Skytte Brock (nicball68)

Hi-C, split-pool recognition of interactions by tag extension (SPRITE) and genome architecture mapping (GAM) are powerful technologies utilized to probe chromatin interactions genome wide, but how faithfully they capture three-dimensional (3D) contacts and how they perform relative to each other is unclear, as no benchmark exists. Here, we compare these methods in silico in a simplified, yet controlled, framework against known 3D structures of polymer models of murine and human loci, which can recapitulate Hi-C, GAM and SPRITE experiments and multiplexed fluorescence in situ hybridization (FISH) single-molecule conformations. We find that in silico Hi-C, GAM and SPRITE bulk data are faithful to the reference 3D structures whereas single-cell data reflect strong variability among single molecules. The minimal number of cells required in replicate experiments to return statistically similar contacts is different across the technologies, being lowest in SPRITE and highest in GAM under the same conditions. Noise-to-signal levels follow an inverse power law with detection efficiency and grow with genomic distance differently among the three methods, being lowest in GAM for genomic separations >1 Mb.Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence-enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.The innate immune response is critical for recognizing and controlling infections through the release of cytokines and chemokines. However, severe pathology during some infections, including SARS-CoV-2, is driven by hyperactive cytokine release, or a cytokine storm. The innate sensors that activate production of proinflammatory cytokines and chemokines during COVID-19 remain poorly characterized. In the present study, we show that both TLR2 and MYD88 expression were associated with COVID-19 disease severity. Mechanistically, TLR2 and Myd88 were required for β-coronavirus-induced inflammatory responses, and TLR2-dependent signaling induced the production of proinflammatory cytokines during coronavirus infection independent of viral entry. TLR2 sensed the SARS-CoV-2 envelope protein as its ligand. In addition, blocking TLR2 signaling in vivo provided protection against the pathogenesis of SARS-CoV-2 infection. Overall, our study provides a critical understanding of the molecular mechanism of β-coronavirus sensing and inflammatory cytokine production, which opens new avenues for therapeutic strategies to counteract the ongoing COVID-19 pandemic.The prerequisite function of vimentin for the epithelial-mesenchymal transition (EMT) is not clearly elucidated yet. Here, we show that vimentin phosphorylated by PLK1, triggers TGF-β-signaling, which consequently leads to metastasis and PD-L1 expression for immune suppression in lung adenocarcinoma. The clinical correlation between expression of both vimentin and PLK1, and overall survival rates of patients was significant in lung adenocarcinoma