Blinking-Based Multiplexing: A New Approach for Differentiating Spectrally Overlapped Emitters
Blinking-Based Multiplexing: A New Approach for Differentiating Spectrally Overlapped Emitters
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DOI：
10.1021/acs.jpclett.2c01252
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发表时间：
2022-06-09
影响因子：
5.7
通讯作者：
Wustholz, Kristin L.
中科院分区：
文献类型：
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作者：
DeSalvo, Grace A.;Hoy, Grayson R.;Wustholz, Kristin L.
多色单分子成像被广泛应用于解答生物学和材料科学的问题。然而，大多数研究依赖于光谱不同的荧光探针或时间密集的顺序成像策略来实现多重成像。在这里，我们介绍了基于闪烁的多路复用（BBM），这是一种简单的方法，可以根据光谱重叠的发射器的固有闪烁动态来区分它们。利用相同的采集设置，获得了玻璃上数百个罗丹明6G和CdSe/ZnS量子点的闪烁动态，并用变化点检测算法进行了分析。虽然观察到大量的闪烁异质性，但分析得出的闪烁度量具有93.5%的分类准确率。我们进一步证明，通过使用深度学习算法进行分类，BBM的准确率高达96.6%。该概念验证研究表明，可以根据单个发射器的固有闪烁动态准确分类，而无需探测其光谱颜色。
Multicolor single-molecule imaging is widely applied to answer questions in biology and materials science. However, most studies rely on spectrally distinct fluorescent probes or time-intensive sequential imaging strategies to multiplex. Here, we introduce blinkingbased multiplexing (BBM), a simple approach to differentiate spectrally overlapped emitters based solely on their intrinsic blinking dynamics. The blinking dynamics of hundreds of rhodamine 6G and CdSe/ZnS quantum dots on glass are obtained using the same acquisition settings and analyzed with a change point detection algorithm. Although substantial blinking heterogeneity is observed, the analysis yields a blinking metric with 93.5% classification accuracy. We further show that BBM with up to 96.6% accuracy is achieved by using a deep learning algorithm for classification. This proof-of-concept study demonstrates that a single emitter can be accurately classified based on its intrinsic blinking dynamics and without the need to probe its spectral color.