Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how image restoration based on deep learning extends the range of biological phenomena observable by microscopy. On seven concrete examples we demonstrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to 10-fold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME.

Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate tradeoffs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how deep learning enables biological observations beyond the physical limitations of microscopes. On seven concrete examples we illustrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how isotropic resolution can be achieved even with a 10-fold under-sampling along the axial direction, and how diffraction-limited structures can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software.

In light microscopy, refractive index mismatches between media and sample cause spherical aberrations that often limit penetration depth and resolution. Optical clearing techniques can alleviate these mismatches, but they are so far limited to fixed samples. We present Iodixanol as a non-toxic medium supplement that allows refractive index matching in live specimens and thus substantially improves image quality in live-imaged primary cell cultures, planarians, zebrafish and human cerebral organoids.DOI: http://dx.doi.org/10.7554/eLife.27240.001

ObjectiveThe role and mechanisms of insulin receptor internalization remain incompletely understood. Previous trafficking studies of insulin receptors involved fluorescent protein tagging at their termini, manipulations that may be expected to result in dysfunctional receptors. Our objective was to determine the trafficking route and molecular mechanisms of functional tagged insulin receptors and endogenous insulin receptors in pancreatic beta-cells.MethodsWe generated functional insulin receptors tagged with pH-resistant fluorescent proteins between domains. Confocal, TIRF and STED imaging revealed a trafficking pattern of inter-domain tagged insulin receptors and endogenous insulin receptors detected with antibodies.ResultsSurprisingly, interdomain-tagged and endogenous insulin receptors in beta-cells bypassed classical Rab5a- or Rab7-mediated endocytic routes. Instead, we found that removal of insulin receptors from the plasma membrane involved tyrosine-phosphorylated caveolin-1, prior to trafficking within flotillin-1-positive structures to lysosomes. Multiple methods of inhibiting caveolin-1 significantly reduced Erk activation in vitro or in vivo, while leaving Akt signaling mostly intact.ConclusionsWe conclude that phosphorylated caveolin-1 plays a role in insulin receptor internalization towards lysosomes through flotillin-1-positive structures and that caveolin-1 helps bias physiological beta-cell insulin signaling towards Erk activation.