Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes.

We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance.

Three-dimensional structured illumination microscopy with enhanced axial resolution.

The axial resolution of three-dimensional structured illumination microscopy (3D SIM) is limited to ∼300 nm. Here we present two distinct, complementary methods to improve axial resolution in 3D SIM with minimal or no modification to the optical system. We show that placing a mirror directly opposite the sample enables four-beam interference with higher spatial frequency content than 3D SIM illumination, offering near-isotropic imaging with ∼120-nm lateral and 160-nm axial resolution. We also developed a deep learning method achieving ∼120-nm isotropic resolution. This method can be combined with denoising to facilitate volumetric imaging spanning dozens of timepoints. We demonstrate the potential of these advances by imaging a variety of cellular samples, delineating the nanoscale distribution of vimentin and microtubule filaments, observing the relative positions of caveolar coat proteins and lysosomal markers and visualizing cytoskeletal dynamics within T cells in the early stages of immune synapse formation.

Haploinsufficiency leads to neurodegeneration in C9ORF72 ALS/FTD human induced motor neurons.

An intronic GGGGCC repeat expansion in C9ORF72 is the most common cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), but the pathogenic mechanism of this repeat remains unclear. Using human induced motor neurons (iMNs), we found that repeat-expanded C9ORF72 was haploinsufficient in ALS. We found that C9ORF72 interacted with endosomes and was required for normal vesicle trafficking and lysosomal biogenesis in motor neurons. Repeat expansion reduced C9ORF72 expression, triggering neurodegeneration through two mechanisms: accumulation of glutamate receptors, leading to excitotoxicity, and impaired clearance of neurotoxic dipeptide repeat proteins derived from the repeat expansion. Thus, cooperativity between gain- and loss-of-function mechanisms led to neurodegeneration. Restoring C9ORF72 levels or augmenting its function with constitutively active RAB5 or chemical modulators of RAB5 effectors rescued patient neuron survival and ameliorated neurodegenerative processes in both gain- and loss-of-function C9ORF72 mouse models. Thus, modulating vesicle trafficking was able to rescue neurodegeneration caused by the C9ORF72 repeat expansion. Coupled with rare mutations in ALS2, FIG4, CHMP2B, OPTN and SQSTM1, our results reveal mechanistic convergence on vesicle trafficking in ALS and FTD.