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Abstract Single-molecule fluorescence experiments have transformed our understanding of complex materials and biological systems. Whether single molecules are used to report on their nano-environment or provide for localization, understanding their blinking dynamics (i.e., stochastic fluctuations in emission intensity under continuous illumination) is paramount. We recently demonstrated another use for blinking dynamics called blink-based multiplexing (BBM), where individual emitters are classified using a single excitation laser based on blinking dynamics, rather than color. This study elucidates the structure-activity relationships governing BBM performance in a series of model rhodamine, BODIPY, and anthraquinone fluorophores that undergo different photo-physical and-chemical processes during blinking. Change point detection and multinomial logistic regression analyses show that BBM can leverage spectral fluctuations, electron and proton transfer kinetics, as well as photostability for molecular classification—even within the context of a shared blinking mechanism. In doing so, we demonstrate two- and three-color BBM with ≥ 93% accuracy using spectrally-overlapped fluorophores.
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Blinking-Based Multiplexing (BBM): Harnessing Molecular Photophysics for Single-Emitter Classification
Single-molecule fluorescence approaches have revolutionized biological and materials microscopy. However, many questions can only be addressed by multicolor imaging of multiple targets, a capability that is limited by the small subset of available, well-performing, and spectrally-distinct fluorescent probes. We recently introduced an alternative single-molecule multiplexing approach termed blinking-based multiplexing (BBM), wherein individual molecules are classified on the basis of their intrinsic blinking dynamics. We demonstrate accurate (>93.5%) binary classification of spectrally-overlapped rhodamine and quantum dot emitters using BBM, even when substantial blinking heterogeneity is observed. Classification can be accomplished using change point detection (CPD) analysis of blinking dynamics or a deep learning (DL) algorithm, the latter of which provides up to 96.6% accuracy. Here, we use CPD and DL algorithms to probe the excitation power, environmental, and molecular dependence of BBM. In addition to providing new opportunities in single-molecule spectroscopy and imaging, BBM represents a new take on single-molecule research, where blinking dynamics can be harnessed for more than just traditional localization or nanoreporting.
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- Award ID(s):
- 2102099
- PAR ID:
- 10406729
- Date Published:
- Journal Name:
- Bulletin of the American Physical Society
- ISSN:
- 0003-0503
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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