Blinking-Based Multiplexing: A New Approach for Differentiating Spectrally Overlapped Emitters

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.

Automated summary

This study introduces a blinking-based multiplexing approach to differentiate spectrally overlapped emitters. By analyzing the blinking dynamics of fluorescent probes on glass, a blinking metric with 93.5% classification accuracy is obtained. Furthermore, using a deep learning algorithm for classification, up to 96.6% accuracy is achieved.