Biology subjects

Qian, X.

Publications and source records attributed to Qian, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Perinatal Reduction of Genetically Aberrant Neurons from Human Cerebral Cortex

Human neurons are postmitotic and long-lived, requiring precise genomic regulation to maintain function over a lifetime. Normal neuronal function is highly dependent on gene dosage, with copy number variants (CNVs) and heterozygous point mutations associated with a host of neurodevelopmental and neuropsychiatric conditions [1-5]. Here, we investigated the landscape of somatic CNVs arising in fetal human brains, and how they change over development, to understand the processes that generate normal neuronal genomes. We sequenced 2,158 single neurons from human postmortem brain using two distinct single cell whole genome sequencing (scWGS) approaches. Tn5-transposase based (TbA) scWGS of 1,327 neurons from 16 individuals ranging in age from gestational week 14 to 90 years old resulted in 8,765 CNVs. Primary template amplification (PTA) was used to assess for CNV from 831 neurons from 12 individuals. Up to 46% of neurons in prenatal cortex showed aberrant genomes, characterized by widespread CNVs of multiple chromosomes, but this proportion reduces by 4-5 fold after birth across the two scWGS approaches (p=7.5x10-6, Fisher meta-analysis). We identified micronuclei in the developing cortex in situ, reflecting chromosomal material missegregated during neurodevelopment [6-8]. Neurons with widespread CNVs were eliminated in the perinatal period, while neurons with smaller CNV burden slowly declined during postnatal aging. CNVs in surviving neurons were depleted for genes that are dosage-sensitive or involved in neurodevelopmental disorders (p<0.05), suggesting selective elimination of neurons with CNVs involving these critical genes. We surveyed 44,861 nuclei with 10X Genomics scATAC/RNAseq and determined that neurons with high CNV burdens also showed abnormal expression of synaptic gene sets, suggesting that abnormal synaptic gene regulation contributes to neuronal elimination. Elimination of defective neuronal genomes during synaptogenesis may represent a critical process of genome quality control and a vulnerable target of factors that contribute to neurodevelopmental disease.

neuroscience↗

bioRxiv2024

Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks

Across the primate cortex, neurons that perform similar functions tend to be spatially grouped together. This biological principle extends to many other species as well, reflecting a common way of organizing sensory processing across diverse forms of life. In the visual cortex, this biological principle manifests itself as a modular organization of neuronal clusters, each tuned to a specific visual property. The tendency toward short connections is widely believed to explain the existence of such an organization in the brains of many animals. However, the neural mechanisms underlying this phenomenon remain unclear. Here, we use artificial deep neural network models to demonstrate that a topographical organization akin to that in the primary, intermediate, and high-level human visual cortex emerges when units in these models are locally laterally connected and their weight parameters are tuned by top-down credit assignment. The emergence of modular organization without explicit topography-inducing learning rules or objective functions challenges their necessity and suggests that local lateral connectivity alone may suffice for the formation of topographic organization across the cortex. Furthermore, the incorporation of lateral connections in deep convolutional networks enhances their robustness to subtle alterations in visual inputs, such as those designed to deceive the model (i.e. adversarial examples), indicating an additional role for these connections in learning robust representations.

neuroscience↗

bioRxiv2024

Metabolomics profiling reveals association of fermented herbal feed additive, growth performance and gut microbiota in piglets

Fermented Chinese medicine (FCM), as a functional feed additive, has been widely recognized to play a significant role in protecting the intestinal health of piglets and enhancing productive performance. However, the relationship between the active components of FCM, gut microbiota, and their beneficial effects on animal performance remains unclear. In this study, metabolomics analysis revealed a significant increase in the main contents of lactic acid and propionic acid in FCM, while most glycosides and their derivatives decreased after three days of microbial fermentation. Subsequently, piglets were fed a basic diet supplemented with 1% FCM, while the control group received only the basic diet. The results indicated a significant increase in feed intake and average daily gain within 14 days (P<0.05) due to FCM supplementation. Additionally, FCM significantly improved feed efficiency from 1.76 to 1.50 (P<0.05). Meanwhile, piglets in the FCM group exhibited significantly lower frequencies of diarrhea and coughing, indicating better health. Moreover, high-throughput sequencing analysis revealed higher abundances of Lactobacillus agilis, Megasphaera elsdenii, Ligilactobacillus, and Veillonellaceae in piglets fed with FCM. In summary, FCM increased the content of active ingredients through microbial fermentation and regulated the intestinal microbiota to improve the health of piglets. FCM offers a promising potential for enhancing production performance and economic efficiency in the livestock industry.

microbiology↗

bioRxiv2024

Combining Scalable Organ Chip Platform with Deep Learning-Based Imaging Analysis for Cancer Therapeutic Screening

Functional precision oncology represents an emerging approach to genomic approaches by testing treatment options directly on patient-derived models. Current assays, including the use of patient-derived xenograft (PDX) and patient-derived organoid (PDOs), faces major barriers in clinical use due to technical challenges, such as standardization, cost, assay time, scalability, and faithful mimicry of patient tumor microenvironment (TME). Here, we introduce an Organ Chip (OC) device constructed entirely from thermoplastic materials, free of porous membrane or other barrier structures, and optimized for high-content imaging (HCI). This automation-compatible device supports tissue-specific extracellular matrices and coculture for a wide spectrum of organ and disease types, including the TME. As a proof-of-concept, we demonstrate the growth of pancreatic, lung, and colon cancer cell lines and primary lung cancer cells and the testing of cancer drugs in these models. HCI-based phenotypic profiling enabled accurate quantification of drug response, with better performance than traditional biochemical assays. Moreover, we developed a deep-learning method for assessing drug responses using bright field images. The integration of a low-cost, scalable, and faithful OC models with automatic high-content image analysis represents a significant stride towards functional precision oncology and cancer drug discovery.

bioengineering↗

bioRxiv2024

Spatial Single-cell Analysis Decodes Cortical Layer and Area Specification

The human cerebral cortex, pivotal for advanced cognitive functions, is composed of six distinct layers and dozens of functionally specialized areas1,2. The layers and areas are distinguished both molecularly, by diverse neuronal and glial cell subtypes, and structurally, through intricate spatial organization3,4. While single-cell transcriptomics studies have advanced molecular characterization of human cortical development, a critical gap exists due to the loss of spatial context during cell dissociation5,6,7,8. Here, we utilized multiplexed error-robust fluorescence in situ hybridization (MERFISH)9, augmented with deep-learning-based cell segmentation, to examine the molecular, cellular, and cytoarchitectural development of human fetal cortex with spatially resolved single-cell resolution. Our extensive spatial atlas, encompassing 16 million single cells, spans eight cortical areas across four time points in the second and third trimesters. We uncovered an early establishment of the six-layer structure, identifiable in the laminar distribution of excitatory neuronal subtypes by mid-gestation, long before the emergence of cytoarchitectural layers. Notably, while anterior-posterior gradients of neuronal subtypes were generally observed in most cortical areas, a striking exception was the sharp molecular border between primary (V1) and secondary visual cortices (V2) at gestational week 20. Here we discovered an abrupt binary shift in neuronal subtype specification at the earliest stages, challenging the notion that continuous morphogen gradients dictate mid-gestation cortical arealization6,10. Moreover, integrating single-nuclei RNA-sequencing and in situ whole transcriptomics revealed an early upregulation of synaptogenesis in V1-specific Layer 4 neurons, suggesting a role of synaptogenesis in this discrete border formation. Collectively, our findings underscore the crucial role of spatial relationships in determining the molecular specification of cortical layers and areas. This work not only provides a valuable resource for the field, but also establishes a spatially resolved single-cell analysis paradigm that paves the way for a comprehensive developmental atlas of the human brain.

neuroscience↗

bioRxiv2024

Grifola frondosa polysaccharide Ameliorates Inflammation and Insulin Resistance by Regulating Macrophage Polarization of liver in Type 2 Diabetes Mellitus Rats

Type 2 diabetes mellitus (T2DM) is a common metabolic disease characterized by a lack of insulin secretion, insulin resistance (IR), and hyperglycemia. Given its high prevalence and multifarious complications, diabetes is globally ranked as the third leading cause of mortality. It is critical to discover efficient medication substitutes in order to lessen the drawbacks and adverse effects of traditional diabetes medication therapy. Previous studies have shown that Grifola frondosa polysaccharide (GFP) have the positive effects in regulating blood glucose and insulin resistance, but the understanding of its regulatory mechanism is still limited. Therefore, this study aimed to investigate the effects of GFP on liver inflammation induced by a high-fat diet (HFD) combined with streptozotocin (STZ) in type 2 diabetic rats and to explore its possible mechanisms. The results showed that GFP intervention reduced weight loss and hyperglycemia symptoms, as well as lowered FINS, HOMA-IR, IPGTT-AUC, and IPITT-AUC to different degrees in T2DM rats. At the same time, after GFP intervention, the secretion level of inflammatory factors (TNF-, IL-1{beta}, MCP-1) was down-regulated and the secretion level of anti-inflammatory factor (IL-10) was up-regulated in the liver tissue of T2DM rats. Furthermore, GFP reduced macrophage infiltration in liver tissue, inhibited macrophage M1-type polarization, and promoted M2-type polarization. These results suggest that GFP intervention could attenuate the hepatic inflammatory response in T2DM rats; possible mechanisms for this effect include hepatic macrophage infiltration and M1/M2 polarization. Summary statementThis study revealed the improvement effect of GFP on hepatic inflammation and insulin resistance in T2DM rats and to explore the possible key roles of hepatic macrophages and their potential mechanisms.

immunology↗

bioRxiv2024

Test Retest Reliability of Meta Analytic Networks During Naturalistic Viewing

Functional connectivity analyses have given considerable insights into human brain function and organization. As research moves towards clinical application, test-retest reliability has become a main focus of the field. So far, the majority of studies have relied on resting-state paradigms to examine brain connectivity, based on its low demand and ease of implementation. However, the reliability of resting-state measures is mostly moderate, potentially due to its unconstrained nature. Recently, naturalistic viewing paradigms have gained popularity because they probe the human brain under more ecologically valid conditions, thereby possibly increasing reliability. Therefore, we here compared the reliability of graph metrics extracted from resting-state and naturalistic viewing in functional networks, across two sessions. We show that naturalistic viewing can increase reliability over resting-state, but that its effect varies between stimuli and networks. Furthermore, we demonstrate that the effect of naturalistic viewing differs between two cohorts with Asian and European cultural backgrounds. Taken together, our study encourages the use of naturalistic viewing to increase reliability, but emphasizes the need to carefully select the appropriate stimulus and network for the respective research question.

neuroscience↗

bioRxiv2024

Biologically Interpretable VAE with Supervision for Transcriptomics Data Under Ordinal Perturbations

AO_SCPLOWBSTRACTC_SCPLOWLatent variable models such as the Variational Auto-Encoders (VAEs) have shown impressive performance for inferring expression patterns for cell subtyping and biomarker identification from transcriptomics data. However, the limited interpretability of their latent variables obscures deriving meaningful biological understanding of cellular responses to different external and internal perturbations. We here propose a novel deep learning framework, EXPORT (EXPlainable VAE for ORdinally perturbed Transcriptomics data), for analyzing ordinally perturbed transcriptomics data that can incorporate any biological pathway knowledge in the VAE latent space. With the corresponding pathway-informed decoder, the learned latent expression patterns can be explained as pathway-level responses to perturbations, offering direct interpretability with biological understanding. More importantly, we explicitly model the ordinal nature of many real-world perturbations into the EXPORT framework by training an auxiliary ordinal regressor neural network to capture corresponding expression changes in the VAE latent representations, for example under different dosage levels of radiation exposure. By incorporating ordinal constraints during the training of our proposed framework, we further enhance the model interpretability by guiding the VAE latent space to organize perturbation responses in a hierarchical manner. We demonstrate the utility of the inferred guided latent space for downstream tasks, such as identifying key regulatory pathways associated with specific perturbation changes by analyzing transcriptomics datasets on both bulk and single-cell data. Overall, we envision that our proposed approach can unravel unprecedented biological intricacies in cellular responses to various perturbations while bringing an additional layer of interpretability to biology-inspired deep learning models.

bioinformatics↗

bioRxiv2024

Structural insights into sphingosine 1-phosphate receptor 4 activation

S1PR4 is one of five subtypes of sphingosine 1-phosphate receptors (S1PRs) that regulate immune cell functioning, with functional distinctions to other subtypes. S1PR1-targeted modulators caused serious cardiac and vascular adverse effects because S1PR1 was expressed throughout the whole body. Since S1PR4 was only expressed in lung and lymphoid cells, S1PR4-targeted modulators might not trigger these side effects. However, the development of S1PR4-specific agonists is greatly hindered because of the lack of activated S1PR4 structure. Here, we resolved cryo-EM structures of activated S1PR4 and revealed the structural mechanism of ligand recognition, receptor activation, and Gi coupling. Our results offered structural templates for the development of selective S1PR4 agonists with improved safety profiles.

biochemistry↗

bioRxiv2024

Using split protein reassembly strategy to optically control PLD enzymatic activity

Phospholipase D (PLD) and phosphatidic acid (PA) play a spatio-temporal role in regulating diverse cellular activities. Although current methodologies enable optical control of the subcellular localization of PLD and by which influence local PLD enzyme activity, the overexpression of PLD elevates the basal PLD enzyme activity and further leads to increased PA levels in cells. In this study, we employed a split protein reassembly strategy and optogenetic techniques to modify superPLD (developed by Jeremy Baskin group). We splited this variants into two HKD domains and fused these domains with optogenetic and chemogenetic elements and by which we achieved control of the two HKD interaction and then restored the PLD enzymatic activity.

cell biology↗

bioRxiv2023

Investigating the validity and interpretability of longitudinal brain age underlying cognition in Asian children and older adults

Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore, most brain age models are trained and tested on cross-sectional data from primarily Caucasian, adult participants. It is thus unclear how well these models generalize to non-Caucasian participants, especially children. Here, we tested a previously published deep learning model on Singaporean elderly participants (55 - 88 years old) and children (4 - 11 years old). We found that the model directly generalized to the elderly participants, but model finetuning was necessary for children. After finetuning, we found that the rate of change in brain age gap was associated with future executive function performance in both elderly participants and children. We further found that lateral ventricles and frontal areas contributed to brain age prediction in elderly participants, while white matter and posterior brain regions were more important in predicting brain age of children. Taken together, our results suggest that there is potential for generalizing brain age models to diverse populations. Moreover, the longitudinal change in brain age gap reflects developing and aging processes in the brain, relating to future cognitive function.

neuroscience↗

bioRxiv2023

Ggnbp2 regulates synaptic development and autophagy in motor neurons

AbstractGenome-wide association studies (GWAS) have identified numerous candidate ALS risk variants, but their cellular functions are often unknown. Recent studies have identified a variant of GGNBP2 that results in increased expression. To better understand how this gene might contribute to disease, we investigated the function of Drosophila Ggnbp2 (dGgnbp2) in motor neurons. Loss of function studies showed that dGgnbp2 is required for motor neuron synaptic development. A human transgene completely rescued these phenotypes indicating that the gene is functionally conserved between humans and flies. Overexpression of dGgnbp2 caused severe locomotor defects in adult flies, consistent with ALS pathology. At the cellular level, dGgnbp2 regulated autophagy, a process commonly defective in ALS. Both overexpression and removal of dGgnbp2 reduced levels of the phosphorylated lipid, PI(3)P, an essential component of autophagosomes. Our study provides strong evidence that Ggnbp2 functions in motor neurons to regulate a cellular process commonly defective in ALS. TeaserThis study investigated the function of the ALS risk variant GGNBP2, in flies, and showed that it regulates autophagy in motor neurons.

neuroscience↗

bioRxiv2023

MUSTANG: MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance

AO_SCPLOWBSTRACTC_SCPLOWSpatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. We here present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on two real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in cellular characterization of tissue samples under the study. MUSTANG is publicly available at at https://github.com/namini94/MUSTANG

bioinformatics↗

bioRxiv2023

Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems

Biological systems often create materials with intricate structures to achieve specialized functions. In comparison, precise control of structures in man-made materials has been challenging. Here, we report a serendipitous discovery of insect cuticle peptides (ICPs) spontaneously forming nanocapsules through a single-step solvent exchange process, where the concentration gradient resulting from mixing of water and aceton drives the localization and self-assembly of the peptides into hollow nanocapsules. The underlying driving force is the intrinsic affinity of the peptides for a particular solvent concentration, while the diffusion of water and acetone creates a gradient interface that triggers peptide localization and self-assembly. This gradient-mediated self-assembly offers a transformative pathway towards next-generation drug delivery systems based on peptide nanocapsules.

bioengineering↗

bioRxiv2023

Rapid Prototyping of Thermoplastic Microfluidic 3D Cell Culture Devices by Creating Regional Hydrophilicity Discrepancy

Microfluidic three-dimensional cell culture devices that enable the recapitulation of key aspects of organ structures and functions in vivo represent a promising preclinical platform to improve translational success during drug discovery. Essential to these engineered devices is the spatial patterning of cells from different tissue types within a confined microenvironment. Traditional fabrication strategies lack the scalability, cost-effectiveness, and rapid prototyping capabilities required for industrial applications, especially for processes involving thermoplastic materials. Here, we introduce an approach to pattern fluid guides inside microchannels by establishing differential hydrophilicity using pressure-sensitive adhesives as masks and a subsequent selective coating with a biocompatible polymer. We identified optimal coating conditions using polyvinylpyrrolidone, which resulted in rapid and consistent hydrogel flow in both the open-chip prototype and the fully bonded device containing additional features for medium perfusion. We tested the suitability of our device for dynamic 3D cell culture by growing human hepatocytes in the device under controlled fluid flow for a 14-day period. Additionally, we demonstrated the potential of using our device for pharmaceutical high-throughput screening applications, such as predicting drug-induced liver injury. Our approach offers a facile strategy of rapid prototyping thermoplastic microfluidic organ chips with varying geometries, microstructures, and substrate materials.

bioengineering↗

bioRxiv2023

cProSite: A web based interactive platform for online proteomics, phosphoproteomics, and genomics data analysis

We developed cProSite, a website that provides online genomics, proteomics, and phosphoproteomics analysis for the data of The National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium (CPTAC). This tool focuses on comparisons and correlations between different proteins and mRNAs of tumors and normal tissues. Our website is designed with biologists and clinicians in mind, with a user-friendly environment and fast search engine. The search results of cProSite can be used for clinical data validation and provide useful strategic information to identify drug targets at proteomic, phosphoproteomic, or genomic levels. The site is available at http://cprosite.ccr.cancer.gov. SignificanceAn interactive database for the expression and correlation for proteins, proteomic phosphorylations, and mRNA levels has been developed for analyzing the molecular alterations between tumors and normal tissues of various tumor types.

bioinformatics↗

bioRxiv2023

Reciprocal polarization imaging of complex media.

The vectorial evolution of polarized light interaction with a medium can reveal its microstructure and anisotropy beyond what can be obtained from scalar light interaction. Anisotropic properties (diattenuation, retardance, and depolarization) of a complex medium can be quantified by polarization imaging by measuring the Mueller matrix. However, polarization imaging in the reflection geometry, ubiquitous and often preferred in diverse applications, has suffered a poor recovery of the mediums anisotropic properties due to the lack of suitable decomposition of the Mueller matrices measured inside a backward geometry. Here, we present reciprocal polarization imaging of complex media after introducing reciprocal polar decomposition for backscattering Mueller matrices. Based on the reciprocity of the optical wave in its forward and backward scattering paths, the anisotropic diattenuation, retardance, and depolarization of a complex medium are determined by measuring the backscattering Mueller matrix. We demonstrate reciprocal polarization imaging in various applications for quantifying complex non-chiral and chiral media, uncovering their anisotropic microstructures with remarkable clarity and accuracy. Reciprocal polarization imaging will be instrumental in imaging complex media from remote sensing to biomedicine and will open up new applications of polarization optics in reflection geometry.

biophysics↗

bioRxiv2023

Single shot dendritic cell targeting SARS-CoV-2 vaccine candidate induces broad and durable systemic and mucosal immune responses

Current COVID-19 vaccines face certain limitations, which include waning immunity, immune escape by SARS-CoV-2 variants, limited CD8+ cellular response, and poor induction of mucosal immunity. Here, we engineered a Clec9A-RBD antibody construct that delivers the Receptor Binding Domain (RBD) from SARS-CoV-2 spike protein to conventional type 1 dendritic cells (cDC1). We showed that single dose immunization with Clec9A-RBD induced high RBD-specific antibody titers with a strong T-helper 1 (TH1) isotype profile and exceptional durability, whereby antibody titers were sustained for at least 21 months post-vaccination. Uniquely, affinity maturation of the antibody response was observed over time, as evidenced by enhanced neutralization potency and breadth across the sarbecovirus family. Consistently and remarkably, RBD-specific T-follicular helper cells and germinal center B cells were still detected at 12 months post-immunization. Increased antibody-dependent cell-mediated cytotoxicity (ADCC) activity of the immune sera was also measured over time with comparable efficacy against ancestral SARS-CoV-2 and variants, including Omicron. Furthermore, Clec9A-RBD immunization induced a durable poly-functional TH1-biased cellular response that was strongly cross-reactive against SARS-CoV-2 variants, including Omicron, and with robust CD8+ T cell signature. Lastly, Clec9A-RBD single dose systemic immunization primed effectively RBD-specific cellular and humoral mucosal immunity in lung. Taken together, Clec9A-RBD immunization has the potential to trigger robust and sustained, systemic and mucosal immune responses against rapidly evolving SARS-CoV2 variants.

immunology↗