Department of Biochemistry and Molecular Biology and Physics of Life Sciences (PhyLife) Center, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark.
Sensors (Basel). 2022 Jun 23;22(13):4731. doi: 10.3390/s22134731.
The phase separation and aggregation of proteins are hallmarks of many neurodegenerative diseases. These processes can be studied in living cells using fluorescent protein constructs and quantitative live-cell imaging techniques, such as fluorescence recovery after photobleaching (FRAP) or the related fluorescence loss in photobleaching (FLIP). While the acquisition of FLIP images is straightforward on most commercial confocal microscope systems, the analysis and computational modeling of such data is challenging. Here, a novel model-free method is presented, which resolves complex spatiotemporal fluorescence-loss kinetics based on dynamic-mode decomposition (DMD) of FLIP live-cell image sequences. It is shown that the DMD of synthetic and experimental FLIP image series (DMD-FLIP) allows for the unequivocal discrimination of subcellular compartments, such as nuclei, cytoplasm, and protein condensates based on their differing transport and therefore fluorescence loss kinetics. By decomposing fluorescence-loss kinetics into distinct dynamic modes, DMD-FLIP will enable researchers to study protein dynamics at each time scale individually. Furthermore, it is shown that DMD-FLIP is very efficient in denoising confocal time series data. Thus, DMD-FLIP is an easy-to-use method for the model-free detection of barriers to protein diffusion, of phase-separated protein assemblies, and of insoluble protein aggregates. It should, therefore, find wide application in the analysis of protein transport and aggregation, in particular in relation to neurodegenerative diseases and the formation of protein condensates in living cells.
蛋白质的相分离和聚集是许多神经退行性疾病的标志。这些过程可以使用荧光蛋白构建体和定量活细胞成像技术(如光漂白后荧光恢复(FRAP)或相关的光漂白荧光损失(FLIP))在活细胞中进行研究。虽然在大多数商业共聚焦显微镜系统上获取 FLIP 图像很简单,但对这些数据的分析和计算建模具有挑战性。在这里,提出了一种新的无模型方法,该方法基于 FLIP 活细胞图像序列的动态模式分解(DMD)解析复杂的时空荧光损失动力学。结果表明,基于其不同的运输和因此的荧光损失动力学,DMD 可对诸如细胞核、细胞质和蛋白质凝聚物等亚细胞区室进行明确区分。通过将荧光损失动力学分解为不同的动态模式,DMD-FLIP 将使研究人员能够单独研究每个时间尺度的蛋白质动力学。此外,结果表明 DMD-FLIP 在去噪共聚焦时间序列数据方面非常有效。因此,DMD-FLIP 是一种用于无模型检测蛋白质扩散障碍、相分离蛋白质组装和不溶性蛋白质聚集体的简单易用的方法。因此,它应该在分析蛋白质运输和聚集方面得到广泛应用,特别是与神经退行性疾病和活细胞中蛋白质凝聚物的形成有关。