Biology subjects

Xie, Y.

Publications and source records attributed to Xie, Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Phosphoinositide Variant Fuels 53BP1 Oligomerization and Higher-Order Assembly in the DNA Damage Response

53BP1 nuclear bodies are dynamic structures with properties resembling biomolecular condensates, but the molecular determinants that govern 53BP1 higher-order assembly at DNA double-strand breaks (DSBs) remain to be established. Here, we show that 53BP1 condensation is stimulated by phosphatidylinositol 3-phosphate (PI(3)P) in vitro through a highly specific interaction with its C-terminal tandem BRCT domain (tBRCT). Consequently, mutational inactivation of the 53BP1 tBRCT domain compromised PI(3)P binding and suppressed 53BP1 optodroplet formation in vivo. We further show that rapid 53BP1 clustering following DNA damage precedes its stable assembly on DSB-flanking chromatin, requires its tBRCT, and is suppressed by sequestration of nuclear PI(3)P. Taken together, our findings identify PI(3)P binding as a mechanism that promotes the formation and maturation of 53BP1 condensate-like assemblies on damaged chromatin.

cell biology↗

bioRxiv2026

Cardiac oxidative stress monitoring enabled by hierarchical mechanical adaptation

Soft bioelectronics have advanced cardiac monitoring through electrophysiological tracking, yet this alone cannot resolve the metabolic pathology essential to surgical decision-making. However, real-time molecular sensing on beating hearts remains unresolved due to deformation-induced sensor failure and stress-induced metabolite artifacts. This challenge is exemplified by ischemia-reperfusion injury (IRI), a major cardiac surgery complication characterized by reactive oxidative species (ROS) bursts, where true pathological ROS signals being confounded by mechanotransduction-induced ROS artifacts. Herein, we propose an enzymatic cardiac oxidative stress biosensor (E-cardiac) with hierarchical mechanical adaptation: macro-scale biofluid-mediated contact, micro-scale fiber reorganization, and nano-scale enzymatic confinement within gold nanoarches dissipate interfacial stress. This produces ultrathin ([~]460 nm), soft (0.79 kPa) E-cardiac with robust electrochemical stability (100% strain), low detection limit (380 nM), rapid adhesion (<3 s), stable biosensing on beating heart, as well as minimal invasive deployment capability. Mechanical analysis and cellular studies confirm mitigated stress-induced ROS and absent PIEZO channel activation. Validated across cardiomyocytes, ex vivo tissues, multi-species ischemia models (mouse, rat, rabbit, pig), rat ischemia-reperfusion injury, and Langendorff hearts simulating graded perfusion deficits, E-cardiac quantitatively differentiates IRI severity (sham < ischemia < reperfusion) as well as detecting the "ECG blind window". The E-cardiac platform provides real-time metabolic feedback for surgical guidance during cardiac procedures, enabling timely intervention before irreversible damage.

bioengineering↗

bioRxiv2026

Learning latent conformational landscapes encoded in cryo-EM

A protein populates a landscape of structural states, abundant snapshots of which are sampled in every cryo-EM experiment. Current analysis averages these snapshots into single density maps or partitions them into discrete classes, discarding the continuous dynamics encoded in the data. Continuous latent-space methods offer a promising alternative, yet whether their learned representations are physically grounded remains unresolved. Here, we realize cryo-EM as structural landscape microscopy, in which latent density directly reflects the probabilistic distribution of molecular states. A central question is whether such a landscape reflects physical reality. For integrin v{beta}8, the learned landscape shows strong agreement with independently derived molecular dynamics simulations, supporting its physical plausibility. We then apply the landscape to structural states that conventional cryo-EM cannot resolve. For LIS1-mediated dynein activation, the landscape reveals a spectrum of states from dominant conformations to low-population intermediates defined by distinct binding modes, including a previously unresolved state. For the KCTD5/CUL3NTD/G{beta}{gamma} complex, the landscape resolves continuous conformational pathways directly from experimental data. Probability-guided particle selection further improves reconstruction quality.

molecular biology↗

bioRxiv2026

Metaplastic sleep regulation in Drosophila determined by microscale circadian neural dynamics

The biophysical mechanisms by which circadian clock neurons integrate temporal coding signals to regulate sleep remain elusive. Here, using Drosophila, we identify Rabphilin (Rph) in DN1p clock neurons as a key stabilizer of the metaplasticity setpoint governing circadian regulation of sleep. Rph protein levels are elevated at night relative to daytime and modulate stochastic process of DN1p membrane potential dynamics linked to variability in synaptic activity at connections between DN1p neurons and their downstream postsynaptic partners. We find that Rph acts as a bidirectional regulator of synaptic plasticity thresholds. Under dim nocturnal light stimulation, Rph knockdown permits synaptic potentiation, whereas synthesized Rph introduction induces synaptic depression. In contrast, under optogenetic manipulation mimicking daytime spiking in DN1p neurons, these effects are reversed. We further show that spike-timing-dependent plasticity emerges when postsynaptic spiking is engaged, with nocturnal dim light conditions determining the direction of plasticity. Together, these findings establish a mechanistic link between microscale circadian neural dynamics and hierarchical metaplastic regulation, demonstrating how circadian regulation of sleep dynamically balances stability and adaptive flexibility through circadian setpoints and environmental nocturnal light interactions. Significance StatementWe show that circadian metaplasticity regulates sleep through membrane potential dynamics. Circadian clock neurons implement flexible metaplasticity, whereby the direction can be determined by internal circadian setpoints and interactions with nocturnal environmental light. This mechanism engages spike-timing-dependent plasticity to determine plasticity polarity. Our findings identify membrane potential dynamics as a computational substrate for physiological state control, linking molecular mechanisms to circuit-level circadian regulation of sleep. Together, they reframe sleep regulation as an active metaplastic process that hierarchically integrates microscale circadian neural dynamics to optimize circuit function.

neuroscience↗

bioRxiv2026

Single-molecule spatial genomics reveals the multi-scale organization and plasticity of extrachromosomal DNA in glioblastoma

Extrachromosomal DNA (ecDNA) is a major driver of intratumoral heterogeneity and is associated with poor clinical outcomes across cancers, yet how individual ecDNA molecules are organized and regulated within intact tumors remains unknown. Here, we leveraged single-molecule, multi-modal spatial genomics to resolve the three-dimensional chromatin organization and transcriptional activity of individual EGFR-containing ecDNA molecules in glioblastoma (GBM) cells in vitro, in orthotopic xenografts, and in patient-derived GBM tissue. At the larger scale, we find that distinct GBM molecular and functional states emerge depending on the local cellular environment. EGFR expression was markedly different between GBM subpopulations, and perturbations of EGFR dosage shifted GBM cellular states. ecDNA expression was modulated by multiple mechanisms, including variation in copy number, chromatin organization, DNA sequence, and chromosomal reintegration, which were simultaneously measured within the same cells. At the single-molecule scale, ecDNA adopts a physically expanded chromatin configuration with larger ecDNA molecules having higher transcriptional activity and interaction with active transcriptional machinery. ecDNA regulation was coordinated within cells and across GBM states, and ecDNA copy number, structure, and transcription were spatially organized across the tumor architecture. Co-culturing GBM cells with neurons recapitulated key features of infiltrative regions, including lower EGFR expression, reduced ecDNA copy number, and increased chromosomal reintegration, suggesting a causal role for the microenvironment in shaping ecDNA regulation. Collectively, these findings support a model in which GBM states and ecDNA are linked, plastic, and influenced by microenvironmental contexts, revealing a previously inaccessible layer of genome organization underlying tumor heterogeneity and malignant cell behavior.

genomics↗

bioRxiv2026

Identifying genetic regulations on immune cell type proportions and their impacts on autoimmune diseases

Genetic regulation of immune cell composition plays a crucial role in the etiology of complex diseases, yet remains poorly understood. We propose a unified analytical framework that integrates genome-wide association studies (GWAS) of cell type proportions with cell-type-wide association studies (cWAS) to systematically characterize both the genetic regulation of immune cell composition and its downstream effects on disease risk. Using single-cell RNA sequencing data from the OneK1K cohort, we conducted a GWAS of immune cell-type proportions with a depth-weighted quasi-binomial model designed for bounded, overdispersed traits. We identified 47 genome-wide significant loci influencing eight fine-labeled immune cell subtypes. Leveraging these identified genetic effects, we further imputed genetically regulated proportions (GRPs) using polygenic risk score (PRS)-based imputation and assessed their associations with complex diseases through cWAS. We identified five significant cell type-disease associations, including two with type 1 diabetes, two with Crohns disease, and one with ulcerative colitis. Together, our results demonstrate that cell type proportions observed in scRNA-seq can reveal regulatory loci and offer insights into how genetic variations regulate immune cell type proportions to affect disease risk. Although we focused on immune single-cell data, our framework is applicable to other tissues or cellular compositions as scRNA-seq datasets expand. Author SummaryGenome-wide association studies (GWASs) have uncovered many disease-associated signals, yet most lie in noncoding regions and are difficult to interpret. Mapping GWAS signals to the relevant cell types is therefore important for better understanding the biological mechanisms that drive disease. A major challenge is that observed gene expression and measured cell-type proportions can be influenced by environmental factors and disease status. In contrast, genotypes are less affected by these factors, making them more reliable for interpreting factors of diseases. Moreover, the cell-type proportions are bounded and often skewed, so standard GWAS models that rely on Gaussian assumptions may lose power. To address this, we developed a quasi-binomial approach that better matches the data and improves discovery while controlling false positives. In real data, our method identified more genetic loci associated with cell-type proportions than a traditional linear model. To further investigate how genetic variation regulates immune cell composition to influence disease risk, we integrated our results with disease GWAS summary statistics to identify immune cell types that may contribute to disease susceptibility. Together, our results link disease-associated GWAS signals to specific immune cell types and provide insights into the cellular mechanisms that may underlie these diseases.

genetics↗

bioRxiv2026

Quiescence improves Candida albicans survival of fungicidal drug exposure

Quiescence, defined as the reversible exit from mitotic division and proliferative growth, is the predominant state of all microbes. Despite its prevalence, the properties and consequences of quiescence in Candida albicans, an opportunistic fungal pathogen, remain largely unexplored. In this study, we characterized the morphological, molecular, and biophysical properties of quiescent C. albicans cells and assessed the effects of quiescence on antifungal drug efficacy. Quiescent cells that were induced via carbon starvation in rich and minimal media underwent distinct morphological changes upon entry into quiescence; this included an increase in cell buoyant density, altered fluidity of the cytoplasm and nucleus, and remodeling of mitochondria. Most C. albicans cells arrested in an unbudded G1/G0 state, although a significant fraction of cells had budded morphologies and 4N DNA content, indicating that they arrested at other cell cycle phases. Both budded and unbudded quiescent cells efficiently re-entered the cell cycle upon nutrient replenishment, with time-to-quiescence exit varying depending on the total nutritional quality of the medium. Quiescence was associated with large-scale gene expression remodeling, including downregulation of ribosomal biogenesis genes and upregulation of autophagy and stress response pathways. Notably, a greater proportion of quiescent cells than proliferative cells survived exposure to the commonly used antifungal drugs micafungin, caspofungin, and amphotericin B in genetically diverse strains. Thus, quiescence is a distinct cellular state with important implications for antifungal drug efficacy in C. albicans. Author SummaryWe show that Candida albicans, a common fungal pathogen, can enter a reversible, non-dividing state when starved of carbon. Starved cells become smaller and denser, reorganize their mitochondria, change how densely packed the inside of the cell and its nucleus are, and switch on stress-protection and internal recycling programs while reducing protein synthesis activity. Most cells have ceased to actively divide, but many retained budded shapes and could restart growth when nutrients returned; the timing of recovery depended on the nutritional environment in which quiescence was initiated. Critically, quiescent cells from laboratory and clinical strains exhibited greater survival than proliferative cells when exposed to widely used fungicidal drugs including micafungin, caspofungin, and amphotericin B. These findings indicate that quiescence is an active, adaptive physiological state that helps Candida albicans survive hostile environmental conditions such as temperature stress and drug exposure. Accounting for the metabolic state of fungal cells in diagnostics and drug development may improve treatment outcomes.

microbiology↗

bioRxiv2026

Longitudinal modality prediction learns gene regulatory patterns: insights from a single-cell competition

Simultaneous measurement of chromatin, transcriptomic, and proteomic features in single cells opens new avenues for modeling interactions between molecular layers during dynamic biological processes. Predicting one modality from another - such as inferring gene expression from chromatin profiles or protein abundance from RNA - has the potential to reveal regulatory relationships and enhance downstream analyses. However, conventional approaches for predicting gene regulation have largely failed and method development in modality prediction for regulatory inference has been limited. To explore effective modeling strategies and stimulate innovation, we generated a purpose-built longitudinal multimodal benchmarking dataset that captures early hematopoietic differentiation and organized the largest single-cell data competition to date, receiving over 27,000 submissions from 1,602 competitors worldwide. In our extensive analysis of the competition results, we demonstrated that top-performing approaches outperform state-of-the-art methods, and uncover how best-performing models captured biologically meaningful regulatory relationships between modalities. With ablation studies of the winning models, we identified feature-engineering strategies, model architectures and cross-validation schemes that are crucial for outstanding performance, and provide simplified, reproducible, light-weight code for state-of-the-art models. Together, the benchmark and analyses serve as an evaluation standard and guide future method development, including recently emerging foundation models, to advance our understanding of regulatory interactions in longitudinal, multimodal single-cell data.

bioinformatics↗

bioRxiv2026

Local Tumor Microenvironment Niches Correlate With Survival And Immunotherapy Response In Human Glioblastoma

Background: Glioblastoma (GBM) is an aggressive form of primary brain cancer. Recent efforts to characterize GBM using single-cell or spatial transcriptomics have revealed tremendous intra-tumoral heterogeneity among malignant cells and across different tumor areas. However, most studies have focused on malignant cells, and the spatial and cellular heterogeneity of the tumor microenvironment (TME) remains poorly understood. Consequently, it is unclear how TME compositions and organizations influence clinical outcomes for patients. Results: Spatial transcriptomics on 25 tumors, integrated with single-cell RNA sequencing and histological analyses, enabled the estimation of cellular composition within the TME across 46,000 spots (55-m wide). This analysis identified spatial associations between mesenchymal-like cancer cells and monocyte-derived macrophages and revealed six unique niches (N1-N6) characterized by distinct cellular composition and biological pathway activations. Deconvolution of large-scale bulk RNA-seq datasets using a spatial transcriptomics reference revealed that tumor niche composition significantly associated with patient survival and response to immunotherapy. Specifically, the N1 niche, characterized by the enrichment of mesenchymal-like cancer cells and monocyte-derived macrophages alongside hypoxic signature, was associated with lower overall survival. In contrast, the N5 niche, characterized by the enrichment of oligodendrocyte progenitor-like cancer cells and microglia-derived tumor-associated macrophages, was associated with longer patients survival. Analysis of data from patients treated with immunotherapy identified that niches N1 and N3, characterized by mixed mesenchymal-like and astrocyte-like cells, were associated with poor response to anti-PD-1 immune checkpoint inhibitors. Conclusions: Our results demonstrate that GBM exhibits significant spatial heterogeneity within the TMEs, comprising distinct niche classes, and that the specific niche compositions are associated with overall survival and immunotherapy responsiveness. Our results suggest incorporation of TME niche categories as biomarkers for risk stratification and therapeutic decisions for GBM patients.

cancer biology↗

bioRxiv2026

Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma

BackgroundCurrent risk stratification for Ewing sarcoma relies heavily on clinical factors such as metastatic status, failing to capture histologic heterogeneity as a potential prognostic indicator. Although pathology reports contain rich biological data, this information remains locked in unstructured narrative text, limiting large-scale retrospective analyses. We aimed to validate the utility of Large Language Models (LLMs) for scalable data abstraction and to identify prognostic histologic features from a large multi-institutional cohort. MethodsWe conducted a retrospective cohort study using data from six Childrens Oncology Group (COG) clinical trials. We utilized an LLM-based pipeline (OpenAI o3) to extract structured variables--including immunohistochemical (IHC) markers and CD99 staining patterns--from digitized, Optical Character Recognition (OCR)-processed pathology reports. Extraction accuracy was validated against a human-annotated ground truth (n=200) and cross-validated against senior experts (n=48). We assessed the association between extracted features and Overall Survival (OS) using Kaplan-Meier analysis and multivariable Cox proportional hazards regression, adjusting for metastatic status. FindingsWe analyzed 931 diagnostic pathology reports from 185 institutions spanning over 21-years. The LLM achieved a weighted average accuracy of 94% across 17 IHC markers; in a cross-validation subset, the LLM outperformed human annotators (weighted average accuracy over 15 IHC markers: LLM o3: 98.1%, a pediatric resident 91.4%, and a pediatric oncologist 95.9%). Survival analysis identified Neuron-Specific Enolase (NSE) and S100 as significant prognostic biomarkers. After adjusting for metastatic status, NSE positivity was associated with significantly inferior survival (HR 2.15, 95% CI 1.15-4.02, p=0.016); this risk was most pronounced in patients with non-metastatic disease (HR 5.64, p=0.0055). Conversely, S100 positivity was associated with improved survival (HR 0.58, 95% CI 0.34-1.00, p=0.046). InterpretationLLM-assisted extraction of pathology variables is highly accurate and scalable, capable of unlocking "dark data" from historical clinical trials. We identified NSE as a potent risk factor and S100 as a protective marker in Ewing sarcoma, particularly in localized disease. These findings suggest that AI-derived histologic data can refine risk stratification and, if validated, warrant inclusion in future prospective trials. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar for articles published up to December 2025, using terms such as "Ewing sarcoma", "prognosis", "risk stratification", "large language models", "natural language processing", and "pathology report extraction". Current risk stratification for Ewing sarcoma relies predominantly on clinical variables, specifically the presence of metastatic disease and tumor size. While histologic heterogeneity is well-documented, it is rarely incorporated into risk models because extracting structured data from narrative pathology reports is labor-intensive. Small, single-institution studies have suggested potential prognostic roles for markers like Neuron-Specific Enolase (NSE) or S100, but results have been inconsistent and limited by small sample sizes. Furthermore, while Large Language Models (LLMs) have demonstrated potential for extracting clinical data, their application to "rescue" data from legacy, multi-institutional clinical trial documents (scanned PDF images) for rare disease biomarker discovery has not been validated at scale. Added value of this studyTo our knowledge, this is the largest study to utilize AI to extract histologic data from Ewing sarcoma pathology reports, aggregating 931 patients from six distinct Childrens Oncology Group (COG) clinical trials spanning over 21 years. We validated an LLM-based pipeline that converted noisy, optically character-recognized text from diverse institutions into structured data with 98.1% accuracy, outperforming human annotators. This scalable approach revealed that NSE positivity is a significant independent risk factor for mortality (HR=2.15), particularly in patients with non-metastatic disease where it confers a more than 5-fold increased risk of death. Conversely, we identified S100 positivity as a protective factor associated with improved survival (HR=0.58). Implications of all the available evidenceThis study demonstrates that LLMs can reliably unlock "dark data" from historical clinical trials, rendering vast archives of unstructured medical documents accessible for retrospective analysis. The identification of NSE and S100 as robust prognostic biomarkers suggests that these widely available immunohistochemical stains provide valuable information beyond standard diagnostic information. These findings support the integration of automated data extraction tools in research workflows and suggest that NSE and S100 status should be considered in the design of future risk-stratified clinical trials for Ewing sarcoma.

bioinformatics↗

bioRxiv2026

A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia

The basal ganglia (BG) underlie motor control, reward processing, and many neurological and psychiatric disorders, but a comprehensive epigenomic and 3D-genome atlas of the human BG is lacking. Here we present a multimodal single-cell atlas profiling DNA methylation and 3D chromatin conformation in 261,331 nuclei (snm3C-seq) across eight subregions, resolving 12 classes, 31 subclasses, and 59 groups. Harmonized under the HMBA basal-ganglia consensus taxonomy, this atlas integrates with matched RNA, ATAC-seq, and histone-modification data across five regulatory layers. We identify millions of cell-type- and region-specific differentially methylated regions enriched for distinct transcription factor motifs and link them to disease-associated heritability. Neuron-specific loops dominate cell-type-specific 3D contact remodeling, while most non-neuron-specific loops are constitutive. Among spiny projection neurons (SPNs), chromatin loops, rather than TAD boundaries, distinguish D1, D2, and eccentric SPN subclasses, with eccentric SPNs showing the most loop-level reorganization among the three. We characterize STR D2 SMYD2-HTR7 SPN, a newly recognized POU6F2 D2-SPN subtype, and reveal region-specific methylation and contact gradients of disease-associated genes, including CADM1 and PDE8B. Integrative gene-regulatory networks reconstruct cell-type-resolved enhancer-promoter links to interpret Parkinsons disease risk variants at SNCA. Finally, MERFISH spatial profiling combined with cross-species Patch-seq identifies non-SPN neuronal subtypes, including a MOXD1 striosomal STR FS PTHLH-PVALB GABA subtype with distinct electrophysiology, partitioning across the striatal matrix-striosome boundary. HighlightsO_LIA multimodal single-cell atlas maps DNA methylation and 3D genome architecture across human basal ganglia cell types and subregions. C_LIO_LINeuron-specific loops dominate cell-type-specific 3D contact remodeling in the human BG, whereas most non-neuron-specific loops are constitutive. C_LIO_LISpiny Projection Neuron (SPN) subtypes exhibit regionally organized epigenomic and 3D genome signatures that align with dorsal-ventral identities. C_LIO_LIChromatin loops are the primary distinguishing feature among D1, D2, and eccentric SPN subclasses, with eccentric SPNs being the most 3D-reorganized. C_LIO_LIIntegrated regulatory maps link cell-type-specific enhancers to disease-associated genetic risk in the human basal ganglia. C_LIO_LINon-SPN interneurons differ in distribution and in transcriptional and epigenetic identity across the matrix-striosome boundary. C_LI

neuroscience↗

bioRxiv2026

Hierarchy in neuronal representations of multiple tasks in prefrontal cortex

The ability to perform multiple tasks is one of the fundamental hallmarks of general intelligence in the brain. However, the underlying neural mechanisms remain largely unknown. We trained macaque monkeys to perform four tasks with identical spatial layouts but different cognitive demands, and recorded activities of thousands of prefrontal neurons using two-photon calcium imaging. Within the same neuronal population, we identified a multitask neural geometry composed of multiple low-dimensional subtask spaces, each encoding spatial information within a specific subtask. These subtask spaces shared a ring-like representational structure, forming a generalized spatial code across different subtasks. Task separation arose from separable bases and offsets of subtask spaces, modulated primarily by meta-task features. Thus, the neural geometry is organized by a three-level hierarchical structure: location codes are nested within subtask spaces, which are grouped as different meta-tasks. This hierarchy supported generalization across both locations and tasks, and explained monkeys error behaviors. Together, the hierarchically organized neural geometry underlies flexible multitask behavior.

neuroscience↗

bioRxiv2026

MNS induces antiviral protection and suppresses inflammation

BackgroundIdentifying safe and broad-spectrum antiviral and anti-inflammatory agents remains an urgent need in infectious and inflammatory diseases. Here, we demonstrated that MNS (NSC170724), a small-molecule nitrovinyl benzodioxole, enhanced antiviral defense while limiting excessive inflammation. MethodsThe antiviral activity of MNS was evaluated in multiple cell lines and mouse infection models across DNA and RNA viruses. Virus-induced and LPS-induced inflammatory responses were assessed using RT-qPCR, ELISA and western blotting. Bulk RNA-seq and ATAC-seq were performed to define transcriptional and epigenetic mechanisms. ResultsMNS significantly suppressed viral infection in vitro and improved survival in four lethal viral infection models, accompanied by reduced viral loads and attenuated tissue injury. MNS also diminished virus-triggered and LPS-triggered inflammatory cytokine production in macrophages and multiple mouse organs, and protected mice from LPS-induced endotoxic lethality. Multi-omics profiling showed that MNS broadly repressed LPS-induced inflammatory transcriptional programs and reversed chromatin accessibility gains across promoters and transcription start sites. Joint analysis of RNA-seq and ATAC-seq data demonstrated consistent downregulation of pivotal inflammatory pathways, such as NF-{kappa}B, Toll-like receptor, and TNF signaling. ConclusionsWith potent activity against viral replication and inflammation in cellular and animal models, MNS emerges as a promising candidate for the treatment of viral infections and hyperinflammatory conditions.

systems biology↗

bioRxiv2026

SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsia

ObjectiveThe immune system plays a role in the occurrence and progression of numerous pregnancy complications, particularly preeclampsia (PE). This study aims to identify critical immune biomarkers via machine learning and assess their predictive ability. MethodsGene expression data were retrieved from the GEO database, while immune-related genes were obtained from the ImmPort repository. Differential expression analysis was then conducted to identify immune genes associated with PE. Different immune-related genes (DIRGs) were subjected to functional and pathway enrichment analysis. We adopted protein-protein interaction (PPI) networks for exploring the connections among various DIRGs and integrated two machine-learning to pinpoint candidate biomarkers in PE. Diagnostic performance was assessed via ROC curve analysis, with predictive accuracy further quantified using nomogram calibration. Findings were validated through integrated computational and experimental analyses. In silico validation utilized additional GEO datasets, while experimental confirmation involved qRT-PCR and IHC assessment of placental tissues. We developed a nomogram to predict PE utilizing two immune-related genes. Cellular composition was inferred from transcriptomic data using CIBERSORT deconvolution.. ResultsWe identified 66 differentially expressed genes (DEGs) and 10 DIRGs between PE pregnancies and normotensive pregnancies. The GO analyses revealed that the DIRGs were enriched in gonadotropin secretion, the regulation of gonadotropin secretion, and the regulation of endocrine processes. Functional annotation revealed enrichment in cytokine and neuroactive ligand-receptor pathways. SERPINA3 and NDRG1 emerged as top-performing biomarkers (training AUCs: 0.812 and 0.866; external validation: 0.795 and 0.781), with overexpression validated in clinical specimens. Both genes inversely regulated M2 macrophage abundance (P < 0.05). ConclusionPE is fundamentally an immune-mediated disorder. SERPINA3 and NDRG1 can be identified as key immune genes associated with M2 macrophages, and these findings provide novel perspectives for the diagnosis and pathogenesis of PE.

immunology↗

bioRxiv2026

Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain

Histone modifications underpin the cell-type-specific gene regulatory programs that drive the remarkable cellular diversity of the mammalian brain. Here, we jointly profiled four histone modifications and the transcriptome in 2.5 million nuclei from the adult mouse brain. By integrating these data with chromatin accessibility, DNA methylation, and three-dimensional genome organization, we constructed a unified epigenomic atlas spanning over 100 brain cell subclasses, and assigned active, primed, and repressive chromatin states across 81% of the genome. Active chromatin states marked by combinatorial histone modifications better nominate candidate enhancers with functional support than chromatin accessibility alone, while H3K27me3- and H3K9me3-marked chromatin states delineate distinct modes of cell-type-specific gene repression. Finally, this multimodal resource enables deep learning models that predict cell-type-specific epigenomic features and gene expression from DNA sequence, providing a comprehensive framework for annotating the brain regulatory genome and interpreting non-coding disease risk variants. HIGHLIGHTS- A single-cell epigenome atlas of transcription and four histone modifications across 2.5M mouse brain cells - Multimodal integration maps chromatin states across [~]81% of the adult mouse brain genome - Cell-type-resolved chromatin landscapes reveal regulatory programs mediated by enhancers, Polycomb and H3K9me3 - Deep learning models predict cell-type-specific epigenomic features and gene expression from DNA sequence

genomics↗

bioRxiv2026

Accessible and reproducible mesoscale fMRI at 5.0 T: A Pulseq-based open framework for human laminar mapping

Mesoscale, layer-specific functional MRI (fMRI) enables noninvasive access to cortical microcircuitry, yet widespread adoption has been constrained by a reliance on ultra-high field ([≥]7.0 T) systems and proprietary pulse sequences. To bridge this gap and enhance accessibility, we developed an open-source framework at 5.0 T for mapping laminar brain activity. This framework integrates a Pulseq-based 3D vascular space occupancy (VASO) sequence with an end-to-end data acquisition and analysis pipeline. At matched sub-millimeter resolution (0.8 mm in-plane), the Pulseq-based 3D implementation increased slab coverage by [~]1.82-fold and improved temporal signal-to-noise ratio by [~]1.50-fold relative to a vendor-provided 2D-VASO sequence. Validated using a finger-tapping paradigm, individual cerebral blood volume-weighted (VASO) laminar activation profiles consistently revealed the canonical "double-peak" pattern, with distinct superficial and deep peaks in the primary motor cortex. These profiles exhibited excellent cross-visit reliability (r = 0.80), and peak depths showed good spatial reliability (ICC = 0.69 for deep layers; ICC = 0.58 for superficial layers). Between-subject reproducibility was high (r = 0.86). Deploying the identical Pulseq protocol at an independent imaging site reproduced the characteristic double-peak laminar profiles (r = 0.63). At the group level, 5.0 T laminar profiles closely matched established 7.0 T findings, robustly resolving both deep and superficial peaks despite the lower field strength. Notably, for each participant, a single 13-minute VASO run was sufficient to resolve reliable laminar activation patterns that exhibited high consistency with multi-run averages (r = 0.78), highlighting the potential for high-throughput population studies or clinical research settings. The Pulseq-based 3D VASO sequence file, image reconstruction pipeline, and data analysis scripts are openly available to facilitate the adoption of this framework. This work establishes a practical route towards more accessible and reproducible mesoscale fMRI for studying human laminar functional architecture.

neuroscience↗

bioRxiv2026

Single-cell Multiome Analysis of Chromatin State and Transcriptome in the Human Basal Ganglia

The basal ganglia play essential roles in motor control, emotion, learning and reward processing. Their dysfunction contributes to many neurological and psychiatric disorders. However, the gene regulatory programs defining basal ganglia cell-type identity and function remain poorly understood, limiting interpretation of disease-associated non-coding variants. Here, we present the first single-cell multiome atlas of histone modifications and transcriptomes across eight basal ganglia regions from neurotypical adult human donors. Joint profiling reveals cell-type-specific deployment of active and repressive cis-regulatory elements and gene regulatory networks, and suggests a combinatorial homeobox transcription factor code underlying cell identity. Integration with matched spatial transcriptomic MERFISH data uncovers regional heterogeneity of epigenomic landscapes. Comparative analysis between human and mouse medium spiny neurons uncovers conservation of core gene regulatory features. This atlas interprets non-coding risk variants of neuropsychiatric disorders and supports the development of a deep learning model to predict gene regulation and functional effects of disease-associated variants. HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes and three histone modifications across eight human basal ganglia regions characterizes active and repressive chromatin states at cell-type resolution. C_LIO_LICell-type-specific gene regulatory programs decode combinatorial homeobox TF grammar governing the identity and diversification of basal ganglia neurons. C_LIO_LIIntergrative analyses link noncoding neuropsychiatric risk variants to specific cell types, regulatory elements, and candidate target genes. C_LIO_LIA sequence-to-function deep-learning model predicts gene regulation from DNA sequence and prioritizes functional disease-associated variants. C_LI

genomics↗

bioRxiv2026

PlotGDP: an AI Agent for Bioinformatics Plotting

High-quality bioinformatics plotting is important for biology research, especially when preparing for publications. However, the long learning curve and complex coding environment configuration often appear as inevitable costs towards the creation of publication-ready plots. Here, we present PlotGDP (https://plotgdp.biogdp.com/), an AI agent-based web server for bioinformatics plotting. Built on large language models (LLMs), the intelligent plotting agent is designed to accommodate various types of bioinformatics plots, while offering easy usage with simple natural language commands from users. No coding experience or environment deployment is required, since all the user-uploaded data is processed by LLM-generated codes on our remote high-performance server. Additionally, all plotting sessions are based on curated template scripts to minimize the risk of hallucinations from the LLM. Aided by PlotGDP, we hope to contribute to the global biology research community by constructing an online platform for fast and high-quality bioinformatics visualization. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/702995v3_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@73c77dorg.highwire.dtl.DTLVardef@12ddfc6org.highwire.dtl.DTLVardef@be5963org.highwire.dtl.DTLVardef@de90b6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO PlotGDP: an AI Agent for Bioinformatics Plotting C_FIG

bioinformatics↗