Biology subjects

Yan, X.

Publications and source records attributed to Yan, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

YY1-concentration-dependent formation of mechanically distinct DNA condensates through different interaction mechanisms

The transcription factor Yin Yang 1 (YY1) plays roles in chromatin organization, combining sequence-specific DNA recognition via zinc finger domains with multivalent interactions mediated by intrinsically disordered regions. While YY1 has been implicated in phase separation and enhancer-promoter communication, how its structured and intrinsically disordered regions cooperate to shape DNA-protein assemblies remains unclear. Here, we used single-molecule DNA curtain fluorescence imaging to dissect the molecular basis of YY1-DNA assembly. We found that YY1 induces higher-order assembly in a concentration-dependent manner. At moderately high concentrations, YY1 formed weakly-linked DNA condensates in which dynamic, liquid-like YY1 molecules are scaffolded by relatively immobile, solid-like DNA. At high concentrations, zinc finger-mediated bridging dominates, producing strongly-linked condensates. Distinct domain-deletion mutants selectively impaired either weakly-(soft) or strongly-linked (hard) DNA condensate formation, indicating that these architectures are generated by separate domain-dependent mechanisms. Our findings establish a domain-level framework for DNA condensate formation and highlight how transcription factors can integrate specific and non-specific DNA interactions to control the material state of chromatin and would influence genome regulation.

biophysics↗

bioRxiv2026

ERFMTDA: Predicting tsRNA-disease associations using an enhanced rotative factorization machine

MotivationtRNA-derived small RNAs (tsRNAs) have emerged as a novel class of regulatory molecules implicated in the pathogenesis of many human diseases, making them as promising biomarkers and therapeutic targets. However, existing computational methods for tsRNA-disease association prediction often overlook explicit biological attributes and complex feature interactions, limiting their predictive performance. ResultsWe propose ERFMTDA, an enhanced rotative factorization machine framework for predicting potential tsRNA-disease associations. ERFMTDA explicitly models complex interactions among heterogeneous biological features while integrating latent structural representations derived from the global association matrix. In addition, a biologically informed negative sampling strategy based on motif-level sequence similarity is introduced to improve the reliability of negative samples. Extensive experiments demonstrate that ERFMTDA consistently outperforms eleven state-of-the-art methods. Case studies on diabetic retinopathy and hepatocellular carcinoma further confirm its ability to prioritize biologically meaningful tsRNA-disease associations. Availability and implementationThe source codes and datasets of ERFMTDA are available at https://github.com/lanbiolab/ERFMTDA.

bioinformatics↗

bioRxiv2026

Dynamic optimization of extrachromosomal DNA copy number drives tumour evolution

Extrachromosomal DNA (ecDNA) is common in human cancers and is associated with poor clinical outcomes, yet how ecDNA-driven genetic heterogeneity is translated into functional heterogeneity remains unclear. Using single-cell multiomics sequencing and multiplexed IF-FISH, we show that asymmetric inheritance of ecDNA generates copy number heterogeneity that propagates to gene expression programs, including oncogenic signaling and cellular stress responses. Transgenerational live-cell lineage tracking directly shows that ecDNA heterogeneity arises within only a few cell divisions and modulates daughter cell division timing in a copy number-dependent manner, a property not observed for evenly inherited chromosomal amplicons. We identify an optimal middle ecDNA copy number range that maximizes proliferative fitness at baseline, while drug selection pressure induced by low-dose CHK1 inhibition selects for cells with a new optimal range at low ecDNA copy numbers. These low ecDNA copy number cells pre-exist in the population and can be generated de novo, driving copy number shifts promoting drug resistance. In vivo experiments further demonstrate that shifts toward ecDNA copy numbers that are optimal under the tumour microenvironment enhance tumourigenicity. Together, these findings establish ecDNA copy number plasticity as a central driver of tumour evolution.

cancer biology↗

bioRxiv2026

Neural signatures of impaired semantic contextualization in Autism Spectrum Disorder

Social and communicative deficits are defining characteristics of autism spectrum disorder (ASD). Some theories suggest that these challenges, among other autistic traits, may arise from differences in predictive coding, or how the brain uses context to predict and interpret incoming information. This idea has the potential to link symptoms of autism to specific neurocomputational processes, and is especially promising for communication, whose impairment is a hallmark of ASD. Here we leveraged the ability of large language models (LLMs) to quantify semantic contextualization to analyze a unique dataset of responses from hippocampal neurons obtained during language listening in three mild-to-severe autistic individuals with comorbid epilepsy. Key elements of semantic coding were preserved in all three individuals with ASD: single-neuron response dynamics, representation of word-word semantic relationships, and patterns of context-dependent shifts in meaning. However, relative to controls, ASD resulted in reduced neural signatures of contextualization: (1) neuronal responses were aligned with earlier, less contextual layers of GPT-2, (2) ASD patients had lower effective dimensionality of the neural subspace predicting semantics, (3) neural representations of word meaning were less influenced by preceding context, and (4) neural signatures of lexical surprisal were reduced. Together, these results support theories of autism that emphasize impairments in contextualization, and highlight the power of LLMs as a tool for quantifying the computational basis of neurodevelopmental disorders.

neuroscience↗

bioRxiv2026

Neuromodulatory Control of Cortical Function: Cell-Type Specific Regulation of Neuronal Information Transfer

Neuromodulatory systems such as dopamine and acetylcholine enable cortical circuits to rapidly shift between behavioral states, yet it remains unclear how receptor-specific signaling reshapes what single neurons compute. Here we combined whole-cell recordings from excitatory and inhibitory neurons in layer 2/3 of mouse somatosensory cortex with information-theoretic analyses under frozen-noise stimulation. We quantified stimulus-to-spike information transfer and profiled each neuron across four functional domains: passive membrane biophysics, action potential dynamics, intrinsic adaptation currents, and input feature selectivity captured by spike-triggered averages. Activation of D1, D2, or M1 receptors produced cell-type and receptor-specific changes in fractional information and firing output, demonstrating that neuromodulators regulate encoding beyond simple gain control. Unsupervised clustering revealed that receptor activation reshapes neuronal functional identities, consistent with a dynamic computational phenotype. To identify the organizing principle underlying these transitions, we applied multi-set correlation and factor analysis and found that neuromodulation systematically reorganizes covariance among functional domains. In excitatory neurons, dopaminergic activation led to a decreased coordination between input feature selectivity and other functional properties while strengthening coordination among response based features such as spike dynamics and adaptation. Inhibitory neurons, by contrast, generally exhibited increased coordination across domains. These findings identify neuromodulation as a reconfiguration signal that reshapes not only individual cellular properties but also the architecture linking them, thereby dynamically expanding the computational repertoire of cortical circuits.

neuroscience↗

bioRxiv2026

Characterization of nanoparticles and fluorescent recombinant extracellular vesicles with three high-sensitivity flow cytometers

High-sensitivity flow cytometry (FC) allows multiparametric analysis of nanoparticles (NPs) and extracellular vesicles (EVs). With new instruments available, studies that evaluate their performance using the same materials in a controlled environment are required. Here, we performed a comparative study to investigate the capabilities of three flow cytometers, namely the NanoFCM (NF), BD Influx (IF) and CytoFLEX LX (CF). Firstly, we analyzed a mixed population of silica NPs (SiNPs, 68, 91, 114 and 155 nm) by using light-scatter based detection thresholds (SSC, FSC, VSSC) across a concentration range from 106 to 109 particles/mL. Next, we analyzed fluorescent recombinant EVs (rEVs) by comparing light-scatter based thresholding (488 nm SSC available for all platforms), the combination of SSC thresholding with a fluorescent gate, and fluorescent thresholding for their qualitative and quantitative analysis. We here provide the strengths and limitations for each platform regarding the analysis of differently sized NPs at different sample concentrations.

cell biology↗

bioRxiv2026

scAmp analyzes focal gene amplifications at single-cell resolution

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common driver of tumor progression and is associated with acquired drug resistance and poor patient survival. While whole genome sequencing (WGS) studies have revealed the landscape of genes amplified on ecDNA in tumors, it remains challenging to study the subclonal heterogeneity and functional (e.g., transcriptomic) consequences of ecDNA on tumors. To address this, we introduce scAmp: a probabilistic algorithm for detecting and analyzing ecDNA from single-cell datasets. We demonstrate scAmps improved accuracy over WGS approaches on well-characterized cell-lines and its applicability to clinical histopathology. We further showcase scAmp by analyzing 73 patient tumors profiled with single-cell ATAC-seq, where we analyze the subclonal evolution of ecDNA+ subclones and identify the effect of ecDNA amplifications on the chromatin accessibility landscape of cancer cells. Together, we anticipate that scAmp will broadly enable further studies - both retrospective and prospective - that dissect critical questions of how ecDNA affect cancer cells and the tumors in which they reside.

cancer biology↗

bioRxiv2026

Artificial Intelligence of Things-Enhanced Automated Surveillance System for Global Antimicrobial Resistance in Food Supply Chain

Antimicrobial resistance (AMR) threatens food safety across the farm-to-fork continuum. Real-time surveillance is crucial to mitigate its global escalation, yet conventional antimicrobial susceptibility testing (AST) remains slow, labor-intensive, and impractical for large-scale monitoring. We developed an Artificial Intelligence of Things (AIoT)-integrated multiplex microfluidic platform enabling automated AMR surveillance of pathogens in food supply chain. Each node combines a single-board AIoT controller (Orange Pi 5B), portable incubator, colorimetric microfluidic chips, and environmental sensors, reducing costs by 98% compared with standard AST. A lightweight YOLOv5 model embedded in the controller achieved >99% accuracy in identifying bacterial growth and inhibition under antibiotic pressure, showing 96% and 95% agreement with standard results for Salmonella and Campylobacter, respectively. Data are synchronized to a cloud server for real-time aggregation and early resistance warning. This fully automated and low-cost system minimizes human error and workload, providing a scalable sample-to-answer solution for AMR surveillance in global agri-food system.

microbiology↗

bioRxiv2026

Decomposing multi-scale dynamic regulation from single-cell multiomics with scMagnify

Deciphering the highly coupled regulatory circuits that drive cellular dynamics remains a fundamental goal in biology. However, capturing the multi-scale time-lagged dynamics and combinatorial regulatory logic of gene regulation remains computationally challenging. Here we present scMagnify, a deep-learning-based framework that leverages multiomic single-cell assays of chromatin accessibility and gene expression via nonlinear Granger causality to reconstruct and decompose multi-scale gene regulatory networks (GRNs). Benchmarking on both simulated and real datasets demonstrates that scMagnify achieves superior performance. scMagnify employs tensor decomposition to systematically identify combinatorial TF modules and their activation profiles across different time-lags. It enables a hierarchical dissection of the regulatory landscape-- from the activity of individual regulator to the combinatorial logic of regulatory modules and intercellular communications. We applied scMagnify to human hematopoiesis and mouse pancreas development, where it successfully recovered known lineage-driving regulators and provided novel insights into the combinatorial logic that governs cell fate decisions. Furthermore, in the context of kidney injury, scMagnifys intracellular communication module mapped key signaling-to-transcription cascades linking microenvironment cues to pathological epithelial cell changes. In summary, scMagnify provides a powerful and versatile computational framework for dissecting the multi-scale regulatory logic that governs complex biological processes in development and disease.

bioinformatics↗

bioRxiv2026

SPCoral: diagonal integration of spatial multi-omics across diverse modalities and technologies

Spatial multi-omics is indispensable for decoding the comprehensive molecular landscape of biological systems. However, the integration of multi-omics remains largely unresolved due to inherent disparities in molecular features, spatial morphology, and resolution. Here we developed SPCoral for diagonal integration of spatial multiomics across adjacent slices. SPCoral extracts spatial covariation patterns via graph attention networks, followed by the use of optimal transport to identify high-confidence anchors in an unsupervised, feature-independent manner. SPCoral utilizes a crossmodality attention network to enable seamless cross-resolution feature integration alongside robust cross-omics prediction. Comprehensive benchmarking demonstrates SPCorals superior performance across different technologies, modalities and varied resolutions. The integrated multi-omics representation further improves spatial domain identification, effectively augments experimental data, enables cross-modal association analysis, and facilitates cell-cell communication. SPCoral exhibits good scalability with data size, reveals biological insights that are not attainable using a single modality. In summary, SPCoral offers a powerful framework for spatial multi-omics integration across various technologies and biological scenarios.

bioinformatics↗

bioRxiv2026

Flexible perception of face attributes under naturalistic visual constraints

How does human brain adapt face perception to naturalistic visual constraints? While faces are typically perceived under dynamic and uncertain conditions, most studies use static presentation and enough temporal exposure, leaving the neural mechanisms of adaptive face processing poorly understood. Here, we employed steady-state evoked potential electroencephalography (EEG) to track how perception of multiple face attributes (age, emotion, gender, and race) adjusts to two ecologically relevant scenarios: (1) faces gradually sharpening from blur to clear (mimicking approaching from a distance), and (2) faces presented with incrementally increasing exposure times (mimicking brief, flashed encounters). By progressively increasing sensory input (via spatial frequency content in Experiment 1 and exposure time in Experiment 2) in a stepwise manner, we show that emotion and race categorization emerge early, even under high blur (e.g., 4.89 cycles/image) or brief exposures (e.g., 41.7-50 ms), reflecting coarse visual processing. In contrast, age discrimination requires higher clarity (e.g., 7.31 cycles/image) but shorter exposure (e.g., 41.7 ms) if clear images are presented. In both scenarios, gender processing exhibits the strongest dependence on clarity (e.g., 10.94 cycles/image) and time (e.g., 66.7 ms). Representational similarity analyses further show that reliable response patterns for emotion and race emerge earlier in the posterior brain region relative to those for gender in both experiments. Together, these results identify a flexible temporal order for face perception that adapts to naturalistic visual constraints, bridging the gap between controlled laboratory paradigms and naturalistic social vision.

neuroscience↗

bioRxiv2026

Semantic axes in the brain support analogical representations

ABSTRACTIn language models of word meaning, directions in the embedding space often correspond to semantic features that can be reused across different words. For example, a single direction corresponding to gender may differentiate word pairs like "boy/girl", "uncle/aunt" and "king/queen". Here we show that the same principle governs semantically driven neural responses in the human brain. We recorded populations of single neurons during podcast listening and identified word sets with consistent meaning differences. Across fifteen sets, including gender, plural, and negation, we observed consistent vectorial directions, resulting in parallelogram structures within the neural manifold. Deviation from parallelism in large language models (LLMs) predicted corresponding deviations in brain-derived parallelism. Among pronouns, vectors corresponding to case, number and person exhibited parallelogram structures individually and, collectively, obeyed the principle of commutativity, resulting in a prismatic structure. Finally, different semantic variables were preferentially associated with discrete groups of neurons, consistent with energy-efficiency theories. Together, these results establish a geometric foundation for the neural encoding of word meaning.

neuroscience↗

bioRxiv2026

Large-scale brain network differences during deliberate and spontaneous mind-wandering in a sustained attention task: An electroencephalography source-level connectivity study

Mind-wandering (MW), the shift of attention away from an ongoing task, can be classified as either deliberate or spontaneous, depending on whether these internal thoughts arise intentionally or unintentionally. Previous functional magnetic resonance imaging-based resting state studies have shown that spontaneous MW traits are positively correlated with coupling within the default mode network (DMN), whereas deliberate MW traits are positively correlated with coupling both within the DMN and between the DMN and control-or attention-related networks. However, state-based functional connectivity during each MW episode remains unknown. Addressing this knowledge gap, we investigated how deliberate and spontaneous MW differentially involve state-based large-scale network interactions during a sustained attention task using electroencephalography (EEG), which captures rapid network dynamics. Thirty-one participants performed the gradual-onset continuous performance task using 64-channel EEG. Mental states were classified via experience-sampling probes as on-task, deliberate, or spontaneous MW. EEG data from 1-s pre-probe epochs were analyzed using source estimation and the weighted phase lag index to assess functional connectivity within and across the DMN, control network (CN), dorsal attention network (DAN), and salience network (SN) in the delta, theta, alpha, and beta frequency bands. Relative to both spontaneous MW and the on-task state, deliberate MW was characterized by significantly stronger alpha band functional connectivity. A prominent connectivity cluster was centered on the right frontal operculum-insula of the SN, linking regions across the SN, CN, and DMN. In addition, deliberate MW exhibited enhanced SN-DAN-DMN coupling compared with the on-task state. By contrast, spontaneous MW did not show significant connectivity differences relative to the on-task state in any frequency band. Taken together, these results suggest that alpha band network interactions centered on the SN may contribute to the intentional regulation of internally directed thought during deliberate MW.

neuroscience↗

bioRxiv2026

Integration of early-stage cryopreservation and cell cycle modulation into a flexible kidney organoid differentiation system

Kidney organoids derived from human pluripotent stem cells (hPSCs) represent a promising platform for modeling nephrogenesis and renal diseases. However, conventional differentiation protocols are continuous and time-sensitive, limiting their scalability and reproducibility. Here, we developed a method to pause and resume organoid formation through cryopreservation at an early differentiation stage using a chemically defined formulation that maintains high post-thaw viability and differentiation potential. We further found that synchronizing hPSCs in the G1 phase with PD-0332991 enhanced post-thaw organoid formation and transcriptional fidelity, while G2 phase enrichment with Ro-3306 promoted the development of SLC12A3-positive distal convoluted tubules. The post-thaw organoids exhibited well-organized nephron architecture and function comparable to uninterrupted cultured controls. This platform proved effective for modeling BK polyomavirus (BKV) infection, drug-induced nephrotoxicity, and renal fibrosis. Together, our cryopreservation and cell cycle synchronization strategy provides a flexible, practical framework to advance organoid-based research and translation.

developmental biology↗

bioRxiv2026

Diet-Driven Gut Microbiome Differences Between Cohabiting Han and Yi Ethnic Groups in Liangshan, China

BackgroundWhile ethnic variations in gut microbiota are well-documented, their persistence in genetically distinct populations sharing the same geographic environment remains poorly understood. This study investigates the Han and Yi ethnic groups cohabiting rural Southwest China, where contrasting dietary traditions persist under identical environmental conditions. MethodsWe analyzed fecal samples from 100 healthy adults (50 Yi/50 Han) using 16S rRNA gene sequencing (V3-V4 region). Dietary intake was quantified via three 24-hour recalls validated with plasma biomarkers. Multivariate analyses (DESeq2, PERMANOVA) controlled for age, sex, and BMI covariates. ResultsYi individuals exhibited significantly higher Prevotellaceae (log2FC=1.82, padj=0.028) and Succinivibrionaceae (log2FC=2.15, padj=0.013) abundances, corresponding to their buckwheat-rich diet (58.5 {+/-} 12.3% vs Han 3.3{+/-}1.8%, p<0.001) Han microbiota showed enriched Bacteroidaceae (log2FC=1.24, padj=0.041), associated with higher animal fat intake (23.6% vs Yi 1.8%, p<0.001) Dietary factors (buckwheat/animal fat intake) explained greater microbiota variation than ethnic genetic ancestry (PERMANOVA R{superscript 2}=0.10 vs 0.07, p < 0.05) ConclusionCohabiting Han and Yi populations maintain distinct gut microbiota compositions primarily driven by dietary differences. These findings highlight diet as a stronger determinant than genetic ancestry in shaping microbial communities under shared environments. Future studies should validate these observations with metagenomic sequencing.

microbiology↗

bioRxiv2025

A cell membrane-hybridized nanocarrier-based RNA delivery system for ocular neovascular diseases

Ocular neovascularization is a common pathological feature of various blinding eye diseases. Current anti vascular endothelial growth factor (VEGF) therapies face challenges such as low response rates, frequent injections, and multiple complications. RNA-based therapeutics offer advantages including strong targeting capability and reversible modulation, but encounter obstacles like low delivery efficiency, susceptibility to degradation, and lack of targeting specificity. This study developed an endothelial cell membrane-hybridized lipid nanoparticle that achieve specific targeting of neovascular endothelial cells through "homing effects". The carrier exhibits excellent biocompatibility, high small interfering RNA (siRNA) delivery efficiency, and lysosomal escape capability, effectively penetrating the vitreous barrier. In a laser-induced choroidal neovascularization mouse model, it significantly enhanced drug accumulation at the lesion site, providing a safe and effective delivery strategy for RNA therapy of ocular neovascular diseases.

bioengineering↗

bioRxiv2025

Ultrahigh-Throughput Forster Resonance Energy Transfer-Activated Droplet Sorting for Terminator Polymerase Engineering

Droplet microfluidic screening systems enable high-throughput, labor-saving enzyme directed evolution by employing fluorescence, absorbance, and Raman-activated sorting strategies for library screening. Forster resonance energy transfer (FRET) - a nanoscale technique for monitoring intramolecular/intermolecular conformational changes - is yet to be integrated into this process. We upgraded a single-channel sorter to a dual-channel one without redesigning the microscopy setup, which can monitor FRET signals during enzyme reactions in droplets at kilohertz rates. We applied this upgraded sorter to improve the incorporation efficiency of KOD DNA polymerase towards reversible terminator deoxyribonucleotides, a property crucial for its application in next-generation sequencing (NGS). Our data show that a single-round sorting can achieve 30-fold enrichment of active variants. Five KOD variants enabling 100-cycle single-end runs of DNA sequencing were identified using a novel cyclic reversible termination (CRT) substrate featuring a terminator group {approx} 5-fold bulkier than the classical azidomethyl moiety. We also engineered a metagenome-derived novel polymerase, and a variant achieving 90% terminator incorporation efficiency within 2 minutes was identified after two rounds of enrichment. In sum, we provide a practical setup for dual-channel FRET-based droplet sorting, and demonstrate its ability in terminator polymerases engineering, thereby broaden the scope of microfluidic applications.

biochemistry↗

bioRxiv2025

DTCFinder: a bench-to-bits toolkit for label-free, whole-spectrum analysis of disseminated and circulating tumor cells in liquid biopsies

Disseminated (DTCs) and circulating tumor cells (CTCs) are rare but pivotal in understanding cancer metastasis and advancing liquid biopsy-based diagnostics. Traditional detection approaches, hindered by their dependency on epithelial markers or morphological features, impair the accurate identification of varied DTC/CTC phenotypes within liquid biopsies. We describe DTCFinder, a bench-to-bits, label-free method integrating high-throughput single-cell RNA sequencing and machine learning, enabling unbiased detection, in-depth molecular characterization, and precise tissue-of-origin tracing of DTCs/CTCs across various types of liquid biopsies, even with ultra-scarce DTC/CTC presence in peripheral blood and low sequencing depth. DTCFinder offers a robust and versatile toolkit for liquid biopsy-based cancer diagnostics, enhancing our understanding of cancer metastasis.

cancer biology↗