SEARCH · Search PubMed

Results for “spatial transcriptome”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12Linked to original sources

PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗

PubMed2026

Spatial profiling of the spleen in mouse and human myelofibrosis reveals complement-driven immune-stromal interactions as a therapeutic target.

Splenomegaly is a defining feature of myelofibrosis, yet the contribution of splenic mesenchymal stroma to disease progression remains unclear. We combined spatial and single-nucleus transcriptomics of patient spleens with spatial and single-cell transcriptomics, as well as imaging analyses, of murine spleens to map extramedullary hematopoiesis niches. Activated red pulp reticular cells localize near hematopoietic stem and progenitor cells, and early disease is characterized by marginal zone disruption with lymphoid depletion preceding stromal remodeling. Trajectory analyses reveal a shift in reticular cells from hematopoiesis-supportive to inflammatory and pro-fibrotic states, driven by macrophage- and megakaryocyte-derived signals that activate complement and induce tumor necrosis factor α (TNF-α), transforming growth factor β (TGF-β), extracellular matrix, and Thbs1 programs. Non-hematopoietic complement component C3 deficiency or pharmacological C3 inhibition suppresses these pathways, restores splenic architecture, and reduces splenomegaly and bone marrow fibrosis. These findings identify complement-dependent stromal reprogramming as a mechanism governing hematopoietic niches and as a targetable axis in myelofibrosis.

Animals↗

PubMed2026

An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.

The immunoregulatory architecture of human oral tissues remains poorly defined. We present an integrated single-cell and spatial proteotranscriptomic atlas profiling >250,000 single-cell transcriptomes and >4 million spatially resolved cells across 13 niches. Using our AI-enabled AstroSuite, we defined neighborhoods and interaction modules, revealing peri-epithelial fibroblast-centered hubs enriched in effector cytokines. We harmonized fibroblast subtypes (universal, immune, peri-epithelial, peri-vascular, peri-neural, antigen-presenting cell [APC]-like, stress responsive, and myofibroblasts) with stress-responsive subtypes partitioning between mucosae and glands (type I and II). Spatial multiomics mapped ligand-receptor programs and identified mucosal stress-responsive fibroblasts as putative immunoregulatory hubs. Niche-aware integration of healthy and diseased datasets revealed fibroblast rewiring into inflammatory and reparative niches. Disease neighborhoods exhibited expansion of major histocompatibility complex (MHC)-I+, MHC-II+, and programmed cell death ligand 1 (PD-L1)+ fibroblasts and predicted spatial engagement with T cells at tertiary lymphoid structures. Together, this atlas identifies fibroblasts as central regulators of structural immunity and provides a scalable framework to target stromal-immune interactions across barrier organs.

Journal Article↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2026

BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction.

MOTIVATION: Hypergraph-based models for brain disorder prediction mainly adopt imaging-derived hypergraphs as propagation backbones. However, the entanglement of topology construction and feature propagation leaves regional representations weakly constrained by underlying biological organization, making them vulnerable to subject-specific variation and noise, particularly in heterogeneous multimodal settings. RESULTS: We present BriGHT, a Brain transcriptome-reGularized Hypergraph framework for mulTimodal disorder prediction. BriGHT employs a transcriptome-derived structural reference as a soft anchoring prior to regularize neuroimaging ROI embeddings, stabilizing representation geometry while preserving disease-relevant subject-specific variation. BriGHT further incorporates a reliability-aware fusion module to estimate subject-specific modality reliability from prediction confidence, cross-modal consistency, and decision certainty, enabling adaptive integration under heterogeneous modality quality. Experiments on three neuroimaging cohorts (ADNI, ADHD-200, REST-meta-MDD) and four modalities (VBM, fMRI, FDG, AV45) demonstrate that BriGHT consistently outperforms competing graph/hypergraph learning methods across six brain disorder prediction tasks. Perturbation analyses show that BriGHT benefits from the spatial correspondence between transcriptomic modules and imaging ROIs, rather than from arbitrary hypergraph regularization alone. Ablation and meta-analytic interpretability analyses support the contribution of transcriptomic anchoring and adaptive fusion to robust and biologically meaningful brain disorder prediction. AVAILABILITY: The software is publicly available at: https://github.com/Yaolab-fantastic/BriGHT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed2026

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans↗

PubMed2026

The chemical landscape of plant surface metabolites: Acylsugars as models of ecological function and structural diversity.

Plants produce a multifunctional assortment of specialized metabolites that play important roles in defense, environmental adaptation, and ecological interactions. Among these compounds, acylsugars, nonvolatile metabolites produced primarily in glandular trichomes of Solanaceae species, have emerged as informative model systems for understanding plant surface chemistry. Differences in acyl chain length, branching pattern, saturation, and attachment position generate extensive chemical diversity that influences herbivore deterrence, pathogen resistance, and the physicochemical properties of leaf surfaces. Recent advances in analytical chemistry, particularly liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), have greatly improved the ability to separate structurally related acylsugar isomers and characterize metabolite complexity at high resolution. When integrated with genomics, transcriptomics, and emerging spatial metabolomics approaches, these analytical tools provide new insights into acylsugar biosynthesis, pathway regulation, evolutionary diversification, and ecological function across plant species. This review positions acylsugars, particularly those of Solanum species, as model systems for understanding how structural diversity, spatial localization, and specialized metabolism shape ecological and physiological function at plant surfaces. We examine acylsugar structural diversity, biosynthetic pathways, ecological and physiological functions, and interactions with environmental and atmospheric processes. Major challenges, including extensive isomeric complexity, incomplete pathway characterization, and difficulties linking chemical structure to biological function, are discussed alongside emerging opportunities in integrative omics, crop improvement, sustainable pest management, and environmental monitoring. Overall, acylsugars provide a powerful model for linking molecular structure, spatial localization, and ecological function, offering broader insight into how specialized metabolism shapes plant adaptation, defense, and environmental interactions.

Acylsugars↗

PubMed2025

SpatioMark: quantifying the impact of spatial proximity on cell phenotype.

MOTIVATION: As research advances in spatially resolving the biological archetype of various diseases, technologies that capture the spatial relationships between cells are demonstrating increasing value. Whilst there are an increasing number of analytical methods being developed to identify the complex web of interactions between cells, the downstream impacts of these cell-cell relationships are under explored. RESULTS: We present SpatioMark, a statistical framework that simplifies the assessment of gene or protein expression changes within a cell type that are associated with the spatial proximity to other cell types. We demonstrate its performance across spatial proteomics and transcriptomics datasets. We link identified relationships with differences in patient survival. We highlight key challenges in identifying changes in molecular markers associated with the localization of cells. We propose correction strategies that reduce artefact-induced relationships. AVAILABILITY AND IMPLEMENTATION: SpatioMark is implemented in the Statial R package on Bioconductor: https://bioconductor.org/packages/release/bioc/html/Statial.html.

Humans↗

PubMed2026

CoxFormer enables spatial omics inference with multimodal generative modeling.

Gene co-expression maps transcriptome-wide gene-gene relationships, yet high-quality estimates cover less than half the genome. Meanwhile, spatial omics either profiles restricted in situ panels or lacks cellular resolution. Extending co-expression transcriptome-wide could overcome these limitations by inferring unassayed gene expression at subcellular resolution. Here we show that CoxFormer integrates literature-derived gene knowledge with co-expression networks from bulk tissues and large-scale single-cell atlases to learn 512-dimensional representations for 32,016 human genes. These embeddings capture functional gene relationships and serve as a generative prior for spatial inference across platforms and modalities. Without requiring a matched single-cell RNA-sequencing reference, CoxFormer supports four applications beyond measured genes: histology-based expression imputation, gene activity prediction from chromatin accessibility, subcellular super-resolution inference, and pathological region detection. Together, CoxFormer extends gene embedding from gene- and cell-level tasks to whole-transcriptome spatial inference, providing a unified framework for biological analysis beyond the limited gene coverage of current spatial omics technologies.

Humans↗

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed2024

The burgeoning spatial multi-omics in human gastrointestinal cancers.

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Humans↗

PubMed2026

ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.

MOTIVATION: Spatial multi-omics technologies jointly profile transcriptomes, proteins and chromatin accessibility in situ, enabling integrative analysis of tissue organization across molecular layers. However, most existing graph-based integration methods rely on independently constructed modality-specific k-nearest-neighbor graphs. When auxiliary modalities are sparse or noisy, these graphs can become topologically discordant, propagate spurious edges, weaken cross-modal alignment, and reduce spatial domain resolution. RESULTS: We present Anchored RNA for Integrated Spatial Embedding (ARISE), an RNA expression anchored framework for spatial multi-omics integration. ARISE defines a shared-edge topology by intersecting RNA feature-similarity and spatial-proximity graphs, encodes auxiliary modalities on this common scaffold, and integrates them through inside-out hierarchical fusion. We further show theoretically that graph intersection minimizes false-positive edges within a broad class of k-of-r graph fusion rules, providing a principled basis for topology anchoring. Across various spatial multi-omics benchmarks spanning simulated and real datasets in bi-modal and tri-modal settings, ARISE improves spatial domain identification, cross-modal consistency, and preservation of tissue structure relative to existing methods. Furthermore, the learned representation supports biologically meaningful downstream analyses, including marker-based domain annotation, pathway enrichment, and cis-regulatory inference, indicating that ARISE yields a robust and interpretable framework for spatial multi-omics integration. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XiangxiangWang-code/ARISE. The archived version used in this study is available at https://doi.org/10.6084/m9.figshare.32686137.v2.

Multiomics↗

PubMed2026

RNF43 Mutations Are Associated With the Classical Molecular Subtype, Vigorous Antitumor Immune Responses, and Prolonged Survival in Pancreatic Adenocarcinoma.

RNF43 mutations were correlated with microsatellite status in colorectal cancer and with fewer and later recurrences in pancreatic ductal adenocarcinoma (PDAC). Here, we undertake a detailed assessment of RNF43 mutations in PDAC. A total of 313 PDACs (308 microsatellite stable [MSS] and 5 microsatellite-instable [MSI] cases) underwent next-generation sequencing (Oncomine Tumor Mutation Load assay; Thermo Fisher). Spatial analyses (NanoString) classified PDACs according to their transcriptomic and proteomic immune signaling. Fluorescent imaging was used to define spatial compartments (tumor: pancytokeratin+/CD45- and leukocytes: pancytokeratin-/CD45+). Each of 20 PDACs with RNF43 mutations (RNF43mut) and without RNF43 mutations (RNF43wt) underwent multiplex immunofluorescence analysis to determine immune status. A total of 153 PDACs (22 RNF43mut and 131 RNF43wt cases) underwent bulk RNA sequencing to assign into molecular subtypes. Overall, 24 RNF43 mutations were identified (22 MSS PDACs and 2 MSI PDACs). The incidence of RNF43 mutations in MSS PDACs (7.1%) was consistent with The Cancer Genome Atlas (6.7%). However, RNF43 mutations were more frequent among MSI PDACs (40%). Additionally, RNF43mut had differential frequencies of other mutations (including Wnt pathway genes), higher tumor mutational burden values (5.5 mut/mb vs 1.67 mut/mb; P < .01), and significantly longer overall survival (47 vs 18 months; P < .0001) than RNF43wt. Moreover, RNF43mut exhibited significantly higher densities of CD8+ T lymphocytes, dendritic cells, and B lymphocytes (P < .001) and an upregulation of ITGAX, CD11c, CD8, and HLA-DR compared with RNF43wt. Patients with RNF43mut PDACs were more often of the classical molecular subtype (20/22, 90.9%). RNF43mut PDACs showed high tumor mutational burden values, suggesting increased neoantigen load coupled with an abundance of antigen-presenting immune cells and an upregulation of immune determinants promoting antigen presentation. All this contributes to stronger antitumor immune responses and improved clinical outcomes.

Humans↗

PubMed2025

Balancing LncRNA H19 and miR-675 Bioconversion as a Key Regulator of Embryonic Myogenesis Under Maternal Obesity.

BACKGROUND: Maternal obesity (MO) impairs fetal skeletal muscle development, but the underlying mechanisms remain poorly defined. The regulatory roles of lncRNA H19 and its first exon derived microRNA675 (miR675) in prenatal muscle development remain to be examined. H19/Igf2 are in the same imprinting cluster with H19 expressed from the maternal allele while Igf2 expresses paternally. H19 contains a G-rich loop, and KH-type splicing regulatory protein (KHSRP) mediates the biogenesis of pre-miRNAs containing G-rich loops, which depends on its phosphorylation by AKT, a key mediator of IGF2 signalling. This study aims to depict the elusive function of these regulators that are affected by MO during embryonic myogenesis. METHODS: Single-cell transcriptomic sequencing and GeoMx spatial RNA sequencing were performed to identify the differentially expressed genes between embryos from MO and control (CT) mice. Both E11.5 and E13.5 embryos were collected and analysed to validate the sequencing data. The roles of H19 and miR657 in myogenesis were further analysed in P19 embryonic cells via CRISPR/dCas9-mediated H19 activation and inhibition. The epigenetic changes of H19 were analysed by methylated DNA immunoprecipitation, and allele-targeted analysis of H19 was performed by crossing C57BL/6J and CAST/EiJ mice. RESULTS: Transcriptomic analysis showed that MO embryos contained less differentiated myocytes (1.34%) than CT embryos (2.86%). Myogenesis-related GO biological processes were down-regulated in the MO embryonic myotome region. MO embryos showed lower expression of myogenic transcription factors such as Myf5, Myod1, Myog, Mef2c and Myh3 (p < 0.05). MO altered epigenetic modifications of the H19 genomic cluster, showing a decreased methylation level in H19 imprinting control region (p < 0.05) and a diallelic expression pattern of H19, which elevated its expression in MO embryos. Overexpression of H19 inhibited myogenesis in P19 cells, but miR675 promoted myogenesis, suggesting the critical regulatory roles of bioconversion of H19 to miR675. A KHSRP mediates the biogenesis of miR675, a process that relies on its phosphorylation by IGF2/AKT signalling. Knocking-down of KHSRP and inhibition of AKT abolished miR675 biogenesis. MO suppressed IGF2/AKT signalling and blocked KHSRP-dependent miR675 biogenesis in embryos. CONCLUSIONS: We found differential effects of H19 and miR675 on embryonic myogenesis. MO up-regulates H19 but blocks its miR675 bioconversion via suppressing IGF2/AKT/KHSRP signalling axis. Myogenesis in MO embryos was impeded due to the highly accumulated H19 and blocked miR675 biogenesis.

RNA, Long Noncoding↗

PubMed2025

Parabacteroides goldsteinii mitigates parkinsonism in LRRK2 mutant mice by reducing neuroinflammation through Gut-Brain axis.

INTRODUCTION: Alterations in the gut microbiota accompanied by intestinal inflammation are early features of Parkinson's disease (PD). Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene represent a common genetic risk factor for PD and inflammatory bowel disease. Parabacteroides goldsteinii has been reported to alleviate intestinal and systemic inflammation. However, whether modulation of the gut microenvironment at early disease stage can attenuate PD progression remains unclear. OBJECTIVE: To investigate the impact of P. goldsteinii colonization prior to the onset of motor dysfunction on PD progression. METHODS: We established a germ-free PD mouse model carrying the LRRK2 G2019S mutation and administered P. goldsteinii orally at the pre-symptomatic stage to evaluate its effects on motor performance and PD-related neuropathology. Spatial and bulk RNA transcriptomic analyses of brain tissue, together with cytokine profiling, were conducted to assess central changes. To investigate gut immunomodulatory mechanisms, we performed intestinal bulk and single-cell RNA sequencing, spectral flow cytometry as well as cellular bioenergetic analyses. RESULTS: Germ-free conditions partially alleviated PD-like phenotypes in LRRK2 G2019S mice. Colonization with P. goldsteinii at 5-months of age, prior to motor symptom onset, further improved locomotor performance, reduced neuronal α-synuclein aggregations, and mitigated microglial activation and dopaminergic neurodegeneration. Neuroprotection was mediated through enhanced noncanonical neuronal IL-12 receptor-dependent neurotrophic support without activating the canonical STAT4 phosphorylation pathway, along with suppression of microglial activation and downregulation of LRRK2 kinase activity. At the intestinal level, P. goldsteinii suppressed TLR4-driven inflammation, expanded anti-inflammatory intraepithelial CD4+CD8αα+ T cells, promoted dendritic cell and macrophage differentiation, upregulated epithelial tight-junction genes, and improved mitochondrial bioenergetics in intestinal cells. CONCLUSION: P. goldsteinii colonization attenuates the progression of LRRK2-associated parkinsonism by restoring intestinal homeostasis and reducing neuroinflammation. These findings underscore the therapeutic potential of modulating the gut-immune-brain axis during the prodromal stage of PD.

Animals↗

PubMed2026

CpG hypermethylation and WNT/AP-1 cooperativity define the epigenetic landscape and a clinical subgroup of high-risk pediatric adrenocortical carcinoma.

Pediatric adrenocortical tumors are rare, clinically heterogeneous neoplasms with unpredictable outcomes and limited treatment options. Through integrated multi-omic analysis of 214 pediatric adrenocortical tumors combining DNA methylation profiling, transcriptomics, chromatin accessibility, and spatial deconvolution, we identify four distinct risk groups. A high-risk subgroup is characterized by CpG island hypermethylation, chromosomal instability, and dismal survival. These tumors exhibit transcriptional co-activation of WNT signalling and activator protein-1 transcriptional programs and display balanced admixture of zona glomerulosa and zona fasciculata/reticularis-like cells. Spatial analysis reveals zona glomerulosa cells as WNT signaling hubs driving intercellular crosstalk. Mechanistically, the histone deacetylase inhibitor entinostat reverses promoter methylation, silences activator protein-1 activity, and induces apoptotic reprogramming in tumor models. These findings establish a molecular framework for risk stratification and identify actionable therapeutic vulnerabilities, providing an essential resource for studying this molecularly uncharted pediatric malignancy.

Humans↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗

PubMed2026

Airway microbiome diversity, intramucosal bacteria, and spatial immunity in asthmatic adults and controls.

RATIONALE: Asthma is characterized by disruption of the thoracic airway mucosae and loss of microbial diversity. Spatial profiling of the mucosal transcriptome may systematically discover mechanisms for microbial influences on immunity. OBJECTIVES: We investigated relationships between clinical measures, microbial communities, and the host mucosal transcriptome within different strata of bronchial biopsies in subjects with and without asthma. METHODS: We performed bronchoscopy in 65 asthmatic adults and 44 healthy controls, quantifying bacterial operational taxonomic units (OTUs) in bronchial brushings by 16S ribosomal RNA (rRNA) gene amplicon sequences. Biopsy histologic features were scored blind to diagnosis. Following 16S rRNA in situ hybridization of 44 biopsies, bacterial foci were scored in epithelium, basement membrane, and stroma. Global human gene expression was quantified in epithelial and stromal compartments using digital spatial profiling. MEASUREMENTS AND MAIN RESULTS: Clinical asthma was independently predicted by basement membrane abnormalities (BaseMA), endobronchial bacterial diversity, and circulating eosinophil counts, but not by specific OTU abundances. 16S rRNA staining revealed bacteria within epithelium and mucosa of all biopsies. Intramucosal bacteria counts correlated negatively with spatially organized coexpression networks encoding antigen-specific immunity, neutrophil functions, and matrix activation, whereas BaseMA correlated positively with the adaptive immunity module. Eosinophil counts correlated with epithelial bacterial counts and senescence pathways. Clinical asthma was accompanied by upregulation of a regulatory T-cell network. CONCLUSIONS: Asthma and its related phenotypes are accompanied by complex mucosal events that extend beyond eosinophilic pathways. Components of diverse airway microbiota may modify immunity by beneficial interactions within the mucosa.

Humans↗