Rapid, Accurate Classification of Single Emitters in Various Conditions and Environments for Blinking-Based Multiplexing
Rapid, Accurate Classification of Single Emitters in Various Conditions and Environments for Blinking-Based Multiplexing
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DOI：
10.1021/acs.jpca.3c00917
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发表时间：
2023-04-06
影响因子：
2.9
通讯作者：
Wustholz，Kristin L.
中科院分区：
文献类型：
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作者：
Hoy，Grayson R.;DeSalvo，Grace A.;Wustholz，Kristin L.
虽然单分子成像在生物学和材料科学中得到了广泛的应用，但大多数研究都受到依赖于光谱不同的荧光探针的限制。我们最近介绍了基于闪烁的多路复用（BBM），一种简单的方法来区分光谱重叠的单发射器的基础上，他们的内在闪烁动力学。最初的概念验证研究实施了两种发射器分类方法：经验推导的度量和深度学习算法，这两种方法都有明显的缺点。在此，将多项逻辑回归（LR）分类应用于各种实验条件下的罗丹明6 G（R6 G）和CdSe/ZnS量子点（QD）（即，激发功率和仓时间）和环境（即，玻璃对聚合物）。我们证明，LR分析是快速和概括性的，和95%的分类准确率是经常观察到的，即使在复杂的聚合物环境中，多个因素导致闪烁的异质性。在这样做时，该研究（1）揭示了实验条件（即，Pexc= 1.2 μW和tbin = 10 ms），优化了QD和R6 G的BBM，以及（2）证明了通过多项式LR的BBM可以准确地对发射体和环境进行分类，为单分子成像的新机会打开了大门。
Although single-molecule imaging is widely applied in biology and materials science, most studies are limited by their reliance on spectrally distinct fluorescent probes. We recently introduced blinking-based multiplexing (BBM), a simple approach to differentiate spectrally overlapped single emitters based solely on their intrinsic blinking dynamics. The original proof-of-concept study implemented two methods for emitter classification: an empirically derived metric and a deep learning algorithm, both of which have significant drawbacks. Here, a multinomial logistic regression (LR) classification is applied to rhodamine 6G (R6G) and CdSe/ZnS quantum dots (QDs) in various experimental conditions (i.e., excitation power and bin time) and environments (i.e., glass versus polymer). We demonstrate that LR analysis is rapid and generalizable, and classification accuracies of 95% are routinely observed, even within a complex polymer environment where multiple factors contribute to blinking heterogeneity. In doing so, this study (1) reveals the experimental conditions (i.e.,Pexc= 1.2 μW andtbin= 10 ms) that optimize BBM for QD and R6G and (2) demonstrates that BBM via multinomial LR can accurately classify both emitter and environment, opening the door to new opportunities in single-molecule imaging.