The stoichiometry of protein complexes is precisely regulated in cells and is fundamental to protein function. Singe-molecule fluorescence imaging based photobleaching event counting is a new approach for protein stoichiometry determination under physiological conditions. Due to the interference of the high noise level and photoblinking events, accurately extracting real bleaching steps from single-molecule fluorescence traces is still a challenging task. Here, we develop a novel method of using convolutional and long-short-term memory deep learning neural network (CLDNN) for photobleaching event counting. We design the convolutional layers to accurately extract features of steplike photobleaching drops and long-short-term memory (LSTM) recurrent layers to distinguish between photobleaching and photoblinking events. Compared with traditional algorithms, CLDNN shows higher accuracy with at least 2 orders of magnitude improvement of efficiency, and it does not require user-specified parameters. We have verified our CLDNN method using experimental data from imaging of single dye-labeled molecules in vitro and epidermal growth factor receptors (EGFR) on cells. Our CLDNN method is expected to provide a new strategy to stoichiometry study and time series analysis in chemistry.
Live-cell single-molecule fluorescence imaging has become a powerful analytical tool to investigate cellular processes that are not accessible to conventional biochemical approaches. This has greatly enriched our understanding of the behaviors of single biomolecules in their native environments and their roles in cellular events. Here, we review recent advances in fluorescence-based single-molecule bioimaging of proteins in living cells. We begin with practical considerations of the design of single-molecule fluorescence imaging experiments such as the choice of imaging modalities, fluorescent probes, and labeling methods. We then describe analytical observables from single-molecule data and the associated molecular parameters along with examples of live-cell single-molecule studies. Lastly, we discuss computational algorithms developed for single-molecule data analysis.
Stimulated emission depletion (STED) nanoscopy is an emerging super-resolution imaging platform for the study of the cellular structure. Developing suitable fluorescent probes of small size, good photostability, and easy functionalization is still in demand. Herein, we introduce a new type of surface-engineered gold nanoclusters (Au NCs) that are ultrasmall (1.7 nm) and ultrabright (QY = 60%) for STED bioimaging. A rigid shell formed by l-arginine (l-Arg) and 6-aza-2-thiothymine (ATT) on the Au NC surface enables not only its strong fluorescence in aqueous solution but also its easy chemical modification for specific biomolecule labeling. Au NCs show remarkable performance as STED nanoprobes, including high depletion efficiency, good photobleaching resistance, and low saturation intensity. Super-resolution imaging has been achieved with these Au NCs, and targeted nanoscopic imaging of cellular tubulin has been demonstrated. Moreover, the circular structure of lysosomes in live cells has been revealed. As a Au NC is also an ideal probe for electron microscopy, dual imaging of Aβ42 aggregates with the single labeling probe of Au NCs has been realized in correlative light and electron microscopy (CLEM). This work reports, for the first time, the application of Au NCs as a novel probe in STED and CLEM imaging. With their excellent properties, Au NCs show promising potential for nanoscale bioimaging.
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