We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) 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 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to 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 (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of ~1.4-fold laterally and ~3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.data, which we deconvolved to yield high SNR 'ground truth'. We then used 30 of these volumes for training and held out volumes for testing network performance. Using the same training and test data, we compared four networks: RCAN, CARE, SRResNET 20 , and ESRGAN 21 . SRResNet and ESRGAN are both class-leading deep residual networks used in image super-resolution, with ESRGAN winning the 2018 Perceptual Image Restoration and Manipulation challenge on perceptual image super-resolution 22 .For the mEmerald-Tomm20 label, RCAN, CARE, ESRGAN, and SRResNET predictions all provided 88 clear improvements in visual appearance, structural similarity index (SSIM) and peak signal-to-noise-89 ratio (PSNR) metrics relative to the raw input (Fig. 1b), also outperforming direct deconvolution on the noisy input data (Supplementary Fig. 1). The RCAN output provided PSNR and SSIM values competitive with the other networks (Fig. 1b), prompting us to investigate whether this performance held for other organelles. We thus conducted similar experiments for fixed U2OS cells with labeled actin, endoplasmic reticulum (ER), golgi, lysosomes, and microtubules (Supplementary Fig. 2), acquiring 15-23 volumes of training data and training independent networks for each organelle. In almost all cases, RCAN performance met or exceeded the other networks (Supplementary Fig. 3, Supplementary Table 3).An essential consideration when using any deep learning method is understanding when network performance deteriorates. Independently training an ensemble of networks and computing measures of network disagreement can provide insight into this issue 9,16 , yet such measures were not generally predictive of disagreement between ground truth and RCAN output (Supplementary Fig. 4). Instead, we found that estimating the per-pixel SNR in the raw input (Methods, Supplementary Fig. 4) seemed to better correlate with network ...
SUMMARYCancer cell migration through and away from tumors is driven in part by migration along aligned extracellular matrix, a process known as contact guidance (CG). To concurrently study the influence of architectural and mechanical regulators of CG sensing, we developed a set of CG platforms. Using flat and nanotextured substrates with variable architectures and stiffness, we show that CG sensing is regulated by substrate stiffness and define a mechanical role for microtubules and actomyosin-microtubule interactions during CG sensing. Furthermore, we show that Arp2/3-dependent lamellipodia dynamics can compete with aligned protrusions to diminish the CG response and define Arp2/3- and Formins-dependent actin architectures that regulate microtu-bule-dependent protrusions, which promote the CG response. Thus, our work represents a comprehen-sive examination of the physical mechanisms influ-encing CG sensing.
XPB and XPD subunits of TFIIH are central genome caretakers involved in nucleotide excision repair (NER), although their respective role within this DNA repair pathway remains difficult to delineate. To obtain insight into the function of XPB and XPD, we studied cell lines expressing XPB or XPD ATPase-deficient complexes. We show the involvement of XPB, but not XPD, in the accumulation of TFIIH to sites of DNA damage. Recruitment of TFIIH occurs independently of the helicase activity of XPB, but requires two recently identified motifs, a R-E-D residue loop and a Thumb-like domain. Furthermore, we show that these motifs are specifically involved in the DNAinduced stimulation of the ATPase activity of XPB.Together, our data demonstrate that the recruitment of TFIIH to sites of damage is an active process, under the control of the ATPase motifs of XPB and suggest that this subunit functions as an ATP-driven hook to stabilize the binding of the TFIIH to damaged DNA.
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