 million) Raman datasets.

Low-content quantification in powders using Raman spectroscopy: a facile chemometric approach to sub 0.1% limits of detection.

A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, subsampling, and chemometrics was demonstrated using a piracetam-proline model. The method involved a 5-step process: collection of a relatively large number of spectra (8410) from each sample by Raman mapping, meticulous data pretreatment to remove spectral artifacts, use of a 0-100% concentration range partial least-squares (PLS) regression model to estimate concentration at each pixel, use of a more accurate, reduced concentration range PLS model to calculate analyte concentration at each pixel, and finally statistical analysis of all 8000+ concentration predictions to produce an accurate overall sample concentration. The relative prediction accuracy was ∼2.4% for a 0.05-1.0% concentration range, and the limit of detection was comparable to high performance liquid chromatography (0.03% versus 0.041%). For data pretreatment, we developed a unique cosmic ray removal method and used an automated baseline correction method, neither of which required subjective user intervention and thus were fully automatable. The method is applicable to systems which cannot be easily analyzed chromatographically, such as hydrate, polymorph, or solvate contamination.