Biology subjects

Tang, C.

Publications and source records attributed to Tang, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Chemical Synthesis of Thrombostasin: An Anticoagulant Protein Produced by the Horn Fly Haematobia irritans

Thrombostasin is a cysteine-free anticoagulant protein produced by the horn fly Haematobia irritans, featuring an unusual stretch of seven consecutive aspartate residues within the polypeptide sequence. Here, we report the first chemical synthesis of native thrombostasin and a hepta-glutamate analogue, together with assessment of their thrombin inhibitory and anticoagulant activity.

molecular biology↗

bioRxiv2026

DeMoP: A Language-Model-Guided Mixture-of-Experts Framework for Cancer Prognosis

Integrating heterogeneous clinical and molecular data for cancer prognosis remains challenging because their dimensionality, semantics and distributions differ across patients and cohorts. Here we present DeMoP, a language-model-guided mixture-of-experts framework that serializes structured patient profiles as natural-language sequences and learns adaptive prognostic representations from clinical variables, copy-number alterations, and gene descriptions. DeMoP combines a fine-tuned DeBERTa-v3-large encoder, attention-based token pooling, and a residual mixture-of-experts prediction head. In held-out tests from two independent pan-cancer cohorts, GENIE (63,090 patients) and TCGA (4,123 patients), DeMoP outperformed the conventional machine-learning and deep-learning baselines evaluated, achieving AUROCs of 0.939 and 0.805 and class-1 F1 scores of 0.72 in both cohorts. A GENIE-trained model transferred directly to TCGA with an overall class-1 F1 score of 0.62. Gene-level ablations recovered established cancer-associated genes and highlighted less-studied candidates. DeMoP provides a unified approach to heterogeneous biomedical data integration, cross-cohort outcome prediction, and model interpretation.

bioinformatics↗

bioRxiv2026

ACCREDIT: A Quality-Aware Agentic Engine for Cell-resolved Cross-modal Image Registration with Dynamic Iterative Tuning

Spatial omics across complementary modalities is transforming our understanding of tissue architecture. Realizing this potential requires accurate and robust registration of cross-platform molecular images with hematoxylin-and-eosin (H&E) sections, the primary morphological reference for pathology. Existing methods, however, often fail silently when image orientation is unknown, image contrast is inverted, or tissue overlap is incomplete, producing erroneous registrations without alerting users or attempting recovery. Here, we present ACCREDIT, a quality-aware agentic framework that redefines cross-modal registration as an adaptive decision-making process rather than a one-shot computation. ACCREDIT combines deterministic registration pipelines with a reference-free composite quality score that automatically evaluates registration quality and rejects plausible but biologically incorrect registrations. When registration quality is insufficient, a large language model (LLM)-based rescue agent autonomously diagnoses failure modes and selects targeted recovery strategies, while an optional strategy-learning module captures expert-validated corrections for future reuse. Across Xenium, CODEX, cell-boundary, and IHC-to-H&E registration tasks, ACCREDIT outperformed competing methods by detecting registration failures and improving alignment quality through automated recovery and rescue. Ultimately, ACCREDIT enables robust integration of histology and spatial molecular profiling, providing a foundation for translating spatial omics into routine H&E-based pathology workflows.

bioinformatics↗

bioRxiv2026

Reconstructing lineage-constrained gene-program dynamics with PhyloFM

Cell-fate dynamics are defined not only by where cells end, but by when fate programs emerge, diverge, and reshape population structure. Much recent effort has been devoted to reconstructing these dynamics from temporally resolved single-cell RNA-seq and spatial transcriptomics. However, it is difficult because these measurements only provide destructive snapshots instead of continuous molecular histories. On the other hand, a diverse set of lineage-tracing methods are now available which could serve as temporal constraints to connect the dots. The challenge is how to utilize and unify these diverse ancestry records in the reconstruction of a continuous cell state dynamics. Here we introduce PhyloFM, a lineage-constrained flow-matching framework that maps heterogeneous lineage information onto a cell-fate-anchor topology and learns a continuous velocity field together with relative cell-abundance changes on a topology-regularized latent geometry. From the same integrated trajectories, PhyloFM predicts population transport, future fate probabilities, commitment timing and branch-resolved projected gene- and module-level dynamics. We tested PhyloFM across five lineage-tracing settings spanning C. elegans embryogenesis, LARRY hematopoiesis, pandaCREST ventral-midbrain development, zebrafish heart regeneration and spatial eTracer tumours. In C. elegans, our method improved population transport by 17.3% and lineage-grounded dynamic error by 16.3% relative to state-of-the-art dynamical baselines, with transport error reduced by 26.6% relative to additional lineage-aware control. Independent EPIC GFP reporter traces support the inferred timing of proneural, ciliated-neuron and late-selector programs, with 67% of matched reporter genes showing Pearson r > 0.6. On the spatial eTracer tumour benchmark, PhyloFM increased clone-grounded fate accuracy by 70.4% relative to the best spatial baseline. Together, these analyses show that diverse lineage records can serve as biological constraints for reconstructing continuous, interpretable molecular fate dynamics from single-cell snapshots.

bioinformatics↗

bioRxiv2026

Electron counting enables cryo-electron ptychography for near-atomic-resolution cryo-electron microscopy

Cryo-electron ptychography is an emerging technique for studying radiation-sensitive biological specimens, developed from four-dimensional transmission electron microscopy (4D-STEM). Although ptychography has achieved ultrahigh resolution beyond conventional transmission electron microscopy limits for radiation-resistant samples, its application to frozen hydrated biological specimens currently remains at sub-nanometer resolution. Here we overcome this limitation by implementing electron counting with a hybrid-pixel detector, establishing key technical foundations for near-atomic-resolution cryo-ptychography. This counting approach significantly improves weak signal detection in convergent-beam electron diffraction, enabling ptychographic reconstruction at doses below 1 e-/[A]{superscript 2}. Additionally, we found beam-induced motion is effectively eliminated within single scans, suggesting conventional cryoEMs dose-fractionation approach may need reevaluation. Demonstrating high contrast under both low-dose and tilted conditions, along with achieving 3.59 [A] resolution for the [~]700 kDa T20S proteasome, we validate cryo-electron ptychography as a viable general imaging modality that could complement or surpass conventional phase-contrast cryoEM methods.

biophysics↗

bioRxiv2026

Medin-Induced Pro-inflammatory and Prothrombotic Activation of Coronary Artery Endothelial Cells: A Potential Novel Mediator Linking Aging and Atherosclerosis

BackgroundAge is the most important risk factor for coronary artery disease (CAD) independent of traditional risk factors. Aging induces classic pro-inflammatory and prothrombotic vascular phenotypic changes whose molecular mediators remain poorly understood. Medin is a common cleavage product protein that accumulates in vasculature with aging and shown to cause endothelial dysfunction. Its role in CAD is unknown. The study aimed to evaluate the effects of medin on human coronary artery endothelial cell (HCAEC) pro-inflammatory and prothrombotic activation and establish the relationship between medin and coronary atherosclerosis in human decedents. MethodsHCAECs were exposed to physiologic dose of medin (5 {micro}M) for 20 hours and ribonucleic acid sequencing (RNAseq) with signaling pathway analyses and reverse transcription polymerase chain reaction of select pro-inflammatory and prothrombotic genes performed. Corresponding protein expression was measured by Western blot or enzyme linked immunosorbent assay in HCAECs exposed to medin (5 {micro}M) without or with nuclear factor-{kappa}B (NF{kappa}B) inhibitor RO106-9920 (10 {micro}M). Coronary arteries from 40 deceased individuals underwent immunohistochemistry and medin and plaque burden were quantified and their relationship evaluated. ResultsRNAseq showed predominant pro-inflammatory gene expression changes induced by medin. HCAECs treated with medin showed increased phosphorylated NF{kappa}B, elevated protein expression of interleukin (IL)-6, IL-8, monocyte chemotactic protein (MCP)-1, intercellular adhesion molecule (ICAM)-1, vascular cell adhesion molecule (VCAM)-1 and plasminogen activator inhibitor (PAI)-1 and reduced protein expression of thrombomodulin; these changes were reversed by RO106-9920 co-treatment. In human tissues, coronary artery medin strongly correlated with plaque burden (R=0.76, p<0.0001) and coronary macrophage content (R=0.72, p<0.0001). Coronary arteries from decedents with myocardial infarction had higher medin than those without (5.53{+/-}2.67% versus 0.02{+/-}0.02%, p=0.0005). ConclusionsMedin induced NF{kappa}B-mediated endothelial cell pro-inflammatory and prothrombotic activation and was strongly associated with coronary plaque burden and inflammation. Medin is a novel candidate mediator linking aging and coronary atherosclerosis.

physiology↗

bioRxiv2026

An allosteric pocket in KV1.3 defines a distinct chemical space for immunomodulator design

KV1.3 is a validated autoimmune drug target, yet all disclosed inhibitors converge on a narrow set of conserved binding sites, suffering from poor selectivity or clinical failure. Whether KV1.3 harbors a distinctive, druggable pocket amenable to selective targeting has remained unknown. Here we report MPiN, a highly selective, state-dependent KV1.3 inhibitor with in vivo efficacy in a mouse psoriasis model, which engages a previously uncharacterized extracellular allosteric pocket framed by the PP1-PP2 turret loops, the pore helix and the outer S5/S6 helices, acting through a bidirectional pore-to-sensor coupling that simultaneously constricts the selectivity filter and facilitates the voltage sensor toward activation. Unexpectedly, despite the extensive structural conservation of this pocket across KV1 paralogs, subtype selectivity is dictated by the peripheral residues G427 and H451, which define pocket geometry without directly contacting the ligand, thereby establishing a geometry-driven "non-contact selectivity" mechanism. By opening an unrecognized, structurally distinct chemical space on KV1.3 and redefining how selectivity is achieved within a conserved channel family, this work lays the structural and conceptual foundation for rational, structure-guided design of next-generation KV1.3 immunomodulators.

biochemistry↗

bioRxiv2026

Ecological inference and contaminant detection from fungal microbiome data with q2-fungal-traits

Fungi are key members of microbial communities, yet microbiome surveys often lack trait-based information required for ecologically meaningful interpretation of mycobiome data. To demonstrate the value of fungal trait-based phenotyping in microbiome research, we re-analyzed N=3,221 samples across four case studies spanning human, agricultural, and environmental systems. In human cancer and vineyard datasets, trait-based analysis detected fungi producing macroscopic fruiting bodies, likely introduced via airborne spore dispersal, indicating widespread contributions of transient or contaminant fungi that can confound interpretation of sequencing data from tumor biopsies and grape berries. In sourdough fermentations, filamentous fungi were highly abundant alongside traditionally-recognized yeast and occupied distinct ecological niches. In forest soils, increasing habitat disturbance was associated with increased prevalence and abundance of plant pathogens, and a marked decline in ectomycorrhizal and lichenized fungi. These changes were accompanied by a shift toward large-spored taxa in urban soils, consistent with enhanced stress tolerance. To facilitate broader adoption of fungal phenotyping in microbiome studies, we introduce q2-fungal-traits, a QIIME2 plugin for automated integration of fungal taxonomy derived from marker-gene or shotgun metagenome sequencing surveys with ecological and functional trait data. The plugin assigns lifestyle-related traits and spore size estimates through hierarchical taxonomic matching and integrates directly into standard microbiome workflows. Our case studies demonstrate that integrating trait-based ecology with mycobiota datasets can generate novel findings and testable hypotheses, enabling inference of the functional (ir)relevance of community constituents. Our work contributes to bridging the gap between descriptive community profiling and functional ecology in microbiome research.

microbiology↗

bioRxiv2026

A regulatory TRIF/IL-1R1 axis controls T-dependent IgA production in the intestines

Mucosal IgA is critical for controlling the microbiota and preventing pathogenic infection of the epithelium. The innate signals that regulate the generation of IgA remain poorly defined. Here, we identified TRIF and IL-1R1 as immune checkpoints for intestinal IgA production. In the absence of infection, Trif-/- and Il1r1-/- mice exhibited markedly elevated stool IgA and IgA-bound commensals. We show that IL-1R1 restricts IgA class-switching in a B cell-intrinsic manner, while TRIF acts extrinsically of B cells and shapes the Peyers patch microenvironment. Loss of either Trif or Il1r1 enhanced retinoic acid metabolism and allowed premature Ccnd3 upregulation in naive B cells, favoring both IgA class-switching and differentiation into germinal center B cells. During oral vaccination, the absence or blockade of the TRIF/IL-1R1 pathway increased antigen-specific IgA production without affecting seric antigen-specific IgG levels. These findings unveil novel and local signaling targets to promote robust antigen-specific mucosal immunity.

immunology↗

bioRxiv2026

Benchmarking Static Gene Regulatory Network Reconstruction and Dynamic Transition Probing in Single-Cell Foundation Models.

Single-cell foundation models may encode gene regulatory information, but it remains unclear which model components capture this signal and how it compares with conventional inference methods. Here, we introduce a unified benchmark that evaluates gene regulatory network (GRN) reconstruction from six single-cell foundation models and three classical baselines across six datasets and four reference network types. We disentangle three sources of regulatory signal within each model--pretrained token embeddings, final-layer hidden states, and attention-derived scores. Under a strict zero-shot setting, scGPT token-embedding similarity outperforms classical baselines on STRING and ChIP-seq references, recovers core transcription factors, and best preserves reference network topology. Moreover, static GRNs cannot test whether learned gene-gene relationships are predictive of expression dynamics, we therefore introduce dynamic transition probing, which iteratively applies a models reconstruction head to drive early-cell profiles toward late-cell states without temporal supervision. We find pretrained models capture meaningful developmental transitions, with scFoundation showing the strongest overall performance. Together, our results show that single-cell foundation models encode transferable regulatory and dynamical priors, but how well these priors can be recovered depends on model architecture, pretraining design, and extraction strategy.

systems biology↗

bioRxiv2026

ZeaMiC: a Publicly Available Culture Collection of Maize Root-Associated Bacteria

Plant-associated microbiota are composed of hundreds of microbial species. For many of them, little is known about their individual functions and even less is known about their emergent community-level traits. While culture-independent methods provide valuable insights into the composition, diversity, and functional potential of plant-associated microbiota, culture-dependent methods are essential for reductionist lines of inquiry into the roles of individual species and their interactions within a community. Here, we present ZeaMiC, a publicly available culture collection of root-associated bacteria from Zea mays (maize). This resource comprises 88 isolates obtained from diverse soils and several maize genotypes, with live cultures available through DSMZ (German Collection of Microorganisms and Cell Cultures) both as single stocks and as cost-effective bundles (https://www.dsmz.de/collection/catalogue/microorganisms/microbiota/zeamic). To maximize relevance, isolates were selected to be representative of maize root-associated microbiomes in the Corn Belt of the United States, based on abundance-occupancy patterns from previously published root microbiome data, phylogenetic diversity, and literature-based evidence of functional importance. Whole-genome sequencing and annotation revealed genes associated with root colonization, plant growth promotion, and nutrient cycling, including functions such as chemotaxis, biofilm formation, secretion systems, hormone modulation, and phosphate solubilization. This collection serves as a community resource for future mechanistic studies of plant-microbe and microbe-microbe interactions, filling the gap in our understanding of the ecological interactions in plant microbiomes.

microbiology↗

bioRxiv2026

Minimal biophysical rules are sufficient for the emergence of computational intelligence at the neuronal scale

How intelligence emerges from the brains complex microscopic physical system is a central question for neuroscience and artificial intelligence. Constrained by the genomic bottleneck that precludes synapse-by-synapse specification, we propose and validate a microscopic structure-function concise-constraint sufficiency hypothesis. We develop the Neuro-Informed Generative Connectome (NIGC) framework, and show that connectomes generated under a concise set of biophysical constraints (geometric embedding, node propensity modulation, a global energy budget and maximum-entropy selection) closely match the structural statistics of a measured mouse V1 microcircuit (similarities, 0.997). In parallel, using the generated connectome as the fixed reservoir of an echo state network (ESN), training only a linear readout achieves 90% accuracy on an auditory multi-classification task. Moreover, multidimensional biologically consistent functional phenotypes, such as hierarchical transmission delays and low-dimensional spatiotemporal trajectories, are obtained without fitting functional matrices or time courses. Further, by combining single-constraint ablations, pathological perturbations and cross-modal validation, we clarify how specific structural constraints map onto functional consequences. Together, these results delineate sufficient conditions for computational intelligence emergence at the microscopic scale, and provide an auditable benchmark for first-principles understanding of brain construction.

neuroscience↗

bioRxiv2026

A novel proliferative candidate genes panel for idiopathic pulmonary fibrosis: insights from integrated bulk and single-cell RNA sequencing

BackgroundIdiopathic pulmonary fibrosis (IPF) remains a fatal interstitial lung disease with limited diagnostic specificity and therapeutic options. This study integrates bulk and single-cell RNA sequencing (RNA-seq) to identify novel biomarkers and elucidate molecular mechanisms underlying IPF pathogenesis. MethodsWe prospectively enrolled 14 treatment-naive IPF patients and 6 controls. Bulk RNA-seq was performed on bronchoalveolar lavage fluid (BALF), while single-cell RNA-seq analyzed lung tissues from 4 IPF patients and 3 controls. Differentially expressed genes (DEGs) were identified (|log2FC| >1, FDR <0.05), followed by functional enrichment, protein-protein interaction (PPI) network analysis, and cell-type-specific expression profiling. Results1. DEG Identification: Bulk RNA-seq revealed 108 DEGs (24 upregulated, 84 downregulated). KEGG enrichment analysis of DEGs revealed that upregulated genes were mainly enriched in inflammation and immune pathways (such as NF-{kappa}B signaling pathway, Fc epsilon RI signaling pathway, B cell receptor signaling pathway, phagosome, Fc gamma R-mediated phagocytosis), pyrimidine metabolism, cell cycle, and PI3K-Akt signaling pathway. 2. PPI Network: Module analysis identified a proliferative gene module 1 (NUF2, CEP55, ANLN, TTK, TK1, MYBL2, CCNA2, RRM2, CDT1) linked to cell division and cycle regulation. 3. Single-Cell Insights: scRNA-seq of 30,477 cells delineated 11 populations. Module 1 genes exhibited predominant expression in proliferating cells, Module 1 signature score of proliferating cells was significantly higher in IPF than in control group. 4. Pathogenic Links: Key genes (e.g., CEP55, TTK) were associated with PI3K/AKT signaling, epithelial-mesenchymal transition (EMT), and anti-apoptotic pathways, mirroring oncogenic mechanisms. ConclusionThis multi-omics approach uncovers a proliferation-centric gene module in IPF, revealing shared molecular pathways with tumorigenesis. Our findings highlight novel diagnostic biomarkers and suggest repurposing cell cycle inhibitors as potential therapies. Future studies should validate these targets in preclinical models to advance precision medicine for IPF.

genetics↗

bioRxiv2026

3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network

Spatially resolved transcriptomics (SRT) is a promising new technology that enables simultaneous analysis of gene expression and spatial information for biomedical research. However, the existing statistical and deep learning algorithms used for analyzing SRT data rely solely on two-dimensional (2D) spatial coordinates, which limits their ability to accurately identify spatial domains, spatially variable genes, cell-to-cell communications, and developmental trajectories in a three-dimensional (3D) spatial manner. To address these limitations, we introduced Spa3D, which utilized the anti-leakage Fourier transform and graph convolutional neural network model to reconstruct 3D-based spatial structures from multiple 2D SRT slices. We demonstrate that Spa3D is appliable to analyze data from various SRT technology platforms and outperforms state-of-art methods by: (I) improving spatial domain identification through 3D reconstruction, (II) elucidating cell-cell communication landscape in the 3D cellular organization, (III) modeling of organ-level tempo-spatial development patterns in a 3D fashion, and (IV) annotating 3D spatial trajectory that are not captured by 2D spatial coordinates. Key pointsO_LIMost existing spatial omics analysis methods rely on 2D data, limiting their ability to capture full spatial and developmental tissue complexity C_LIO_LISpa3D incorporates physical z-axis distances, enabling accurate 3D modeling even when adjacent slices vary in tissue structure and composition C_LIO_LISpa3D reconstructs true 3D spatial structures from 2D SRT slices using graph convolutional networks and anti-leakage Fourier transforms C_LIO_LISpa3D enhances spatial domain detection, revealing detailed cell-cell communication and organ-level development patterns across multiple spatial omics platforms C_LIO_LISpa3D enables novel biological discoveries by revealing spatial features and trajectories not detectable using traditional 2D transcriptomic analysis approaches C_LI

bioinformatics↗

bioRxiv2025

IFN-I exacerbates the inflammatory response of epithelial cells to Chlamydia trachomatis infection by enhancing TLR3 expression

The inflammation induced by Chlamydia trachomatis infection in the female genital tract (FGT) can have severe consequences. Recent observations in women and mice infected with Chlamydia suggest that type I interferon (IFN-I) may have deleterious effects. This study aimed at elucidating the consequences of IFN-I production on C. trachomatis-induced inflammation in epithelial cells and the molecular pathway(s) involved. We showed that combination of IFN-I and Chlamydia resulted in a stronger induction of inflammation than Chlamydia alone, while IFN-I alone had no effect. Inhibiting AKT and mTOR, but not silencing STAT1, significantly attenuated the synergetic effect between IFN-I and bacteria on inflammation. Inhibition of ERK also blocked this synergistic effect, although ERK were not activated by IFN-I. We hypothesized that IFN-I enhanced the expression of a pathogen recognition receptor of the host, thereby potentiating detection of the bacteria and the subsequent inflammatory response. IFN-I, but not C. trachomatis infection, increased the expression of Toll-like receptor 3 (TLR3), and silencing or knocking-out TLR3 prevented the synergetic effect between infection and IFN-I. Furthermore, our data support the presence of dsRNA in infected cells and the activation of the MAPK/ERK and AP-1/ATF2 signaling cascades downstream of TLR3. Taken together, our data demonstrate that IFN-I exacerbates the host inflammatory response triggered by Chlamydia by increasing TLR3 expression and activation, leading to hyperinflammation. The identified signaling cascades represent potential targets for therapeutical intervention to limit tissue damage upon Chlamydia infection.

microbiology↗

bioRxiv2025

Adapting Epigenetic Clocks for Cell-Free DNA High-Throughput Sequencing Data

Background.Circulating cell-free DNA (cfDNA or ccfDNA) methylation sequencing holds promise for developing epigenetic aging clocks in minimally invasive aging assessment applications. However, current clocks--primarily trained on array-based data--do not readily generalize to methylation profiles of cfDNA, due to stochasticity and uncertainty arising from the limited input amounts of cfDNA and technical characteristics of high-throughput sequencing (HTS) platform. Due to lack of training to correct this uncertainty, direct application of legacy clocks to HTS data will inevitably produce unreliable estimates, introduce predictive discordance and undermine the trustworthiness of epigenetic biomarkers. Despite this urgent challenge, a systematic, model-agnostic framework for adapting existing epigenetic clocks to HTS-based cfDNA data remains lacking. Methods.Here, we generated a dedicated benchmark dataset comprising paired cfDNA and genomic DNA (gDNA), which was replicated and profiled across two methylation arrays and two targeted HTS platforms. We evaluated 53 epigenetic clocks for CpG coverage, reproducibility, cross-platform consistency, and age prediction accuracy. Further, we systematically explored and tested multiple adaptation strategies including depth filtering, beta-value imputation, and transfer learning via model distillation aiming at improving performance and clinical concordance of legacy epigenetic clocks on cfDNA HTS datasets. Results.Our results show that inherent technical noise of HTS platforms compromises diagnostic precision, presenting as a significant burden for applying legacy clocks on cfDNA HTS data. However, this technical limitation can be systematically neutralized. By enforcing stringent sequencing depth protocols ([≥]10x ideally 20x), employing robust algorithmic stabilization (L2-heavy clocks and imputation) and transfer learning, we can effectively isolate genuine physiological aging signals from technical artifacts. Ultimately, an adaptation pipeline incorporating all these methods effectively improved age prediction accuracy and, closed the gap between epigenetically induced aging status and clinically assessed realities. Besides, the pipeline demonstrated improved diagnostic sensitivity for neurodegenerative conditions such as amyotrophic lateral sclerosis, establishing a reliable foundation for non-invasive clinical monitoring. Conclusions.This study provided a comprehensive framework and practical guidelines for adapting epigenetic clocks to HTS-based cfDNA data, paved the path for more general application of cfDNA-based aging assessment and liquid biopsies.

bioinformatics↗

bioRxiv2025

Derivation, Characterizations, and Applications of Rabbit Haploid Embryonic Stem Cells

Haploid embryonic stem cells (haESCs), which contain a single set of chromosomes, provide powerful systems for investigating gene function and creating gene-edited animal models. haESCs have been derived from several mammalian species including human, mouse and rats, yet not from rabbits. Here, we report the derivation of parthenogenetic haESCs of rabbit (rbPhESCs) using an optimized culture medium. These cells maintain a stable haploid karyotype during long-term culture and own pluripotency features comparable to diploid embryonic stem cells derived from rabbit fertilized embryos. By integrating gene trapping cassette into ROSA26 locus, rbPhESCs offer an ideal platform for high-throughput functional genomic screening, as shown in haESCs from other mammals. Consistent with their parthenogenetic origin, rbPhESCs durably preserve maternal-specific imprinting patterns even after extended culture in serum-containing conditions, providing a valuable platform for generating disease models deficient in maternally expressed imprinted genes. Therefore, our findings expand the repertoire of mammalian haESCs and establish rbPhESCs as a valuable platform for genetic studies and biotechnological applications in rabbits.

cell biology↗

bioRxiv2025

State-dependent release of extracellular particles with distinct α2,6-sialylation patterns and small RNA cargo related to neuroinflammation

Neuroinflammation is a significant contributor to neurodegenerative diseases, including Alzheimers disease, Parkinsons disease, and related dementias; yet peripheral biomarkers for neuroinflammation remain an unmet medical need. Microglia, the resident immune cells of the central nervous system, play a dual role in maintaining homeostasis under physiological conditions and driving neuronal damage when chronically dysregulated. One mechanism by which microglia influence their environment is through the release of extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs), which can serve as biomarkers the reflect cellular states. Here, we systematically isolated and characterized microglia-derived EVs and NVEPs under pro- and anti-inflammatory conditions and profiled their small RNA cargo by small RNA sequencing. We validated these findings in human iPSC-derived microglia and further recapitulated them in EVs and NVEPs from mouse brain and plasma. Using an engineered mouse model, we were able to isolate plasma microglia-specific EVs in vivo and demonstrated that their RNA cargo reflects their inflammatory state. Importantly, microglial EVs and NVEPs display distinct 2,6-sialylation patterns and small RNA signatures implicated in neurological diseases. These findings demonstrate that microglia-derived EVs and NVEPs cargo reflect microglial cellular state and establish them as putative minimally non-invasive biomarkers of early-stage neurodegenerative diseases.

neuroscience↗