Biology subjects

Yin, Y.

Publications and source records attributed to Yin, Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Sexual dimorphism in the complete connectome of the Drosophila male central nervous system

Sex differences in behaviour exist across all animals, typically under strong genetic regulation. In Drosophila, fruitless/doublesex transcription factors can identify dimorphic neurons but their organisation into functional circuits remains unclear. We present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and nerve cord, fully proofread and annotated including fruitless/doublesex expression and 11,691 types. We provide the first comprehensive comparison between male and female brain connectomes to synaptic resolution, finding 7,205 isomorphic, 114 dimorphic, 262 male-specific and 69 female-specific types. This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours and the impact of dimorphic elements. Sex-specific/dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or arbours. Numerous circuit switches reroute sensory information to form antagonistic circuits controlling opposing behaviours.

neuroscience↗

bioRxiv2025

Quantitative Prevalence, Chemical Speciation, and ECM-Embedded Networks of Biogenic Silicon in Animal Tissues Revealed by Refined Analytical Methods

Silicon, abundant in Earths biosphere, plays crucial roles in many organisms. However, its biological functions in animals remain inadequately characterized, primarily constrained by methodological limitations in quantifying, identifying, and visualizing biogenic silicon in tissues. Here, we refined the molybdenum blue colorimetric method (MB) for more accurate silicon quantification, developed integrated mass spectrometry techniques to delineate chemical natures of biogenic silicon, and optimized micro-X-ray fluorescence (micro-XRF) imaging for spatial mapping in animals. Employing these methodologies, we demonstrated that silicon abundance in animals substantially exceeds prior estimates--surpassing essential elements like iron. We also revealed the chemical architectures of diverse silicon compounds and visualized tissue-specific distributions in mice and humans, indicating its predominant localization within extracellular matrix (ECM) and suggesting the formation of silicified networks. These findings establish silicon as a structurally and quantitatively significant element with profound biological implications, warranting its recognition as an essential factor in human physiology and pathology.

biochemistry↗

bioRxiv2025

spCorr: flexible and scalable inference of spatially varying correlation in spatial transcriptomics

Spatial transcriptomics has transformed our ability to explore gene expression within its tissue context, enabling us to dissect subtle yet biologically significant variations in situ. While numerous computational methods have been proposed for detecting Spatially Varying Genes (SVGs) expression by modeling each individual gene separately, much less effort has been devoted to understanding how correlations between genes change across space. Such Spatially Varying Correlations (SVCs) are critical for understanding biological processes such as gene regulatory mechanisms shaped by local tissue environments, yet existing tools remain limited for this task. To address this gap, we present spCorr, a flexible and scalable regression framework for studying SVCs. spCorr provides interpretable, spot-level estimates of gene correlation and detects gene pairs whose correlations vary across locations or between tissue domains. Through extensive simulations and real-data analyses, we show that spCorr achieves high detection power, reliably controls the False Discovery Rate (FDR), and is computationally efficient. Importantly, spCorr reveals biologically meaningful correlation patterns that highlight fine-scale tissue structures, gene module functions, and region-specific interactions, offering new opportunities to study coordinated gene regulation in spatial transcriptomics.

bioinformatics↗

bioRxiv2025

The Connectome Interpreter Toolkit

Complete synaptic wiring diagrams, or connectomes, of whole brains open new opportunities for studying the structure-function relationship of neural circuits. However, the large number of nodes and edges in the graphs makes the analysis challenging. Here, we present the Connectome Interpreter, an open-source software toolkit for efficient graph exploration to find polysynaptic pathways, compute the effective connectivity and receptive fields for arbitrarily deep neurons, slice out subcircuits, and construct non-linear but differentiable circuit models, implemented using efficient approaches tailored to large connectomes with abundant divergent and convergent connections, such as that of the fruit fly Drosophila melanogaster. Our approach delivers results orders of magnitude faster than conventional methods on consumer hardware. We demonstrate the capabilities of our toolkit with select applications, including quantifying the density of polysynaptic connections in the whole adult fruit fly brain, exploring the necessity for non-linearities in circuit modeling, and combining known function of neurons with the connectome to aid in formulating hypotheses of circuit function.

neuroscience↗

bioRxiv2025

Morphology-guided convolutional Graph Neural Network decodes optically barcoded nanoparticles for one-pot, purification-,amplification-, and enzyme-free femtomolar nucleic-acid diagnostics

Affordable, accurate, and rapid point-of-care diagnostic tests remain elusive due to inherent trade-offs between performance and cost. Conventional nucleic acid tests offer high sensitivity but require complex, expensive steps such as amplification and purification, whereas lateral flow assays are simple and low-cost but lack the necessary sensitivity for many applications. To bridge this gap, we present a miniaturized and simplified chip-based platform that combines three components into a single diagnostic pipeline: we use (i) spectrally distinct silver and gold nanoparticles that form analyte-dependent clusters with unique spectral fingerprints, (ii) a one-pot, enzyme- and purification-free assay on a chip integrated with a high-throughput automated low-cost microscope, and (iii) a morphology-guided convolutional Graph Neural Network that embeds morphology information into convolutional kernels and performs graph-based relational learning across particle-level features. This integration captures spectral, spatial, and morphological quantification at the particle level, rather than relying on bulk spectral shifts, thereby overcoming the limitations of contemporary nanoparticle assays and image-level deep learning approaches. Processing up to 5000 particles per image using only <5 GB GPU memory, Mc-GNN achieves femtomolar sensitivity with 98.2% recall for synthetic DNA and 94.8% for SARS-CoV-2 RNA from whole virus, despite variations in nanoparticle selection and sample complexity. By embedding morphological information into the biosensing pipeline, our diagnostic platform is computationally efficient, smartphone-compatible and is readily extensible to new analytes and multiplexing, offering a scalable solution for a fieldable diagnostic tool.

bioengineering↗

bioRxiv2025

Application of MT3 in the Development and Immunogenicity Evaluation of GnRH Vaccines

Research on immune castration vaccines is of great significance for animal management. In this study, five copies of GnRH were linked to two MT3s by a flexible linker, GGGGS, to construct a recombinant plasmid, pET30a-GnRH5-MT3, which was expressed and purified using E. coli BL21(DE3) cells. The recombinant protein was best expressed under the induction conditions of 25 with 0.25 mM IPTG, with a purity of up to 80% after purification. It was mixed with the external adjuvant ISA 206 and immunized mice by subcutaneous injection. Serum levels of GnRH-specific Antibody, T, FSH and LH were detected by ELISA, and testicular tissue morphology was observed by HE staining. The results showed that GnRH5-MT3 recombinant protein combined with external adjuvant could efficiently induce GnRH-specific antibody production in male mice (P < 0.01-0.001) and significantly reduce the Serum LH, FSH and T concentrations (P < 0.01-0.001). The immunized mice had sparse cells in the seminiferous tubules of the testis, significantly reduced cells at various developmental stages, and almost no mature spermatozoa. This study provides experimental basis for further application of the vaccine in animal immune castration.

immunology↗

bioRxiv2025

Class A PBPs reinforce the septal cell wall following initial synthesis by SEDS-bPBP pairs during bacterial cytokinesis

Bacterial cytokinesis requires the coordinated action of septal peptidoglycan (PG) synthases to ensure efficient and accurate septum formation. Two major types of synthases contribute: SEDS-bPBP and bifunctional class A PBPs (aPBPs). How their activities are organized in space and time has remained unclear. Using Deinococcus radiodurans, which often contains both constricting and closed septa in the same cell, we show that septal PG synthesis proceeds through distinct steps at different regions. Primary synthesis at the leading edge by SEDS-bPBP pairs drives constriction, whereas aPBPs act later at the lagging edge and in closed septa to thicken the wall, enhance cross-linking, and reinforce septal integrity. At the single-molecule level, both synthase types move directionally to perform processive PG synthesis at distinct sites. We also observed spatiotemporal separation of SEDS-bPBP and aPBP synthases in Staphylococcus aureus, suggesting that secondary septal PG synthesis is a conserved mechanism securing successful cytokinesis.

microbiology↗

bioRxiv2025

Mapping antigenic evolution of influenza A virus using deep learning-based prediction of hemagglutination inhibition titers

Seasonal influenza remains a significant public health challenge through unpredictable antigenic drift, where accumulated mutations enable immune evasion and necessitate vaccine updates. Comprehensive antigenic characterization has been hindered by the absence of real-world A(H1N1)pdm antigenic mapping and post-2012 A(H3N2) maps due to insufficient pair-wise hemagglutination inhibition (HAI) titrations. Here, we present an end-to-end transformer model that predicts HAI titers directly from viral genetic sequences with error under two-fold, comparable to experimental variability. This approach enables rapid and high-throughput augmentation of HAI titrations across viral isolates, allowing for constructing large-scale antigenic maps. Several novel evolutionary patterns emerged from our analysis. Our antigenic mapping identified three A(H3N2) clusters between 2012-2022 with transitions occurring approximately every 6 years. Notably, genetically diverse co-circulating subclades 3C.2a and 3C.3a (2015-2020) formed a single antigenic cluster. A(H1N1)pdm formed three less temporally distinct clusters, with a novel cluster emerging post-COVID-19. Using interpretable analysis, we identified mutation sites linked to antigenic cluster transitions that align with previous laboratory findings. Key A(H3N2) mutations primarily occurred within major antigenic epitopes, while A(H1N1) showed fewer key mutations, some outside recognized antigenic regions. Our deep learning approach accelerates antigenic characterization in surveillance at global scale and can be transferred to other variable pathogens, providing an actionable bridge between genomic sequencing and vaccine strain selection.

evolutionary biology↗

bioRxiv2025

Artificial Intelligence-driven Whole-brain Cell Mapping with Highly Multiplexed In Situ Hybridization

Recent advances in three-dimensional single-cell-resolution imaging have begun to link organ-wide and cellular level research in development and disease research. Harnessing the power of whole-mount cell staining and tissue-clearing, it became possible to quantify the cell populations throughout an intact organ. While powerful, whole-organ imaging remains limited by the inability to stain a broad range of molecular markers simultaneously and by the lack of an analytical scheme to precisely quantify the cell population. Here, we present a highly multiplexed whole-mount staining technique, utilizing the repeated application of fluorescent in situ hybridization. This technique, termed mFISH3D, was designed by extensively dissecting the chemical basis of hybridization reactions in fixed tissue. mFISH3D enabled the visualization of 10 types of mRNAs in an intact mouse brain and has been demonstrated in various biological specimens including the human brain. To achieve unprecedented levels of accuracy in spatial cell mapping, we developed artificial intelligence (AI)-driven workflow using self-supervised learning, significantly reducing the need for extensive manual annotations. The integration of mFISH3D with our AI solution sets a standard for high-dimensional tissue analysis, provides a new systematic framework for analyzing complex cellular ecosystems and enables comprehensive investigation of selective cellular vulnerabilities in diseases.

neuroscience↗

bioRxiv2025

Clonal lineage tracing of innate immune cells in human cancer

Innate immune cells constitute the majority of the tumor microenvironment (TME), where they mediate both natural anti-tumor immunity and immunotherapy responses. While single-cell T- and B-cell receptor sequencing has provided fundamental insights into the clonal dynamics of human adaptive immunity, the lack of appropriate tools has precluded similar analysis of innate immune cells. Here, we describe a method that leverages somatic mitochondrial DNA (mtDNA) mutations to reconstruct clonal lineage relationships between single cells across cell types in native human tissues. We jointly sequenced single-cell transposase-accessible chromatin and mtDNA to profile n=124,958 cells from matched tumor, non-involved lung tissue (NILT), and peripheral blood of early-stage non-small cell lung cancer (NSCLC) patients, as well as n=93,757 cells from matched tumor and peripheral blood of ovarian cancer patients. Single-cell concomitant profiling of lineage and cell states of thousands of immune cells resolved clonality across cell types, tissue sites, and malignancies. Clonal tracing of innate immune cells demonstrates that TME-resident myeloid subsets, including macrophages and type 3 dendritic cells (DC3), are clonally linked to both circulating and tissue-infiltrating monocytes. Further, we identify distinct DC-biased and macrophage-biased myeloid clones, enriched in the tumor and NILT, respectively, and find that their circulating monocyte precursors exhibit distinct epigenetic profiles, suggesting that myeloid differentiation fate may be predetermined before TME infiltration. These results delineate the clonal pathways of intratumoral myeloid cell recruitment and differentiation in human cancer and suggest that remodeling of the tumor myeloid compartment may be peripherally programmed.

genomics↗

bioRxiv2025

Measure Sodium Transport in Cells with NMR

Sodium ions (Na+) are fundamental to numerous physiological functions, such as maintaining electrolyte balance, enabling nerve impulse transmission, and facilitating muscle contraction. Dysregulation of Na+ transport across cell membranes is implicated in a range of health issues, including metabolic syndromes, neurological conditions, and cardiovascular diseases. However, current methods for assessing cellular Na+ activity often face limitations; they can be invasive or fail to capture dynamic changes. In this study, we introduce a non-invasive 23Na nuclear magnetic resonance (NMR) methodology designed to directly quantify the transport rate of sodium ions in living cells. Our technique integrates relaxation exchange spectroscopy (REXSY) with a multi-site exchange model, enabling the investigation of Na+ transport dynamics on a timescale of sub-seconds. A key advantage is its ability to differentiate between intracellular and extracellular Na+ pools based on the endogenous NMR relaxation difference, thereby avoiding the need for potentially disruptive exogenous reagents. Experiments conducted on human cell lines successfully demonstrated the techniques capacity to distinguish between various physiological states, such as when ion channels are pharmacologically blocked or activated. The resulting measurements of Na+ transport rates and intracellular Na+ fractions show a clear correlation with cellular metabolic activity, offering valuable quantitative markers for monitoring transmembrane ion dynamics in vitro.

cell biology↗

bioRxiv2025

Circulating tumor cell heterogeneity and MMP9 expression in clinical correlation with breast cancer

ObjectivesThis study aimed to investigate the association between the heterogeneity of circulating tumor cell (CTC) subtypes and the expression patterns of matrix metalloproteinase 9 (MMP9) with clinicopathological parameters in breast cancer patients. The distribution of CTC subtypes, MMP9 expression levels, and their influence on tumor progression. MethodsA total of 303 breast cancer patients were enrolled in this study. The association between CTCs subtypes in peripheral blood, MMP9 expression and clinical or pathological data of patients were analyzed. ResultsThe number of mesenchymal CTCs increased significantly with the number of lymph node metastases (P < 0.05), and high MMP9 expression was positively correlated with lymph node involvement (P < 0.05). The proportion of intermediate to high MMP9 expression was significantly higher in mixed CTCs compared to epithelial and mesenchymal CTCs (P < 0.05). High MMP9 expression in tumor tissues was associated with increased CTCs production and induction of epithelial-mesenchymal transition (EMT) (P < 0.05), as well as an elevated number of MMP9-positive CTCs (P < 0.05). ConclusionsThese findings suggest that MMP9 is positively associated with CTCs production and EMT process, and MMP9 may contribute to tumor metastasis by promoting CTC shedding and EMT by degrading the extracellular matrix. We recommend compliance with the Mesh guidelines: https://www.ncbi.nlm.nih.gov/mesh/

cancer biology↗

bioRxiv2025

Sexually-dimorphic neurons in the Drosophila whole-brain connectome

Sexual dimorphisms in the brain arise from differences in cell number, morphology, and connectivity, and underlie sex-specific behaviors. In Drosophila, these differences are largely governed by the transcription factors Fruitless (Fru) and Doublesex (Dsx), expressed in neurons implicated in reproductive and social behaviors. However, how these neurons are integrated into the connectome to drive different behaviors in different sexes remains unclear. Here, we identify 98 putative Fru- or Dsx-expressing cell types ([~]1700 neurons) in the female whole-brain connectome by matching electron microscopy reconstructions to light microscopy images. We then analyze their connectivity within the whole-brain network. Fru/Dsx neurons are strongly interconnected, but are not part of a private network. We measure distances to the sensory periphery to examine multisensory interactions, and map connections to descending neurons that drive behavior. We further cluster Fru/Dsx neurons based on whole-brain connectivity, and uncover functional groupings. We analyze a subset of strongly connected Fru/Dsx neurons to make functional predictions, and test some of these with experiments. Together, this work provides a brain-wide framework for understanding the organization of sexually dimorphic circuits and establishes a resource for dissecting the neural basis of social and sexual behaviors.

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

High-throughput linear amplification for single-cell template strand sequencing with sci-L3-Strand-seq

Despite the many advances in single cell genomics, detecting structural rearrangements in single cells, particularly error-free sister-chromatid exchanges, remains challenging. Here we describe sci-L3-Strand-seq, a combinatorial indexing method with linear amplification for DNA template strand sequencing that cost-effectively scales to millions of single cells, as a platform for mapping mitotic crossover and resulting genome instability events. We provide a computational framework to fully leverage the throughput, as well as the relatively sparse but multifaceted genotype information within each cell that includes strandedness, digital counting of copy numbers, and haplotype-aware chromosome segmentation, to systematically distinguish seven possible types of mitotic crossover outcomes. We showcase the power of sci-L3-Strand-seq by quantifying the rates of error-free and mutational crossovers in thousands of cells, enabling us to explore enrichment patterns of genomic and epigenomic features. The throughput of sci-L3-Strand-seq also gave us the ability to measure subtle phenotypes, opening the door for future large mutational screens. Furthermore, mapping clonal lineages provided insights into the temporal order of certain genome instability events, showcasing the potential to dissect cancer evolution. Altogether, we show the wide applicability of sci-L3-Strand-seq to the study of DNA repair and structural variations.

genomics↗

bioRxiv2025

TRiC-assisted folding of class I HDAC family proteins regulated by distinct co-chaperone and cofactor networks

Class I histone deacetylases (HDACs), including HDAC1, HDAC2, HDAC3, and HDAC8, are essential for diverse cellular processes. Although the chaperonin TRiC is implicated in the activation of class I HDACs, the underlying mechanisms remain elusive. Using cryo-electron microscopy (cryo-EM), cross-linking mass spectrometry (XL-MS), and biochemistry analyses, we established class I HDACs as novel TRiC substrates and elucidate TRiC-assisted folding of HDAC1 and HDAC3 during its ATPase cycle, orchestrated by distinct co-chaperone and cofactor networks. In the closed TRiC chamber, both HDAC1 and HDAC3 adopt near-native states with shared binding interfaces. However, their open-state configurations diverge: Hsp70 and PDCD5 engage atop and within TRiC, respectively, for HDAC3, whereas prefoldin (PFD) binds atop TRiC for HDAC1, suggesting roles in substrate delivery and folding modulation. Furthermore, an unexpected bent conformation of CCT4, detected in TRiC-HDAC1 complex, may facilitate co-chaperone dissociation from TRiC. In contrast, HDAC8 folds independently of TRiC. Our study reveals the mechanism governing TRiC-assisted folding of class I HDACs in orchestration of dynamic co-chaperone/cofactor network, shielding new lights on the sophisticated regulatory landscape of TRiC, and open promising avenues for designing peptides or small molecules to selectively modulate TRiC-assisted folding of class I HDACs and other substrates.

molecular biology↗

bioRxiv2025

Neurocomputational Mechanisms Linking Future Interaction Prospects to Reactive and Proactive Costly Punishment

Costly punishment within social bonds manifests as both reactive penalties for transgressions and proactive sanctions against fair behaviors, yet a unified neurocomputational framework remains elusive. Combining neuroimaging and computational modeling, this study examined how future interaction prospects--a key element of social bonds--modulate reactive and proactive punishment. Participants acted as second- or third-party punishers, responding to allocation offers from individuals with or without future interactions. Results showed that participants imposed harsher punishment on both fair and unfair offers from individuals they did not expect to interact with, aligning with lower self-reported social closeness. Heightened provocation sensitivity, reflected in dorsal anterior cingulate cortex activity, drove the increased reactive punishment toward wrongdoers without future interactions, whereas punishment of future-interacting transgressors was associated with enhanced dorsolateral and dorsomedial prefrontal cortex (dlPFC, dmPFC) activity. A stronger preference for relative advantage and reduced harm sensitivity, linked to dlPFC and temporoparietal junction activity, mediated greater proactive punishment towards non-future-interacting individuals. Moreover, proactive punishment and underlying neurocognitive processes followed a collective retaliation pattern, increasing after unfair (vs. fair) behavior from another non-future-interacting individual. These findings establish a unified neurocomputational framework for understanding how future interaction prospects regulate costly punishment, integrating reactive and proactive motives within social bonds.

neuroscience↗

bioRxiv2025

An approach to differentiating biological from chemical sulfide oxidation on corroded concrete sewer surfaces

Hydrogen sulfide (H2S)-induced sewer corrosion largely impacts the longevity of infrastructure worldwide. The corrosion incorporates two pathways, i.e., chemical and biological oxidation of H2S to sulfuric acid; however, their rates are difficult to differentiate on corroded sewer pipelines, limiting understanding and application of effective strategies for corrosion control. Here, this study develops and reports a kinetic approach to determining rates of chemical and biological sulfide oxidation. By measuring the total sulfide uptake rate of sewer corrosion concretes, extensive experimental data was obtained for calibration and validation of the proposed kinetic model. The modelling results fit with measured data indicating that both chemical and biological sulfide oxidation can be well-described by the developed model. Model predictions revealed that along with sewer corrosion (i.e., pH decrease), chemical sulfide oxidation rate was significantly accelerated, along with an increase in biological rate. This result suggests that the chemical sulfide oxidation process should not be overlooked for seriously corroded concretes, and synergistic strategies to suppress both biological and chemical rates are required. Specifically, the chemical process can predominate sulfide oxidation with a gas-H2S concentration higher than 300 ppm. The work provides a useful tool for future corrosion studies and also valuable insights for sewer corrosion management practices. SynopsisThis study reports a kinetic approach to determining chemical and biological H2S oxidation rates of sewer concretes. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/653906v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1cfd16aorg.highwire.dtl.DTLVardef@bd9677org.highwire.dtl.DTLVardef@e1610forg.highwire.dtl.DTLVardef@ab3dfc_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗