Ordabayev Yerdos A, Friedman Larry J, Gelles Jeff, Theobald Douglas L
Department of Biochemistry, Brandeis University, Waltham, United States.
Elife. 2022 Mar 23;11:e73860. doi: 10.7554/eLife.73860.
Multi-wavelength single-molecule fluorescence colocalization (CoSMoS) methods allow elucidation of complex biochemical reaction mechanisms. However, analysis of CoSMoS data is intrinsically challenging because of low image signal-to-noise ratios, non-specific surface binding of the fluorescent molecules, and analysis methods that require subjective inputs to achieve accurate results. Here, we use Bayesian probabilistic programming to implement Tapqir, an unsupervised machine learning method that incorporates a holistic, physics-based causal model of CoSMoS data. This method accounts for uncertainties in image analysis due to photon and camera noise, optical non-uniformities, non-specific binding, and spot detection. Rather than merely producing a binary 'spot/no spot' classification of unspecified reliability, Tapqir objectively assigns spot classification probabilities that allow accurate downstream analysis of molecular dynamics, thermodynamics, and kinetics. We both quantitatively validate Tapqir performance against simulated CoSMoS image data with known properties and also demonstrate that it implements fully objective, automated analysis of experiment-derived data sets with a wide range of signal, noise, and non-specific binding characteristics.
多波长单分子荧光共定位（CoSMoS）方法能够阐明复杂的生化反应机制。然而，由于图像信噪比低、荧光分子的非特异性表面结合以及需要主观输入才能获得准确结果的分析方法，CoSMoS数据的分析本质上具有挑战性。在此，我们使用贝叶斯概率编程来实现Tapqir，这是一种无监督机器学习方法，它纳入了基于物理的CoSMoS数据整体因果模型。该方法考虑了由于光子和相机噪声、光学不均匀性、非特异性结合和斑点检测导致的图像分析中的不确定性。Tapqir不是仅仅产生一个可靠性未指定的二元“斑点/无斑点”分类，而是客观地分配斑点分类概率，从而允许对分子动力学、热力学和动力学进行准确的下游分析。我们既针对具有已知特性的模拟CoSMoS图像数据定量验证了Tapqir的性能，也证明了它能够对具有广泛信号、噪声和非特异性结合特征的实验衍生数据集进行完全客观、自动化的分析。
