Biology subjects

Qian, Y.

Publications and source records attributed to Qian, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Engineered translation release factor 1 suppresses disease-causing nonsense mutations and enables multi-site nonstandard amino acids incorporation

Nonsense mutations convert sense codons into premature termination codons (PTC), resulting in early termination of translation of mRNAs, underlie [~]11% of human genetic diseases. Restoring translation of genes carrying nonsense mutations remains a major therapeutic challenge. Here we present OPENER (Overexpression of Protein-engineered eRF1 for Nonsense Elision and Readthrough), a strategy to efficiently suppress nonsense mutations based on engineered variants of the eukaryotic translation termination factor eRF1. Through saturation mutagenesis of the human eRF1 N-domain, we identified variants, notably eRF1S77E, that promote efficient readthrough of all three PTCs (UAG, UGA, UAA) and rescue 12 pathogenic PTCs reporters in multiple disease contexts. Systemic delivery of OPENER-S77E via AAV8 in a Duchenne muscular dystrophy mouse model (DmdQ995X) restored dystrophin expression, improved muscle function, and ameliorated pathology without detected toxicity. Furthermore, when combined with orthogonal engineered aminoacyl-tRNA synthetase/tRNA pairs, OPENER-S77E enabled efficient, multi-site incorporation of nonstandard amino acids (BocK and AzF) into a single protein in mammalian cells, achieving efficiency of 27.77% and 19.83%, respectively. The incorporation efficiency of BocK was further enhanced to 32.80% in conditional eRF1-knockout cells. OPENER thus provides a platform for treating nonsense-mediated diseases and expands the synthetic biology toolkit for precise protein engineering.

synthetic biology↗

bioRxiv2025

Identification of Vector Organisms and Their Carried Pathogens by Metagenomic Sequencing

Vector surveillance is vital for public health, yet traditional methods often face limitations. This study assessed the use of metagenomic sequencing for analyzing mosquitoes, cockroaches, flies, and rodents from Zhongshan, China. The results revealed a high concordance between metagenomic and Sanger sequencing for species identification (Kappa = 0.667, P = 0.011). Metagenomic analysis identified vector-specific dominant bacteria, including Wolbachia in mosquitoes, Blattabacterium sp. in cockroaches, Bartonella tribocorum and Leptospira interrogans in rodents, and Vagococcus teuberi in flies. It also detected numerous classical pathogens, such as Enterococcus faecalis, L. interrogans, and Klebsiella pneumoniae. Among the identified fungi and viruses, 19 were human pathogens, while seven posed a threat to agriculture, notably Pepper mild mottle virus and Tomato brown rugose fruit virus. These findings systematically characterize the microbial communities associated with various vectors, highlight the potential of metagenomics for broad pathogen screening, and underscore its utility in large-scale vector surveillance and public health protection. ImportanceVectors are major carriers of numerous diseases and present risks fot cross-border transmission. Existing detection methods for vectors and their pathogens remain inefficient, insensitive, and prone to difficulties in species identification. Despite extensive efforts in vector identification, the potential threats posed by their associated pathogens remain unclear, demonstrating the need for more effective diagnostic strategies. Metagenomic sequencing offers a promising solution to this challenge. In this study, we applied metagenomic sequencing to vectors collected from urban and port areas in Zhongshan, China, to evaluate its efficacy in pathogen detection. Overall, our work contributes to a better understanding of the public health risks posed by vector-borne pathogens.

microbiology↗

bioRxiv2025

Epithelial FOXP3 Drives Pancreatic Fibrosis through O-glycosylated IL-6

BACKGROUND & AIMSThe transcription factor FOXP3, known for specifying regulatory T cell fate, is unexpectedly expressed in the epithelium of precancerous pancreatic lesions. Here we investigate its non-immune role in initiating pancreatic fibrosis, a process driving therapeutic resistance and organ failure in pancreatic ductal adenocarcinoma (PDAC) with unknown origins in early neoplasia. METHODSUsing human tissues and genetically engineered mouse models, we analysed FOXP3 expression in premalignant lesions. We employed epithelial-specific FOXP3 knockout and knock-in strategies to determine its functional impact on fibrogenesis and neoplasia progression. Mechanistic studies included chromatin immunoprecipitation, glycomic analyses, and signalling assays. RESULTSFOXP3 was consistently expressed in the epithelial compartment of human and murine precancerous pancreas. Epithelial-specific deletion of FOXP3 attenuated pancreatic fibrosis and delayed neoplasia, whereas its knock-in induced spontaneous stromal activation and accelerated PanIN progression. Mechanistically, epithelial FOXP3 directly transactivated the glycosyltransferase GALNT1. GALNT1, in turn, mediated O-glycosylation of interleukin (IL)-6, which was essential for its rapid secretion. CONCLUSIONSOur study establishes epithelial-derived FOXP3 as a master regulator of early pancreatic fibrocarcinogenesis. It drives a glycosylation-dependent amplification loop for IL-6 signalling, orchestrating sustained stromal activation. This pathway represents a promising target for intercepting pancreatic fibrosis and carcinogenesis at its origin.

cancer biology↗

bioRxiv2025

Cross-species connectome comparisons reveal the network attributes of memory capacity and time series prediction

The brains connectome provides a powerful blueprint for designing efficient neural networks, yet the impact of incorporating its intricate, non-random architecture into machine learning models remains underexplored. Here, we integrate empirical structural connectomes from four model species--fruit fly, mouse, rat, and macaque--as the recurrent layer in echo state networks (ESNs). We demonstrate that biologically realized networks, particularly the macaque connectome, achieve superior performance in chaotic time series prediction and exhibit higher memory capacity compared to randomly shuffled controls. This computational advantage correlates with small-world topology, which scales with phylogenetic level. Crucially, we identify that weakly connected but highly central nodes are essential for optimal network dynamics; their targeted perturbation significantly degrades performance. Furthermore, functional connectomes from Alzheimers disease patients show computational deficits resembling those induced by weak-tie disruption in healthy networks. Our findings establish that evolved connectome topology is fundamental to efficient information processing, providing key principles for bio-inspired artificial intelligence.

neuroscience↗

bioRxiv2025

In Silico Post-screening of Anti-polymerization Agents to Treat Sickle Cell Disease

Sickle cell disease (SCD) is a genetic disorder that affects approximately 100,000 individuals in the United States and millions globally. Although curative therapies, such as stem cell transplant and gene therapy, are available, their application is limited by high cost and donor availability. Conseuently, drug therapy remains the most feasible treatment option for the majority of patients with SCD. To date, only four drugs have been approved by the FDA, but none of these treatments comprehensively address all SCD-related symptoms or crises, suggesting the pressing need for developing new drugs. Several high-throughput screening campaigns have been performed for SCD drug discovery based on the in vitro sickling of red blood cells (RBCs) and they have identified several hits. However, it is challenging to replicate the organ-specific oxygen level and physiological deoxygenation time in these in vitro RBC sickling assays. Furthermore, these assays do not consider the pharmacokinetics (PK) and pharmacodynamics (PD) of the identified drugs, which are essential to determine whether the drugs can provide robust and sustained efficacy in treated patients with SCD. To address these technical gaps, we have developed a computational platform to perform post-screening analysis of potential anti-sickling agents. This platform sequentially combines PK/PD models with a kinetic model of RBC sickling, enabling efficient prediction of the dosage-dependent therapeutic efficacy of various anti-sickling agents based on patient-specific hematological factors and organ-specific oxygen levels. We first demonstrate the effectiveness of our integrated platform by showcasing the therapeutic efficacy of two FDA-approved drugs, Hydroxyurea (HU) and voxelotor. Next, we evaluate the therapeutic efficacy of two potential anti-sickling agents under clinical trial, namely Bitopertin and osivelotor. Our findings suggest that Bitopertin exhibits anti-sickling effects that are considerably less pronounced than those of HU and voxelotor. On the other hand, osivelotor can achieve similar anti-sickling effects as voxelotor with significantly lower doses due to its improved PK properties. Furthermore, we show the versatility of the proposed platform in predicting the anti-sickling effect of multi-agent therapies and evaluate the conse-quences of drug noncompliance. In particular, our analysis indicates that noncompliance with voxelotor may result in rapid increases in RBC sickling, whereas osivelotor is likely to mitigate noncompliance-induced adverse effects due to improved PK properties. We further quantify the relationship between drug dosage and the duration of noncompliance that leads to loss of thera-peutic efficacy for voxelotor and osivelotor, providing guidance for optimizing dosage strategies to reduce the risk associated with noncompliance. In summary, our in silico platform serves as a valuable tool for post-screening analysis of potential anti-sickling agents by considering their PK and anti-sickling efficacy under patient-specific hemoglobin level and organ-specific oxygen level, thereby gaining insights into their potential therapeutic efficacy alone or in combination before clinical trials.

bioengineering↗

bioRxiv2025

PIWIL3-piRNA Pathway Is Essential for Rabbit Oogenesis and Embryogenesis via Broad Regulation of the Transcriptome and Proteome

Female infertility represents a significant challenge in reproductive medicine. Although it is known to be primarily due to oogenic and early embryonic failures, the molecular mechanisms underlying these failures remain elusive. The Piwi-piRNA pathway is crucial for gametogenesis in diverse organisms. Yet, its role in mammalian female fertility is unclear. This is partly because most studies are done in mice, which contain only PIWIL1, PIWIL2, and PIWIL4 that are essential for spermatogenesis but not for oogenesis. PIWIL3 emerges in higher mammals and is highly expressed in human oocytes. Interestingly, female PIWIL3-knockout golden hamsters exhibit only reduced fertility without detectable defects in oogenesis. This has left the function of PIWIL3 in higher mammals, including humans, unexplored. Here, we discovered that PIWIL3 in the rabbit (Oryctolagus cuniculus) shares high homology with human PIWIL3. It is the predominant PIWI protein in oocytes in both species. Using CRISPR-cas9-mediated knockout, we demonstrated that rabbit PIWIL3 is essential for female fertility. Its loss leads to severe defects in oogenesis. Moreover, embryos lacking maternal PIWIL3 arrest developmentally at the 8-cell stage. Mechanistically, rabbit PIWIL3 binds [~]18-nucleotide piRNAs, mirroring the behavior of human PIWIL3, and is critical for piRNA biogenesis. Moreover, it regulates transcriptomic and proteomic landscapes and silences a broad array of transposons during late oogenesis yet activates another set of transposons during early embryogenesis. These findings establish PIWIL3 as a pivotal dual regulator of gene expression and transposon activity, which is essential for oogenesis and early embryogenesis in non-rodent mammals, potentially including humans.

developmental biology↗

bioRxiv2025

Distinct neurogenic pathways shape the diversification and mosaic organization of cortical output channels

The cerebral cortex broadcasts its output to subcortical regions through the projections of diverse extratelencephalic neurons (ETs) derived from either direct (dNG-ETd) or indirect (iNG-ETi) neurogenesis, but the differential contributions of these neurogenic pathways to cortical output organization remain unknown. Establishing a lineage-based viral targeting strategy in mice, we uncover that ETi massively amplifies and diversifies ETd projections across the cortex. Whereas ETd primarily targets the forebrain and midbrain, ETi dominates the hindbrain (e.g., brainstem/spinal action centers, lemniscal sensory stations), hypothalamic, and neuromodulatory regions with cortical area-specific projections. Numerous corticofugal subpopulations derive solely from iNG-ETi. Area-specific spinal projections of ETi but not ETd emerge from the postnatal pruning of an initial cortex-wide population. In the motor cortex, ETd or ETi activation induced distinct movements: either head-trunk, or additionally, coordinated orofacial-forelimb movements, respectively. Thus, two foundational neurogenic pathways with distinct evolutionary history differentially shape the area-specific diversification of cortical output channels.

neuroscience↗

bioRxiv2025

An Adaptive Visuomotor Transformation Reservoir Embedded in the Vertebrate Brain

Adaptive visuomotor transformation requires sensorimotor circuits to generate accurate, robust, and flexible outputs, but its underlying cellular mechanism remains poorly understood. Leveraging a zebrafish mesoscopic connectome, we built a biologically constrained spiking neural network of the optic tectum (OT), a conserved vertebrate center for visuomotor transformation, and drove the model with real retinal inputs. Integrating in silico and biological interrogations, we revealed that the OT functions as a biologically structured reservoir complemented by serotonergic systems to implement adaptive visuomotor transformation. Within the tectal reservoir, inhibitory interneurons with layer-matched axonal arborizations suppress task-unrelated pathways to ensure visuomotor accuracy, while excitatory interneurons with deep-layer terminating axons reinforce task-related pathways to enhance noise robustness. Furthermore, visually responsive OT-projecting serotonergic subsystems reweight competing pathways to confer visuomotor flexibility. Thus, our findings delineate how specific interneuron-type-embodied push-pull-like mechanisms coordinate with serotonergic neuromodulation to drive adaptive visuomotor transformation, offering a mechanistic framework for neural computational architectures.

neuroscience↗

bioRxiv2025

Neural hierarchy for coding articulatory dynamics in speech imagery and production

Mental imagery is a hallmark of human cognition, yet the neural mechanisms underlying these internal states remain poorly understood. Speech imagery--the internal simulation of speech without overt articulation--has been proposed to partially share neural substrates with actual speech articulation. However, the precise feature encoding and spatiotemporal dynamics of this neural architecture remain controversial, constraining the understanding of mental states and the development of reliable speech imagery decoders. Here, we leveraged high-resolution electrocorticography recordings to investigate the shared and modality-specific cortical coding of articulatory kinematic trajectories (AKTs) during speech imagery and articulation. Applying a linear model, we identified robust neural dynamics in frontoparietal cortex that encoded AKTs across both modalities. Shared neural populations across the middle premotor cortex, subcentral gyrus, and postcentral-supramarginal junction exhibited consistent spatiotemporal stability during the integrative articulatory planning. In contrast, modality-specific populations for speech imagery and articulation were somatotopically interleaved along the primary sensorimotor cortex, revealing a hierarchical spatiotemporal organization distinct from shared encoding regions. We further developed a generalized neural network to decode multi-population neural dynamics. The model achieved high syllable prediction accuracy for speech imagery (79% median accuracy), closely matching the performance of speech articulation (81%). This model robustly extrapolated AKT decoding to untrained syllables within each modality while demonstrating cross-modal generalization across shared populations. These findings uncover a somato-cognitive hierarchy linking high-level supramodal planning with modality-specific neural manifestation, revolutionizing an imagery-based brain-computer interface that directly decodes thoughts for synthetic telepathy.

neuroscience↗

bioRxiv2025

Large-scale single-neuron recording using the uFINE array in human cortex

Monitoring neural population activity at single-cell resolution is essential for driving fundamental research and clinical innovations. However, translating advanced recording techniques from animal models to humans remains a significant challenge. Flexible neural electrodes have recently emerged as powerful tools for large-scale single-unit recordings due to their superior biocompatibility and high recording density. Here, we demonstrate reliable, high-density single-unit recordings during intraoperative procedures in human patients using ultra-Flexible Implantable Neural Electrode (uFINE) arrays. The uFINE array exhibited sufficient mechanical robustness to maintain structural integrity throughout surgical operations. We successfully recorded 616 single units from 10 patients, with up to 135 single units simultaneously recorded. The flexibility of uFINE array minimized signal disturbances from brain pulsations, enabling stable and continuous single-unit detection. Stimulus and response tuning were observed at the level of individual neurons in awake patients. This uFINE-based recording approach offers unique opportunities to investigate human-specific cognitive functions and develop next-generation brain-machine interfaces.

bioengineering↗

bioRxiv2025

High-efficiency likelihood inference of shared proteomic architectures across 50 complex human traits

Advancements in genetic correlation estimation have elucidated genome-wide pleiotropys influence on phenotypic correlations among human complex traits and diseases. However, the role of proteomic domains in these correlations remains underexplored. Traditional genetic correlation analysis assumptions, including the minute effects of SNPs and their linkage disequilibrium, do not suit proteomic data. We present a novel method, Likelihood-based Estimation for Proteomic Correlation (LEAP), tailored to provide unbiased estimation of shared proteomic architectures between trait pairs. LEAP notably decreases computational demands by approximately 1000-fold compared to conventional bivariate linear mixed models. We applied LEAP to data from the UK Biobank Pharma Proteomics Project, identifying 585 significant proteomic correlations among 1,225 pairs of 50 biochemical, anthropometric, and behavioral traits. Furthermore, we quantified the distinct proteomic and genetic contributions to phenotypic correlations, highlighting significant gender differences. This study provides a comprehensive computational approach for proteomic correlation estimation, clarifying the specific roles of genomics and proteomics in complex trait correlations. Our findings not only advance the understanding of proteomic contributions to phenotypic traits but also suggest potential applications for evaluating shared omics architectures in other domains such as transcriptomics and metabolomics.

bioinformatics↗

bioRxiv2025

Gene expression plasticity in the antibiotic environment is predominantly adaptive in the budding yeast

Phenotypic plasticity plays a key role in adaptation to fluctuating environments. However, its evolutionary significance remains debated, with conflicting views on whether it is actively maintained by natural selection or a neutral consequence of molecular constraints. In this study, we investigate the evolutionary role of gene expression plasticity in yeast populations exposed to antibiotic and osmotic stress. Using mutation accumulation (MA) lines to separate the effects of natural selection from genetic drift, we compare gene expression responses (referred to as plastic change) between 22 natural strains, 21 MA lines, and their progenitor under non-stressed and stressed conditions. Our results show that, in the antibiotic environment, gene expression plasticity is selectively maintained, as indicated by its reduction in magnititude, reversal in directionality, and loss of stress-responsive pathways in MA lines. In contrast, plasticity in the osmotic stress condition appears neutral, with random variation across MA lines. This study provides direct evidence for the adaptive role of gene expression plasticity in the antibiotic environment and sheds light on its molecular mechanisms.

evolutionary biology↗

bioRxiv2025

Charting Postnatal Heart Development Using In Vivo Single-Cell Functional Genomics

The transition at birth, marked by increased circulatory demands and rapid growth, necessitates extensive remodeling of the hearts structure, function, and metabolism. This transformation requires precise spatial and temporal coordination among diverse cardiac cell types; central to this process is cardiomyocyte maturation, yet the regulatory mechanisms driving these changes remain poorly understood. Here, we present a temporal and spatial atlas of postnatal hearts by integrating single-nucleus transcriptomics with image-based spatial transcriptomics, which uncovers the dynamic regulatory networks of cardiomyocyte maturation. To functionally interrogate candidate regulators in vivo, we developed Probe-based Indel-detectable Perturb-seq (PIP-seq), a high-throughput platform that uses probe-based chemistry to directly capture sgRNA expression, perturbation status, and transcriptomic profiles at single-nucleus resolution. Applying PIP-seq to postnatal cardiac development identified 21 novel regulators of cardiomyocyte maturation, highlighting critical nodal points in this process. Our study establishes a high-resolution framework for dissecting postnatal heart development, underscoring the integrative and highly ordered roles of microenvironment and intercellular communication in cardiomyocyte maturation. Importantly, PIP-seq enables systematic, high-throughput exploration of gene function and networks underlying complex biological processes in their native in vivo context.

developmental biology↗

bioRxiv2025

BayesCurveFit: Enhancing Curve Fitting in Drug Discovery with Bayesian Inference

Accurate curve fitting is central to quantifying dose-response relationships in drug discovery. However, commonly used regression-based methods are ill-suited for early prescreening assays, where measurements are sparse and experimental noise can dominate. Bayesian approaches have been developed previously, but they are rarely optimized for such data-limited, noise-prone conditions. We present BayesCurveFit, a Bayesian framework specifically designed for robust dose-response inference under data scarcity. The workflow integrates calibrated initialization, stochastic optimization, adaptive posterior sampling, and probabilistic mixture modeling within a unified Bayesian pipeline, enabling reliable parameter estimation and uncertainty quantification from few observations. By modeling residuals with a Gaussian-Laplace hybrid distribution, BayesCurveFit remains robust to outliers where ordinary least squares and conventional regression methods fail. Simulation studies and real screening benchmarks demonstrate that BayesCurveFit outperforms state-of-the-art regression-based methods in recovering true dose-response relationships under limited sampling. In addition, a Bayesian measure of significance - posterior error probability - offers interpretable probabilistic confidence for response classification. Together, these features establish a general and easy-to-use Bayesian framework for analyzing dose-response data in early screening experiments.

bioinformatics↗

bioRxiv2025

Inferring Latent Behavioral Strategy from the Representational Geometry of Prefrontal Cortex Activity

Behavioral tasks can be solved employing various strategies. Sometimes, different strategies result in the same observable behavior, making them latent. In this study, we infer the latent behavioral strategy used by monkeys in a working memory updating task by comparing the representational geometry of two prefrontal regions --the lateral prefrontal cortex (LPFC) and the prearcuate cortex (PAC)--with that of recurrent neural network (RNN) models trained to solve the task using different strategies. We found that neural activity patterns in both LPFC and PAC align with only one of the proposed strategies, suggesting that monkeys employ this latent strategy to perform the task. These findings open avenues for investigating the processes that lead to strategy learning and the decision-making mechanisms that determine which strategies are chosen when multiple options are available.

neuroscience↗

bioRxiv2025

Reusing wasted bioresources for crop protection: Calcinated oyster shell powder enhances rhizospheric microbial-mediated suppression of root-knot nematodes

The root-knot nematode (RKN) Meloidogyne incognita is one of the most destructive plant-parasitic nematodes (PPNs) affecting crop production worldwide. Our earlier study revealed that calcinated oyster shell powder (OSP) effectively suppressed tobacco RKN disease. However, the suppression mechanism of OSP against RKNs remain unknown. Our study revealed that calcinated OSP reduced the tobacco root-knot index by more than 38% by inhibiting the migration of second-stage juveniles of Meloidogyne incognita (J2) in the soil. Furthermore, OSP reduced the J2 density in the tobacco rhizosphere by 43.69% and significantly increased the soil pH by 0.68; moreover, OSP increased the contents of soil exchangeable calcium (ExchCa) and exchangeable magnesium (ExchMg) by more than 50%. Moreover, soil properties, including ExchMg, ExchCa and pH, enhanced the microbial-mediated suppression of J2. Specifically, Proteobacteria and Gemmatimonadota dominated the microbial community suppressing RKN, and fungal richness contributed to the suppression of RKNs; in addition, Chloroflexi and Acidobacteria dominated the microbial community promoting RKN prosperity. Our study revealed that the reuse of wasted oyster shell powder as an innovative antagonist is a promising avenue for ecofriendly RKN management strategies. IMPORTANCEThere is increased demand for ecofriendly RKN management strategies, and this study is the first to propose that the reuse of wasted oyster shell powder as an innovative antagonist against RKNs is effective, and calcinated oyster shell powder can reduce tobacco root-knot index and Meloidogyne incognita (J2) density by inhibiting the migration of J2s and enhancing the microbially mediated suppression of J2s in the tobacco rhizosphere. Moreover, several soil microbial and physicochemical indicators, including the contents of soil exchangeable calcium, exchangeable magnesium, pH and the relative abundance of Proteobacteria, Gemmatimonadota, Chloroflexi and Acidobacteria in plant rhizosphere were suggested to guide the development of ecofriendly PPN management strategies.

microbiology↗

bioRxiv2025

A data-driven modeling framework for mapping genotypes to synthetic microbial community functions

Microbial communities play a central role in transforming environments across Earth, driving both physical and chemical changes. By harnessing these capabilities, synthetic microbial communities, assembled from the bottom up, offer valuable insights into the mechanisms that govern community functions. These communities can also be tailored to produce desired outcomes, such as the synthesis of health-related metabolites or nitrogen fixation to improve plant productivity. Widely used computational models predict synthetic community functions using species abundances as inputs, making it impossible to predict the effects of species not included in the training data. We bridge this gap using a data-driven community genotype function (dCGF) model. By lifting the representation of each species to a high-dimensional genetic feature space, dCGF learns a mapping from community genetic feature matrices to community functions. We demonstrate that dCGF can accurately predict communities in a fixed environmental context that are composed in part or entirely from new species with known genetic features. In addition, dCGF facilitates the identification of species roles for a community function and hypotheses about how specific genetic features influence community functions. In sum, dCGF provides a new data-driven avenue for modeling synthetic microbial communities using genetic information, which could empower model-driven design of microbial communities.

systems biology↗

bioRxiv2024

Simple and rapid lectin-based detection of native glycoRNAs

The current detection methods for glycoRNAs include metabolic labeling of living cells or animals and rPAL that directly detects native glycoRNAs by periodate oxidation and aldehyde ligation. Both of them have some limitations. Here we reported a simple and rapid detection of native glycoRNAs by using lectins. The method involves in several simple procedures: isolation of total RNA, northern blotting and detection with lectins. The advantages of this method include high sensitivity, very simple procedures and broader applications. We used this method detecting glycoRNA expression in the RNA samples from different species. We also detect the glycoRNA expression across the varieties of mouse and human tissues and compared with other detection methods. We are in the first to detect free glycoRNAs in the varieties of human biofluids. Overall, this simple and rapid method will provide a new tool for study of glycoRNA biology and clinical diagnosis in future.

biochemistry↗