Mattamira Chiara, Ward Alyssa, Krishnan Sriram Tiruvadi, Lamichhane Rajan, Barrera Francisco N, Sgouralis Ioannis
Department of Mathematics, University of Tennessee, Knoxville, TN.
Department of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN.
Biophys J. 2025 Aug 20. doi: 10.1016/j.bpj.2025.08.014.
With the growing adoption of single-molecule fluorescence experiments, there is an increasing demand for efficient statistical methodologies and accurate analysis of the acquired measurements. Existing analysis frameworks, such as those that use kinetic models, often rely on strong assumptions on the dynamics of the molecules and fluorophores under study that render them inappropriate for general purpose step counting applications, especially when the systems of study exhibit uncharacterized dynamics. Here, we propose a novel Bayesian nonparametric framework to analyze single-molecule fluorescence data that is kinetic model independent. For the evaluation of our methods, we develop four Markov Chain Monte Carlo samplers, ranging from elemental to highly sophisticated, and demonstrate that the added complexity is essential for accurate data analysis. We apply our methods to experimental data obtained from total internal reflection fluorescent photobleaching assays of the EphA2 receptor tagged with GFP. In addition, we validate our approach with synthetic data mimicking realistic conditions and demonstrate its ability to recover ground truth under high- and low-signal/noise ratio data, establishing it as a versatile tool for fluorescence data analysis.
随着单分子荧光实验的日益普及，对高效统计方法以及对所获取测量值进行准确分析的需求也在不断增加。现有的分析框架，例如那些使用动力学模型的框架，通常对所研究分子和荧光团的动力学做出很强的假设，这使得它们不适用于通用的步长计数应用，特别是当所研究的系统表现出未表征的动力学时。在此，我们提出一种新颖的贝叶斯非参数框架来分析与动力学模型无关的单分子荧光数据。为了评估我们的方法，我们开发了四种马尔可夫链蒙特卡罗采样器，从基本的到高度复杂的，并证明增加的复杂性对于准确的数据分析至关重要。我们将我们的方法应用于从用绿色荧光蛋白（GFP）标记的EphA2受体的全内反射荧光漂白测定中获得的实验数据。此外，我们用模拟实际条件的合成数据验证了我们的方法，并证明了它在高信噪比和低信噪比数据下恢复真实情况的能力，将其确立为荧光数据分析的通用工具。
