Biology subjects

Rui, Y.

Publications and source records attributed to Rui, Y..

9 recordsLinked to original sources

bioRxiv2026

Coronavirus nucleocapsid proteins differentially antagonize RIG-I-like receptor signaling to shape pathogenicity

Coronaviruses cause human infections with divergent clinical outcomes. Viral proteins likely play important roles in the severity of human diseases but are poorly defined. In this study, we discovered that viral nucleocapsid (N) proteins from various human coronaviruses have distinct abilities to antagonize human innate immune responses. N proteins from more pathogenic coronaviruses, such as SARS-CoV and SARS-CoV-2, have more potent activities specifically against human RIG-I-like receptor (RLR) activation than less pathogenic coronaviruses including OC43 and HKU1. Mechanistically, the SARS-CoV-2 N protein interacts with RLR pathway viral RNA sensors MDA5 and RIG-I and inhibits their interaction with MAVS. The CTD of SARS-CoV-2 N protein is an important region for RLR antagonism. Consequently, N proteins from more pathogenic coronaviruses promote viral replication more effectively than N proteins from less pathogenic coronaviruses. Blocking N protein function may be a promising antiviral strategy.

microbiology↗

bioRxiv2025

Cellulose Synthase Complex and Remorin Nanodomains Mediate Stress Resilience Through Cell Wall-Plasma Membrane Attachments

HighlightsO_LIThe plasma membrane forms attachments to the plant cell wall that are revealed by hyperosmotic shock and correlate with tolerance to stress. C_LIO_LICellulose Synthase Complex (CSC) clusters and REMORIN (REM) nanodomains localize to cell wall-plasma membrane attachment sites. C_LIO_LICSC density at the plasma membrane determines the extent of cell wall-plasma membrane attachment under hyperosmotic stress. C_LIO_LIREMs rapidly form nanodomains under hyperosmotic stress and are associated with SHOU4/4L, CSC exocytosis inhibitors that limit CSC density at the plasma membrane. C_LI The outer cell surface of an organism is the frontline for detecting and responding to environmental stimuli. In plants, this interface consists of the plasma membrane that lies beneath the cell wall and remains associated with it through attachment sites. These wall-membrane attachments become evident upon hyperosmotic shock, when severe water loss causes the membrane to retract from the wall. Despite their long-standing observation, the molecular identity and function of these attachments remain poorly understood. Here, we identified two mechanisms governing wall-membrane attachments: one dependent on the Cellulose Synthase Complex (CSC), whose density at the plasma membrane positively correlates with resistance to hyperosmotic stress, and the other on REMORIN (REM), which acts antagonistically to the CSC mechanism. Using proximity-labeling proteomics, we identified SHOU4/4L as REM-associated proteins that mediate this antagonism. Together, our findings reveal how wall-membrane attachments are patterned to mediate plant cell resilience under water stress.

plant biology↗

bioRxiv2023

Using UAV-based temporal spectral indices to dissect changes in the stay green trait in wheat

Stay green (SG) in wheat, a beneficial trait for increasing yield and stress resistance, needs to be supported by analysis of the underlying genetic basis. Spectral reflectance indices (SIs) provide non-destructive tools to evaluate crop temporal senescence. However, few SI-based SG quantification pipelines for analyzing diverse wheat panels in the field are available. Here, we first applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages during two field seasons. Based on over 12,000 SIs data set, four SIs (NDVI, GNDVI, NDRE and OSAVI) were selected to develop relative stay green scores (RSGS) and the senescence of wheat populations occurs mainly at four developmental stages stage 1 (S1) to S4, accounting for the final SG indicators. A RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 SNPs significantly associated with RSGS and 1,085 corresponding candidate genes in the two seasons; 15 QTL overlapped or were adjacent to known SG-related QTL or genes and the remaining QTL were novel. Finally, we selected three superior candidate genes (TraesCS6B03G0356400, TraesCS2B03G1299500, and TraesCS2A03G1081100) as examples by transcriptomes, gene annotation, and gene-based association analysis for further analysis and found that utilization of superior SG-related variation in China gradually increased following the Green Revolution. The study provides a useful reference for further SG-related gene discovery of favorable variations in diverse wheat panels.

genetics↗

bioRxiv2022

Imaging the structure and dynamic activity of retinal microglia and macrophage-like cells in the living human eye

PurposeWe recently showed how a refined sequential detection pattern and image processing pipeline for multi-offset adaptive optics scanning light ophthalmoscopy (AOSLO) can increase the contrast of weakly scattering inner retinal structures, including microglia. However, sequential detection was still time-consuming, preventing dynamics from being monitored over short intervals (< 3 mins). Here we show that simultaneous fiber-bundle (FB) detection can overcomes this limitation to reveal the structure and dynamic activity of microglia and macrophage-like cells in healthy and diseased retinae. MethodsWe designed and implemented a custom 7-fiber optical FB with one central confocal fiber and six larger fibers for multi-offset detection in AOSLO at a single focal plane. We imaged the ganglion cell layer at several locations at multiple timepoints (from minutes to weeks) in 8 healthy participants and in 4 patients with ocular infections or inflammation, including ocular syphilis and posterior uveitis. Microglia and immune cells were manually segmented to quantify cell morphometry and motility. ResultsFiber-bundle detection reduced single acquisition time to 20-30 seconds, enabling imaging over larger areas and monitoring of dynamics over shorter intervals. Presumed microglia in healthy retinas had an average diameter of 12.8 m and with a spectrum of morphologies including circular cells and elongated cells with visible processes. We also detected the somas of putative macroglia, potentially astrocytes, near the optic nerve head. Microglia moved slowly in normal eyes (0.02m/sec, on average) but speed increased in patients with active infections or inflammation (up to 2.37m/sec). Microglia activity was absent in a patient with chronic uveitis that was quiescent but apparent over short intervals in an active uveitis retina. In a patient with ocular syphilis imaged at multiple timepoints during treatment, macrophage-like cells containing granular internal structures were seen. Decreases in the quantity and motility of these immune cells were correlated with improvements to vision and other structural and systemic biomarkers. ConclusionsFB-AOSLO enable simplified optical setup with easy alignment and implementation. We imaged the fine-scale structure and dynamics of microglia and macrophage-like cells during active infection and inflammation in the living eye for the first time. In healthy eyes we also detected putative glia cell near the optic nerve head. FB-AOSLO offers promise as a powerful tool for detecting and monitoring retinal inflammation and infection in the living eye over short response to treatment.

bioengineering↗

bioRxiv2022

Spatial Transcriptomics Prediction from Histology jointly through Transformer and Graph Neural Networks

The rapid development of spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations, and the corresponding hematoxylin and eosin-stained histology images. Since histology images are relatively easy and cheap to obtain, it is promising to leverage histology images for predicting gene expression. Though several methods have been devised to predict gene expression using histology images, they dont simultaneously include the 2D vision features and the spatial dependency, limiting their performances. Here, we have developed Hist2ST, a deep learning-based model using histology images to predict RNA-seq expression. At each sequenced spot, the corresponding histology image is cropped into an image patch, from which 2D vision features are learned through convolutional operations. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial (ZINB) distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. Hist2ST was tested on the HER2-positive breast cancer and the cutaneous squamous cell carcinoma datasets, and shown to outperform existing methods in terms of both gene expression prediction and following spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.

bioinformatics↗

bioRxiv2020

Spatial frequency-based image reconstruction to improve image contrast in multi-offset adaptive optics ophthalmoscopy

Off-axis detection methods in adaptive optics (AO) ophthalmoscopy can enhance image contrast of translucent retinal structures such as cone inner segments and retinal ganglion cells layer neurons. Here, we propose a 2D optical model showing that the phase contrast produced by these methods depends on the offset orientation. While one axis provides an asymmetric light distribution, hence a high phase contrast, the perpendicular axis provides a symmetric one, thus a substantially lower contrast. We support this model with in-vivo human data acquired with a multi-offset AO scanning light ophthalmoscope. Then, using this finding, we provide a post-processing method, named Spatial frequency-based iMAge ReconsTruction (SMART), to optimally combine images from different off-axis detector orientations, significantly increasing the structural cellular contrast of in-vivo human retinal neurons such as conne inner segment, putative rods and retinal ganglion cells.

bioengineering↗

bioRxiv2020

Improvements to multi-offset adaptive optics scanning laser ophthalmoscopy for in vivo imaging of individual retinal ganglion cell layer neurons in humans

Previous work has shown that multi-offset detection in adaptive optics scanning laser ophthalmoscopy (AOSLO) can be used to image retinal ganglion cells (RGCs) in monkeys and humans. However, though images of RGCs in anesthetized monkeys with high light levels produced high contrast images of RGCs, images from humans failed to reach the same contrast due to several drawbacks in the previous dual-wavelength multi-offset approach. Our aim here was to design and build a multi-offset detection pattern for humans at safe light levels that could reveal the retinal ganglion cell layer neurons with a contrast, robustness and acquisition time approaching results only previously obtained in monkeys. Here, we present a new imaging system using only one light source, compared to the previous dual-wavelength used on monkeys. Our single-wavelength solution allows for increased light power and eliminates problematic chromatic aberrations. Then, we demonstrate that a radial multi-offset detection pattern with an offset distance of 8-10 Airy Disk Diameter (ADD) is optimal to detect photons multiply scattered in all directions from RGCs thereby enhancing their contrast. This new setup and image processing pipeline led to improved imaging of retinal ganglion cells using multi-offset imaging in AOSLO.

bioengineering↗

bioRxiv2020

Improvements to strip-based digital image registration for robust eye-tracking and to minimize distortions in images from scanned ophthalmic imaging systems

Retinal image-based eye tracking from scanned ophthalmic imaging systems, such as scanning laser ophthalmoscopy, has allowed for precise real-time eye tracking at sub-micron resolution. To achieve real-time processing rates, strip-based image registration methods for real-time applications have several constraints that limit their performance. This trade-off is acceptable for many imaging and psychophysical applications but when the objective is precise eye motion measurement over time, a high error tolerance can be consequential. Dropped strips in these applications can complicate FEMs quantification. Some light starved imaging applications, such as autofluorescence retinal imaging, also require the retention and registration of as much of the data as possible to increase the signal to noise ratio in the final integrated or averaged image. We show here that eye motion can be extracted from image sequences from scanned imaging systems more consistently when the constraints of real-time processing are lifted, and all data is available at the time of registration. This is enabled with additional image processing steps to achieve a more robust solution. Our iterative approach identifies and discards distorted frames, detects coarse motion to generate a synthetic reference frame and then uses it for fine scale motion tracking with improved sensitivity over a larger area. We demonstrate its application here to tracking scanning laser ophthalmoscopy (TSLO) and adaptive optics scanning light ophthalmoscopy (AOSLO). We show that it can successfully capture most of the eye motion across each image sequence, leaving only between 0.04-3.39% of non-blink frames untracked, even with low quality images, while simultaneously minimizing image distortions induced from eye motion. These improvements will facilitate precise FEMs measurement in TSLO and longitudinal tracking of individual cells in AOSLO.

bioengineering↗

bioRxiv2018

Combinatorial library of biodegradable polyesters enables delivery of plasmid DNA to polarized human RPE monolayers for retinal gene therapy

AbstractEfficient gene delivery into hard-to-transfect cells is still a challenge despite significant progress in the development of various gene delivery tools. Non-viral and synthetic polymeric nanoparticles offer an array of advantages for gene delivery over the viral vectors and high in demand as they are safe to use, easy to synthesize and highly cell-type specific. Here we demonstrate the use of a high-throughput screening (HTS) platform to screen for biodegradable polymeric nanoparticles (NPs) that can transfect human retinal pigment epithelial (RPE) cells with high efficiency and low toxicity. These NPs can deliver plasmid DNA (pDNA) to RPE monolayers more efficiently compared to the commercially available transfection reagents without interfering the global gene expression profile of RPE cells. In this work, we have established an HTS platform and identified synthetic polymers that can be used for high efficacy non-viral gene delivery to human RPE monolayers, enabling gene loss- and gain-of-function studies of cell signaling and developmental pathways. This platform can be used to identify the optimum polymer, weight-to-weight ratio of polymer to DNA, and the dose of NP for various retinal cell types.

bioengineering↗