Lens-based optical microscopy failed to discern fluorescent features closer than 200 nm for decades, but the recent breaking of the diffraction resolution barrier by sequentially switching the fluorescence capability of adjacent features on and off is making nanoscale imaging routine. Reported fluorescence nanoscopy variants switch these features either with intense beams at defined positions or randomly, molecule by molecule. Here we demonstrate an optical nanoscopy that records raw data images from living cells and tissues with low levels of light. This advance has been facilitated by the generation of reversibly switchable enhanced green fluorescent protein (rsEGFP), a fluorescent protein that can be reversibly photoswitched more than a thousand times. Distributions of functional rsEGFP-fusion proteins in living bacteria and mammalian cells are imaged at <40-nanometre resolution. Dendritic spines in living brain slices are super-resolved with about a million times lower light intensities than before. The reversible switching also enables all-optical writing of features with subdiffraction size and spacings, which can be used for data storage.

The nanoscale organization of the F-actin cytoskeleton in neurons comprises membrane-associated periodical rings, bundles, and longitudinal fibers. The F-actin rings have been observed predominantly in axons but only sporadically in dendrites, where fluorescence nanoscopy reveals various patterns of F-actin arranged in mixed patches. These complex dendritic F-actin patterns pose a challenge for investigating quantitatively their regulatory mechanisms. We developed here a weakly supervised deep learning segmentation approach of fluorescence nanoscopy images of F-actin in cultured hippocampal neurons. This approach enabled the quantitative assessment of F-actin remodeling, revealing the disappearance of the rings during neuronal activity in dendrites, but not in axons. The dendritic F-actin cytoskeleton of activated neurons remodeled into longitudinal fibers. We show that this activity-dependent remodeling involves Ca 2+ and NMDA receptor-dependent mechanisms. This highly dynamic restructuring of dendritic F-actin based submembrane lattice into longitudinal fibers may serve to support activity-dependent membrane remodeling, protein trafficking and neuronal plasticity.

Traditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task. Its potential is highlighted on various imaging tasks, such as live-cell and multicolor imaging and multimodal optimization. This online optimization routine can be integrated to various imaging systems to increase accessibility, optimize performance and improve overall imaging quality.

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