Fluorophore labels that transiently and repetitively bind to a target (“exchangeable” or “renewable” labels) lead to a continuous renewal of the fluorescence signal. This dynamic labeling approach minimizes photobleaching and was beneficially exploited in various super-resolution microscopy methods. Here, we report two new developments using exchangeable fluorophores: first, we report fast and long-time live-cell super-resolution microscopy using a weak-affinity protein label and a neural network. Second, we report a novel design for exchangeable DNA labels that show higher brightness and lower background. Together, these two developments further increase the application range for exchangeable fluorophore labels in super-resolution fluorescence microscopy.
Single-molecule localization microscopy (SMLM) in combination with DNA barcoding (DNA-PAINT) enables easy-to-implement multi-target super-resolution imaging. However, image acquisition is slow because of the need to spatio-temporally isolate single emitters and to collect sufficient statistical data to generate a super-resolved image. Here, we bypass this limitation by utilizing a neural network, DeepSTORM, that can predict super-resolved SMLM images from high-emitter density data. This reduces the acquisition time 10- to 20-fold, enabling image acquisition as short as one minute. Integrating weak-affinity DNA labels allows precise control of single-molecule emitter densities, which enables recording of training, ground truth, and testing data from the same sample. Sequential imaging of multiple targets using different DNA barcodes with the same fluorophore enables aberration-free multi-target imaging (Exchange-PAINT). The constant exchange of fluorophore labels at target sites minimizes signal loss for long acquisition times, which allows imaging large samples in a matter of minutes. The concept is transferable to other weak-affinity, non-covalent fluorophore labels.
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