Univ. Lille, CNRS, UMR 8516 - LASIRE - Laboratory of advanced spectroscopy, interactions, reactivity and environment, Cité scientifique, Bâtiment C5, 59000 Lille, France.
Chemometrics Group, Dept. of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, Martí I Franquès, 1, 08028 Barcelona, Spain.
Anal Chem. 2021 Sep 21;93(37):12504-12513. doi: 10.1021/acs.analchem.1c01284. Epub 2021 Sep 8.
Time-resolved fluorescence spectroscopy (TRFS), i.e., measurement of fluorescence decay curves for different excitation and/or emission wavelengths, provides specific and sensitive local information on molecules and on their environment. However, TRFS relies on multiexponential data fitting to derive fluorescence lifetimes from the measured decay curves and the time resolution of the technique is limited by the instrumental response function (IRF). We propose here a multivariate curve resolution (MCR) approach based on data slicing to perform tailored and fit-free analysis of multiexponential fluorescence decay curves. MCR slicing, taking as a basic framework the multivariate curve resolution-alternating least-squares (MCR-ALS) soft-modeling algorithm, relies on a hybrid bilinear/trilinear data decomposition. A key feature of the method is that it enables the recovery of individual components characterized by decay profiles that are only partially describable by monoexponential functions. For TRFS data, not only pure multiexponential tail information but also shorter time delay information can be decomposed, where the signal deviates from the ideal exponential behavior due to the limited time resolution. The accuracy of the proposed approach is validated by analyzing mixtures of three commercial dyes and characterizing the mixture composition, lifetimes, and associated contributions, even in situations where only ternary mixture samples are available. MCR slicing is also applied to the analysis of TRFS data obtained on a photoswitchable fluorescent protein (rsEGFP2). Three fluorescence lifetimes are extracted, along with the profile of the IRF, highlighting that decomposition of complex systems, for which individual isomers are characterized by different exponential decays, can also be achieved.
时间分辨荧光光谱学（TRFS），即测量不同激发和/或发射波长的荧光衰减曲线，为分子及其环境提供了特定和敏感的局部信息。然而，TRFS依赖于多指数数据拟合，从测量的衰减曲线中推导出荧光寿命，并且该技术的时间分辨率受到仪器响应函数（IRF）的限制。我们在这里提出了一种基于数据切片的多变量曲线解析（MCR）方法，用于对多指数荧光衰减曲线进行定制且无需拟合的分析。MCR 切片以多变量曲线解析交替最小二乘法（MCR-ALS）软建模算法为基本框架，依赖于混合双线性/三线性数据分解。该方法的一个关键特点是，它能够恢复由仅部分可由单指数函数描述的衰减曲线特征的各个组件。对于 TRFS 数据，不仅可以分解纯多指数尾部信息，还可以分解更短的时间延迟信息，其中由于时间分辨率有限，信号偏离理想的指数行为。通过分析三种商业染料的混合物并表征混合物的组成、寿命和相关贡献，验证了所提出方法的准确性，即使仅提供三元混合物样品也是如此。MCR 切片也应用于对光可切换荧光蛋白（rsEGFP2）获得的 TRFS 数据的分析。提取了三个荧光寿命，以及 IRF 的轮廓，突出表明可以实现对复杂系统的分解，其中各个异构体的特征是不同的指数衰减。
