The identification of photobleaching steps in single molecule fluorescence imaging is a well-established procedure for analysing the stoichiometries of molecular complexes. Nonetheless, the method is challenging with protein fluorophores because of the high levels of noise, rapid bleaching and highly variable signal intensities, all of which complicate methods based on statistical analyses of intensities to identify bleaching steps. It has recently been shown that deep learning by convolutional neural networks can yield an accurate analysis with a relatively short computational time. We describe here an improved use of such an approach that detects bleaching events even in the first time point of observation, and we have included this within an integrated software package incorporating fluorescence spot detection, colocalisation, tracking, FRET and photobleaching step analyses of single molecules or complexes. This package, known as FluoroTensor, is written in Python with a self-explanatory user interface.

中文翻译：

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FluoroTensor：通过深度学习识别和跟踪多色单分子成像中的共定位分子及其化学计量

单分子荧光成像中光漂白步骤的识别是分析分子复合物化学计量的成熟程序。尽管如此，由于高水平的噪声、快速漂白和高度可变的信号强度，该方法对于蛋白质荧光团具有挑战性，所有这些都使基于强度统计分析来识别漂白步骤的方法变得复杂。最近的研究表明，卷积神经网络的深度学习可以在相对较短的计算时间内产生准确的分析。我们在这里描述了这种方法的改进使用，即使在第一个观察时间点也可以检测漂白事件，并且我们已将其包含在集成软件包中，该软件包包含荧光点检测、共定位、跟踪、FRET 和单分子的光漂白步骤分析或复合物。这个包称为 FluoroTensor，是用 Python 编写的，具有不言自明的用户界面。