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

Artificial Intelligence-Empowered Spectroscopic Single Molecule Localization Microscopy

Spectroscopic single-molecule localization microscopy (SMLM) has revolutionized the visualization and analysis of molecular structures and dynamics at the nanoscale level. The technique of combining high spatial resolution of SMLM with spectral information, enables multicolor super-resolution imaging and provides insights into the local chemical environment of individual molecules. However, spectroscopic SMLM faces significant challenges, including limited spectral resolution and compromised localization precision because of signal splitting and the difficulties in analyzing complex, multidimensional datasets, that limit its application in studying intricate biological systems and materials. The recent integration of artificial intelligence (AI) with spectroscopic SMLM has emerged as a powerful approach for addressing these challenges. Here, it is reviewed how AI-based methods applied to spectroscopic SMLM enhance and expand the capabilities of these applications. Recent advancements in AI-driven data analysis for spectroscopic SMLM, including improved spectral classification, localization precision, and extraction of rich spectral information from unmodified point-spread functions are discussed, further examining their applications in biological studies, materials science, and single-molecule reaction analysis, which highlight how AI provides new insights into molecular behavior and interactions. The AI-empowered approach adds new dimensions of information and provides new opportunities and insights into the nanoscale world of rapidly evolving field of spectroscopic SMLM.

Blinking characteristics of organic fluorophores for blink-based multiplexing

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.

Fluorescence Bar-Coding and Flowmetry Based on Dark State Transitions in Fluorescence Emitters

Reversible dark state transitions in fluorophores represent a limiting factor in fluorescence-based ultrasensitive spectroscopy, are a necessary basis for fluorescence-based super-resolution imaging, but may also offer additional, largely orthogonal fluorescence-based readout parameters. In this work, we analyzed the blinking kinetics of Cyanine5 (Cy5) as a bar-coding feature distinguishing Cy5 from rhodamine fluorophores having largely overlapping emission spectra. First, fluorescence correlation spectroscopy (FCS) solution measurements on mixtures of free fluorophores and fluorophore-labeled small unilamellar vesicles (SUVs) showed that Cy5 could be readily distinguished from the rhodamines by its reversible, largely excitation-driven trans-cis isomerization. This was next confirmed by transient state (TRAST) spectroscopy measurements, determining the fluorophore dark state kinetics in a more robust manner, from how the time-averaged fluorescence intensity varies upon modulation of the applied excitation light. TRAST was then combined with wide-field imaging of live cells, whereby Cy5 and rhodamine fluorophores could be distinguished on a whole cell level as well as in spatially resolved, multiplexed images of the cells. Finally, we established a microfluidic TRAST concept and showed how different mixtures of free Cy5 and rhodamine fluorophores and corresponding fluorophore-labeled SUVs could be distinguished on-the-fly when passing through a microfluidic channel. In contrast to FCS, TRAST does not rely on single-molecule detection conditions or a high time resolution and is thus broadly applicable to different biological samples. Therefore, we expect that the bar-coding concept presented in this work can offer an additional useful strategy for fluorescence-based multiplexing that can be implemented on a broad range of both stationary and moving samples.

Rapid, Accurate Classification of Single Emitters in Various Conditions and Environments for Blinking-Based Multiplexing

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., P_exc = 1.2 μW and t_bin = 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.

Programmed Control of Fluorescence Blinking Patterns based on Electron Transfer in DNA

Fluorescence imaging uses changes in the fluorescence intensity and emission wavelength to analyze multiple targets simultaneously. To increase the number of targets that can be identified simultaneously, fluorescence blinking can be used as an additional parameter. To understand and eventually control blinking, we used DNA as a platform to elucidate the processes of electron transfer (ET) leading to blinking, down to the rate constants. With a fixed ET distance, various blinking patterns were observed depending on the DNA sequence between the donor and acceptor units of the DNA platform. The blinking pattern was successfully described with a combination of ET rate constants. Therefore, molecules with various blinking patterns can be developed by tuning ET. It is expected that the number of targets that can be analyzed simultaneously will increase by the power of the number of blinking patterns.