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PubMed2026

ModiCal: A Targeted Calibration Workflow for Site-Specific m5C Validation by Nanopore Direct RNA Sequencing.

Accurate identification of RNA 5-methylcytidine (m5C) at the single-nucleotide resolution remains a central challenge in nanopore direct RNA sequencing (DRS). Current global scanning and modification-aware basecalling methods enable transcriptome-wide profiling but often yield high false-positive rates and lack site-specific accuracy. To address this, we repurposed ModiDeC, originally a de novo multimodification classifier, into a targeted, high-precision validation tool for RNA modification sites with prior biochemical knowledge. This was implemented through a three-step calibration workflow that alternates between biochemical and computational modules using the well-characterized m5C2278 site in 25S rRNA as a starting point. Baseline training uses short synthetic RNAs carrying either a methylated or unmodified C2278 as ground truth, followed by IVT-derived calibration and validation in methyltransferase knockout yeast. The baseline model accurately detected the bona fide m5C2278 site but initially produced off-target predictions. Iterative retraining with unmodified IVT signals progressively reduced and ultimately eliminated false positives while maintaining a strong signal at the bona fide site. The final model retained enzyme-dependent detection in wild-type versus knockout yeast and, when explicitly targeted, was also able to detect the second rRNA site, C2870, which remained invisible in the initial analysis. Application to native human prerRNA processing intermediates further resolved two distinct m5C deposition regimes on 28S rRNA, while generalization to dengue virus genomic RNA confirmed that the same calibration logic transfers across diverse RNA contexts. Together, this study establishes a reproducible and transferable framework that integrates biochemical validation with iterative neural network refinement, providing a route toward reliable site-specific m5C confirmation by nanopore direct RNA sequencing.

RNA Methylation↗

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

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↗

PubMed2026

Penicillium melinii promotes root growth through subtle host reprogramming across model and crop species.

Root development is highly responsive to microbial interactions, yet the mechanisms by which beneficial fungi promote root growth remain incompletely understood. Here, we identified Penicillium melinii 'isolate 2' through a screen of endophytic fungi isolated from Arabidopsis and characterized it as a promoter of root development in both Arabidopsis and crop species. We combined phenotyping in vitro, rhizotron, greenhouse and field assays with reporter and mutant analyses, transcriptomics, phytohormone profiling and sequencing and annotation of the fungal genome to investigate the basis of this interaction. P. melinii consistently stimulated root growth and modified root architecture across experimental systems and host species. These effects were associated with subtle but reproducible host transcriptional reprogramming, supporting a model in which the fungus fine-tunes endogenous developmental programmes rather than broadly perturbing stress or growth pathways. Genetic and reporter analyses further suggested that this interaction modulates root branching through localized developmental reprogramming. Genomic analysis provided a framework for understanding the fungal traits associated with this beneficial interaction. The conservation of the response across model and crop species supports the relevance of P. melinii as both a useful experimental system to study beneficial plant-fungus interactions and a promising candidate for improving root traits and crop performance.

Penicillium melinii↗

PubMed2026

Blood-derived gene expression profiles associated with dietary microalgae oil intake and methane emission variation in lambs.

BACKGROUND: Minimising methane (CH4) emissions from livestock production is a global priority, and feed modifications, such as supplementing diets with microalgae, have previously been shown to help reducing enteric CH4 production. This study explored blood-derived host gene expression profiles from twenty lambs supplemented with increasing levels of microalgae oil to investigate their transcriptional responses associated with varying microalgae oil levels while also exploring the host systemic responses towards varied CH4 productions. RESULTS: Findings revealed no significant changes in CH4 production with increasing levels of microalgae oil intake through phenotypic analysis (P = 0.18). However inter-individual variations in CH4 production ranged from 27.02 to 47.86 g/day throughout the study period. Blood RNA-Sequencing identified 64 significant genes including DHCR7, DHCR24, HMGCS1, INSIG1, LSS, MSMO1, and SQLE, which were involved in lipid metabolism, and steroid biosynthesis that became enriched alongside increasing microalgae oil intake levels thereby contributing to a positive impact on lambs' metabolic functions. Additionally, seven significant blood-expressed host genes (NME4, MARCHF3, PLXNB3, LOC132657460, LOC121819234, LOC105603087, LOC101116551) functionally enriched in nucleotide metabolic pathways and immune responses were identified to have significant positive associations with increasing CH4 production. Importantly, this study found no overlap between genes associated with microalgae oil intake and those linked to CH4 emissions. CONCLUSIONS: Findings suggest that microalgae oil intake and inter-individual variations in CH₄ production are associated with distinct blood-derived transcriptional responses. Although such signals should be interpreted as proxies for systemic host responses rather than direct measures of rumen-specific processes, these results emphasise the importance of considering host-associated molecular variations alongside dietary CH₄-mitigation strategies.

Animals↗

PubMed2025

Exploring diagnostic m6A regulators in primary open-angle glaucoma: insight from gene signature and possible mechanisms by which key genes function.

PURPOSE: The purpose of this study was to interrogate the potential role of N6-methyladenosine (m6A) regulators in the process of trabecular meshwork (TM) tissue damage in patients with primary open-angle glaucoma (POAG). METHODS: Firstly, the expression profile of m6A regulators in TM tissues of POAG patients was comprehensively analyzed by bioinformatics analysis; Plasmid transfection and siRNA gene interference were used to enhance or weaken the expression levels of YTHDC2 in human trabecular meshwork cells (HTMCs); Cell migration ability was detected by transwell chamber assay; Immunofluorescence staining assay was used to evaluate the expression of extracellular matrix (ECM) related proteins. RESULTS: Through the analysis of GSE27276 database, 5 m6A regulators with different expression in POAG were screened out. The results of random forest model showed that these 5 m6A regulators exhibited diagnostic potential and were characteristic genes of POAG. All POAG samples could be effectively divided into two groups based on the expression levels of these 5 hub m6A regulators. Immune cell infiltration analysis indicated that the levels of activated CD8+ T cells and regulatory T cells were different in the two subtypes. HTMC oxidative stress cell model and TGF-β2 stimulation cell model were further constructed to verify the expression of the aforementioned hub m6A regulators, and it was found that YTHDC2 mRNA showed the same expression trend in both models. The silencing of YTHDC2 enhanced the migration ability of HTMCs and increased the synthesis ability of ECM. However, when YTHDC2ΔYTH, which lacks the YTH domain, is overexpressed in HTMCs, there is no significant change in the ECM synthesis ability. CONCLUSIONS: The differentially expressed m6A regulators in TM tissues may serve as potential diagnostic biomarkers for POAG. And, in HTMCs, the expression level of YTHDC2 mRNA was changed under oxidative stress or TGF-β2 intervention, and then exerted its regulation on cell migration and ECM synthesis capability through m6A modification, which may be an important part of the disease process of POAG.

Humans↗

PubMed2026

Decoding glioblastoma evolution and heterogeneity through mechanistic modeling: implications for clinical translation.

Glioblastoma (GBM) is one of the most aggressive and lethal primary brain tumors in adults, characterized by dynamic clonal evolution and extensive genomic, cellular, spatial, and microenvironmental heterogeneity. Multi-omics studies have revealed that GBM follows complex evolutionary trajectories involving genetic, epigenetic, transcriptional, and immune-microenvironmental remodeling as tumors grow, adapt to the brain microenvironment, and acquire therapeutic resistance. Increasing evidence suggests that GBM may originate from aberrant neural stem or progenitor cells, including those residing in the subventricular zone, and that glioblastoma stem cells (GSCs) contribute to tumor propagation, heterogeneity, and recurrence. A key conceptual challenge is to reconcile hierarchical cancer stem cell models, in which GSCs are viewed as relatively stable tumor-propagating subpopulations, with dynamic state plasticity models, in which stem-like properties can be reversibly acquired or lost during transitions among proneural-like, mesenchymal-like, invasive, and therapy-tolerant states. Recent advances in single-cell profiling, spatial transcriptomics, lineage tracing, organoid culture, 3D bioprinting, genetically engineered models, and artificial intelligence (AI)-assisted computational modeling have substantially improved the ability to study these processes. However, no currently available model fully recapitulates human GBM heterogeneity, recurrence, treatment history, and tumor-microenvironment interactions. Therefore, model selection should be guided by clearly defined mechanistic questions rather than by reliance on any single platform. This review summarizes current advances in in vitro, ex vivo, in vivo, and computational models for studying GBM evolution and heterogeneity, and discusses how integrated model pipelines may improve preclinical drug testing, treatment-response prediction, and precision neuro-oncology.

Humans↗

PubMed2021

RP-REP Ribosomal Profiling Reports: an open-source cloud-enabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting.

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user's own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

AMI↗

PubMed2026

Complement expression profiles in human glomerular mesangial cells, endothelial cells, podocytes and proximal tubular epithelial cells.

BACKGROUND: Local expression of complement components in the kidney has been reported sporadically in both diseased and normal kidneys. This study aimed to comprehensively characterize the expression of complement components in human glomerular mesangial cells (GMCs), glomerular endothelial cells (GECs), podocytes, and proximal tubular epithelial cells (PTECs) in non-diseased renal tissue. METHODS: Complement expression in cultured human renal intrinsic cells was initially evaluated using reverse transcription polymerase chain reaction and immunofluorescence staining. These findings were further examined using publicly available single-cell RNA-sequencing datasets and 10×Genomics single-cell RNA sequencing of non-diseased human kidney tissue. The analyses focused on complement components involved in the initiation of the classical, lectin, and alternative pathways, as well as components shared among these activation pathways, terminal pathway components, complement regulators, and complement receptors. RESULTS: Complement components unique to the initial phase for classical pathway (C1S, C1R, C2, C4), lectin pathway (MBL2, FCN1, MASP1), alternative pathway (CFB, CFD), and the C3 component shared by the three activation pathways were detected in these cells. The components shared by the terminal pathways including C5, C6, C7, C8 and C9 exhibited lower expression, while complement regulators (CFH, CFI, CD55/DAF, CD46/MCP, CD59, C4BPB, PROS1/Protein S) or receptors (CD93/C1QR1, CR1), particularly membrane-bound proteins, such as DAF, MCP and CD59, which inhibit complement activation and the formation of the membrane attack complex, showed relatively high expression. CONCLUSION: These results showed that all four types of intrinsic renal cells expressed multiple complement components associated with the classical, lectin, and alternative pathways. In non-diseased kidney tissue, complement regulatory molecules involved in the control of complement activation showed relatively higher expression, whereas components of the terminal complement pathway were expressed at relatively lower levels, suggesting that renal intrinsic cells maintain a locally poised but tightly regulated complement system.

Humans↗

PubMed2026

High-throughput single-cell proteomics and transcriptomics from same cells with a nanoliter-scale, spin-transfer approach.

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

Single-Cell Analysis↗

PubMed2026

Proteogenomic features define subtypes of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a biologically heterogeneous B-cell malignancy. Although genomics and transcriptomics have delineated parts of the MCL disease spectrum, proteomics remains largely unexplored. Here, we conducted a comprehensive proteogenomic analysis integrating genomics, transcriptomics, and proteomics on peripheral blood samples from 27 patients with MCL and 4 healthy donors to investigate the translational and posttranslational dimensions of MCL. Our study identified 1296 downregulated and 468 upregulated proteins in MCL cells. The splicing pathways were significantly upregulated at both the mRNA and protein levels, suggesting a critical role for aberrant RNA splicing in MCL pathogenesis. Integration of proteomic data with genetic aberrations revealed immunoglobulin heavy chain variable mutational status and CCND1 mutation are associated with distinctive transcriptomic and proteomic profiles, which correspond to significant differences in clinical outcomes. A multiomics molecular stratification model incorporating proteomic data showed superior predictive power for patient survival compared with single-omics models (concordance index, 0.83 vs 0.74). This study provides, to our knowledge, the first comprehensive proteogenomic profile of MCL, offering novel insights into its molecular mechanisms and clinical behavior. The identification of molecular subtypes and prognostic protein signatures underscores the potential of proteomics to guide precision medicine strategies for MCL.

Humans↗

PubMed2026

Optics-free spatial genomics for mapping mammalian brain aging by IRISeq.

Spatial transcriptomics has emerged as a transformative approach for in situ mapping of cellular heterogeneity and interactions, yet existing methods often compromise throughput, cost and tissue coverage. Here we introduce Imaging Reconstruction using Indexed Sequencing (IRISeq): an optics-free, cost-effective platform that leverages spatial interaction mapping by indexed sequencing to profile tissues at adjustable sizes and resolutions (5-50 µm). We applied IRISeq to map gene expression across more than 70 coronal sections from both adult and aged mouse brains, including wild-type and two lymphocyte-deficient models (Rag1 and Prkdc mutants) and generated more than 460,000 spatial transcriptome profiles. Our integrated analysis with 783,264 single-cell transcriptomes revealed region-specific aging signatures that are lymphocyte dependent, notably a downregulation of interferon signaling and inflammation in ventricular regions upon lymphocyte depletion, alongside mutant-specific upregulation of senescence pathways. Furthermore, lymphocyte deficiency was linked to preserved abundance of ependymal cells that line the brain's ventricles and to distinct microglial state dynamics, highlighting a key role for lymphocytes in driving inflammatory processes during brain aging. Overall, IRISeq provides a high-throughput and cost-effective solution for spatially resolved transcriptomic profiling, opening new avenues for elucidating region-specific cellular mechanisms underlying aging and identifying potential therapeutic targets to preserve brain homeostasis.

Animals↗

PubMed2026

Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Advances in spatially resolved technologies enable the simultaneous acquisition of diverse data modalities within a tissue slice while preserving critical spatial context, which presents unprecedented opportunities to decipher intricate tissue heterogeneity. However, existing computational approaches lack the intrinsic flexibility to universally process both spatial multi-modal and multi-omics data. Here, we introduce STransformer, a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short-range cellular interactions and tissue-wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity. Systematic evaluations across diverse species, tissue types, and data modalities highlight its profound versatility. For spatial multi-modal data, STransformer delineates intricate anatomical structures in the human cortex, uncovers pathological mechanisms in Alzheimer's disease, and characterizes dynamic spatiotemporal developmental trajectories during chicken cardiogenesis. Scaling to spatial multi-omics data, STransformer synergizes spatial transcriptomic and proteomic profiles to decipher intricate immune microenvironments within the human tonsil, and jointly analyzes spatial epigenomic and transcriptomic data to infer regulatory mechanisms in the mouse embryonic brain. Consequently, STransformer serves as a highly versatile and robust analytical framework for advancing our understanding of tissue heterogeneity and disease pathogenesis.

Multiomics↗

PubMed2025

Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer.

To unravel the mechanism of immune activation and suppression within tumors, a critical step is to identify transcriptional signals governing cell-cell communication between tumor and immune/stromal cells in the tumor microenvironment. Central to this communication are interactions between secreted ligands and cell-surface receptors, creating a highly connected signaling network among cells. Recent advancements in in situ-omics profiling, particularly spatial transcriptomic (ST) technology, provide unique opportunities to directly characterize ligand-receptor signaling networks that power cell-cell communication. In this paper, we propose a novel statistical method, LRnetST, to characterize the ligand-receptor interaction networks between adjacent tumor and immune/stroma cells based on ST data. LRnetST utilizes a directed acyclic graph model with a novel approach to handle the zero-inflated distributions of ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation. Application of LRnetST to ST data of high-grade serous ovarian tumor samples revealed both common and distinct ligand-receptor regulations across different tumors. Some of these interactions were validated through both a MERFISH dataset and a CosMx SMI dataset of independent ovarian tumor samples. These results cast light on biological processes relating to the communication between tumor and immune/stromal cells in ovarian tumors. An open-source R package of LRnetST is available on GitHub at https://github.com/jie108/LRnetST.

Humans↗

PubMed2021

Comparative cellular analysis of motor cortex in human, marmoset and mouse.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Animals↗

PubMed2025

Transcript-guided targeted cell enrichment for scalable single-nucleus RNA sequencing.

Large-scale single-cell atlases have revealed many aging- and disease-associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular analyses. To address this, we developed EnrichSci-a scalable, microfluidics-free platform that combines hybridization chain reaction RNA fluorescence in situ hybridization (FISH) with combinatorial indexing to profile single-nucleus transcriptomes of target cell types with full gene-body coverage. Applied to oligodendrocytes in the aging mouse brain, EnrichSci uncovered aging-associated molecular dynamics across distinct oligodendrocyte subtypes, revealing both shared and subtype-specific gene expression changes. Additionally, we identified aging-associated exon-level signatures missed by conventional gene-level analyses, highlighting post-transcriptional regulation as a critical dimension of cell-state dynamics in aging. By coupling transcript-guided enrichment with a scalable sequencing workflow, EnrichSci provides a versatile approach to decode dynamic regulatory landscapes in diverse cell types from complex tissues.

Animals↗

PubMed2025

Novel insights into hypoxia-driven transcriptomic and epigenetic landscapes in grade 3 meningioma.

BACKGROUND: Meningiomas are among the most prevalent central nervous system (CNS) tumors, with up to 20% of cases exhibiting recurrence or aggressive behavior. Hypoxia is a key driver of malignant transformation and therapeutic resistance, yet its molecular basis in meningioma remains poorly understood. METHODS: We conducted integrative transcriptomic and epigenomic profiling of IOMM-Lee cells (grade 3 meningioma) cultured under hypoxic (0.2% O₂) and normoxic conditions. RNA-sequencing and Illumina MethylationEPIC v2.0 data were analyzed in R using DESeq2 and minfi, respectively. Functional enrichment, transcription-factor binding analysis, and pathway mapping (clusterProfiler, enrichR) were performed. Findings were cross-validated in public meningioma datasets, in Indian meningioma patient cohort and cell line via RT-qPCR, and azacytidine-based demethylation assay. Functional role of the candidate gene was elucidated in vitro via cellular assays. RESULTS: Hypoxia triggered a canonical HIF1A-driven transcriptional program activating glycolytic and angiogenic pathways while downregulating genes associated with DNA repair and replication in meningioma. Several differentially expressed genes (DEGs) were identified as known oncogenes, tumor-suppressors, or associated with immune regulation and stemness. Promoter motif analysis identified HIF1, SP1, TP53, BRCA1, and E2F1 as enriched transcriptional regulators. We validated hypoxia and HIF1-mediated regulation of some of the top DEGs. DNA-methylation analysis revealed epigenetic silencing of RTN4IP1 and ZBTB7C under hypoxia, reversible upon azacytidine treatment. Integrative comparison with patient datasets highlighted SLITRK2, PDE4C, SGCD, and LRP1B as hypoxia-responsive genes associated with poor prognosis. Several hypoxia-regulated genes also showed significant correlation with known hypoxia biomarkers, VEGFA and CA9. IGFBP3 and NDRG1 were among the top hypoxia-associated upregulated genes, and IGFBP3 expression was linked to advanced meningioma grades. Knockdown of IGFBP3 via siRNA in hypoxia-treated IOMM-Lee cells was associated with reduced cell proliferation and migration. CONCLUSIONS: This study presents the first integrated transcriptomic–epigenomic landscape of hypoxia in grade 3 meningioma, uncovering regulatory networks and candidate biomarkers with prognostic and therapeutic potential. These findings provide a foundation for future translational studies targeting hypoxia-driven tumor progression in meningioma.

Humans↗

PubMed2026

Extracellular vesicle miR-93-5p cargo regulates glomerular endothelial cell damage in Alport syndrome.

Modulation of miRNA expression in glomerular cells is associated with renal disease. Here, we investigated the role of miR-93-5p in mitigating glomerular damage in Alport syndrome and whether the disease-modifying activity of extracellular vesicles from human amniotic fluid stem cells (hAFSC-EVs) is mediated by their miR-93-5p cargo. We identified downregulation of miR-93-5p specifically in glomerular endothelial cells in Alport syndrome along disease progression. Silencing of miR-93-5p in hAFSC-EVs changed the transcriptomic and proteomic profile, regulating EV disease-modifying activity. Compared with naive hAFSC-EVs, silenced hAFSC-EVs did not rescue glomerular endothelial function in vitro and did not restore kidney function in vivo. We established that hAFSC-EVs regulate VEGFR1 and VEGFR2 signaling by miR-93-5p cargo transfer, highlighting that miR-93-5p can restore glomerular endothelial cell biology. Spatial transcriptomics analysis of hAFSC-EV-injected kidneys showed that these EVs can reverse pathways altered during disease progression by stimulating proregenerative processes, specifically in the glomerulus, by regulating miR-93-5p targets. Alteration of glomerular endothelial cell transcriptomics and miR-93-5p targets was also confirmed in biopsies of patients with Alport syndrome using spatial molecular imaging. We demonstrated the critical role of miR-93-5p in glomerular endothelial cells and the capability of hAFSC-EVs to regulate miR-93-5p and its targets in Alport syndrome.

Humans↗