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Abstract
RESOLFT fluorescence nanoscopy can nowadays image details far beyond the diffraction limit. However, signal to noise ratio (SNR) and temporal resolution are still a concern, especially deep inside living cells and organisms. In this work, we developed a non-deterministic scanning approach based on a real-time feedback system which speeds up the acquisition up to 6-fold and decreases the light dose by 70–90% for in vivo imaging. Also, we extended the information content of the images by acquiring the complete temporal evolution of the fluorescence generated by reversible switchable fluorescent proteins. This generates a series of images with different spatial resolution and SNR, from conventional to RESOLFT images, which combined through a multi-image deconvolution algorithm further enhances the effective resolution. We reported nanoscale imaging of organelles up to 35 Hz and actin dynamics during an invasion process at a depth of 20–30 µm inside a living Caenorhabditis elegans worm.
Long acquisition time and high illumination intensities needed in super-resolution imaging often generate photobleaching and phototoxicity. Here the authors develop a non-deterministic scanning approach based on a real-time feedback system that enables faster acquisition with lower light doses for in vivo imaging.
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1 KTH Royal Institute of Technology, Department of Applied Physics and Science for Life Laboratory, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746)
2 Istituto Italiano di Tecnologia, Molecular Microscopy and Spectroscopy, Genoa, Italy (GRID:grid.25786.3e) (ISNI:0000 0004 1764 2907)
3 PSL Research University, CNRS, Laboratoire Physico-Chimie Curie, Institut Curie, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282); Sorbonne Université, Paris, France (GRID:grid.462844.8) (ISNI:0000 0001 2308 1657); Université Paris Descartes, Paris, France (GRID:grid.10992.33) (ISNI:0000 0001 2188 0914)
4 PSL Research University, CNRS, Laboratoire Physico-Chimie Curie, Institut Curie, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282); Sorbonne Université, Paris, France (GRID:grid.462844.8) (ISNI:0000 0001 2308 1657)