Detecting brief changes in time-series data remains a major challenge in fields where short-lived states carry meaning. In single-molecule localisation microscopy, this problem is particularly acute as fluorescent molecules used to tag protein oligomers display heterogenous photophysical behaviour that can complicate photobleach step analysis; a key step in resolving nanoscale protein organisation. Existing methods often require extensive filtering or prior calibration, and can fail to accurately account for blinking or reversible dark states that may contaminate downstream analysis. In this paper, an extension to RJMCMC is proposed for change point detection with heterogeneous temporal dynamics. This approach is applied to the problem of estimating per-frame active fluorophore counts from one-dimensional integrated intensity traces derived from Fluorescence Localisation Imaging with Photobleaching (FLImP), where compound change point pair moves are introduced to better account for short-lived events known as blinking and dark states. The approach is validated using simulated and experimental data, demonstrating improved accuracy and robustness when compared with current photobleach step analysis methods and with the existing analysis approach for FLImP data. This Compound RJMCMC (CRJMCMC) algorithm performs reliably across a wide range of fluorophore counts and signal-to-noise conditions, with signal-to-noise ratio (SNR) down to 0.001 and counts as high as seventeen fluorophores, while also effectively estimating low counts observed when studying EGFR oligomerisation. Beyond single molecule imaging, this work has applications for a variety of time series change point detection problems with heterogeneous state persistence. For example, electrocorticography brain-state segmentation, fault detection in industrial process monitoring and realised volatility in financial time series.

翻译：检测时间序列数据中的短暂变化在短暂状态具有意义的领域中仍然是一个主要挑战。在单分子定位显微术中，这个问题尤为突出，因为用于标记蛋白质寡聚体的荧光分子表现出异质的光物理行为，这可能使光漂白步骤分析复杂化；而该分析是解析纳米尺度蛋白质组织的一个关键步骤。现有方法通常需要大量过滤或先验校准，并且可能无法准确解释可能污染下游分析的闪烁或可逆暗态。本文提出了一种针对具有异质时间动态的变点检测问题的可逆跳转马尔可夫链蒙特卡洛（RJMCMC）扩展方法。该方法应用于从荧光定位成像与光漂白（FLImP）导出的一维积分强度迹线中估计每帧活性荧光团数量的问题，其中引入了复合变点对移动，以更好地解释被称为闪烁和暗态的短暂事件。该方法通过模拟和实验数据进行了验证，与当前的光漂白步骤分析方法以及现有的FLImP数据分析方法相比，展示了更高的准确性和鲁棒性。这种复合RJMCMC（CRJMCMC）算法在广泛的荧光团数量和信噪比条件下均能可靠运行，信噪比（SNR）低至0.001，荧光团数量高达十七个，同时也能有效估计在研究EGFR寡聚化时观察到的低数量。除了单分子成像，这项工作对于各种具有异质状态持续性的时间序列变点检测问题都有应用价值。例如，皮层脑电图的脑状态分割、工业过程监控中的故障检测以及金融时间序列中的已实现波动率。