Nanoscale BioPhotonics Laboratory, School of Chemistry, National University of Ireland, Galway, Galway, Ireland.
Nanoscale BioPhotonics Laboratory, School of Chemistry, National University of Ireland, Galway, Galway, Ireland.
Anal Chim Acta. 2018 Feb 13;1000:132-143. doi: 10.1016/j.aca.2017.11.031. Epub 2017 Nov 22.
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 (S) 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 (S) 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.
各向异性分辨多维发射光谱（ARMES）为具有复杂重叠发射带的蛋白质等多荧光团系统提供了有价值的见解。该方法结合多维荧光、各向异性和化学计量学，有助于区分具有非常相似发射特性的荧光团。在这里，我们解决了一个关键问题，即使用两个标准样品集：甘油中的苝和具有重叠发射但各向异性不同的赤藓红 B 和荧光素 B 的混合物，从荧光团混合物中准确提取光谱和各向异性信息所需的化学计量学方法的标准化。我们首次展示了如何使用多元曲线分辨（MCR）从使用全同步荧光扫描（TSFS）和激发发射矩阵（EEM）测量方法收集的数据中准确地对组件各向异性进行建模。选择这些数据集是为了避免存在内滤效应（IFE）或Förster 共振能量转移（FRET），因为内滤效应或 Förster 共振能量转移会使荧光发射去极化或降低数据三线性。这使得非三线性 TSFS 数据在使用正确的数据增强策略进行建模后可以产生准确的组件各向异性数据，但是 EEM 数据在采用最佳约束（非负性和物种之间的对应关系）后证明更加准确。对于苝（S）和荧光素 B，它们的各向异性都非常弱（<0.06），尽管光谱恢复非常出色，但由于相对噪声贡献较大，建模的各向异性值相当准确（真实值的±20%）。然而，对于苝（S）和赤藓红 B，它们的各向异性较大（>0.2），使用总三线性约束构建的双线性和三线性 EEM 模型，产生了没有任何旋转不确定性且非常准确（真实值的±4%）的各向异性值。因此，这些样品系统为验证 ARMES 的光谱测量和化学计量数据分析元素提供了简单而稳健的测试系统。
