Data for: Optimal inference of molecular interaction dynamics in FRET microscopy

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible—a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.

Methods
Synthetic data were generated using MATLAB based on the photophysical model described in the article. Experimental data were taken under fluorescence microscopes. See the article for details.

基于强度的延时荧光共振能量转移（FRET）显微镜技术一直是研究细胞过程的核心工具，可将原本无法直接观测的分子相互作用转化为荧光时间序列。然而，从观测数据中推断分子互作动态仍是一个极具挑战的逆问题，在测量噪声与光漂白效应不可忽视的场景下尤为如此——这在单细胞分析中属于常见情况。传统方法多采用代数方式处理时间序列数据，但这类方法不可避免会累积测量噪声、降低信噪比（SNR），限制了FRET显微镜的应用边界。本文提出了一种替代的概率性方法B-FRET，该方法可普遍适配标准的三立方体FRET成像数据。基于滤波理论，B-FRET实现了统计最优的分子互作推断路径，从而大幅提升了信噪比。我们首先通过模拟数据验证了B-FRET的有效性，随后将其应用于真实实验数据，包括来自单个细菌细胞的、本底噪声极高的活体内FRET时间序列，以此揭示了原本隐藏在噪声中的信号转导动态。
方法
本研究基于文献中记载的光物理模型，使用MATLAB生成合成数据集。实验数据采集自荧光显微镜平台，详细信息请参阅原文。