In recent years, there have been significant advances in quantifying molecule copy number and protein stoichiometry with single-molecule localization microscopy (SMLM). However, as the density of fluorophores per diffraction-limited spot increases, distinguishing between detection events from different fluorophores becomes progressively more difficult, affecting the accuracy of such measurements. Although essential to the design of quantitative experiments, the dynamic range of SMLM counting techniques has not yet been studied in detail. Here, we provide a working definition of the dynamic range for quantitative SMLM in terms of the relative number of missed localizations or blinks and explore the photophysical and experimental parameters that affect it. We begin with a simple two-state model of blinking fluorophores, then extend the model to incorporate photobleaching and temporal binning by the detection camera. From these models, we first show that our estimates of the dynamic range agree with realistic simulations of the photoswitching. We find that the dynamic range scales inversely with the duty cycle when counting both blinks and localizations. Finally, we validate our theoretical approach on direct stochastic optical reconstruction microscopy (dSTORM) data sets of photoswitching Alexa Fluor 647 dyes. Our results should help guide researchers in designing and implementing SMLM-based molecular counting experiments.

近年来，使用单分子定位显微镜 (SMLM) 在量化分子拷贝数和蛋白质化学计量方面取得了重大进展。然而，随着每个衍射极限点的荧光团密度增加，区分来自不同荧光团的检测事件变得越来越困难，影响了此类测量的准确性。尽管对定量实验的设计至关重要，但尚未详细研究 SMLM 计数技术的动态范围。在这里，我们根据错过的定位或眨眼的相对数量提供定量 SMLM 动态范围的工作定义，并探索影响它的光物理和实验参数。我们从一个简单的闪烁荧光团的两态模型开始，然后扩展模型以结合检测相机的光漂白和时间分箱。从这些模型中，我们首先表明我们对动态范围的估计与光开关的实际模拟一致。我们发现在计算眨眼和定位时，动态范围与占空比成反比。最后，我们验证了我们对光开关 Alexa Fluor 647 染料的直接随机光学重建显微镜 (dSTORM) 数据集的理论方法。我们的结果应该有助于指导研究人员设计和实施基于 SMLM 的分子计数实验。我们发现在计算眨眼和定位时，动态范围与占空比成反比。最后，我们验证了我们对光开关 Alexa Fluor 647 染料的直接随机光学重建显微镜 (dSTORM) 数据集的理论方法。我们的结果应该有助于指导研究人员设计和实施基于 SMLM 的分子计数实验。我们发现在计算眨眼和定位时，动态范围与占空比成反比。最后，我们验证了我们对光开关 Alexa Fluor 647 染料的直接随机光学重建显微镜 (dSTORM) 数据集的理论方法。我们的结果应该有助于指导研究人员设计和实施基于 SMLM 的分子计数实验。