FluoroTensor: identification and tracking of colocalised molecules and their stoichiometries in multi-colour single molecule imaging via deep learning.
FluoroTensor: identification and tracking of colocalised molecules and their stoichiometries in multi-colour single molecule imaging via deep learning.
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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。
DOI：
10.1101/2023.11.21.567874
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Wills M
中科院分区：
文献类型：
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作者：
Wills M
单分子荧光成像中光漂白步骤的识别是用于分析分子复合物的化学计量的成熟程序。尽管如此，该方法是具有挑战性的蛋白质荧光团，因为高水平的噪声，快速漂白和高度可变的信号强度，所有这些复杂的方法的基础上的强度的统计分析，以确定漂白步骤。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。我们在这里描述了一种改进的使用这种方法，检测漂白事件，即使在第一时间点的观察，我们已经包括在一个集成的软件包，将荧光斑点检测，共定位，跟踪，FRET和光漂白步骤分析的单分子或复合物。这个软件包被称为FluoroTensor，是用Python编写的，有一个不言自明的用户界面。
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.