Omer Travis, Zhao Lingling, Intes Xavier, Hahn Juergen
Rensselaer Polytechnic Institute, Department of Biomedical Engineering, 110 8th Street, Troy, New York 12180, United States.
Rensselaer Polytechnic Institute, Departments of Biomedical Engineering and Chemical & Biological Engineering, 110 8th Street, Troy, New York 12180, United States.
J Biomed Opt. 2014 Aug;19(8):086023. doi: 10.1117/1.JBO.19.8.086023.
Fluorescence lifetime imaging (FLIM) aims at quantifying the exponential decay rate of fluorophores to yield lifetime maps over the imaged sample. When combined with Förster resonance energy transfer (FRET), the technique can be used to indirectly sense interactions at the nanoscale such as protein–protein interactions, protein–DNA interactions, and protein conformational changes. In the case of FLIM-FRET, the fluorescence intensity decays are fitted to a biexponential model in order to estimate the lifetime and fractional amplitude coefficients of each component of the population of the donor fluorophore (quenched and nonquenched). Numerous time data points, also called temporal or time gates, are typically employed for accurately estimating the model parameters, leading to lengthy acquisition times and significant computational demands. This work investigates the effect of the number and location of time gates on model parameter estimation accuracy. A detailed model of a FLIM-FRET imaging system is used for the investigation, and the simulation outcomes are validated with in vitro and in vivo experimental data. In all cases investigated, it is found that 10 equally spaced time gates allow robust estimation of model-based parameters with accuracy similar to that of full temporal datasets (90 gates).
荧光寿命成像(FLIM)旨在量化荧光团的指数衰减率,以生成成像样本上的寿命图。当与福斯特共振能量转移(FRET)相结合时,该技术可用于间接检测纳米尺度上的相互作用,如蛋白质-蛋白质相互作用、蛋白质-DNA相互作用以及蛋白质构象变化。在FLIM-FRET的情况下,荧光强度衰减被拟合为双指数模型,以估计供体荧光团群体(淬灭和未淬灭)各组分的寿命和分数振幅系数。通常采用大量的时间数据点,也称为时间门,来准确估计模型参数,这导致采集时间长且计算需求大。这项工作研究了时间门的数量和位置对模型参数估计准确性的影响。使用FLIM-FRET成像系统的详细模型进行研究,并通过体外和体内实验数据验证模拟结果。在所研究的所有情况下,发现10个等间距的时间门能够以与完整时间数据集(90个门)相似的精度对基于模型的参数进行稳健估计。