Biology subjects

Guan, W.

Publications and source records attributed to Guan, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Transdermal diagnosis and therapy using an integrated acoustofluidic patch

Transdermal biosensing and drug delivery provide convenient, pain-free, and user-friendly solutions for personalized therapy. However, challenges remain in detecting and treating acute diseases, primarily due to current limitations in effective detection of transdermal biomarkers and administration of therapeutics to an individual within a short time window in a real-world environment. Here, we integrate a compact acoustofluidic patch for transdermal sampling, sensing, and delivery in a rapid and automated manner. By leveraging acoustic streaming and 3D-printed microfluidic designs, acoustofluidic sampler and injector modules are assembled for penetration of the skin stratum corneum, precise sampling of skin interstitial fluids, and controlled percutaneous release of therapeutics, respectively. By incorporating an electrochemical sensing module, the integrated patch can be further developed to achieve in situ and rapid detection of transdermal biomarkers and automated transdermal delivery of therapeutics via closed-loop control. As a proof-of-concept application, we demonstrate our approach can detect and reverse life-threatening acute allergic reactions in a mouse model of anaphylaxis by rapid sampling and sensing transdermal histamine followed by automated transdermal delivery of epinephrine. The innovative acoustofluidic patch may enable a new kind of transdermal devices for personalized therapy of various acute diseases.

bioengineering↗

bioRxiv2025

Structural basis of DNA N6-adenine methylation in eukaryotes

As a transcription-associated epigenetic mark abundantly present in many unicellular eukaryotes, DNA N6-adenine methylation (6mA) is maintained by the AMT1 complex. However, the structure-function relationship of the AMT1 complex remains to be fully elucidated. Here, we employ AlphaFold3 (AF3) for de novo structural modeling of the AMT1 complex, complemented by molecular dynamics simulations. Functional relevance of key structural features, especially those of the AMT1 ternary complex with the double-stranded DNA (dsDNA) substrate and the cofactor, are validated by extensive mutagenesis, both in vivo and in vitro. Our analysis reveals the structural basis for DNA substrate recognition, base flipping, and catalysis in this prototypical eukaryotic DNA 6mA-methyltransferase. It also allows us to delineate the reaction pathway for processive methylation involving translocation of the AMT1 complex along the dsDNA. As the active site is conserved across the MT-A70 family of eukaryotic N6-adenine methyltransferases, the structural insight will facilitate rational design of specific inhibitors.

molecular biology↗

bioRxiv2025

Mechanism of SK2 channel gating and its modulation by the bee toxin apamin and small molecules

Small-conductance calcium-activated potassium channel 2 (SK2) serves a variety of biological functions by coupling intracellular calcium dynamics with membrane potential. SK2 modulators are in development for the treatment of neurological and cardiovascular diseases, though the mechanisms of pharmacological modulation remain incompletely understood. We determined structures of an SK2-4 chimeric channel in Ca2+-bound and Ca2+-free conformations and in complex with the bee toxin apamin, a small molecule inhibitor, and a small molecule activator. The structures revealed that the S3-S4 linker forms a hydrophobic constriction at the extracellular opening of the pore. Apamin binds to this extracellular constriction and blocks the exit of potassium ions. Furthermore, we identified a structurally related SK2 inhibitor and activator that bind to the transmembrane domains. The compounds exert opposing effects on gating by differentially modulating the conformation of the S6 helices. These results provide important mechanistic insights to facilitate the development of targeted SK2 channel therapeutics.

pharmacology and toxicology↗

bioRxiv2025

Probe-Free Multiplexed RPA Detection Via Single-Molecule Nanopore Sensing and Deep Learning Classification

Recombinase Polymerase Amplification (RPA) is a rapid, sensitive, and isothermal method for nucleic acid amplification that has gained widespread use in diagnostic applications. While well-suited for single-target assays, extending RPA to multiplex detection remains technically challenging despite the multiplexed RPA being essential for tasks such as differential diagnosis and inclusion of internal controls. Existing multiplex RPA strategies rely on proprietary probes (e.g., Exo, Fpg, Nfo), which require complex design, are susceptible to cross-reactivity, and often depend on sophisticated optical instrumentation, limiting their scalability. To address these limitations, here, we developed a probe-free multiplex RPA assay that distinguishes targets by amplicon length, using solid-state nanopore detection of single molecules and deep neural network (DNN)-based classification. Using the Monkeypox (Mpox) as a model, we designed and validated a multiplex RPA assay targeting both the Mpox gene and the human RNase P gene as an internal control. We systematically evaluated the nanopore size requirement for detecting DNA amplicons ranging from 50 to 500 base pairs and found that [~]7 nm pores provided optimal performance for amplicons between 75 and 500 bp, balancing high event rates without pore clogging. Using single-molecule translocation events as input, we trained and optimized a DNN to classify amplicons by target. The model achieved 94.2% accuracy at the single-molecule event level, which translated to 100% accuracy at the population-level target call. While our current system demonstrates duplex detection, the strategy is inherently scalable to higher multiplex levels. These findings establish a new framework for multiplex RPA that eliminates the need for complex probe design and optical detection, paving the way for robust, scalable, and accessible molecular diagnostics.

molecular biology↗

bioRxiv2025

Respiratory Airway Secretory Cells act as Immune Sentinels in Human Distal Airways

Pulmonary immunity in the human distal respiratory airways is essential for lung function but remains poorly explored, mainly due to limited physiologically relevant models. Here, we develop distal lung organoids containing respiratory airway secretory (RAS) cells, a recently identified epithelial population unique to the distal airways of humans and large mammals, using human pluripotent stem cells (hPSCs). Lineage tracing identified RAS cells as descendants of SOX9brightNKX2-1bright progenitors. Single-cell transcriptomics elucidated that RAS cells exhibited a distinct immune-competent phenotype with enriched genes associated with viral host entry and pattern recognition receptor signaling. Functionally, RAS cells infected by respiratory syncytial virus (RSV) exhibited enhanced antiviral activity marked by upregulation of interferon-stimulated genes (ISGs) and complement component 3 (C3). In contrast, bacterial flagellin or Pseudomonas aeruginosa (PAO1) triggered a TLR5-driven immune response in RAS cells, inducing complement system activation, a response suppressed by selective TLR5 inhibition. Notably, in chronic obstructive pulmonary disease (COPD), RAS cells displayed dysregulated immune activation, characterized by elevated expression of adaptive immune mediators. Together, our findings identify RAS cells as previously unrecognized sensors and effectors of mucosal immunity in the human distal airways and reveal their roles in viral infection, bacterial stimulation, and chronic inflammatory lung disease, providing potential therapeutic targets for respiratory diseases.

cell biology↗

bioRxiv2025

Solid-State Nanopore Sizing for cfDNA Sample Quality Control in Point-of-Need Sequencing

DNA sequencing is a powerful tool for diagnosing conditions like infectious diseases and cancers. Even though current workflows demand rigorous quality control (QC) of DNA samples, this QC is typically limited to lab settings, despite recent advances in portable nanopore sequencers. For personalized healthcare to truly benefit from the portable sequencer, QC must be performed right where the samples are processed. Here, we present a solid-state nanopore device that provides label-free, controlled quantification and qualification of cell-free DNA (cfDNA). We demonstrated the use of a 1 kbp double-stranded DNA internal marker at a known concentration to measure the concentration of a representative cfDNA target in the presence of genomic DNA. We also found that nanopores with diameters ranging from 6 to 19 nm yield consistent measurements, with a maximum coefficient of variation (CV) of less than 15%. Moreover, analyzing data from multiple nanopores over longer acquisition times can reduce the uncertainty to below 10% CV. Finally, we applied our nanopore QC assay to a plasma cfDNA sample and compared the results with those from a capillary electrophoresis (CE) assay. Both methods produced highly correlated measurements, demonstrating the potential of our nanopore QC assay for effective cfDNA assessment at the point of need. Table of Contents Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/643726v1_ufigl1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1131713org.highwire.dtl.DTLVardef@4ffeb1org.highwire.dtl.DTLVardef@1da48e3org.highwire.dtl.DTLVardef@1e7697a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Point-Of-Need One-Pot Multiplexed RT-LAMP Test For Detecting Three Common Respiratory Viruses In Saliva

Respiratory viral infections pose a significant global public health challenge, partly due to the difficulty in rapidly and accurately distinguishing between viruses with similar symptoms at the point of care, hindering timely and appropriate treatment and limiting effective infection control and prevention efforts. Here, we developed a multiplexed, non- invasive saliva-based, reverse transcription loop-mediated isothermal amplification (RT- LAMP) test that enables the simultaneous detection of three of the most common respiratory infections, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Influenza (Flu), and respiratory syncytial virus (RSV), in a single reaction via specific probes and monitored in real-time by a machine-learning-enabled compact analyzer. Our results demonstrate that the multiplexed assay can effectively detect three target RNAs with high accuracy. Further, testing with spiked saliva samples showed strong agreement with reverse transcription polymerase chain reaction (RT-PCR) assay, with area under the curve (AUC) values of 0.82, 0.93, and 0.96 for RSV, Influenza, and SARS-CoV-2, respectively. By enabling the rapid detection of respiratory infections from easily collected saliva samples at the point of care, the device presented here offers a practical and efficient tool for improving outcomes and helping prevent the spread of contagious diseases. SignificanceThis research presents an innovative approach to respiratory infection diagnostics by combining a one-pot isothermal molecular test with machine learning-based analysis to simultaneously detect SARS-CoV-2, Influenza, and RSV in saliva samples. The battery- powered portable analyzer features novel machine-learning-assisted fluorescence detection for multiplexed reporter quantification, eliminating the need for traditional filter- based optical components and enabling adaptation to new targets without hardware changes. The test demonstrates high accuracy in detecting single and co-infections in spiked saliva samples, providing a rapid, cost-effective point-of-need solution. This tool can expand testing access, improve patient outcomes, and support more effective disease control, particularly in resource-limited or decentralized healthcare settings.

bioengineering↗

bioRxiv2024

LP-Micro Offers Interpretable Disease Outcome Prediction by Leveraging Microbial Biomarkers and Their Time-Varying Effects

Information generated from longitudinally-sampled microbial data has the potential to illuminate important aspects of development and progression for many human conditions and diseases. Identifying microbial biomarkers and their time-varying effects can not only advance our understanding of pathogenetic mechanisms, but also facilitate early diagnosis and guide optimal timing of interventions. However, longitudinal predictive modeling of highly noisy and dynamic microbial data (e.g., metagenomics) poses analytical challenges. To overcome these challenges, we introduce a robust and interpretable machine-learning-based longitudinal microbiome analysis framework, LP-Micro, that encompasses: (i) longitudinal microbial feature screening via a polynomial group lasso, (ii) disease outcome prediction implemented via machine learning methods (e.g., XGBoost, deep neural networks), and (iii) interpretable association testing between time points, microbial features, and disease outcomes via permutation feature importance. We demonstrate in simulations that LP-Micro can not only identify incident disease-related microbiome taxa but also offers improved prediction accuracy compared to existing approaches. Applications of LP-Micro in two longitudinal microbiome studies with clinical outcomes of childhood dental disease and weight loss following bariatric surgery yield consistently high prediction accuracy. The identified critical early predictive time points are informative and aligned with clinical expectations.

bioinformatics↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2024

Dynorphin modulates motivation through a pallido-amygdala cholinergic circuit

The endogenous opioid peptide dynorphin and its receptor {kappa}-opioid receptor (KOR) have been implicated in divergent behaviors, but the underlying mechanisms remain elusive. Here we show that dynorphin released from nucleus accumbens dynorphinergic neurons exerts powerful modulation over a ventral pallidum (VP) disinhibitory circuit, thereby controlling cholinergic transmission to the amygdala and motivational drive in mice. On one hand, dynorphin acts postsynaptically via KORs on local GABAergic neurons in the VP to promote disinhibition of cholinergic neurons, which release acetylcholine into the amygdala to invigorate reward-seeking behaviors. On the other hand, dynorphin also acts presynaptically via KORs on dynorphinergic terminals to limit its own release. Such autoinhibition keeps cholinergic neurons from prolonged activation and release of acetylcholine, and prevents perseverant reward seeking. Our study reveals how dynorphin exquisitely modulate motivation through cholinergic system, and provides an explanation for why these neuromodulators are involved in motivational disorders, including depression and addiction.

neuroscience↗

bioRxiv2024

Chemically induced cell plasticity enables the generation of high-fidelity embryo model

Embryo models open exciting opportunities for improving our understanding of development and advancing medicine. However, current models depend on intricate procedures and the integration of separately generated cell types. Here, we aimed to establish a chemical-only strategy to induce cellular plasticity and generate embryo founder-like cells. Within 60 hours, chemical-induced pluripotent stem cells activated early lineage specifiers, Cdx2 and Gata6, while maintaining Oct4/ Pou5f1 expression. Following a second 36-hour chemical treatment, founder-like cells developed into all blastocyst fates and self-assembled into embryo-like structures. These progressed into high-fidelity late-streak gastrulation stages within 4.5 days, displaying primitive streak formation via epithelial-to-mesenchymal transition, along with germ-layer tissues, amnion, and ectoplacental cone. Fate trajectories and molecular signatures from founder-like cells to chemically-induced embryo model (ci-MO) were defined, revealing remarkable similarity up to Embryonic day (E) 7.5 embryo. Summarily, ci-MO offers a straightforward, rapid, small-molecule-only, and high-fidelity approach to study embryonic development and cell fate specification. HighlightsO_LIGeneration of a direct, efficient, and sophisticated chemically induced embryo model (ci-MO) C_LIO_LITwo-pulse chemical induction reprograms ESCs into founder-like cells capable of developing all embryo fates C_LIO_LIGastrulating ci-MOs display high morphological and molecular accuracy C_LIO_LIci-MOs reconstruct embryo developmental trajectories C_LI

cell biology↗

bioRxiv2023

Specific and comprehensive genetic targeting reveals brain-wide distribution and synaptic input patterns of GABAergic axo-axonic interneurons

Axo-axonic cells (AACs), also called chandelier cells (ChCs) in the cerebral cortex, are the most distinctive type of GABAergic interneurons described in the neocortex, hippocampus, and basolateral amygdala (BLA). AACs selectively innervate glutamatergic projection neurons (PNs) at their axon initial segment (AIS), thus may exert decisive control over PN spiking and regulate PN functional ensembles. However, the brain-wide distribution, synaptic connectivity, and circuit function of AACs remains poorly understood, largely due to the lack of specific and reliable experimental tools. Here, we have established an intersectional genetic strategy that achieves specific and comprehensive targeting of AACs throughout the mouse brain based on their lineage (Nkx2.1) and molecular (Unc5b, Pthlh) markers. We discovered that AACs are deployed across essentially all the pallium-derived brain structures, including not only the dorsal pallium-derived neocortex and medial pallium-derived hippocampal formation, but also the lateral pallium-derived claustrum-insular complex, and the ventral pallium-derived extended amygdaloid complex and olfactory centers. AACs are also abundant in anterior olfactory nucleus, taenia tecta and lateral septum. AACs show characteristic variations in density across neocortical areas and layers and across subregions of the hippocampal formation. Neocortical AACs comprise multiple laminar subtypes with distinct dendritic and axonal arborization patterns. Retrograde monosynaptic tracing from AACs across neocortical, hippocampal and BLA regions reveal shared as well as distinct patterns of synaptic input. Specific and comprehensive targeting of AACs facilitates the study of their developmental genetic program and circuit function across brain structures, providing a ground truth platform for understanding the conservation and variation of a bona fide cell type across brain regions and species.

neuroscience↗

bioRxiv2023

Bacterial diversity and composition on the rinds of specific melon cultivars and hybrids from across different growing regions in the United States

The goal of this study was to characterize the bacterial diversity on different melon varieties grown in different regions of the US, and determine the influence that region, rind netting, and variety of melon has on the composition of the melon microbiome. Assessing the bacterial diversity of the microbiome on the melon rind can identify antagonistic and protagonistic bacteria for foodborne pathogens and spoilage organisms to improve melon safety, prolong shelf-life, and/or improve overall plant health. Bacterial community composition of melons (n = 603) grown in seven locations over a four-year period were used for 16S rRNA gene amplicon sequencing and analysis to identify bacterial diversity and constituents. Statistically significant differences in alpha diversity based on the rind netting and growing region (p < 0.01) were found among the melon samples. Principal Coordinate Analysis based on the Bray-Curtis dissimilarity distance matrix found that the melon bacterial communities clustered more by region rather than melon variety (R2 value: 0.09 & R2 value: 0.02 respectively). Taxonomic profiling among the growing regions found Enterobacteriaceae, Bacillaceae, Microbacteriaceae, and Pseudomonadaceae present on the different melon rinds at an abundance of [≥] 0.1%, but no specific core microbiome was found for netted melons. However, a core of Pseudomonadaceae, Bacillaceae, and Exiguobacteraceae were found for non-netted melons. The results of this study indicate that bacterial diversity is driven more by the region that the melons were grown in compared to rind netting or melon type. Establishing the foundation for regional differences could improve melon safety, shelf-life, and quality as well as the consumers health.

microbiology↗

bioRxiv2022

DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by [~]3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimers disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.

genetics↗

bioRxiv2022

STAMP-Based Digital CRISPR-Cas13a (STAMP-dCRISPR) for Amplification-Free Quantification of HIV-1 Plasma Viral Load

The development of new nucleic acid techniques to quantify HIV RNA in plasma is critical for identifying the disease progression and monitoring the effectiveness of antiretroviral therapy. While RT-qPCR has been the gold standard for HIV viral load quantification, digital assays could provide an alternative calibration-free absolute quantification method. Here, we report the development of a self-digitalization through automated membrane-based partitioning (STAMP) technique to digitalize the CRISPR-Cas13 assay (dCRISPR) for amplification-free and absolute quantification of HIV-1 viral RNAs. The analytical performances of STAMP-dCRISPR were evaluated with synthetic HIV-1 RNA, and it was found samples spanning 4 orders of dynamic range between 100 aM to 1 pM can be quantified as fast as 30 min. We also examined the overall assay from RNA extraction to STAMP-dCRISPR quantification with spiked plasma samples. The overall assay showed a resolution of 42 aM at a 90% confidence level. Finally, a total of 20 clinical plasma samples from patients were evaluated with STAMP-dCRISPR. The obtained results agreed well with the RT-qPCR. Our result demonstrates a new type of easy-to-use, scalable, and highly specific digital platform that would offer a simple and accessible platform for amplification-free quantification of viral RNAs, which could be exploited for the quantitative determination of viral load for an array of infectious diseases.

bioengineering↗

bioRxiv2022

Epigenome-wide meta-analysis of BMI in nine population-based cohorts: examining the utility of methylation as a biomarker of BMI

This study sought to examine the association between DNA methylation and body mass index (BMI) and the potential utility of these cytosine-phosphate-guanine (CpG) sites in predicting metabolic health. We pooled summary statistics from six trans-ethnic EWAS of BMI representing nine cohorts (n=17058), replicated these findings in the Womens Health Initiative (WHI, n=4822) and developed an epigenetic prediction score of BMI. In the pooled EWAS, 1265 CpG sites were associated with BMI (p<1E-7), and 1238 replicated in the WHI (FDR < 0.05). We performed several stratified analyses to examine whether these associations differed between individuals of European descent and individuals of African descent. We found five CpG sites had a significant interaction with BMI by race/ethnicity. To examine the utility of the significant CpG sites in predicting BMI, we used elastic net regression to predict log normalized BMI in the WHI (80% training/20% testing). This model found 397 sites could explain 32% of the variance in BMI in the WHI test set. Individuals whose methylome-predicted BMI overestimated their BMI (high epigenetic BMI) had significantly higher glucose and triglycerides, and lower HDL-cholesterol and LDL-cholesterol compared to accurately predicted BMI. Individuals whose methylome-predicted BMI underestimated their BMI (low epigenetic BMI) had significantly higher HDL-cholesterol and lower glucose and triglycerides. This study identified 553 previously identified and 685 novel CpG sites associated with BMI. Participants with high epigenetic BMI had poorer metabolic health suggesting that the overestimation may be driven in part by cardiometabolic derangements characteristic of metabolic syndrome.

genetics↗

bioRxiv2022

Crosshair, semi-automated targeting for electron microscopy with a motorised ultramicrotome

Volume electron microscopy (EM) is a time consuming process - often requiring weeks or months of continuous acquisition for large samples. In order to compare the ultrastructure of a number of individuals or conditions, acquisition times must therefore be reduced. For resin-embedded samples, one solution is to selectively target smaller regions of interest by trimming with an ultramicrotome. This is a difficult and labour-intensive process, requiring manual positioning of the diamond knife and sample, and much time and training to master. Here, we have developed a semi-automated workflow for targeting with a modified ultramicrotome. We adapted two recent commercial systems to add motors for each rotational axis (and also each translational axis for one system), allowing precise and automated movement. We also developed a user-friendly software to convert X-ray images of resin-embedded samples into angles and cutting depths for the ultramicrotome. This is provided as an open-source Fiji plugin called Crosshair. This workflow is demonstrated by targeting regions of interest in a series of Platynereis dumerilii samples.

cell biology↗

bioRxiv2022

Integrated platform for multi-scale molecular imaging andphenotyping of the human brain

Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multi-scale details of individual cells in the human organ-scale system. To address this challenge, we developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain, by integrating novel chemical, mechanical, and computational tools. The platform includes three key tools: (i) a vibrating microtome for ultra-precision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), (ii) a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and (iii) a computational pipeline for reconstructing 3D connectivity across multiple brain slabs (UNSLICE). We demonstrated the transformative potential of our platform by analyzing human Alzheimers disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain. One-Sentence SummaryWe developed an integrated, scalable platform for highly multiplexed, multi-scale phenotyping and connectivity mapping in the same human brain tissue, which incorporated novel tissue processing, labeling, imaging, and computational technologies.

bioengineering↗