Biology subjects

Zou, Q.

Publications and source records attributed to Zou, Q..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

High-Parameter Spatial Multi-Omics through Histology-Anchored Integration

Spatial omics face challenges in achieving high-parameter, multi-omics co-profiling. Serial-section profiling of complementary panels mitigates technical trade-offs but introduces the spatial diagonal integration problem. To address this, we present SpatialEx and its extension SpatialEx+, computational frameworks leveraging histology as a universal anchor to integrate spatial molecular data across tissue sections. SpatialEx combines a pre-trained H&E foundation model with hypergraph and contrastive learning to predict single-cell omics from histology, encoding multi-neighborhood spatial dependencies and global tissue context. SpatialEx+ further introduces an omics cycle module that encourages cross-omics consistency via slice-invariant mappings, enabling seamless integration without co-measured training data. Extensive validations show superior H&E-to-omics prediction, panel diagonal integration, and omics diagonal integration across various biological scenarios. The frameworks scale to datasets exceeding one million cells, maintain robustness with non-overlapping or heterogeneous sections, and support unlimited omics layers in principle. Our work makes multi-modal spatial profiling broadly accessible.

bioinformatics↗

bioRxiv2024

Increased versatility and convenience: advances and strategy optimization of ReMOT-mediated genetic modification in insects

Genetic modification via gene editing has become a widely adopted and demonstrably effective method in functional gene research within entomology. However, the optimal efficiency and simplicity of delivering exogenous guide RNA-clustered regularly interspaced short palindromic repeats-associated protein 9 (gRNA-Cas9) complexes into target tissues are crucial for successful gene editing. The Receptor-Mediated Ovary Transduction of Cargo (ReMOT) strategy, which simplifies the delivery process, target site selection, technical requirements, and delivery cost compared with embryonic microinjection, enabling efficient editing at the germline level, is gaining increasing attention. Although the feasibility and advantages of this technique have been demonstrated in various insect species, further optimization of operational details and the overcoming of further bottlenecks are still required. This review focuses on advances in developing ReMOT as a valuable technology, exploring its applicability, rationale for selecting the ovary as a delivery target site, factors influencing its efficiency, and recommendations for improvement. The versatility and effectiveness of ReMOT make it a promising method for researchers looking to make precise genetic modifications with greater ease and efficiency.

genetics↗

bioRxiv2024

Bridging the Dimensional Gap from Planar Spatial Transcriptomics to 3D Cell Atlases

Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation using real 3D ST and independent serial sectioning datasets demonstrates that SpatialZ accurately reconstructs virtual slices while preserving cell identities, gene expression patterns, and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells, a scale not feasible experimentally. This dense atlas enables unprecedented capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, and 3D mapping of query tissue sections. While currently validated for spatial transcriptomics, the underlying principles of SpatialZ could potentially be adapted for spatial proteomics, spatial metabolomics, and even spatial multi-omics. Validated through internal and external testing, our computationally generated atlas maintains biological accuracy, providing unprecedented resolution of spatial molecular landscapes and demonstrating the potential of computational approaches in advancing 3D ST.

bioinformatics↗

bioRxiv2024

NicheTrans: Spatial-aware Cross-omics Translation

Spatial omics technologies have revolutionized our studies on tissue architecture and cellular interactions at single-cell resolution. While spatial multi-omics approaches offer unprecedented insights into complex biological systems, their widespread adoption is hindered by technical challenges, specialized requirements, and limited accessibility. To address these limitations, we present NicheTrans, the first spatially-aware cross-omics translation method and a flexible Transformer-based multi-modal deep learning framework. Unlike existing single-cell (non-spatial) translation methods, NicheTrans uniquely incorporates both cellular microenvironment information and flexible integration of multi-modal data, such as morphology and prior knowledge. We validated NicheTrans across diverse biological cases: Parkinsons Disease (PD), Alzheimers Disease (AD), breast cancer, and lymph nodes. Our approach demonstrated superior performance compared to existing single-cell methods, highlighting the crucial role of spatial and multi-modal information in cross-omics translation. Through NicheTrans, we uncovered spatial multi-omics domains that were not detectable through single-omics analysis alone. Model interpretation revealed key molecular relationships, including gene programs associated with dopamine metabolism and amyloid {beta}-associated cell states. Additionally, using translated protein markers as spatial landmarks, we quantified the spatial organization of key glial cell subtypes in the AD brain. NicheTrans represents a powerful tool for generating comprehensive spatial multi-omics insights from more accessible single-omics measurements, making multi-omics analysis more feasible for the broader research community.

bioinformatics↗

bioRxiv2024

Machine learning engineered PoLixNano nanoparticles overcome delivery barriers for nebulized mRNA therapeutics

There continues to be a dearth of competent inhalable mRNA delivery although it holds great potential for addressing a wide variety of refractory diseases. The huge advances seen with parenteral-administered lipid nanoparticle (LNP) have not been translated into nebulized mRNA delivery due to the aggressive nebulization process and insurmountable barriers inherent to respiratory mucosa. Here, we show amphiphilic block copolymers revealed by machine learning (ML) can spontaneously form stabilized nanoparticles (PoLixNano) with the lipids components of LNP and simultaneously impart the PoLixNano with "shield" (shear force-resistant) and "spear" (pulmonary barriers-penetrating abilities) capabilities. We present a ML approach that leverages physicochemical properties and inhaled mRNA transfection profiles of a chemically diverse library of polymeric components to validate the integration of "shield" and "spear" properties as highly predictive indicators of transfection efficiency. This quantitative structure-mRNA transfection prediction (QSMTP) model identifies top-performing amphiphilic-copolymers from more than 10000 candidates and suggests their mucus-penetrating ability outweights the shear force-resistant property in contributing to efficient mRNA transfection. The optimized PoLixNano substantially outperforms the LNP counterpart and mediates up to 1114-times higher levels of mRNA transfection in animal models with negligible toxicities. The PoLixNano promotes overwhelming SARS-CoV-2 antigen-specific sIgA antibody secretion and expansion of TRM cells which collectively confers 100% protection in mice against lethal SARS-CoV-2 challenges and blocks the transmission of Omicron variant between hamsters. PoLixNano also displays versatile therapeutic potential in lung carcinoma and cystic fibrosis models. Our study provides new insights for designing delivery platforms of aerosol-inhaled mRNA therapeutics with clinical translation potential.

synthetic biology↗

bioRxiv2024

Human Brain-Wide Activation of Sleep Rhythms

During sleep, our brain undergoes highly synchronized activity, orchestrated by distinct neural rhythms. Little is known about the associated brain activation during these sleep rhythms, and even less about their functional implications. In this study, we investigated the brain-wide activation underlying human sleep rhythms by employing simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) in 107 participants during nocturnal nap (first half of the night). We identified robust coupling between slow oscillations (SOs) and fast spindles during deep non-rapid eye movement (NREM) sleep (N2/3 stages), with spindle peaks consistently occurring just before the SO UP-state. This SO-spindle coupling was linked to elevated activation in both the thalamus and hippocampus, alongside increased functional connectivity from the hippocampus to the thalamus and from the thalamus to the medial prefrontal cortex (mPFC). An open-ended cognitive state decoding analysis suggested that these activations may relate to episodic memory processes, yet were distinct from task-related networks. Together, these findings highlight the thalamus as a key coordinator of hippocampal-cortical communication during sleep and provide new insights into the mechanisms by which synchronized sleep rhythms may support memory consolidation.

neuroscience↗

bioRxiv2024

NSP4 mutation T492I drives rapid evolution of SARS-CoV-2 toward Omicron

T492I, a mutation encountered in SARS-CoV-2 nonstructural protein 4 (NSP4), enhances viral replication and alters nonstructural protein cleavage, causing potential evolutionary impacts. Through comprehensive comparative analyses based on evolve-and-resequence experiments of SARS-CoV-2 wild-type and Delta strains with or without T492I, we demonstrate that NSP4 T492I not only increases the mutation rate, but also accelerates the emergence of many mutations characteristic for Omicron variants. Accordingly, viral populations that evolved from ancestors with T492I, show Omicron-biased selective forces and increases in viral replication, infectivity, immune evasion capacity, potentials for cross-species transmission and receptor-binding affinity. Aside from enhanced replication, we observed stronger epistasis regarding viral replication and infectivity in T492I than in S N501Y and NSP6 {Delta}SGF; this facilitates the regulation of mutation types, which can drive fast evolution of Omicron specific mutations. Our results highlight the role of an important replication-enhancing mutation in regulating the evolutionary rates and mutational trends of SARS-CoV-2.

evolutionary biology↗

bioRxiv2024

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for the characterizing and understanding of tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multi-modal SRT data. We introduce a multi-modal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks (GCN) and self-attention module for deep embedding of features within unimodal and incorporates similarity contrastive learning for integrating features across modalities. Comprehensive benchmark analysis on various types of spatial data shows superior performance of stMMR in multiple analyses, including spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. In chicken heart development, stMMR reconstruct the spatiotemporal lineage structures indicating accurate developmental sequence. In breast cancer and lung cancer, stMMR clearly delineated the tumor microenvironment and identified marker genes associated with diagnosis and prognosis. Overall, stMMR is capable of effectively utilizing the multi-modal information of various SRT data to explore and characterize tissue architectures of homeostasis, development and tumor.

bioinformatics↗

bioRxiv2023

Robust and Adaptive Deep Model Ensemble Framework Fine-tuned by Structural Information for Drug-Target Interactions

In the fields of new drug development and drug repositioning, drug-target interactions (DTI) play a pivotal role. Although deep learning models have already made significant contributions in this domain, the state-of-the-art models still exhibit shortcomings in predictive performance and issues of false-negative errors. Based on these observations, we constructed a streamlined yet effective base learner model. With our designed adaptive feature weight network, the model can capture key features within drugs (targets). Furthermore, by cross-partitioning the training data, multiple base learners are integrated into a powerful ensemble model named EADTN. The performance of the model is further enhanced as the number of base learners increases. Additionally, we employed a single-linkage clustering algorithm to cluster drugs and proteins and leveraged this clustering information to fine-tune the base learners, which elevates the value of EADTN in real-world applications like drug repositioning and targeted drug development. Our designed substructure importance ranking method also demonstrates the models exceptional capability to recognize key substructures. Benefiting from the models low generalization error capability, we successfully identified false-negative samples within the dataset, revealing new interaction relationships. Experimental results indicate that EADTN consistently outperforms existing state-of-the-art models across multiple datasets. More importantly, the ensemble learning and clustering fine-tuning approaches adopted by our model offer a fresh perspective for related fields.

bioinformatics↗

bioRxiv2023

Elevated ENO2 Disrupts VSMCs Homeostasis Facilitating the Development of Aortic Dissection

OBJECTIVEAortic dissection (AD) is a dangerous cardiovascular disease. However, its regulatory mechanism remains poorly understood. Autophagy is an important pathway for maintaining cellular homeostasis. Recent studies have shown that glycolysis and autophagy are essential for the development of AD. Elevated glycolysis can promote cells autophagy, but the relationship between the two is unclear. Enolase 2 (ENO2) is a glycolytic enzyme. Here, we investigated whether ENO2 can regulate autophagy in vascular smooth muscle cells (VSMCs) involved in the progression of AD. APPROACH AND RESULTSThe levels of ENO2 and autophagy-related proteins in aortic tissues from AD patients were identified by qRT-PCR, Western blot and IHC. We found that autophagosome clearance was impaired in aortic tissues and positively correlated with elevated ENO2 expression. Furthermore, aortic application of smooth muscle-specific adeno-associated virus (AAV) inhibiting ENO2 expression attenuated the development of AD in mice. in vitro experiments, downregulation of ENO2 partially restored PDGF-BB-induced impairment of autophagic flux, as evidenced by reduced expression of Beclin-1, SQSTM1, LC3BII/I and increased levels of LAMP2 in human aortic vascular smooth muscle cells (HAVSMCs). Similarly, increased levels of ENO2 exacerbated the autophagic dysfunction in HAVSMCs. Mechanistically, ENO2 may block autophagic flux through the ENO2-GAP43-ATF4 pathway and disrupt cellular homeostasis. CONCLUSIONSOur data reveal a perturbing role of ENO2 in the homeostasis of VSMCs, suggesting ENO2 as a potential target for intervention in AD.

molecular biology↗

bioRxiv2023

Single-cell transcriptome analysis on the anatomic positional heterogeneity of pig skin

Different anatomic locations of the body skin dermis come from different origins, and its positional hereditary information can be maintained in adults, while highly resolvable cellular specialization is less well characterized in different anatomical regions. Pig is regarded as excellent model for human research in view of its similar physiology to human. In this study, we performed single-cell RNA sequencing of six different anatomical skin regions from the Chenghua pig with superior skin thickness trait. We obtained 215,274 cells, representing seven cell types, among which we primarily characterized the heterogeneity of smooth muscle cells, endothelial cells and fibroblasts. We identified several phenotypes of smooth muscle cell and endothelial cell and presented genes expression of pathways such as the immune response in different skin regions. By comparing differentially expressed fibroblast genes among different skin regions, we considered TNN, COL11A1, and INHBA as candidate genes for facilitating ECM accumulation. These findings of heterogeneity in the main three cell types from different anatomic skin sites will contribute to a better understanding of hereditary information and places the potential focus on skin generation, transmission and transplantation, paving the foundation for human skin priming.

cell biology↗

bioRxiv2023

Identification and Characterization of a Novel Major Facilitator Superfamily (MFS) Efflux Pump SA09310 Mediating Tetracycline Resistance in Staphylococcus aureus

Drug efflux systems have recently been recognized as an important mechanism of multidrug resistance in bacteria. Here, we described the identification and characterization of a novel chromosomally encoded multidrug efflux pump (SA09310) in Staphylococcus aureus. SA09310 is a 43-kDa protein with 12 transmembrane helices. The conserved amino acid sequence motifs of the major facilitator superfamily (MFS) were identified in the protein SA09310, which indicated SA09310 belonged to MFS transporters. Expression of the sa09310 gene was induced by different types of antibiotics, including aminoglycoside, tetracycline, macrolides, and chloramphenicol. The sa09310 gene knockout mutant ({Delta}sa09310) was constructed, and its susceptibility to 30 different antibiotics was evaluated. Mutant{bigtriangleup} sa09310 exhibited increased sensitivity to tetracycline and doxycycline, with 64-fold and 8-fold decreased MICs, respectively. The mechanism of SA09310 mediating tetracycline resistance was demonstrated by its ability to extrude intracellular tetracycline from within the cells into the environment. The efflux activity of SA09310 was further confirmed by EtBr accumulation and efflux assays. In addition, the efflux activity of SA09310 was observed to be blocked by the known efflux pump inhibitor carbonyl cyanide-chlorophenylhydrazone (CCCP), which provided direct evidence that suggested the H+-dependent activity of SA09310 efflux pump. The conservation of SA09310 homologs in Staphylococcus indicated the universal function of these SA09310-like protein clusters. In conclusion, the function-unknown protein SA09310 has been identified and characterized as a tetracycline efflux pump, thereby mediating tetracycline resistance in S. aureus.

microbiology↗

bioRxiv2022

DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization, and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis, functional sequential region discovery, etc. Additionally, DeepBIO supports 9 base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust, and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists, and provide meaningful functional insights at both sequence-level and base-level from biological sequences alone. DeepBIO is publicly available at http://inner.wei-group.net/DeepBIO.

bioinformatics↗

bioRxiv2022

Cellular Harmonics for the Morphology-invariant Analysis of Molecular Organization at the Cell Surface

The spatiotemporal organization of membrane-associated molecules is central to the regulation of the vast signaling network that control cellular functions. Powerful new microscopy techniques enable the 3D visualization of the localization and activation of these molecules. However, quantitatively interpreting and comparing the spatial organization of molecules on the 3D cell surface remains challenging because cells themselves vary greatly in their morphology. Here, we introduce u-signal3D, a framework to assess the spatial scales of molecular organization at the cell surface in a cell-morphology invariant manner. We validated our framework by analyzing both synthetic polka dot patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the frameworks versatility, we further compared the spatial organization of cell surface signaling both within and between cell populations and powered an upstream machine-learning based analysis of signaling motifs. U-signal3D is open source and is available at https://github.com/DanuserLab/u-signal3D.

bioinformatics↗

bioRxiv2022

PHAT: interpretable prediction of peptide secondary structures using hypergraph multi-head attention network and transfer learning

Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, we propose PHAT, a deep graph learning framework for the prediction of peptide secondary structures. The framework includes a novel interpretable deep hypergraph multi-head attention network that uses residue-based reasoning for structure prediction. Our algorithm can incorporate sequential semantic information from large-scale biological corpus and structural semantic information from multi-scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. Our interpretable models are able to highlight the reasoning of structural feature representations and the classification of secondary substructures. We further demonstrate the importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis, highlighting the versatility of our models. To facilitate the use of our model, we establish an online server which is accessible via http://inner.wei-group.net/PHAT/. We expect our work to assist in the design of functional peptides and contribute to the advancement of structural biology research.

bioinformatics↗

bioRxiv2022

Respiratory mucosal vaccination of peptide-poloxamine-DNA nanoparticles provides complete protection against lethal SARS-CoV-2 challenge

The ongoing SARS-CoV-2 pandemic represents a brutal reminder of the continual threat of mucosal infectious diseases. Mucosal immunity may provide robust protection at the predominant sites of SARS-CoV-2 infection. However, it remains unclear whether respiratory mucosal administration of DNA vaccines could confer protective immune responses against SARS-CoV-2 challenge due to the insurmountable barriers posed by the airway. Here, we applied self-assembled peptide-poloxamine nanoparticles with mucus-penetrating properties for pulmonary inoculation of a COVID-19 DNA vaccine (pSpike/PP-sNp). Not only displays the pSpike/PP-sNp superior gene-transfection and favorable biocompatibility in the mouse airway, but pSpike/PP-sNp promotes a tripartite immunity consisting of systemic, cellular and mucosal immune responses that are characterized by mucosal IgA secretion, high levels of neutralizing antibodies, and resident memory phenotype T-cell responses in the lungs of mice. Most importantly, pSpike/PP-sNp completely eliminates SARS-CoV-2 infection in both upper and lower respiratory tracts and enables 100% survival rate of mice following lethal SARS-CoV-2 challenge. Our findings indicate PP-sNp might be a promising platform in mediating DNA vaccines to elicit all-around mucosal immunity against SARS-CoV-2.

immunology↗

bioRxiv2021

Reorganizations of latency structure within white matter from wakefulness to sleep

Previous studies based on resting-state fMRI (rsfMRI) data have revealed the existence of highly reproducible latency structure, reflecting the propagation of BOLD fMRI signals, in white matter (WM). Here, based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data collected from 35 healthy subjects who were instructed to sleep, we explored the alterations of propagations in WM across wakefulness and nonrapid eye movement (NREM) sleep stages. Lagged cross-covariance was computed among voxel-wise time series, followed by parabolic interpolation to determine the actual latency value in-between. In WM, regions including cerebellar peduncle, internal capsule, posterior thalamic radiation, genu of corpus callosum, and corona radiata, were found to change their temporal roles drastically, as revealed by applying linear mixed-effect model on voxel-wise latency projections across wakefulness and NREM sleep stages. Using these regions as seeds, further seed-based latency analysis revealed that variations of latency projections across different stages were underlain by inconsistent temporal shifts between each seed and the remaining part of WM. Finally, latency analysis on resting-state networks (RSNs), obtained by applying k-means clustering technique on group-level functional connectivity matrix, identified a path of signal propagations similar to previous findings in EEG during wakefulness, which propagated mainly from the brainstem upward to internal capsule and further to corona radiata. This path showed inter-RSN temporal reorganizations depending on the paired stages between which the brain transitioned, e.g., it changed, between internal capsule and corona radiata, from mainly unidirectional to clearly reciprocal when the brain transitioned from wakefulness to N3 stage. These findings suggested the functional role of BOLD signals in white matter as a slow process, dynamically modulated across wakefulness and NREM sleep stages, and involving in maintaining different levels of consciousness and cognitive processes.

neuroscience↗

bioRxiv2021

Vaccination-induced rapid protection against bacterial pneumonia via training alveolar macrophage in mice

Vaccination strategies for rapid protection against multidrug-resistant bacterial infection are very important, especially for hospitalized patients who have high risk of exposure to these bacteria. However, few such vaccination strategies exist due to a shortage of knowledge supporting their rapid effect. Here we demonstrated a single intranasal immunization of inactivated whole cell (IWC) of Acinetobacter baumannii elicits rapid protection against A. baumannii-infected pneumonia via training of innate immune response in Rag1-/- mice. Immunization-trained alveolar macrophages (AMs) showed enhanced TNF- production upon restimulation. Adoptive transfer of immunization-trained AMs into naive mice mediated rapid protection against infection. Elevated TLR4 expression on vaccination-trained AMs contributed to rapid protection. Moreover, immunization-induced rapid protection was also seen in Pseudomonas aeruginosa and Klebsiella pneumoniae pneumonia models, but not in Staphylococcus aureus and Streptococcus pneumoniae model. Our data reveal that a single intranasal immunization induces rapid and efficient protection against certain Gram-negative bacterial pneumonia via training AMs response, which highlights the importance and the possibility of harnessing trained immunity of AMs to design rapid-effecting vaccine.

immunology↗