Biology subjects

Jiang, A.

Publications and source records attributed to Jiang, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Complex Patterns of Altered White Matter Structural Connectivity within a 'subjective valuation network' in Treatment-Resistant Depression

BackgroundTreatment-resistant depression (TRD) poses a significant clinical challenge, demanding a deeper understanding of its neurobiological underpinnings to improve therapeutics. We examined white matter microstructure and structural connectivity in TRD, focusing on the subjective valuation network (SVN), which captures motivated behavior, reward processing, and emotional regulation circuits commonly altered in depression. This allowed us to identify potential neuroimaging biomarkers associated with treatment resistance. MethodsDiffusion tensor imaging (DTI) data were acquired from a sample of age-and-gender balanced individuals with TRD (n=44; female = 24, male=11, non-binary/unchecked =7) and non-depressed controls (n=42; female=27, male =12, non-binary/unchecked=5). Tract-Based Spatial Statistics were used to compare whole-brain white matter integrity differences between groups. Probabilistic tractography was then used to assess fractional anisotropy (FA) and white matter structural connectivity within the SVN across groups. SVN was defined a priori based on converging functional connectivity studies and included key regions such as the ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), ventral striatum, and insula and their connecting white matter tracts. Additionally, correlations between clinical measures of depression severity and cognition and structural features of the networks white matter fibers (i.e. FA and structural connectivity) were explored. ResultsCompared to non-depressed controls, individuals with TRD exhibited reduced FA within the left uncinate fasciculus, left inferior fronto-occipital fasciculus, and anterior cingulum, supporting widespread white matter integrity degradations in TRD. No correlations were found between FA and depression severity, suggesting a more specific association with anhedonic features. Whole-network measures of FA and structural connectivity of the SVN did not differ between groups. However, specific subcircuits structural connectivity within the SVN differed between groups; namely, the white matter tracts connecting the insula-vmPFC, striatum-insula, and striatum-vmPFC. Hyperconnectivity emerged for patients with TRD for tracts connecting the insula-vmPFC and striatum-vmPFC region-pairs, with exhibited hypoconnectivity between the striatum-insula. Exploratory analyses for the TRD group indicated the subcircuits with altered structural connectivity within the SVN correlated with depressive severity. This indicates subcircuit network alterations may associate with greater difficulty experiencing pleasure - a core symptom of depression and a potential marker of treatment resistance. ConclusionsThis study provides evidence for wide-spread disruption of white matter microstructure and altered structural connectivity within specific subcircuits of the SVN in TRD. These findings point to an intricate pattern of structural hyper-and-hypo-connectivity within the subcircuits of the SVN which may underlie the core symptoms of TRD. The altered structural connectivity within the SVN may contribute to the pathophysiology of TRD, especially concerning the motivational and emotional deficits associated with anhedonia. Future research employing multimodal neuroimaging techniques and longitudinal designs is warranted to further elucidate the functional consequences of these structural abnormalities and their potential as predictive biomarkers for personalized treatment interventions in TRD. Specifically, investigating how these white matter alterations change with successful treatment or targeted interventions aimed at improving anhedonia could inform more effective therapies for this challenging condition.

neuroscience↗

bioRxiv2025

Striatal dopamine modulates reward-reinforced temporal learning in humans.

The neurobiological mechanisms underlying human time perception remain elusive. Evidence has consistently linked striatal dopamine to timing behaviors, but it is still uncertain how rapid changes in dopamine may modulate human time perception. Many tasks designed to measure time perception utilize instrumental conditioning paradigms that reinforce correctly timed intervals. In these tasks, subjects are shown to improve their performance following repeated presentations of temporal cues - a phenomenon known as temporal learning. We sought to determine the association between rapid changes in human dopamine levels and temporal learning on an interval timing task that tested the reproduction of 1000ms, 3000ms, and 5000ms intervals in the presence and absence of monetary reinforcement. We utilized human voltammetry to measure real-time dopamine concentrations from the striatum of patients with Parkinsons disease while they performed the interval timing task. We first compared task behavior between patients with Parkinsons disease and neurologically healthy controls and found significant differences in the reproduction of 1000ms intervals, but not 3000ms or 5000ms intervals of time. Further, we observed that during 1000ms intervals, increases in striatal dopamine concentrations were associated with increases in temporal errors, but only during the expectation of monetary reinforcement. We also demonstrated that as temporal errors decrease overtime during temporal learning, so do striatal dopamine concentrations. These results suggest that dopamine may be driving temporal learning through the generation of temporal errors in response to positive reinforcement. These findings may have significant implications in our understanding of the role that dopamine plays in time perception. Significance StatementHuman time perception is a fundamental cognitive ability but the interaction between how the human brain perceives time and dopamines role is unclear. This study is the first of its kind to apply human voltammetry to measure rapid changes in dopamine levels associated with temporal learning in the presence and absence of positive reinforcement. We demonstrate that temporal learning may be affected by moment-to-moment changes in dopamine levels, which are also counterintuitively related to the generation of temporal errors in the presence of expectations of rewarding feedback.

neuroscience↗

bioRxiv2025

IL-6 is a Key Factor in the Formation of Gut Tissue Resident Memory T Cells from Naïve T cells

Tissue resident memory CD4+ T cells (TRMs) populate mucosal sites and play a critical role in local immune responses. Gut TRM cells persist for extended periods in the gut mucosa where they rapidly respond to invading pathogens and provide long lasting protection. This study investigates the factors that mediate differentiation of naive CD4+ T cells into cells presenting a gut TRM phenotype. Naive CD4+ T cells were cultured under conditions that mimicked mucosal environments. This included signaling through MAdCAM-1 in the presence of Retinoic Acid (RA) and TGF-{beta}. This combination of stimuli primed naive CD4+ T cells to adopt a TRM phenotype. However, to fully differentiate into TRMs an additional soluble factor provided by memory T cells was required. Our results identified IL-6 as a key factor that induces the expression of TRM -associated markers, including CD69, CD103 and CCR5. This unique combination of stimuli promoted TRM differentiation despite low level proliferation. TRM differentiation was mediated through JAK/STAT signaling, and antagonists that target JAK/STAT pathways suppressed MAdCAM-1 mediated TRM cell formation. Our findings revealed that MAdCAM-1 works together with TGF-{beta}, RA and IL-6 in this process. Such information may aid in the design of next generation adjuvants and effective mucosal vaccines. Additionally, each of these factors may be targeted to treat excessive gut inflammation associated with conditions like inflammatory bowel disease. Overall, these findings provide new strategies aimed at modulating immune responses to invading pathogens and identify therapeutic approaches toward regulating gut inflammation. Author SummaryImmunologists and vaccinologists have long been interested in strategies aimed at boosting immune responses in mucosal tissues where pathogens are first encountered. Gut Tissue resident memory T cells (TRMs) play a central role in gut homeostasis and immunity. They provide a rapid long-lasting protection against pathogens. TRMs must target harmful pathogens and at the same time tolerate harmless commensal bacteria. This balance between immunity and tolerance in the complex environment of gut tissues is essential to the maintenance of gut homeostasis. In this study we have identified key factors present in the gut tissue milieu that are involved in TRM differentiation from naive T cells. These findings point to new therapeutic approaches that can potentially target immune responses in gut tissues and may help in developing effective mucosal vaccines. Conversely, these factors may be targeted in excessive gut inflammation involved in conditions like inflammatory bowel disease (IBD). Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/660664v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@14e6015org.highwire.dtl.DTLVardef@860b93org.highwire.dtl.DTLVardef@61f8c8org.highwire.dtl.DTLVardef@142b48_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Predicting Early Transitions in Respiratory Virus Infections via Critical Transient Gene Interactions

Early detection of respiratory virus infections, such as influenza A (H3N2), is critical for timely intervention and disease management. Conventional biomarkers often overlook the complex and dynamic nature of gene regulatory changes, while existing predictive models frequently lack automation and robust external validation. Thus, we present CRISGI (Critical tran-Sient Gene Interaction), a computational framework that detects early-warning signals of infection by identifying dynamic changes in gene-gene interactions--termed critical transient interactions--from bulk RNA-seq data. CRISGI leverages critical transition (CT) theory to capture a GRNs unstable intermediate state, known as the CT stage, before irreversible phenotypic shifts. Applied to a human challenge study with H3N2, CRISGI identified 128 critical transition edges (128-TER). These were used to train predictive models capable of forecasting symptom status and onset timing. 128-TER was then validated across six temporal transcriptomic datasets involving three respiratory viruses (H3N2, H1N1, HRV). The 128-TER consistently distinguished symptomatic individuals, predicted infection onset, and revealed phenotype-specific enrichment patterns. Notably, CRISGI captured immune-related transitions involving interferon-stimulated genes (e.g., IFIT1, CXCL10), underscoring their role in early host defense. CRISGI advances early-warning biomarker discovery by integrating interaction-level dynamics and predictive modeling. Its reproducibility across viruses highlights shared immune activation pathways, supporting its utility in both research and clinical contexts.

bioinformatics↗

bioRxiv2025

Knowledge-driven annotation for gene interaction enrichment analysis

Gene Set Enrichment Analysis (GSEA) is a cornerstone for interpreting gene expression data, yet traditional approaches overlook gene interactions by focusing solely on individual genes, limiting their ability to detect subtle or complex pathway signals. To overcome this, we present GREA (Gene Interaction Enrichment Analysis), a novel framework incorporating gene interaction data into enrichment analysis. GREA replaces the binary gene hit indicator with an interaction overlap ratio, capturing the degree of overlap between gene sets and gene interactions to enhance sensitivity and biological interpretability. It supports three enrichment metrics: Enrichment Score (ES), Enrichment Score Difference (ESD) from a Kolmogorov-Smirnov-based statistic, and Area Under the Curve (AUC) from a recovery curve. GREA evaluates statistical significance using both permutation testing and gamma distribution modeling. Benchmarking on transcriptomic datasets related to respiratory viral infections shows that GREA consistently outperforms existing tools such as blitzGSEA and GSEApy, identifying more relevant pathways with greater stability and reproducibility. By integrating gene interactions into pathway analysis, GREA offers a powerful and flexible tool for uncovering biologically meaningful insights in complex datasets. The source code is available at https://github.com/compbioclub/GREA.

bioinformatics↗

bioRxiv2025

Multidimensional characterization of cellular ecosystems in Hodgkin lymphoma

The tissue architecture of classic Hodgkin Lymphoma (CHL) is unique among cancers and characterized by rare malignant Hodgkin and Reed-Sternberg cells that co-evolve with a complex ecosystem of immune cells in the tumor microenvironment (TME). The lack of a comprehensive systems-level interrogation has hindered the description of disease heterogeneity and clinically relevant molecular subtypes. Here, we employed an integrative, multimodal approach to characterize CHL tumors using malignant cell sequencing, spatial transcriptomics and imaging mass cytometry. We identified four molecular subtypes (CST, CN913, STB, and CN2P), each characterized by distinct clinical features, mutational patterns, malignant cell gene expression profiles, and spatial architecture involving immune cell populations. Functional modeling of CSF2RB mutations, a characteristic feature of the CST subtype, revealed dysregulated oncogenic signaling and unique TME crosstalk. These findings highlight the significance of multi-dimensional profiling in elucidating patterns of molecular alterations that drive immune ecosystems and underlie therapeutically exploitable vulnerabilities.

cancer biology↗

bioRxiv2024

Temporal dynamics of inflammatory and transcriptome changes in the myocardium in a murine model of cardiac arrest

BackgroundIt is increasingly recognized that inflammatory changes play a significant role in myocardial injury following cardiac arrest. However, the pathophysiological mechanisms responsible for functional changes in the myocardium following the event remain incompletely understood. Here, we characterize the transcriptomic and immunological changes in a mouse model of cardiac arrest to explore the roles of epinephrine and ischemia/reperfusion on myocardial inflammation. Methods and ResultsMale and female mice (C57BL/6J) were divided into three groups: (1) Naive (anesthesia only), (2) Sham (anesthesia + saline injection + epinephrine), and (3) Arrest (8-minute cardiac arrest followed by resuscitation (anesthesia + potassium chloride + epinephrine). We monitored the animals over 0.5, 1, 3, and 7 days with serial echocardiography. Hearts were harvested for transcriptomic analysis and inflammatory changes as assessed by flow cytometry and immunohistochemistry. Plasma samples were collected for cytokine signaling analysis by multiplex ELISA. Our data indicate that the cardiac arrest group have increased infiltrating neutrophils in the myocardium and elevated plasma levels of pro-inflammatory cytokines, peaking at 0.5 days after the initial insult and subsequently resolving. In contrast, Sham mice displayed less pronounced inflammatory changes, which peaked at 1 day after the procedure. Notably, these inflammatory alterations coincided with cardiac stunning, as demonstrated by echocardiography. ConclusionsIn our murine model of cardiac arrest, epinephrine exposure and ischemia/reperfusion induce significant inflammatory changes in the myocardium with increased infiltrating neutrophils and elevated circulating cytokines which subsequently resolve in a time-dependent manner. These changes correlate with improving cardiac function by echocardiography.

immunology↗

bioRxiv2024

Bridging the Gap in Immunotherapy Prediction: The AGAE Score as a Pan Cancer Biomarker for Immune Checkpoint Inhibitor Response

BackgroundImmune checkpoint inhibitor (ICI) therapy efficacy varies among cancer patients, necessitating precise predictive biomarkers for optimized treatment strategies. MethodsWe developed the Adaptive best subset selection algorithm and Genetic algorithm Aided Ensemble learning (AGAE) score through multi-cohort transcriptomic analysis of ICI-treated patients. The AGAE score incorporated gene-pairing, Adaptive Best Subset Selection for feature optimization, and a Genetic Algorithm for optimal basic learner identification. We explored correlations between AGAE score and immune microenvironments using multi-omics data. Potential targets were screened using 17 CRISPR datasets and validated through in vitro and in vivo experiments. ResultsThe AGAE score demonstrated robust predictive power for ICI therapy outcomes, with lower scores correlating with enhanced treatment response. The AGAE score outperformed published signatures and conventional biomarkers. Lower AGAE scores were associated with increased immune cell infiltration, higher immunogenicity, and enhanced antitumor immune activity. The CEP55 was identified as a potential key target driving immune evasion through AGAE scoring and CRISPR screening. Experimental validation showed CEP55 downregulation attenuated tumor cell malignancy and augmented ICI therapy efficacy by modulating T cell responses. ConclusionsThe AGAE score was a potent predictor of ICI therapy efficacy, facilitating refined patient stratification. CEP55s role in the tumor microenvironments immune response highlights its potential as a therapeutic target. Targeted interventions against CEP55 may improve immunotherapy precision.

cancer biology↗

bioRxiv2024

Gut Bacteria Encode Reductases that Biotransform Steroid Hormones

The metabolism of steroids by the gut microbiome affects hormone homeostasis, impacting host development, mental health, and reproductive functions. In this study, we identify the {Delta}4-3-ketosteroid 5{beta}-reductase, 3{beta}-hydroxysteroid dehydrogenase/{Delta}5-4 isomerase, and {Delta}6-3-ketosteroid reductase enzyme families encoded by common human gut bacteria. Through phylogenetic reconstruction and mutagenesis, We show that 5{beta}-reductase and {Delta}6-3-ketosteroid reductase have evolved to specialize in converting diverse 3-keto steroid hormones into their 5{beta}- and {Delta}6-reduced derivatives. We also find that the novel 3{beta}-hydroxysteroid dehydrogenase/{Delta}5-4 isomerase is fused with 5{beta}-reductase in multiple species, streamlining the multi-step conversion of pregnenolone, a steroid hormone precursor, into epipregnanolone. Through metagenomic analysis, we reveal that these enzymes are prevalent in healthy populations, being enriched in females over males. These findings provide the molecular basis for studying microbial steroid metabolism in the gut, offering insights into its potential impact on hormonal health in hosts, especially in the context of womens health.

microbiology↗

bioRxiv2024

A preprocessing toolbox for 2-photon subcellular calcium imaging

Recording the spiking activity from subcellular compartments of neurons such as axons and dendrites during behavior with 2-photon calcium imaging is increasingly common yet remains challenging due to low signal-to-noise, inaccurate region-of-interest (ROI) identification, movement artifacts, and difficulty in grouping ROIs from the same neuron. To address these issues, we present a computationally efficient pre-processing pipeline for subcellular signal detection, movement artifact identification, and ROI grouping. For subcellular signal detection, we capture the frequency profile of calcium transient dynamics by applying Fast Fourier Transform (FFT) on smoothed time-series calcium traces collected from axon ROIs. We then apply band-pass filtering methods (e.g. 0.05 to 0.12 Hz) to select ROIs that contain frequencies that match the power band of transients. To remove motion artifacts from z-plane movement, we apply Principal Component Analysis on all calcium traces and use a Bottom-Up Segmentation change-point detection model on the first principal component. After removing movement artifacts, we further identify calcium transients from noise by analyzing their prominence and duration. Finally, ROIs with high activity correlation are grouped using hierarchical or k-means clustering. Using axon ROIs in the CA1 region, we confirm that both clustering methods effectively determine the optimal number of clusters in pairwise correlation matrices, yielding similar groupings to "ground truth" data. Our approach provides a guideline for standardizing the extraction of physiological signals from subcellular compartments during behavior with 2-photon calcium imaging.

neuroscience↗

bioRxiv2024

Exacerbation of influenza virus induced lung injury by alveolar macrophages and its suppression by pyroptosis blockade in a human lung alveolus chip

Alveolar macrophages (AMs) are the major sentinel immune cells in human alveoli and play a central role in eliciting host inflammatory responses upon distal lung viral infection. Here, we incorporated peripheral human monocyte-derived macrophages within a microfluidic human Lung Alveolus Chip that recreates the human alveolar-capillary interface under an air-liquid interface along with vascular flow to study how residential AMs contribute to the human pulmonary response to viral infection. When Lung Alveolus Chips that were cultured with macrophages were infected with influenza H3N2, there was a major reduction in viral titers compared to chips without macrophages; however, there was significantly greater inflammation and tissue injury. Pro-inflammatory cytokine levels, recruitment of immune cells circulating through the vascular channel, and expression of genes involved in myelocyte activation were all increased, and this was accompanied by reduced epithelial and endothelial cell viability and compromise of the alveolar tissue barrier. These effects were partially mediated through activation of pyroptosis in macrophages and release of pro-inflammatory mediators, such as interleukin (IL)-1{beta}, and blocking pyroptosis via caspase-1 inhibition suppressed lung inflammation and injury on-chip. These findings demonstrate how integrating tissue resident immune cells within human Lung Alveolus Chip can identify potential new therapeutic targets and uncover cell and molecular mechanisms that contribute to the development of viral pneumonia and acute respiratory distress syndrome (ARDS).

bioengineering↗

bioRxiv2024

Clathrin-associated carriers enable recycling through a kiss-and-run mechanism

Endocytosis and recycling control the uptake and retrieval of various materials, including membrane proteins and lipids, in all eukaryotic cells. These processes are crucial for cell growth, organization, function, and environmental communication. However, the mechanisms underlying efficient, fast endocytic recycling remain poorly understood. Here, by utilizing a biosensor and imaged-based screening, we uncover a novel recycling mechanism that couples endocytosis and fast recycling, which we name the clathrin-associated fast endosomal recycling pathway (CARP). Clathrin-associated tubulovesicular carriers containing clathrin, AP1, Arf1, Rab1, and Rab11, while lacking the multimeric retrieval complexes, are generated at subdomains of early endosomes, and then transported along actin to cell surfaces. Unexpectedly, the clathrin-associated recycling carriers undergo partial fusion with the plasma membrane. Subsequently, they are released from the membrane by dynamin and reenter cells. Multiple receptors utilize and modulate CARP for fast recycling following endocytosis. Thus, CARP represents a novel endocytic recycling mechanism with kiss-and-run membrane fusion.

cell biology↗

bioRxiv2024

Receptor tyrosine kinase inhibition leads to regression of acral melanoma by targeting the tumor microenvironment

Acral melanoma (AM) is an aggressive melanoma variant that arises from palmar, plantar, and nail unit melanocytes. Compared to non-acral cutaneous melanoma (CM), AM is biologically distinct, has an equal incidence across genetic ancestries, typically presents in advanced stage disease, is less responsive to therapy, and has an overall worse prognosis. Independent analysis of published genomic and transcriptomic sequencing identified that receptor tyrosine kinase (RTK) ligands and adapter proteins are frequently amplified, translocated, and/or overexpressed in AM. To target these unique genetic changes, a zebrafish acral melanoma model was exposed to a panel of narrow and broad spectrum multi-RTK inhibitors, revealing that dual FGFR/VEGFR inhibitors decrease acral-analogous melanocyte proliferation and migration. The potent pan-FGFR/VEGFR inhibitor, Lenvatinib, uniformly induces tumor regression in AM patient-derived xenograft (PDX) tumors but only slows tumor growth in CM models. Unlike other multi-RTK inhibitors, Lenvatinib is not directly cytotoxic to dissociated AM PDX tumor cells and instead disrupts tumor architecture and vascular networks. Considering the great difficulty in establishing AM cell culture lines, these findings suggest that AM may be more sensitive to microenvironment perturbations than CM. In conclusion, dual FGFR/VEGFR inhibition may be a viable therapeutic strategy that targets the unique biology of AM.

cancer biology↗

bioRxiv2024

Highly Dynamic and Sensitive NEMOer Calcium Indicators for Imaging ER Calcium Signals in Excitable Cells

Endoplasmic/sarcoplasmic reticulum (ER/SR) sits at the heart of the calcium (Ca2+) signaling machinery, yet current genetically encoded Ca2+ indicators (GECIs) lack the ability to detect elementary Ca2+ release events from ER/SR, particularly in muscle cells. Here we report a set of organellar GECIs, termed NEMOer, to efficiently capture ER Ca2+ dynamics with increased sensitivity and responsiveness. Compared to G-CEPIA1er, NEMOer indicators exhibit dynamic ranges that are an order of magnitude larger, which enables up to 5-fold more sensitive detection of Ca2+ oscillation in both non-excitable and excitable cells. The ratiometric version further allows super-resolution monitoring of local ER Ca2+ homeostasis and dynamics. Notably, the NEMOer-f variant enabled the inaugural detection of Ca2+ blinks, elementary Ca2+ releasing signals from the SR of cardiomyocytes, as well as in vivo spontaneous SR Ca2+ releases in zebrafish. In summary, the highly dynamic NEMOer sensors expand the repertoire of organellar Ca2+ sensors that allow real-time monitoring of intricate Ca2+ dynamics and homeostasis in live cells with high spatiotemporal resolution.

synthetic biology↗

bioRxiv2024

Activation of locus coeruleus noradrenergic neuronsrapidly drives homeostatic sleep pressure

Homeostatic sleep regulation is essential for optimizing the amount and timing of sleep, but the underlying mechanism remains unclear. Optogenetic activation of locus coeruleus noradrenergic neurons immediately increased sleep propensity following transient wakefulness. Fiber photometry showed that repeated optogenetic or sensory stimulation caused rapid declines of locus coeruleus calcium activity and noradrenaline release. This suggests that functional fatigue of noradrenergic neurons, which reduces their wake-promoting capacity, contributes to sleep pressure.

neuroscience↗

bioRxiv2024

Synthetic integrin antibodies discovered by yeast display reveal αV subunit pairing preferences with β subunits

Eight of the 24 integrin heterodimers bind to the tripeptide Arg-Gly-Asp (RGD) motif in their extracellular ligands, and play essential roles in cell adhesion, migration, and homeostasis. Despite similarity in recognizing the RGD motif and some redundancy, these integrins can selectively recognize RGD-containing ligands including fibronectin, vitronectin, fibrinogen, nephronectin and the prodomain of the transforming growth factors to fulfill specific functions in cellular processes. Subtype-specific antibodies against RGD-binding integrins are desirable for investigating their specific functions. In this study, we discovered 11 antibodies that exhibit high specificity and affinity towards integrins V{beta}3, V{beta}5, V{beta}6, V{beta}8, and 5{beta}1 from a synthetic yeast-displayed Fab library. Of these, 6 are function-blocking antibodies containing an R(G/L/T) D motif in their CDR3 sequences. We report antibody binding specificity, kinetics, and binding affinity for purified integrin ectodomains as well as intact integrins on the cell surface. We further employed these antibodies to reveal binding preferences of the V subunit for its 5 {beta}-subunit partners: {beta}6={beta}8>{beta}3>{beta}1={beta}5.

cell biology↗

bioRxiv2024

DNA origami vaccine (DoriVac) nanoparticles improve both humoral and cellular immune responses to infectious diseases

Current SARS-CoV-2 vaccines have demonstrated robust induction of neutralizing antibodies and CD4+ T cell activation, however CD8+ responses are variable, and the duration of immunity and protection against variants are limited. Here we repurposed our DNA origami vaccine nanotechnology, DoriVac, for targeting infectious viruses, namely SARS-CoV-2, HIV, and Ebola. The DNA origami nanoparticle, conjugated with infectious-disease-specific heptad repeat 2 (HR2) peptides, which act as highly conserved antigens, and CpG adjuvant at precise nanoscale spacing, induced neutralizing antibodies, Th1 CD4+ T cells, and CD8+ T cells in naive mice, with significant improvement over a bolus control. Pre-clinical studies using lymph-node-on-a-chip systems validated that DoriVac, when conjugated with antigenic peptides or proteins, induced promising cellular and humoral immune responses in human cells. Moreover, DoriVac bearing full-length SARS-CoV-2 spike protein achieved immune responses comparable to current mRNA vaccine platforms while potentially reducing storage constraints. These results suggest that DoriVac holds potential as a versatile, modular vaccine platform, capable of inducing both humoral and cellular immunities, underscoring its potential utility in addressing future pandemics.

immunology↗

bioRxiv2023

ICARUS v3, a massively scalable web server for single cell RNA-seq analysis of millions of cells.

In recent years, improvements in throughput of single cell RNA-seq have resulted in a significant increase in the number of cells profiled. The generation of single cell RNA-seq datasets comprising >1 million cells is becoming increasingly common, giving rise to demands for more efficient computational workflows. Here, we present an update to our single cell RNA-seq analysis web server application, ICARUS (available at https://launch.icarus-scrnaseq.cloud.edu.au/) that allows effective analysis of large-scale single cell RNA-seq datasets. ICARUS v3 utilises the geometric cell sketching method to subsample cells from the overall dataset for dimensionality reduction and clustering that can be then projected to the large dataset. We then extend this functionality to select a representative subset of cells for downstream data analysis applications including differential expression analysis, gene co-expression network construction, gene regulatory network construction, trajectory analysis, cell-cell communication inference and cell cluster associations to GWAS traits. We demonstrate analysis of single cell RNA-seq datasets using ICARUS v3 of 1.3 million cells completed within the hour.

genomics↗