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Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows.
Expansion microscopy (ExM) enables super-resolution fluorescence imaging on standard microscopes by physical expansion of the sample. However, the investigation of interactions between different organisms such as mammalian and fungal cells by ExM remains challenging because different cell types require different expansion protocols to ensure identical, ideally isotropic expansion of both partners. Here, we introduce an ExM method that enables super-resolved visualization of the interaction between NK cells and Aspergillus fumigatus hyphae. 4-fold expansion in combination with confocal fluorescence imaging allows us to resolve details of cytoskeleton rearrangement as well as NK cells' lytic granules triggered by contact with an RFP-expressing A. fumigatus strain. In particular, subdiffraction-resolution images show polarized degranulation upon contact formation and the presence of LAMP1 surrounding perforin at the NK cell-surface post degranulation. Our data demonstrate that optimized ExM protocols enable the investigation of immunological synapse formation between two different species with so far unmatched spatial resolution.