Unraveling the Excited-State Dynamics of Eosin Y Photosensitizers Using Single-Molecule Spectroscopy

Current advances in photocatalytic proximity labeling

Understanding the intricate network of biomolecular interactions that govern cellular processes is a fundamental pursuit in biology. Over the past decade, photocatalytic proximity labeling has emerged as one of the most powerful and versatile techniques for studying these interactions as well as uncovering subcellular trafficking patterns, drug mechanisms of action, and basic cellular physiology. In this article, we review the basic principles, methodologies, and applications of photocatalytic proximity labeling as well as examine its modern development into currently available platforms. We also discuss recent key studies that have successfully leveraged these technologies and importantly highlight current challenges faced by the field. Together, this review seeks to underscore the potential of photocatalysis in proximity labeling for enhancing our understanding of cell biology while also providing perspective on technological advances needed for future discovery.

Resolving Multi-Asperity Contacts at the Nanoscale through Super-Resolution Fluorescence Imaging

Contact mechanics, spanning nanometer to tectonic scales, faces long-standing challenges arising from multiscale random roughness, which hinders experimental validation of theories. Understanding multi-asperity rough contacts is vital for addressing catastrophic consequences of these contacts failing such as earthquakes and for diverse technological applications. To visualize such contacts, we introduce a super-resolution microscopy method utilizing spontaneous millisecond ON/OFF fluorescence blinking of contact-sensitive molecular rotor molecules immobilized on a glass coverslip. This technique achieves ∼55 nm lateral imaging resolution for rough poly(methyl methacrylate) and glass spheres on glass contacts. For soft polymer spheres due to large plastic deformation, the resolution improvement does not significantly affect the area of real contact. However, for hard glass spheres, the real contact area is found to be 2.4 times smaller than that found by diffraction-limited imaging. This study highlights, through direct visualization, the impact of material stiffness on the nanoscale structure within the area of real contact.

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.

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 blinking-based 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.