Biology subjects

Wu, Y.

Publications and source records attributed to Wu, Y..

At least 253 records · Page 14Linked to original sources

bioRxiv2025

Observing biological spatio-angular structures and dynamics with statistical image reconstruction and polarized fluorescence microscopy

Understanding molecular orientation and density distributions is essential for exploring biological structure and function. Polarized fluorescence microscopy (PFM) provides insights into molecular architecture but struggles to resolve three-dimensional (3D) molecular orientation distributions, particularly in densely labeled or structurally complex specimens. To address this, we introduce the efficient generalized Richardson-Lucy (eGRL) algorithm, a robust framework for reconstructing 3D molecular density and orientation (spatio-angular) distributions from PFM data. By modeling the imaging process in spatio-angular hyperspace, we propose a maximum-likelihood solution enhanced by dimensionality reduction and angular domain transformation to overcome computational challenges. eGRL improves accuracy and efficiency across different PFM implementations, enabling use on standard platforms. We utilize our methods to resolve biological spatio-angular structures and dynamics otherwise impossible to resolve, including the tangential alignment of actin filaments in U2OS cells, nanowire-guided cytoskeletal organization in NIH3T3 cells, rotational actin patterns in live HeLa protrusions, and membrane tension-induced anisotropy in live macrophages.

bioengineering↗

bioRxiv2025

Inferring Dynamic Information from Protein Structures by Gaussian Integrals and Deep Learning

Protein conformational flexibility underlies a wide range of biological functions, yet experimentally probing dynamics at atomic resolution remains costly and low-throughput. Here, we present a deep learning framework that predicts protein flexibility directly from static structural descriptors, bypassing the need for molecular dynamics (MD) simulations. Using the ATLAS database of standardized all-atom MD trajectories, we encoded 1,374 protein chains as 30-dimensional Gaussian integral (GI) vectors--global shape and topology invariants of the protein backbone. Principal component analysis of GI profiles revealed four structural clusters with distinct secondary structure compositions and flexibility distributions. We trained an attention-based one-dimensional convolutional neural network (1D-CNN) to classify proteins as flexible or non-flexible based on their root-mean-square fluctuation (RMSF) relative to the dataset-wide mean. The classifier achieved an AUC of 0.772 (95% CI: 0.712-0.826) on an independent test set, with balanced sensitivity and specificity, and identified a small subset of GI components as the most predictive. In a regression setting, a recurrent neural network outperformed other architectures, attaining an R2 of 0.537, though high-flexibility values were systematically underestimated. Cluster-specific analyses indicated that coil-rich and {beta}-sheet-dominated proteins were more amenable to flexibility prediction than -helical proteins, likely due to greater structural heterogeneity. Our results demonstrate that compact GI descriptors preserve sufficient information to recover MD-derived flexibility trends, offering a computationally efficient complement to simulation-based approaches. This framework enables large-scale screening of protein dynamics from structural data alone, with potential applications in structural bioinformatics, drug design, and functional annotation.

bioinformatics↗

bioRxiv2025

Cryo-EM structure of influenza polymerase bound to the cRNA promoter provides insights into the mechanism of virus replication

Influenza virus polymerase (FluPol) synthesizes the complementary RNA (cRNA) and the viral RNA (vRNA) using distinct de novo initiation strategies during genome replication, known as internal and terminal initiation, respectively. The de novo initiation mechanisms, especially the internal initiation process, which includes a template realignment process, is still not well understood. Here, we present a cryo-electron microscopy structure of H5N1 FluPol bound to the cRNA promoter. In combination with structural analyses and structure-guided mutagenesis studies, we identified several previously unreported structural features of FluPol essential for internal initiation. The B loop adopts an "open" conformation, allowing translocation of the 3' terminus of cRNA (3'-cRNA) template into the catalytic cavity. The dynamic of incoming 3'-cRNA template is limited by the priming and realignment loop (PR loop), which facilitates the cRNA template realignment process. An asparagine cluster above the catalytic cavity is required for polymerase activity. Our findings provide structural insights into the mechanism of replication internal initiation of FluPol.

microbiology↗

bioRxiv2025

GPT-4 accurately predicts human emotions and their neural correlates

Emotions are evoked by both internal and external events related to survival challenges. Recent advances in multimodal large language models ((M)LLMs), such as GPT-4, enable them to accurately analyze and describe complex visual scenes, raising the question whether LLMs can also predict human emotional experiences evoked by similar scenes. Here we asked GPT-4 and humans (N = 519) to provide self-reports of 48 unipolar emotions and aFective dimensions for emotionally evocative videos and images. We evaluated GPT-4s emotion ratings using three natural socio-emotional stimulus datasets: two video datasets (234 and 120 videos) and one image dataset (300 images). We found that GPT-4 can predict emotions of human observers with high accuracy. The multivariate emotion structure (correlation matrices of emotions ratings) converged between GPT-4 and humans and across datasets indicating that GPT-4 ratings for diFerent emotions follow similar structural representations as the human evaluations. Finally, we modeled the brains hemodynamic responses for emotions elicited by videos or images in two fMRI datasets (N = 97) with GPT-4 or human-based emotional evaluations to highlight the usefulness of GPT-4 in neuroscientific research. The results showed that the brains emotion circuits can be mapped with high accuracy using GPT-4 emotion ratings as the stimulation model. In conclusion, GPT-4 can predict human emotion ratings to the extent that GPT-4 ratings can also model the associated neural responses. Our results indicate that LLMs provide novel and scalable tools that have broad potential in emotion research, cognitive and aFective neuroscience, and that it can also have practical applications.

neuroscience↗

bioRxiv2025

Geomagnetic and visual cues guide seasonal migratory orientation in the nocturnal fall armyworm, the worlds most invasive insect

The mechanisms guiding nocturnal insect migration remain poorly understood. Although many species are thought to use the geomagnetic field, the sensory basis of magnetic orientation in insects has yet to be clarified. We developed an indoor experimental system to investigate the integration of geomagnetic and visual cues in the seasonal orientation of a globally distributed pest moth, the fall armyworm (Spodoptera frugiperda), a highly invasive species which in the past decade has colonized almost all potentially habitable regions of the globe. Our results demonstrate that fall armyworms require both geomagnetic and visual cues for accurate migratory orientation, with visual cues being indispensable for magnetic orientation. When visual and geomagnetic cues are placed in conflict moths become disoriented, although not immediately, indicating that sensory recognition of the conflict requires time to process. We also show that the absence of visual cues leads to a significant loss of flight stability, which likely explains the disruption in orientation. Our findings highlight that visual cues are critical for stable magnetic orientation in the fall armyworm, offering a basis for future investigations of visual-magnetic integration in noctuid migrants.

animal behavior and cognition↗

bioRxiv2025

Super-resolution microscopy of alpha-synuclein aggregates in brain samples indicates a subset of cells have disrupted protein homeostasis

Nanoscopic aggregates of alpha-synuclein have been observed in Parkinsons disease (PD). However, the processes that occur in-vivo leading to the formation of these small aggregates are not well understood. We used ultra-sensitive single-molecule methods including SIMOA and super-resolution microscopy to quantify and characterise alpha-synuclein aggregates harvested from human brain samples alongside the Line 61 mouse model using different tissue processing methods. While aggregate numbers did not differ between PD and control samples, larger aggregates were detected in PD brain samples. Moreover, different sub-populations of aggregates were obtained by different extraction methods, with diffusible and membrane-bound aggregates producing a more pronounced difference between disease and control samples. Our data suggests that alpha-synuclein aggregates slowly in the brain, leading to formation of larger aggregates in a sub-set of cells.

neuroscience↗

bioRxiv2025

Developmental Connectomics of the Mouse Cerebellum

To uncover the developmental processes that establish the precise patterns of synaptic connectivity in the CNS, we employed a connectomic approach in the mouse cerebellar cortex between birth and 2 weeks of age. There were dramatic quantitative and qualitative changes in the structure and connectivity of cerebellar cells. Parallel fiber synapses onto Purkinje cells increased [~]500-fold, with the most rapid growth taking place a week after birth. To support this profound synaptogenesis, Purkinje cells generated thousands of transient parallel fiber-oriented filopodia that received nascent synapses from parallel fibers. Importantly, we find that granule cells initiate synaptic output onto Purkinje cells only after receiving mossy fiber input, revealing a sequential, input-dependent logic for circuit assembly. In sharp contrast to the concurrent pruning of climbing fiber inputs, parallel fiber connectivity expanded and became highly individualized during development. Despite anatomical overlap, neighboring Purkinje cells share significantly fewer parallel fiber inputs than expected by chance. Moreover, parallel fibers themselves diverged spatially, further enforcing selective input allocation and resulting in highly specific parallel fiber cohorts for each Purkinje cell. Our findings uncover a mechanistic sequence in which early afferent activity and transient cellular structures guide the selective wiring and expansion of parallel fiber input to Purkinje cells, establishing developmental principles that ensure functional specificity in the mature brain.

neuroscience↗

bioRxiv2025

YprA family helicases provide the missing link between diverse prokaryotic immune systems

Bacteria and archaea possess an enormous variety of antivirus immune systems that often share homologous proteins and domains, some of which contribute to diverse defense strategies. YprA family helicases are central to widespread defense systems DISARM, Dpd, and Druantia. Here, through comprehensive phylogenetic and structural prediction analysis of the YprA family, we identify several major, previously unrecognized clades, with unique signatures of domain architecture and associations with other genes. Each YprA family clade defines a distinct class of defense systems, which we denote ARMADA (disARM-related Antiviral Defense Array), BRIGADE (Base hypermodification and Restriction Involving Genes encoding ARMADA-like and Dpd-like Effectors), or TALON (TOTE-like and ARMADA-Like Operon with Nuclease). In addition to the YprA-like helicase, ARMADA systems share two more proteins with DISARM. However, ARMADA YprA homologs are most similar to those of Druantia, suggesting ARMADA is a missing link connecting DISARM and Druantia. We show experimentally that ARMADA protects bacteria against a broad range of phages via a direct, non-abortive mechanism. We also discovered multiple families of satellite phage-like mobile genetic elements that often carry both ARMADA and Druantia Type III systems and show that these can provide synergistic resistance against diverse phages.

microbiology↗

bioRxiv2025

SpatialZoomer: multi-scale feature analysis of spatial transcriptomics

Single-cell resolution spatial transcriptomics (ST) provides a great opportunity to explore the complex cellular contents in different tissues. In this study, we introduce SpatialZoomer, a spectral graph-based method that applies a set of low-pass filters to efficiently extract spatial molecular features from ST data at multiple resolutions or scales, including the single cell scale, the niche scale with dozens of closely interacting cells, and the domain scale with spatially-organized cell contents. The corresponding "critical" scales can be automatically identified by partitioning a cross-scale similarity map. Results show that SpatialZoomer can identify disease-progression signals at specific scales in Alzheimers Disease, and spatial context dependent cell subtypes in tumor microenvironment. The extracted multi-scale features also uncovered spatially heterogeneous niches in cancer. SpatialZoomer also takes advantage of high computational efficiency and low hardware requirements.

bioinformatics↗

bioRxiv2025

Localized Space Coding and Phase Coding Complement Each Other to Achieve Robust and Efficient Spatial Representation

Localized space coding and phase coding are two distinct strategies responsible, respectively, for representing abstract structure and sensory observations in neural cognitive maps. In spatial representation, localized space coding is implemented by place cells in the hippocampus (HPC), while phase coding is implemented by grid cells in the medial entorhinal cortex (MEC). Both strategies have their own advantages and disadvantages, and neither of them meets the requirement of representing space robustly and efficiently in the brain. Here, we show that through reciprocal connections between HPC and MEC, place and grid cells can complement each other to overcome their respective shortcomings. Specifically, we build a coupled network model, in which a continuous attractor neural network (CANN) with position coordinate models place cells, while multiple CANNs with phase coordinates model grid cell modules with varying spacings. The reciprocal connections between place and grid cells encode the correlation prior between the sensory cues processed by HPC and MEC, respectively. Using this model, we show that: 1) place and grid cells interact to integrate sensory cues in a Bayesian manner; 2) place cells complement grid cells in coding accuracy by eliminating non-local errors of the latter; 3) grid cells complement place cells in coding efficiency by enlarging the number of environmental maps stored stably by the latter. We demonstrate that the coupled network model explains the seemingly contradictory experimental findings about the remapping phenomena of place cells when grid cells are either inactivated or depolarized. This study gives us insight into understanding how the brain employs collaborative localized and phase coding to realize both robust and efficient information representation.

neuroscience↗

bioRxiv2025

TESSA: A unified model to detect trajectory-preserved and spatially-variable genes in spatial transcriptomics

Identifying spatially variable genes (SVGs) has been an essential task in spatial transcriptomics. In addition to SVGs detection, there are genes exhibiting expression patterns that are associated with cellular developmental stages or lineage fates across a tissue section. Identifying such genes could provide novel insights into tumor metastasis. Here, we introduce a unified statistical model, termed TrajEctory-preServed and SpAtially-variable gene detection (TESSA), to detect both types of genes. Moreover, we propose a novel strategy to address the inherent double-dipping issue commonly encountered when assessing temporal gene effects in transcriptomics analysis. We demonstrate the testing performance through extensive simulation studies and real applications to several publicly available datasets. Downstream analyses further highlight the potential of our method in identifying genes associated with tumor progression and enhancing spatial domain detection.

bioinformatics↗

bioRxiv2025

Versatile HIV Rev-dependent reporter cell system for stringent and sensitive quantification of viral reservoirs, neutralizing antibodies, and restriction factors

Detecting and measuring HIV reservoirs, neutralizing antibodies, and restriction factors are important for HIV cure research and the development of new therapeutics and vaccines. Here we describe the development and validation of several HIV Rev-dependent indicator cell lines for these purposes. These reporter cells come from different T-lymphoblast cell lines, including MOLT-4-R5, SupT1-R5, CEM-SS, A3R5, and from the adherent TZM cell platform based on HeLa clone JC53. These cells express CD4, CXCR4, and various levels of CCR5. We compared these cell lines for responsiveness to both X4 and R5-tropic viruses, and confirmed that reporter expression in these cells is not affected by stimulation from mitogens but is responsive to HIV Tat and Rev, reducing non-specific reporter induction from the leaky LTR promoter. To validate the sensitivity of the Rev-dependent reporter cell systems, we conducted a viral dilution assay with three primary HIV-1 clade C swarms from an adult in Malawi. We also validated the systems for quantifying antibody neutralization and screening restriction factors; these systems are also sensitive for viral outgrowth assays for quantifying viral reservoirs in clinical and basic research settings. Given that the systems can measure HIV accurately in complex environments with mitogens or other substances, they can be used for versatile applications, such as quantifying latent reservoirs, testing inhibitory compounds, conducting neutralizing antibody assays, and identifying new restriction factors.

microbiology↗

bioRxiv2025

Post-transcriptional Regulation Coordinating Transcription and Translation During Circadian Oscillation and Stress Recovery in Plants

Circadian rhythms orchestrate gene expression to align plant growth and development with daily environmental cycles. However, the post-transcriptional mechanisms that coordinate transcriptional and translational rhythmicity remain incompletely understood. To address this, we analyzed time-series transcriptome and translatome profiles in Arabidopsis seedlings, identifying 5,185 genes with rhythmicity at both levels. These genes were classified into four distinct groups based on phase and amplitude differences between transcription and translation. Circadian mRNAs with high oscillation amplitudes tended to undergo co-translational RNA decay (CTRD), whereas intronless genes displayed the lowest amplitudes, likely due to their mRNA instability and short half-lives. While CTRD and NAD capping modulate amplitude differences, intronless and circadian translational efficiency (TE) influence both phase and amplitude variations. Additionally, CTRD, NAD+ capping and circadian TE facilitate fast recovery of heat-induced genes to normal hemostasias. Collectively, our findings demonstrate that these post-transcriptional regulation shapes both synchronized and decoupled transcription and translation during plants response to diel and environmental dynamics.

genetics↗

bioRxiv2025

γδ T cells modulate anti-tumor immunity in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive neoplasm with limited sensitivity to anti-PD-(L)1 blockade, likely due to the epigenetic silencing of MHC-I. Elucidating MHC-I-independent immune recognition mechanisms is therefore crucial for enhancing treatment responses and improving clinical outcomes in a greater number of patients. Leveraging single cell approaches, we discovered {gamma}{delta} T cell infiltration in biospecimens from patients with SCLC. Despite PD-1 expression, {gamma}{delta} T cells maintained a cytotoxic transcriptional profile, suggestive of an anti-tumor role. Indeed, high {gamma}{delta} T cell infiltration predicted improved response to anti-PD-L1 immunotherapy in patients with SCLC. Moreover, using pre-clinical models, we demonstrated that {gamma}{delta} T cells are effective at tarlatamab (DLL3-CD3 BiTE) redirected SCLC killing and that zoledronate, an FDA-approved compound, can sensitize SCLC cells to {gamma}{delta} T cell-mediated killing. Thus, our findings suggest that engaged {gamma}{delta} T cells are potentially valuable targets for SCLC therapy.

cancer biology↗

bioRxiv2025

Therapeutic Salmonella induces long-term protective trained immunity in NK cells against cancer metastasis

Trained immunity, a form of innate immune memory initially recognized in pathogen defense, has recently been reported to aid in combating cancer. Although trained immunity has been well-characterized in myeloid lineage cells, its presence in innate lymphoid cells, particularly natural killer (NK) cells, remains largely unexplored. Here, we report that a single dose of Salmonella YB1, an attenuated candidate anticancer vaccine strain, provides long-lasting protection against cancer metastasis in mice by inducing trained natural killer (NK) cells. Functional assays coupled with multiomics analysis revealed that Salmonella-trained NK (stNK) cells establish an enduring reprogrammed epigenome characterized by enhanced pro-survival signaling and immune effector functions, resulting in more potent IFN-{gamma} release and cytotoxicity upon secondary stimulation. We further revealed that IL-12 and IL-18 are essential but insufficient for this NK cell training effect. Crucially, stNK cells far outperform the common immune checkpoint therapies, including PD-1 and TIGIT blockade, in suppressing metastasis, underscoring the unique immunological mechanisms in combating metastasis. These findings uncover a novel long-term anti-metastatic function of trained immunity in NK cells, highlighting its potential as an effective antitumor strategy.

immunology↗

bioRxiv2025

Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide vari- ants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alter- ations due to inherent technical limitations. To overcome that, here we introduce an approach for long-read sequencing of single-cell derived subclones, and use it to profile 23 subclones of a mouse melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational frame- work for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by indepen- dent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development of new computational methods.

genomics↗

bioRxiv2025

Disgust propensity, not disgust sensitivity, shapes the reactivity of a subjective disgust circuit in humans

Disgust constitutes an evolutionary adaptive defensive-avoidance response, yet humans vary markedly in their dispositional tendency to experience disgust (disgust propensity) and in their negative appraisal of such experience (disgust sensitivity). Conceptual frameworks and neuroimaging studies suggest that these traits may differentially modulate neural responses to disgust-eliciting stimuli; however, methodological constraints have left their precise roles unresolved. Our comparably large fMRI study (n = 142) therefore aimed to systematically determine how trait disgust modulates neural responses to carefully selected and validated disgust-specific visual stimuli across varying levels of subjective disgust experience. The whole-brain voxel-wise regression analyses revealed a neural dissociation between the two disgust traits, with disgust propensity, but not disgust sensitivity, modulating disgust-related neural activity in the anterior, middle, and posterior insula, as well as the caudate, putamen, thalamus, hippocampus, and parahippocampal gyrus. Mediation and network-level analyses further supported this dissociation by showing that disgust propensity shapes disgust experience via insula - striatal - hippocampal pathways. Together, these findings provide evidence for a neurofunctional dissociation of disgust propensity and sensitivity and elucidate how trait disgust shapes subjective experiences. They further suggest that disgust propensity and the identified systems may represent promising targets for the regulation of disgust-related pathology.

neuroscience↗

bioRxiv2025

Orthogonal and Robust Analytics Enable Reproducible and Scalable Manufacturing of High Purity Extracellular Vesicles Derived from Mesenchymal Stromal Cells

The development of extracellular vesicle (EV) based therapeutics requires robust analytical assays and scalable downstream processing (DSP) strategies to ensure product quality and reproducibility. In this study, we established an analytical toolbox comprising scattering and fluorescence mode nanoparticle tracking analysis (NTA), fluorescence-based flow cytometry (Fl-FC), and multi-detector analytical size exclusion chromatography. Liposomes, selected for their physicochemical similarity to EVs, were used as reference materials to optimize assay parameters, fluorescence labeling conditions, and dynamic range, minimizing artifacts such as photobleaching and masking effects. Using these optimized analytical tools, we designed a scalable DSP workflow for human bone marrow mesenchymal stromal cell (hBM-MSC) derived EVs, incorporating clarification, tangential flow filtration (TFF), ion exchange chromatography (IEX), buffer exchange, and sterile filtration. IEX chromatography resulted in the elution of two cell-secreted populations, with similar scattering signals while eluate 1 showed approximately 30 times higher absorbance signal compared to eluate 2. Transmission electron microscopy revealed that eluate 1 contained non-vesicular extracellular particles (NVEPs), and eluate 2 was enriched in EVs and showed higher expression of positive markers such as CD81 and CD73 using Simple Western. Additionally, the two IEX eluates showed different proteomic and lipidomic profiles. Then the analytical toolbox was utilized to monitor the DSP process in terms of particle recovery and impurity removal throughout the process determining the high purity level of the final EV preparation. Together, these results demonstrate that orthogonal analytics coupled to a scalable DSP yield reproducible MSC-EV preparations while depleting commonly co-isolated NVEPs. This practical framework advances process analytics of MSC-EV manufacturing and supports reporting of identity, purity, and function aligned with existing guidelines in the field. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/672476v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@142ff8aorg.highwire.dtl.DTLVardef@196c0bforg.highwire.dtl.DTLVardef@485c36org.highwire.dtl.DTLVardef@9dff39_HPS_FORMAT_FIGEXP M_FIG C_FIG Liposomes were used as reference materials to optimize single particle analytics as well as multi detector analytical chromatography to assess particle recovery and impurity removal. This optimized analytical toolbox enabled accurate monitoring of downstream processing steps, establishing a robust workflow for reproducible and scalable mesenchymal stromal derived extracellular vesicles bioprocessing.

bioengineering↗