Biology subjects

Xiao, G.

Publications and source records attributed to Xiao, G..

At least 19 recordsLinked to original sources

bioRxiv2026

SpatialOmicsLab: an integrated research environment for AI co-scientists in spatial transcriptomics

Spatial profiling is moving from method development into a central platform for biological discovery and clinical translation, but analyzing such complex data has become the bottleneck of scientific discovery. Translating spatial transcriptomic measurements into biological insight requires reproducible workflows that integrate diverse analytical methods. Although language-model agents can plan and execute multistep analyses, fragmented data and software ecosystems hinder their use as reliable research assistants. In this study, we introduce SpatialOmicsLab, an integrated research environment for spatial transcriptomics comprising 113 curated computational tools, 63 reference datasets and 4,614 task specifications. We also introduce ST-Coscientist, an AI co-scientist that uses this environment to assemble, execute and refine spatial transcriptomic analyses. Within SpatialOmicsLab, ST-Coscientist outperformed expert-led workflows and other scientific agents across six benchmark settings spanning spatial clustering, cell-type deconvolution and spatially variable gene detection. It constructed alignment-aware workflows for three-dimensional MERFISH data and addressed biological questions by selecting and analysing relevant reference datasets. Procedural memory increased the success rate of integrating and validating additional analytical tools from 48% to 95%. Together, SpatialOmicsLab and ST-Coscientist establish an extensible ecosystem for reproducible spatial transcriptomic research, enabling AI co-scientists to address evolving biological questions with an expanding analytical repertoire.

bioinformatics↗

bioRxiv2026

Suppression of RIPK3 by EZH2 contributes to cigarette smoking-induced chemoresistance in lung cancer

Lung cancer remains the leading cause of cancer-related mortality worldwide, with cigarette smoking (CS) representing its primary risk factor. In addition to promoting tumorigenesis, chronic CS exposure contributes to chemotherapy resistance, although the underlying mechanisms remain poorly understood. Here, we established a long-term CS exposure model by repeatedly treating Lewis lung carcinoma (LLC) cells with cigarette smoke extract (CSE). After 4 months of exposure, CSE-treated cells exhibited enhanced proliferation, migration, and resistance to chemotherapy-induced cell death. Mechanistically, chronic CSE exposure suppressed receptor-interacting protein kinase 3 (RIPK3) expression by upregulating the epigenetic regulator enhancer of zeste homolog 2 (EZH2), which promoted repressive histone methylation at the RIPK3 promoter. Loss of RIPK3 impaired chemotherapy-induced cell death primarily by inhibiting ferroptosis rather than necroptosis. Importantly, genetic depletion or pharmacological inhibition of EZH2 restored RIPK3 expression and sensitized lung cancer cells to gemcitabine treatment both in vitro and in vivo. Furthermore, analysis of human lung cancer datasets revealed an inverse correlation between EZH2 and RIPK3 expression, with RIPK3 levels progressively decreasing with smoking history. Collectively, these findings identify the EZH2/RIPK3 axis as a critical mediator of smoking-associated chemoresistance and uncover a previously unrecognized role for RIPK3 in ferroptosis regulation. Targeting EZH2-mediated RIPK3 suppression may represent a promising therapeutic strategy to overcome chemoresistance in lung cancer patients with a history of smoking.

cancer biology↗

bioRxiv2026

Task-Dependent and Cell-Type-Specific Modulation of the Mouse Dorsal Cortex of the Inferior Colliculus

Sensory responses in the auditory system are modulated by behavioral context, and this modulation extends as early as the inferior colliculus (IC). Where within the IC it occurs, which neurons it affects, what behavioral variables drive it, and whether it requires the auditory cortex remain unclear. We imaged the dorsal cortex of the IC (DCIC) with two-photon microscopy in head-fixed mice (n = 7) performing a two-alternative forced-choice tone-discrimination task, with blocks of passive listening to the same tones before and after, while recording orofacial movement, licking, and pupil diameter from synchronized video. Unsupervised clustering of sound-evoked responses in 1479 tracked neurons identified four functional response classes: onset-excited, sustained-inhibited, offset/motor-excited, and transient-inhibited. Task engagement modulated these classes in distinct and outcome-dependent ways, with the sustained-inhibited and transient-inhibited classes more suppressed and the offset/motor-excited class more excited during active trials, and with hit and miss trials driving most of the effect. Population activity discriminated active from passive trials above chance, and this discrimination outlasted the stimulus. A linear encoding model attributed a substantial share of the explained variance in three of the four classes to facial movement, whereas pupil diameter contributed little in any class. The correlation between orofacial motion and activity was itself context-dependent in the sustained-inhibited and transient-inhibited classes, but not in the offset/motor-excited class. Bilateral chemogenetic silencing of the auditory cortex (n = 3 mice, 171 matched neurons) altered task modulation only in the sustained-inhibited class. Task-related modulation in the DCIC is therefore heterogeneous across functionally defined neurons, is closely tied to movement, and depends on the auditory cortex in only one of these populations.

neuroscience↗

bioRxiv2026

GPSNorm: Gaussian Process Spatial Normalization for Spatial Transcriptomics

Spatial transcriptomics technologies enable measurement of gene expression while preserving spatial tissue organization, but they remain highly sensitive to technical variability such as library size differences, slide-level effects, and spatial artifacts. Most existing normalization approaches treat normalization as a preprocessing step and perform downstream analyses on normalized values as fixed inputs, ignoring the uncertainty introduced during normalization. We introduce GPSNorm (Gaussian Process Spatial Normalization), a Bayesian spatial normalization framework that jointly models technical variation, spatial structure, and biological signal within a unified hierarchical model. GPSNorm represents gene expression counts using a negative binomial latent Gaussian model whose spatial component is a Gaussian Markov random field approximating a Gaussian process and performs efficient approximate Bayesian inference using the Integrated Nested Laplace Approximation (INLA), producing posterior estimates that propagate normalization uncertainty into downstream differential expression analysis. In simulations anchored to empirical spatial transcriptomics data, GPSNorm accurately recovers spatial technical structure and improves log-fold change estimation compared with existing normalization methods. Applications to three spatial transcriptomics datasets--including human dorsolateral prefrontal cortex Visium data, a GeoMx COVID-19 lung damage study, and the Spatial Organ Atlas kidney dataset--demonstrate improved preservation of biologically expected spatial patterns and marker gene contrasts. These results show that jointly modeling normalization and downstream inference can improve the robustness and interpretability of spatial transcriptomics analyses.An open-source R implementation of GPSNorm is available at https://github.com/Tiny-Quant/GPSNorm.

bioinformatics↗

bioRxiv2026

Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer

Most spatial transcriptomic analyses of solid tumors focus on individual cell states or single-sample spatial domains rather than on recurrent multicellular tissue architectures shared across patients, and typically depend on predefined cell-type annotations or compartment definitions. We developed STORM (Spatial Topology analysis of Recurrent Motifs), an unsupervised graph-attention variational autoencoder that learns recurrent spatial motifs directly from cell-level graph structure and molecular profiles without cell-type labels, manual annotation, or predefined compartments. We applied to 32 Xenium sections from 16 patients with paired early-onset (EOCRC) and average-onset (AOCRC) colorectal cancer, STORM identified 10 recurrent motifs that self-organized into tumor-parenchymal, stromal, and immune macro-compartments. Among these, the Desmoplastic Fibrotic Barrier (DFB) motif, a CAF- and ECM-rich boundary architecture, was associated with restricted CD8+ T-cell geodesic access to tumor cores independently of CD8+ abundance, as demonstrated by abundance-normalized neighborhood enrichment statistics and within-sample mixed-effects models. EOCRC selectively amplified this barrier-exclusion architecture, exhibiting tighter tumor parenchyma, denser DFB shells, and a DFB-specific ECM activation program that yielded an age-specific prognostic signature in TCGA-COAD. Translation of motif macro-classes to H&E images via a Vision Transformer classifier produced an image-derived DFB-barrier composite that predicted overall survival in advanced-stage TCGA colorectal cancer. STORM provides an annotation-free framework for discovering recurrent spatial motifs and identifies a fibroblast barrier architecture whose topological association with immune exclusion is independent of effector abundance, amplified in early-onset disease, and translatable to a deployable pathology-based prognostic biomarker.

bioinformatics↗

bioRxiv2026

Ergothioneine, alone or combined with vitamin K2, vitamin D3 and magnesium L-threonate, attenuates bone turnover, inflammatory and oxidative disturbances in ovariectomized mice

PurposeEstrogen deficiency drives bone loss through a complex interplay of endocrine, oxidative, inflammatory, and bone-remodeling disturbances. Ergothioneine (EGT) is a diet-derived thiol/thione antioxidant, yet its effects on the estrogen-deficient skeleton remain unknown. This study evaluated whether EGT-alone or combined with vitamin K2, vitamin D3, and magnesium L-threonate-attenuates the skeletal and systemic consequences of ovariectomy (OVX) in mice. MethodsForty-eight female C57BL/6J mice underwent either sham surgery or OVX. For 12 weeks, the mice received a daily oral gavage of one of the following: vehicle, alendronate (1.53 mg/kg), EGT (30 mg/kg/day), EGT with vitamin K2 (40 {micro}g/kg/day) and vitamin D3 (500 IU/kg/day), or EGT with vitamin K2, magnesium L-threonate (350 mg/kg/day), and vitamin D3 (n = 5-6 per group). Outcomes evaluated included the uterine index, tibial micro-computed tomography (micro-CT), distal-femoral histology, serum bone turnover markers (CTX-I, PINP, osteocalcin), sex hormones, TNF-, IL-6, SOD, and MDA. ResultsOVX significantly lowered the uterine index and induced tibial trabecular deterioration. This was accompanied by increased CTX-I, TNF-, IL-6, and MDA, alongside decreased PINP, osteocalcin, and SOD (all P < 0.01 vs. sham). Alendronate successfully restored tibial micro-CT bone-volume fraction (BV/TV) and trabecular number (P < 0.01 vs. OVX). While the EGT-based regimens did not significantly restore tibial micro-CT BV/TV, trabecular thickness, or trabecular number (all P > 0.05 vs. OVX), they significantly increased the trabecular area in distal-femoral histology (14.2-15.0% across regimens vs. 7.6% in OVX; P < 0.05). Furthermore, EGT treatments lowered CTX-I, TNF-, IL-6, and MDA, increased SOD, and partially restored PINP and osteocalcin (P < 0.05-0.01 vs. OVX). Apparent increases in serum estradiol were assay-dependent and are considered exploratory. ConclusionEGT-based nutritional regimens improved the systemic oxidative, inflammatory, and bone-turnover environments associated with estrogen-deficient bone loss and successfully preserved the distal-femoral trabecular area. However, tibial three-dimensional microarchitecture was not restored on micro-CT. Given that histological and micro-CT endpoints were evaluated at different skeletal sites, structural interpretations should be made with caution. These findings support the further evaluation of EGT as a dietary adjunct, warranting subsequent mechanistic and dose-optimization studies.

pharmacology and toxicology↗

bioRxiv2026

Mutant p53 Directs PARP to Regulate Replication Stress and Drive Breast Cancer Metastasis

TP53 mutations occur in 80-90% of triple-negative breast cancers (TNBCs) and drive genomic instability and metastatic progression. Poly (ADP-ribose) polymerase (PARP) is critical for DNA repair and replication fork stability. How oncogenic signaling influences PARP function to sustain proliferation during replication stress remains unclear. Mutant p53 (mtp53) R273H associates tightly with chromatin, forms complexes with PARP, and enhances PARP recruitment to replication forks [1-3]. The C-terminal region of mtp53 mediates mtp53-PARP and mtp53-Poly (ADP-ribose) (PAR) interactions that facilitate S phase progression [4, 5]. The PARP inhibitor talazoparib (TAL) combined with the alkylating agent temozolomide (TMZ) produces synergistic cytotoxicity selectively in mtp53, but not wild-type p53 (wtp53), breast cancer cells and organoids. Herein we evaluated the mechanism of mtp53-associated cell death and tested if this could translate to a preclinical xenograft model. We found that TMZ+TAL treatment induced elevated cleaved PARP and {gamma}H2AX and reduced the metastasis-promoting oncoprotein MDMX. In orthotopic xenografts expressing mtp53 R273H, but not wtp53, combination therapy significantly decreased circulating tumor cells (CTCs) and lung metastases. Transcriptomic profiling of tumors from combination treated animals demonstrated downregulation of MDMX, VEGF, and NF-{kappa}B, consistent with the observed suppression of CTCs and lung metastasis, and increased {gamma}H2AX, indicative of replication stress in mtp53 xenografts. Inhibition of metastasis was also observed in mtp53 R273H WHIM25 and p53-undetectable WHIM6 TNBC patient-derived xenografts (PDX). The mtp53 C-terminal domain (347-393) demonstrated a critical tumor promoting function, as CRISPR-mediated deletion impaired replication fork progression, tumor growth, and metastatic dissemination. DNA fiber combing showed that expression of full-length mtp53 R273H, but not C-terminal deleted {Delta}347-393, supported sustained single-stranded DNA gaps (ssGAPs) following Poly (ADP-ribose) glycohydrolase (PARG) inhibition. These findings support that mtp53 uses C-terminal amino acids to exploit PARP to enable replication stress adaptation and that mtp53 is a predictive biomarker for combined PARP inhibitor and DNA damaging therapies targeting TNBC. Significance statementTP53 mutations are the most common genetic alterations in TNBC and a major driver of replication stress and metastasis. This study shows that missense mutant p53 uses C-terminal amino acids to reprogram PARP activity to maintain tumor cell survival under replication stress. We demonstrate that p53 status governs the response to combined PARP inhibitor (PARPi) and DNA-damaging chemotherapy, establishing an additional molecular basis beyond BRCA1 mutations for treating TNBC with PARPi therapy. These findings reveal a previously unrecognized mechanism by which the mutant p53-PARP axis enables replication stress tolerance and drives cancer metastasis. We show mutation of p53 in TNBC provides an additional biomarker-guided framework to improve PARPi therapeutic outcomes.

cancer biology↗

bioRxiv2026

Gene III Ergothioneine ameliorates alcoholic fatty liver disease: A dual strategy of accelerated ethanol elimination and reducing oxidative stress

BackgroundAlcoholic fatty liver disease (AFLD) is a progressive hepatic pathology triggered by chronic ethanol consumption, serving as the initial stage of severe liver injury. Currently, there are no FDA-approved pharmacological interventions that specifically target alcohol-induced hepatic steatosis or prevent disease progression, highlighting an urgent need for effective preventive strategies. This study evaluated the preventive efficacy and underlying mechanisms of Gene III Ergothioneine (EGT) in a clinically relevant preclinical model. MethodsC57BL/6 mice were randomized into five groups: a Control group, an alcoholic fatty liver Model group, a Positive control group treated with Silybin (100 mg/kg), and three EGT treatment groups (10, 30, and 50 mg/kg). The NIAAA mouse model was utilized to induce alcoholic fatty liver. Various biochemical, histological, and molecular markers were assessed to evaluate liver damage, alcohol metabolism, lipid profiles, oxidative stress, and inflammation. ResultsGene III EGT treatment significantly ameliorated hepatic steatosis and necrosis, as confirmed by H&E and Oil Red O staining. Notably, EGT accelerated alcohol clearance, reducing serum ethanol levels by up to 54.4% in a dose-dependent manner. Furthermore, EGT restored liver function markers (ALT, AST, GGT) and corrected dyslipidemia by lowering TG, TC, and LDL-C while elevating HDL-C. Mechanistically, EGT suppressed pro-inflammatory cytokines (IL-6, IL-1 {beta}) and mitigated oxidative stress by reducing malondialdehyde (MDA) accumulation and restoring superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities. ConclusionGene III Ergothioneine prevents alcoholic liver injury through a dual mechanism: accelerating ethanol metabolism and enhancing hepatocyte antioxidative and anti-inflammatory defenses. These findings position EGT as a promising therapeutic candidate for AFLD management.

pharmacology and toxicology↗

bioRxiv2026

Gene III Ergothioneine Ameliorates Exercise-Induced Fatigue by Attenuating Oxidative Stress, Inflammation, and Modulating the AMPK/PGC-1α Signaling Pathway

BackgroundExercise-induced fatigue is a complex physiological phenomenon involving oxidative stress, inflammation, and metabolic disturbances. Ergothioneine (EGT), a naturally occurring amino acid with potent antioxidant properties, has garnered interest for its potential health benefits. This study aimed to evaluate the anti-fatigue effects of Gene III(R) EGT in a mouse model of exhaustive exercise and to elucidate its underlying mechanisms. MethodsMale C57BL/6 mice were randomly divided into five groups: a control group (CTL), low-dose EGT (EGT-L, 10 mg/kg), medium-dose EGT (EGT-M, 30 mg/kg), high-dose EGT (EGT-H, 50 mg/kg), and a positive control group (Coenzyme Q10, 50 mg/kg). Mice were subjected to a 4-week treadmill training protocol, followed by an exhaustive running test. We measured exercise performance and collected blood and skeletal muscle samples at multiple time points to assess biochemical markers, inflammatory cytokines, antioxidant status, and key signaling proteins. ResultsGene III(R) EGT supplementation, particularly at medium and high doses, significantly extended the time to exhaustion and running distance. Compared to the control group, EGT treatment significantly reduced post-exercise levels of lactic acid (LA), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN). Furthermore, Gene III(R) EGT suppressed the exercise-induced increase in pro-inflammatory cytokines, including IL-1{beta}, IL-6, and TNF-. The anti-fatigue effect of EGT was also associated with a reduction in malondialdehyde (MDA) and an increase in the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px). Mechanistically, EGT promoted the phosphorylation of AMP-activated protein kinase (AMPK) and the expression of peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1) in skeletal muscle, while also increasing the Bcl-2/Bax ratio, suggesting enhanced mitochondrial biogenesis and reduced apoptosis. ConclusionsOur findings demonstrate that Gene III(R) EGT effectively enhances exercise performance and alleviates fatigue. The underlying mechanisms involve the mitigation of oxidative stress and inflammation, as well as the activation of the AMPK/PGC-1 signaling pathway to promote mitochondrial function and cellular protection. These results highlight the potential of Gene III(R) EGT as a nutritional supplement for combating exercise-induced fatigue.

pharmacology and toxicology↗

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

Exploring Multi-Scale Local and Global Features in Whole Slide Images Using State Space Models

Whole slide image (WSI) classification is crucial in computational pathology, yet the gigapixel scale of WSIs makes it challenging to extract discriminative and compact WSI-level features for disease diagnosis. In this paper, we propose MambaWSI, a novel method that leverages the state space model (SSM) for WSI classification by exploring multi-scale local and global features. Unlike existing approaches that sequentially traverse WSI tiles and rely on vanilla SSMs for long-range dependency modeling, we exploit a traversal strategy in a higher-dimensional discrete space that preserves spatial proximity, enabling a first-local-then-global feature extraction process. Furthermore, to align with the clinical workflow of pathologists when examining WSIs at multiple scales, we propose a two-stage hierarchical fusion strategy: inter-scale feature alignment and aggregation, followed by attention-based fusion across magnifications, integrating complementary information from multiple magnifications. Experiments on two datasets demonstrate that MambaWSI outperforms state-of-the-art methods in classification performance.1

pathology↗

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

Ergothioneine: Evaluation of a Novel Antioxidant for Targeting Ocular Oxidative Stress

PurposeTo evaluate L-Ergothioneine (EGT), a naturally occurring amino acid and endogenous antioxidant, as a novel therapeutic agent for oxidative stress-related ocular diseases. This evaluation specifically aimed to address the challenge of targeted ocular delivery by assessing EGT s antioxidant potency, stability, ocular tolerance, and crucially, its ability to reach the posterior segment (fundus) via topical administration. MethodsThis study evaluated EGT as a novel ocular antioxidant by examining its radical scavenging capacity (DPPH assay compared to glutathione, astaxanthin, and coenzyme Q10), stability (at 40{degrees}C/75% relative humidity for six months using HPLC), ocular tolerance (using a New Zealand rabbit model), and fundus delivery efficiency (topical D9-EGT eye drops quantified by LC-MS/MS). ResultsEGT demonstrated significantly superior radical scavenging activity, exhibiting 6.4-fold and 46-fold higher rates than glutathione and coenzyme Q10, respectively, at 50 ppm. It also showed excellent stability, retaining over 97% of its initial concentration after six months, and caused no ocular irritation at any tested concentration (score 0). Importantly, topical administration of EGT resulted in effective fundus delivery, with peak concentrations reached at 0.5 hours post-application (1181 {+/-} 56 ng/g), confirming successful penetration through corneal and scleral barriers. These findings establish EGT as a potent, multi-mechanistic antioxidant characterized by high stability, ocular safety, and exceptional posterior segment penetrance via non-invasive eye drops. ConclusionThese findings establish EGT as a potent, multi-mechanistic antioxidant characterized by high stability, ocular safety, and exceptional posterior segment penetrance via non-invasive eye drops. By overcoming key delivery limitations, EGT presents a promising therapeutic strategy for oxidative stress-related ocular diseases such as age-related macular degeneration and diabetic retinopathy. Further studies are warranted to evaluate its long-term efficacy and clinical translation potential.

pharmacology and toxicology↗

bioRxiv2025

MisTIC: Missegmented Transcript Inference Correction for Improved Spatial Transcriptomics Analysis

Imaging-based spatially resolved transcriptomics (SRT) technologies, such as 10X Xenium, MERSCOPE, and CosMx, have revolutionized our ability to study gene expression within the spatial context of tissues at single-cell resolution. The acquisition of such data relies heavily on cell segmentation algorithms, which often produce imperfect boundaries, leading to transcript misassignment. These misassignments can significantly affect downstream analyses, including cell type identification, differential expression analysis, cell-cell communication, and RNA localization. We present MisTIC (Missegmented Transcript Inference Correction), a variational Bayesian model designed to correct transcript misassignment errors without requiring resegmentation. In benchmarking analyses using synthetic data with simulated transcript misassignment, MisTIC demonstrated high sensitivity and specificity in removing misassigned transcripts. In real data applications, MisTIC effectively enhances cell type identification, reduces ambiguity in differential expression analysis, and improves the detection of cell-cell communication. Furthermore, RNA localization analysis based on data corrected by MisTIC revealed that, in T cells located near cancer-associated fibroblasts compared to those farther away, genes involved in T cell activation, inflammation, and cytotoxicity were depleted from cytoplasmic regions despite not being differentially expressed between the two T cell subsets. In conclusion, MisTIC is a powerful tool for correcting transcript misassignment in SRT data. It not only improves the accuracy of routine analyses but also enables novel investigations that provide deeper insights into the dynamics of gene expression.

bioinformatics↗

bioRxiv2025

Unified Probabilistic Analysis of CyTOF: A Deep Generative Approach using CytoOne

Extracting meaningful biological signals from Cytometry by time-of-flight (CyTOF) data remains challenging due to heterogeneity, data characteristics, and the presence of various technical artifacts. Current analysis workflows typically rely on task-specific tools assembled into pipelines, which often make inconsistent distributional assumptions and fail to fully leverage the structure of the data. We present CytoOne, a unified probabilistic framework tailored for CyTOF data that integrates batch correction, differential analysis, and visualization within a single model. CytoOne is built upon a Bayesian hierarchical architecture inspired by Nouveau Variational Autoencoders (NVAE) and employs a novel quasi zero-inflated softplus-normal (QZIPN) likelihood to flexibly model the sparse and noisy nature of CyTOF measurements. We demonstrate via qualitative and quantitative evaluations that CytoOne effectively approximates both marginal and joint distributions of CyTOF data, removes batch-specific artifacts, enables fine-grained differential expression analysis, and facilitates interpretable embeddings for exploratory analysis.

bioinformatics↗

bioRxiv2025

PDLIM2 in Lung Adenocarcinoma Metastasis

Human and mouse studies have established the unique PDZ-LIM domain-containing protein PDLIM2 as a common tumor suppressor that is especially vital for suppressing the tumorigenesis and therapeutic resistance of lung cancer, the leading cause of cancer-related deaths among both men and women. However, the role of PDLIM2 in tumor metastasis, the predominant cause of cancer morbidity and mortality, is yet to be determined. Here, we report that PDLIM2 repression was positively associated with the metastasis of human lung adenocarcinoma, the major type of non-small cell lung cancer that accounts for more than 40% of all cases of human lung cancer. Interestingly, PDLIM2 repression was also correlated with oncogenic KRAS and/or TP53 mutations, two common drivers of human lung adenocarcinoma that often co-occur. In mice, in comparison to concurrently inducing mutant KRAS expression and TP53 deletion, additional co-ablation of PDLIM2 significantly increased the number and size of lung adenocarcinomas in the lung, and more importantly, the distant metastasis of lung tumor cells. The increased metastasis was accompanied by decreased anti-tumor immunity and increased pro-tumor inflammation. These data demonstrate the role of PDLIM2 in suppressing lung adenocarcinoma metastasis, thereby improving our understanding of this crucial tumor suppressor and lung cancer. They also provide a useful model for studying metastasis and testing new lung cancer treatments in vivo.

cancer biology↗

bioRxiv2025

PDLIM2 Repression: A Common Mechanism in Viral Lung Infection

BackgroundPDLIM2, a PDZ-LIM domain-containing protein expressed highest in the lung and immune cells, serves as a unique tumor suppressor and immune modulator, mainly by turning off the activation of the master transcription factors NF-{kappa}B and STAT3. While its role in cancer is established, the involvement of PDLIM2 in viral infection remains unclear. ResultsHere, we analyzed public gene expression data of blood leukocytes, bronchoalveolar lavage cells, and lung tissues from uninfected healthy humans and those infected with the respiratory virus SARS-CoV-2 or influenza. We found that PDLIM2 expression was repressed by viral infection, and notably, this repression correlated with the severity of infectious diseases. Consistently, the expression level of PDLIM2 was negatively associated with NF-{kappa}B and STAT3 activity across a diverse range of cell types, such as macrophages, monocytes, neutrophils, T cells, alveolar type 1 and 2 epithelial cells, airway epithelial cells, and fibroblasts. Accordingly, cells with low PDLIM2 expression exhibited aberrant activation of signaling pathways essential for cellular functions and immune responses. ConclusionsThese findings highlight PDLIM2 repression as a common mechanism underlying human viral infectious diseases and suggest PDLIM2 as a potential biomarker and therapeutic target for disease prognosis, prevention, and treatment.

pathology↗

bioRxiv2025

Extreme Small-World, Modular, and Rich-Club Topology of Single-Neuron Networks in Mouse Primary Visual Cortex

Understanding whether the canonical topologies of macroscale connectomes, such as small-world architecture, hub dominance, rich-club cores, and modularity, extend to local cortical microcircuitry has remained challenging due to limitations in simultaneously recording large neuronal populations in vivo. Here, using ultra-large-scale, high-resolution calcium imaging, we tracked spontaneous activity from approximately 2,000 neurons across the mouse primary visual cortex (V1). Across multiple mice and correlation thresholds, V1 neuronal networks exhibited hallmark characteristics of efficient brain organization, but in a markedly intensified form compared to macroscopic brain networks. Local clustering coefficients remained an order of magnitude above random levels, while characteristic path lengths approached those observed in random networks, yielding an exceptionally high small-world index that substantially exceeded typical values previously reported at macroscopic scales. Degree distributions followed a power-law, identifying highly connected hub neurons whose interconnections formed a robust rich-club integrative core. Community detection analyses showed robust modularity upon pruning weak connections, indicating functionally specialized neuronal clusters interconnected predominantly through hubs. These findings provide one of the first direct in vivo evidence that single cortical microcircuits not only recapitulate but intensify network topologies observed at macroscopic scale, implying evolutionarily conserved design principles underlying brain organization from neurons to systems.

neuroscience↗