Biology subjects

Sheng, X.

Publications and source records attributed to Sheng, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Biologically grounded cell profiling across microscopy modalities

Microscopy-based cell profiling has broad applications in biological discovery, disease characterization, and phenotypic drug screening. Modern microscopy continues to push the limits of resolution, speed, depth and throughput, but better imaging does not automatically lead to better biomedical discovery and translation. A key bottleneck is feature representation: existing features are either handcrafted or learned as black-box embeddings and often lack explicit biological meaning. Here we propose biological grounding as a first principle for cell profiling and implement it through MorphAgent, an AI agent framework in which each quantitative feature originates from a biological hypothesis and is anchored to a cellular structure or process. By integrating biological knowledge, multimodal reasoning and automated validation, MorphAgent makes biologically grounded feature design systematic and scalable. We show biological grounding fundamentally changes the properties of cell profiling features. Across three microscopy modalities, MorphAgent produces compact, expressive and transferable features for drug screening, cell state characterization and disease morphology analysis. In wide-field Cell Painting, a common assay for phenotypic drug screening, it improves perturbation-retrieval mean average precision by 48% over CellProfiler and 20% over DeepProfiler while using substantially fewer dimensions. In confocal mitochondrial imaging, biologically grounded features transfer across independently acquired datasets and imaging resolutions, supporting accurate aging-state classification. In structured illumination microscopy (SIM) imaging of Tau-labeled samples, biologically grounded super resolution features reveal nanoscale morphologies inaccessible at conventional resolution, enhancing the discrimination of disease-associated mutations. Moreover, biologically grounded features provide a hierarchical organization of cellular morphology, enabling multilevel alignment with transcriptomics data and facilitating mechanistic understanding. Biologically grounded cell profiling thus brings AI reasoning closer to biological mechanisms, advancing biomedical discovery and translation.

bioinformatics↗

bioRxiv2026

Mutation Order and Selection Shape Intratumor Heterogeneity in Tumor Evolution

Cancer progression often requires the accumulation of multiple driver mutations, yet the same set of drivers may be acquired in different orders. How these alternative mutation-order pathways jointly shape tumor clonal structure remains unclear. We develop a multitype branching-process model in which malignant transformation requires two driver mutations and distinguish malignant cells both by their mutation order and by the independent transformation event that founded their clone. Under a successive exponential approximation, we establish point-process limits for the pathway-specific clone-size processes and derive a closed-form expression for the limiting expected Simpson's index of the combined malignant population. When the two mutation orders produce malignant cells with the same net growth rate, the limiting index separates into effective pathway weights, determined by mutation rates and birth-death dynamics at preceding stages, and within-pathway concentration terms, determined by intermediate-to-malignant growth-rate ratios. This decomposition shows that a driver's effect on heterogeneity depends critically on when it is acquired. A strong driver acquired early expands the intermediate lineage and increases the supply of independent malignant founders, whereas the same driver acquired at the final transition strengthens the growth and age advantage of early-founded malignant clones. Under additive fitness effects, these counteracting mechanisms can produce a non-monotone relationship between selective advantage and clonal concentration. We further show that threshold-like non-additive fitness effects can generate highly concentrated malignant populations, while order-dependent terminal fitness leads the faster-growing pathway to dominate asymptotically. Together, these results reveal how mutation order, mutational accessibility, selection, and epistasis jointly determine lineage-level intratumor heterogeneity.

cancer biology↗

bioRxiv2026

RADF: Reference-Anchored Dynamic Flow for Spatial Perturbation Profile Completion

Spatial perturbation profiling is becoming an important tool in functional genomics because it reveals how genetic interventions reshape transcription within intact tissue contexts. However, destructive readout and limited screening capacity leave many perturbation-by-location response profiles unmeasured, motivating the task of spatial perturbation profile completion. The task is to infer the held-out response population at query locations from reported profiles of the same perturbation. Existing methods either generate responses de novo or reuse these profiles without spatial adaptation. These strategies make it difficult to preserve empirical population structure while modeling location-specific variation. Our key insight is that the reported population already defines an empirical response distribution for the target perturbation. To exploit this empirical support, we propose Reference-Anchored Dynamic Flow (RADF), which employs a Sinkhorn-balanced decoder to construct a population-valued anchor in which every reference profile has equal total contribution. Additionally, a bounded dynamic relational flow is used to recompute spatial relations from the evolving expression state and query geometry. Across diverse spatial contexts, RADF reduces macro E-distance by 70.6% compared with an existing state-of-the-art spatial method, highlighting the advantage of combining a reference-supported population anchor with bounded, location-dependent refinement. Code will be made publicly available upon acceptance.

bioinformatics↗

bioRxiv2026

A proteomic atlas of organelle remodeling identifies lysosomal SNX3 as a regulator of Notch signaling in epidermal differentiation

Differentiation of epidermal keratinocytes is accompanied by profound reorganization of intracellular architecture, but how organelle remodeling interfaces with cell fate control is not well understood. Here, we generate a compartment-resolved proteomic map of keratinocyte differentiation and identify extensive remodeling of lysosomes, mitochondria, autophagic vesicles, plasma membrane, and nucleus. Differentiating keratinocytes display coordinated enrichment of lysosomal degradative machinery, vesicular trafficking factors, and mitochondrial metabolic proteins, revealing organelle remodeling as a prominent feature of epidermal differentiation. From the lysosomal proteome, we identify Sorting Nexin 3 (SNX3) as a critical regulator of epidermal homeostasis. SNX3 increasingly localizes to LAMP1-positive vesicles during differentiation, and its loss impairs epidermal differentiation, suppresses Notch signaling, and promotes proliferative gene expression. In vivo, SNX3-deficient skin grafts fail to maintain normal epidermal architecture and instead develop into squamous cell carcinoma. Mechanistically, SNX3 mediates efficient Notch receptor activation, as SNX3 loss reduces nuclear Notch1 and NICD production, whereas NICD re-expression can rescue the differentiation defect. Our study defines a proteomic framework for organelle remodeling during epidermal differentiation and identifies lysosome-associated SNX3 as a key link between endolysosomal trafficking, Notch signaling, and epidermal tissue homeostasis.

cell biology↗

bioRxiv2026

Modeling Multi-Modal Brain Connectomes for Brain Disorder Diagnosis via Graph Diffusion Optimal Transport Network

Network analysis of human brain connectivity provides a fundamental framework for identifying the neurobiological mechanisms that cause cognitive variations and neurological disorders. However, existing diagnostic models often treat structural connectivity (SC) as a fixed or optimal topological scaffold for functional connectivity (FC). This consequently overlooks the higher-order dependencies between brain regions that are critical for characterizing pathological alterations. Moreover, the distinct spatial organizations of SC and FC complicate their direct integration, as naive alignment methods may distort the inherent nonlinear patterns of brain connectivity. To address these limitations, we propose the Graph Diffusion Optimal Transport Network (GDOT-Net), which models disease-related topological evolution and achieves precise alignment between SC and FC. Unlike existing diffusion studies, the proposed model introduces an evolvable brain connectome modeling approach to infer the complex topological structure of brain networks, unveiling higher-order connectivity patterns linked to specific neuropsychiatric disorders. Furthermore, GDOT-Net incorporates a Pattern-Specific Alignment mechanism, leveraging optimal transport to align structural and functional topological representations in a geometry-aware manner. To capture nonlinear topological relationships between brain regions, a Neural Graph Aggregator Module was developed, which adaptively learns complex node interaction patterns in brain networks. By leveraging this module, GDOT-Net generates highly discriminative representations that form a robust basis for the precision diagnosis of brain disorders. Experiments on REST-meta-MDD and ADNI demonstrate that GDOT-Net surpasses SOTA methods in uncovering structural-functional misalignments and disorder-specific subnetworks. The source code is publicly available at this Link.

neuroscience↗

bioRxiv2026

PRISM: Niche-informed Deciphering of Incomplete Spatial Multi-Omics Data

Spatial multi-omics data, characterizing the knowledge of diverse molecular layers, have become indispensable for the in situ analysis of tissue architecture and complex biological processes. Nevertheless, current spatial multi-omics sequencing protocols are often hindered by incompatible protocols, resulting in incomplete spatial multi-omics pairing due to inconsistent field-of-view or spatial resolution. To address this, we present PRISM, a novel computational method tailored for this scenario. PRISM leverages a niche-informed graph to propagate information from paired to unpaired regions, jointly achieving the spatial domain identification and spatial omics imputation. Extensive benchmarking on five diverse simulated and real experimental datasets demonstrated that PRISM outperformed existing methods in spatial multi-omics analysis tasks. Application to the human Parkinsons disease data revealed that PRISM accurately recovered dopamine-associated spatial domains and metabolite distributions masked by incomplete data gaps. PRISM offers a robust solution for bridging the integration gap inherent in incompatible sequencing protocols, thereby facilitating more accurate downstream biological interpretation.

bioinformatics↗

bioRxiv2025

Benchmarking Real-World Applicability of Molecular Generative Models from De novo Design to Lead Optimization with MolGenBench

Structure-based drug design (SBDD) has been profoundly reshaped by the advent of deep generative models, yet their practical impact on drug discovery remains limited. A central issue is the absence of a rigorous, application-oriented benchmark that mirrors the multi-stage, target-aware workflows of real-world pharmaceutical development. Inspired by recent advances in benchmarking for computer vision and large language models, where systematic evaluation has catalysed rapid progress, we introduce MolGenBench, a comprehensive benchmark designed to close the gap between molecular generation algorithms and tangible drug discovery outcomes. MolGenBench integrates a structurally diverse, large-scale dataset spanning 120 protein targets, 5,433 chemical series comprising 220,005 experimentally confirmed active molecules. Beyond conventional de novo generation, it incorporates a dedicated hit-to-lead (H2L) optimization scenario, which represents a critical phase in hit optimization that is seldom evaluated in existing benchmarks. We further introduce novel, pharmaceutically grounded metrics that assess a models ability to both rediscover target-specific actives or progressively optimize compounds for potency. Through extensive evaluation, MolGenBench uncovers significant gaps between current generative models and the demands of real-world drug development, establishing a foundational resource for building generative models with enhanced practical impact and accelerated translational potential.

bioinformatics↗

bioRxiv2025

Development of an efficient PEG-Mediated protoplast transformation system for the medicinal fungus Ophiocordyceps xuefengensis

Ophiocordyceps xuefengensis is a newly identified medicinal fungus with significant pharmacological and economic value, but its genetic manipulation has been impeded by the lack of an efficient transformation system. Here, we established the first stable and highly efficient polyethylene glycol (PEG)-mediated protoplast transformation platform for O. xuefengensis using hygromycin B as a selectable marker. Through systematic optimization of critical parameters such as enzyme composition, enzyme concentration, mycelial age, and digestion conditions, we developed an optimized protocol for protoplast preparation. A high protoplast yield of 9.42x107 CFU/mL was achieved using 4-day-old mycelia with 1.5% lywallzyme 1 and 1.5% snailase digested at 34{whitebullet}C with shaking at 130 rpm for 3.5 h. PY medium containing 0.6 M mannitol significantly enhanced protoplast regeneration. Stable plasmid integration and robust hygR gene expression were confirmed through PCR detection and sustained antibiotic resistance over four successive generations. Furthermore, a controllable expression system was established by using the endogenous promoter that drove the stable expression of the glycoside hydrolase gene cbhI. The result of enzymatic assay confirmed the functional production of CBHI enzyme, demonstrating the credibility of this system. This work provides a reliable genetic toolbox for functional genomics studies, targeted gene manipulation, and strain engineering in O. xuefengensis, facilitating fundamental research and sustainable utilization of this valuable medicinal species. IMPORTANCEThe conservation and sustainable utilization of medicinal fungi represents a critical challenge in biotechnology and natural product research. Ophiocordyceps xuefengensis, a newly identified species with significant pharmacological potential, exemplifies this challenge: its wild resources are diminishing while artificial cultivation remains restricted by limited understanding of its fundamental biology. The lack of genetic tools has impeded progress in elucidating its biosynthetic pathways, regulating fruiting body development, and enhancing metabolite production. We established a highly efficient PEG-mediated transformation system that directly addresses this technological gap. In addition, we demonstrated that this transformation system can support heterologous gene expression using endogenous promoters. This platform enables reliable genetic manipulation in O. xuefengensis, permitting functional genomics studies and targeted bioactive compounds synthesis.

microbiology↗

bioRxiv2025

Instrument-free quantitative detection of analytes using a novel combo lateral flow assay

Lateral flow assays (LFA) are widely regarded as the cheapest, fastest, and easiest point-of-care (PoC) tests available.1,2 A key advantage is that results can be visually interpreted without the need for specialized equipment, enabling on-site applications while keeping costs low.3 However, conventional LFAs are typically qualitative, limiting their use to simply detect the presence or absence of analytes.4 Additional limitations include narrow dynamic ranges and false negatives due to the Hook Effect in sandwich LFAs.5 In this work, we present a combo lateral flow assay (CLFA) that integrates both sandwich and competitive assay formats, enabling the quantitative detection of analytes across a broad concentration range without an external instrument. The precision of CLFA measurements can be further enhanced with artificial intelligence (AI)-based image analysis via a smartphone app. Importantly, CLFA is not subject to the Hook Effect. CLFA is easily implemented by adding just one extra test line to the standard LFA design, resulting in minimal additional manufacturing effort and cost. Given these benefits, we anticipate that CLFA will have broad applications in diagnostics and research applications. Several examples are provided to illustrate its potential.

biochemistry↗

bioRxiv2025

Advancing Ligand Binding Affinity Prediction with Cartesian Tensor-Based Deep Learning

0.We present PBCNet2.0, a cartesian tensor-based Siamese Neural Network for protein-ligand relative binding affinity prediction. Trained on 8.6 million protein-ligand complex structure pairs, PBCNet2.0 achieves zero-shot performance comparable to computationally intensive physics-based simulations. Our prioritization experiments show that PBCNet2.0 speeds up binding affinity optimization by 718% while reducing resource use by 41%. Through extensive retrospective experiments, we demonstrate that PBCNet2.0 intrinsically comprehends protein-ligand interactions, showing high sensitivity to intermolecular interactions and exceptional perception of spatial geometric information. Strikingly, PBCNet2.0 exhibits an emergent capability to predict affinity changes induced by binding residue variations, highlighting its potential for identifying resistance mutation. We prospectively validated these capabilities on two targets ENPP1 and ALDH1B1, where PBCNet2.0 successfully identified affinity shifts arising from subtle molecular interactions and conformational differences, and pinpointed critical binding residues with an 83% hit rate. This combination of computational efficiency, spatial geometric perception of binding site, and generalizable affinity prediction establishes PBCNet2.0 as a transformative tool for developing pharmacological probes for all human proteins.

molecular biology↗

bioRxiv2025

Toti: an integrated multi-omics database to decipher the epigenetic regulation of gene expression in totipotent stem cells

Totipotent cells (TSCs), the origin of mammalian life and foundation to early mammalian embryogenesis, are characterized by the highest differentiation capacity and extensive developmental potential. However, none of the existing embryonic databases have provided epigenetic and transcriptomic resources on totipotency, greatly limiting our understanding of the mechanisms governing the establishment and exit of totipotency. Here, we present Toti, a pioneering multi-omics database exclusively developed for totipotency, covering in vivo, in vitro and genome-edited human and mouse embryonic TSCs, TSC-like cells, pluripotent cells (PSCs), and embryos spanning preimplantation stages, with a total of 8,265 samples. Toti facilitates an in-depth exploration of the molecular mechanisms underlying totipotency by offering Search, Browse and Analysis modules available at http://toti.zju.edu.cn/.

developmental biology↗

bioRxiv2024

A circular engineered sortase for interrogating histone H3 in chromatin

Reversible modification of the histone H3 N-terminal tail is critical in regulating chromatin structure, gene expression, and cell states, while its dysregulation contributes to disease pathogenesis. Understanding the crosstalk between H3 tail modifications in nucleosomes constitutes a central challenge in epigenetics. Here we describe an engineered sortase transpeptidase, cW11, that displays highly favorable properties for introducing scarless H3 tails onto nucleosomes. This approach significantly accelerates the production of both symmetrically and asymmetrically modified nucleosomes. We demonstrate the utility of asymmetrically modified nucleosomes produced in this way in dissecting the impact of multiple modifications on eraser enzyme processing and molecular recognition by a reader protein. Moreover, we show that cW11 sortase is very effective at cutting and tagging histone H3 tails from endogenous histones, facilitating multiplex "cut-and-paste" middle down proteomics with tandem mass tags. This cut-and-paste proteomics approach permits the quantitative analysis of histone H3 modification crosstalk after treatment with different histone deacetylase inhibitors. We propose that these chemoenzymatic tail isolation and modification strategies made possible with cW11 sortase will broadly power epigenetics discovery and therapeutic development.

biochemistry↗

bioRxiv2024

High-Resolution Spatial Mapping of Electrocorticographic Activities with a 4096-Channel, Multiplexed Thin-Film Transistor Array

Advanced brain-machine interfaces (BMIs) demand implantable devices that offer flexibility, high-density and high-throughput capabilities. Conventional neural electrodes are constrained by the wiring strategy. Here we present a flexible and implantable electrocorticography (ECoG) device array (NeuroCam) based on metal-oxide semiconductor thin-film transistors (TFTs), to record brain activities at a large scale. Employing a multiplexing technique, the system is capable to record ECoG signals with up to 4096 channels and a density of 44 sites/mm2, while compressing the fan-in/fan-out leads to around a hundred. In a rabbit model with epilepsy, the NeuroCam array maps abnormal spike-wave discharges with an exceptional spatial resolution (150 m) across extensive brain areas. The device strategy provides a promising route towards high-throughput BMIs with potential applications in fundamental neuroscience studies and practical biomedicine. Significance StatementDeveloping high-throughput neural recording techniques is critical for next-generation brain-machine interfaces (BMIs), but conventional neural electrodes are limited by the difficulty of scaling the lead wires. Here we report an implantable neural interface, based on a flexible thin-film transistor (TFT) array named NeuroCam. Employing an actively switching (multiplexing) technique, NeuroCam is capable to record electrocorticographic (ECoG) signals with up to 4096 channels and a density of 44 sites/mm2, with only 128 input/output leads. Validated in a rabbit model of epilepsy, NeuroCam records spatially resolved neural activities across extensive cortical areas with a resolution of 150 m, and precisely maps the temporal evolution of epileptic waves. NeuroCam shows a promising solution for high-performance BMIs.

bioengineering↗

bioRxiv2024

A biodegradable and restorative peripheral neural interface for the interrogation of neuropathic injuries

Monitoring the early-stage healing of severe traumatic nerve injuries is essential to gather physiological and pathological information for timely interventions and optimal clinical outcomes. While implantable peripheral nerve interfaces provide direct access to nerve fibers for precise interrogation and modulation, conventional non-degradable designs pose limited utilization in nerve injury rehabilitation. Here, we introduce a biodegradable and restorative neural interface for wireless real-time tracking and recovery of long-gap nerve injuries. Leveraging machine learning techniques, this electronic platform deciphers nerve recovery status and identifies traumatic neuroma formation at the early phase, enabling timely intervention and significantly improved therapeutic outcomes. The biodegradable nature of the device eliminates the need for retrieval procedures, reducing infection risks and secondary tissue damage. This research sheds light on bioresorbable multifunctional peripheral nerve interfaces for probing neuropathic injuries, offering vital information for early diagnosis and therapeutic intervention.

bioengineering↗

bioRxiv2024

A Silicon Diode based Optoelectronic Interface for Bidirectional Neural Modulation

The development of advanced neural modulation techniques is crucial to neuroscience research and neuroengineering applications. Recently, optical-based, non-genetic modulation approaches have been actively investigated to remotely interrogate the nervous system with high precision. Here, we show that a thin-film, silicon (Si)-based diode device is capable to bidirectionally regulate in vitro and in vivo neural activities upon adjusted illumination. When exposed to high-power and short-pulsed light, the Si diode generates photothermal effects, evoking neuron depolarization and enhancing intracellular calcium dynamics. Conversely, low-power and long-pulsed light on the Si diode hyperpolarizes neurons and reduces calcium activities. Furthermore, the Si diode film mounted on the brain of living mice can activate or suppress cortical activities under varied irradiation conditions. The presented material and device strategies reveal an innovated optoelectronic interface for precise neural modulations. TeaserA thin-film, silicon (Si)-based diode device is capable to bidirectionally regulate in vitro and in vivo neural activities.

bioengineering↗

bioRxiv2023

Battery-Free Optoelectronic Patch for Photodynamic and Light Therapies in Treating Bacteria-Infected Wounds

Light therapy is an effective approach for the treatment of a variety of challenging dermatological conditions. In contrast to existing methods involving high doses and large areas of illumination, alternative strategies based on wearable designs that utilize a low light dose over an extended period provide a precise and convenient treatment. In this study, we present a battery-free, skin-integrated optoelectronic patch that incorporates a coil-powered circuit, an array of microscale violet and red light emitting diodes (LEDs), and polymer microneedles (MNs) loaded with 5-aminolevulinic acid (5-ALA). This polymer MNs, based on the biodegradable composite materials of polyvinyl alcohol (PVA) and hyaluronic acid (HA), serves as light waveguides for optical access and a medium for drug release into deeper skin layers. Unlike conventional clinical photomedical appliances with a rigid and fixed light source, this flexible design allows for a conformable light source that can be applied directly to the skin. In animal models with bacterial-infected wounds, the experimental group with the combination treatment of metronomic photodynamic and light therapies reduced 2.48 log10 CFU mL-1 in bactericidal level compared to the control group, indicating an effective anti-infective response. Furthermore, post-treatment analysis revealed the activation of proregenerative genes in monocyte and macrophage cell populations, suggesting enhanced tissue regeneration, neovascularization, and dermal recovery. Overall, this optoelectronic patch design broadens the scope for targeting deep skin lesions, and provides an alternative with the functionality of standard clinical light therapy methods.

bioengineering↗

bioRxiv2023

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, are mainly limited to static contractions and cannot fully represent the dynamic modulation of EMG signals during volitional movements. Here, we overcome these limitations by presenting NeuroMotion, an open-source simulator that provides a full-spectrum synthesis of EMG signals during voluntary movements. NeuroMotion is comprised of three modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, such as muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. Here we also provide representative applications of NeuroMotion. We first show how simulated MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regression. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. NeuroMotion is the first full-spectrum EMG generative model to simulate human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this full-spectrum model will complement experimental approaches and facilitate neuromechanical research. Author summaryNeuromechanical studies investigate how the nervous system and musculoskeletal system interact to generate movements. Such studies heavily rely on simulation models, which provide non-measurable variables to complement the experimental analyses. However, the simulation models of surface electromyography (EMG), the core physiological signal widely used in neuromechanical analyses, are limited to static conditions. We bridged this gap by proposing NeuroMotion, the first full-spectrum EMG simulator that can be used to generate EMG signals during voluntary movements. NeuroMotion integrates a musculoskeletal model, a neural network-based EMG generator, and an advanced motoneuron model. With representative applications of this simulator, we show that it can be used to investigate the variabilities of EMG signals during voluntary movement. We also demonstrate that the synthetic signals generated by NeuroMotion can be used to augment experimental data for regressing joint angles. We expect the functionality provided by NeuroMotion, which is provided open-source, will stimulate progress in neuromechanics.

bioengineering↗

bioRxiv2023

A pseudovirus-based method to dynamically mimic SARS-CoV-2-associated cell-to-cell fusion and transmission

SARS-CoV-2 has caused the global tremendous loss and continues to evolve to generate variants. Entry of SARS-CoV-2 into the host cells is primarily mediated by Spike (S), which binds to the host receptor hACE2 and initiates virus-cell membrane fusion. Cell fusion contributes to viral entry, cell-to-cell transmission and tissue damage in COVID-19 patients. Many reporter assays have been developed to study S-mediated cell fusion by equally coculturing S-expressing cells and hACE2-positive cells. However, these strategies cannot fully simulate cell-to-cell fusion and transmission of SARS-CoV-2 infection, in which virions from a single target cell transmit to the neighbor cells and induce syncytia formation. Here, we design a pseudovirus-based method to dynamically mimic cell-to-cell fusion and transmission of SARS-CoV-2. We coculture a small number of pseudovirus-producing 293FT cells and a large number of hACE2-expressing 293T cells, and demonstrate that a single cell producing S-pseudotyped virions can induce significant syncytia of hACE2-positive cells. This pseudovirus-based method is a powerful tool to screen and estimate potential inhibitors of S-driven syncytia. Moreover, this strategy can also be utilized to explore fusogenic ability of SARS-CoV-2 variants. Together, the pseudovirus-based method we report here will be beneficial to drug screening and scientific research against SARS-CoV-2 or future emerging coronavirus.

microbiology↗