Biology subjects

Combs, C. A.

Publications and source records attributed to Combs, C. A..

2 recordsLinked to original sources

bioRxiv2020

Transgelin: A New Gene Involved in LDL Endocytosis Identified by a Genome-wide CRISPR-Cas9 Screen

To identify new genes involved in the cellular uptake of low-density lipoprotein (LDL), we applied a novel whole genome CRISPR/Cas9 knockout-screen on HepG2 cell lines. We identified TAGLN (transgelin), an actin-binding protein, as a new gene involved in LDL endocytosis. In silico validation demonstrated that genetically predicted differences in expression of TAGLN in human populations were associated with plasma lipids (triglycerides, total cholesterol, HDL, and LDL cholesterol) in the Global Lipids Genetics Consortium and lipid-related phenotypes in the UK Biobank. Decreased cellular LDL uptake observed in TAGLN-knockout cells due to decreased LDL receptor internalization, led to alterations in cellular cholesterol content and compensatory changes in cholesterol biosynthesis. Transgelin was also shown to be involved in the actin-dependent phase of clathrin-mediated endocytosis of other cargo besides LDL. The identification of novel genes involved in LDL uptake may improve the diagnosis of hypercholesterolemia and provide future therapeutic targets for the prevention of cardiovascular disease.

cell biology↗

bioRxiv2020

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

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.

bioengineering↗