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At least 793 records · Page 44Linked to original sources
PubMed2026

ARID5A RNA-binding coordinates microglial defense and ferroptosis in iPSC-derived models.

RNA-binding proteins (RBPs) are key regulators of gene expression that shape cellular function in health and disease. However, the roles of RBPs in immune cells within the central nervous system (CNS) remain poorly understood. Here, we identify ARID5A as an RBP highly expressed in microglia and uncover its RNA-mediated regulatory functions using integrated multi-omics analyses of its RNA, DNA, and protein interactions. ARID5A regulates the splicing and translation of its RNA targets, many of which are integral to lysosomal, immune, and iron metabolism pathways. We confirm the functional relevance of this ARID5A-dependent RNA regulatory network by demonstrating that ARID5A modulates lysosomal activity, cytokine secretion, iron accumulation, and ferroptosis in iPSC-derived microglia. We further demonstrate that knockdown of microglial ARID5A reduces neuronal ferroptosis in co-cultures, underscoring the interconnected nature of these pathways. Moreover, in microglia harboring the TREM2-T66M mutation, ARID5A depletion restores dysregulated lysosomal and metabolic functions. Our results highlight the importance of protein-RNA interactions in regulating microglial cell biology.

Microglia↗
PubMed2026

Quantitative RNA modification mapping by mass spectrometry with isobaric tags and nucleobase fragment analysis.

RNA modifications regulate RNA stability, translation, stress responses, and disease processes, yet their function remains poorly understood due to technical limitations in sequence analysis. Here, we present an RNA-specific isobaric tandem mass tagging (RMT) platform for omic-scale quantitative mapping of RNA modifications. The platform combines RNA-specific tags adapted from proteomics with an end-to-end workflow spanning sample preparation through data processing. Validation using synthetic oligonucleotides and total tRNA from Pseudomonas aeruginosa yielded reproducible quantification, with coefficients of variation below 5%. Together with nucleobase fragment analysis, we identified and quantified 24 RNA modifications in PA14 tRNAs, including previously undescribed m2A38 and Gm/Cm39, and assigned their corresponding writer enzymes. Further analyses of tRNAs from writer knockout strains and stressed cells revealed dynamic modification patterns, modification interdependencies, and their potential roles in stress adaptation. This method provides a robust, cost-effective platform for quantitative RNA modification mapping, enabling deeper biological insights.

RNA, Transfer↗
PubMed2026

The DLX/Notch axis is necessary for spatiotemporal regulation of neural cell fate.

Neuronal-glial cell fate switch during forebrain development is highly regulated. DLX transcription factors are necessary for promoting GABAergic interneuron differentiation and migration but the mechanisms for concomitant repression of glial fate in neural progenitors remain elusive. Here, the DLX2 regulatory network dynamic in the developing ventral telencephalon is characterised using a multi-omic approach at single-cell resolution, including single-cell whole genome spatial transcriptomics. We identify a secondary proliferative zone in the ventral subventricular zone and spatiotemporal-context dependent Notch pathway repression by DLX2 in maintaining progenitor populations and facilitating neural differentiation. We find that DLX2 controls cell fate determination by directly repressing Notch signalling genes as well as glial fate-promoting transcription factors, thereby inhibiting early adoption of oligodendroglial differentiation during neurogenesis. Here, we show that temporal cell fate switch is mediated by DLX2 via a multilayer gene regulatory network, redefining current understanding of neuronal-glial cell specification mechanisms in the developing telencephalon.

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

AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework.

Breast cancer's genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial intelligence now enable integration of multi-omics data to reveal drug-gene-disease relationships and generate subtype-specific repurposing hypotheses. In this Review, we examine AI-driven computational approaches from signature-based to multi-modal frameworks and propose an integrated interpretability-driven framework linking mechanistic validation with clinical translation toward more transparent and actionable precision oncology.

Journal Article↗
PubMed2026

Flexible use of conserved motifs constrains genome access in cell type evolution.

Cell types can be organized into related families, but the regulatory mechanisms that define and maintain these families across deep evolutionary time remain unknown. Here, combining single-nucleus multi-omic sequencing with deep learning to analyse the accessible genomes of two groups of vastly divergent animals including flatworms and vertebrates, we find that hundreds of accessibility-dictating sequence motifs partition into distinct yet conserved sets, or 'vocabularies', each associated with a specific cell type family. However, combinatorial relationships among these motifs preferred by individual cell types are largely species specific. Deep-learning models trained on one species accurately predict family-level chromatin accessibility in distantly related species, albeit frequently rely on different motifs from shared vocabularies to reach convergent predictions. By contrast, models trained on individual cell types within a family lose cross-species predictive power, indicating that the regulatory syntax governing cell type-level identity evolves rapidly. We propose a 'collective maintenance' model in which motif vocabularies defining cell type families are evolutionarily stable, while recombination of these motifs generates cell type-specific regulatory programmes. This suggests that family identity is maintained collectively by large, conserved pools of regulatory factors, analogous to the logic of developmental homology, where character identity persists through network-level conservation despite extensive rewiring.

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

Multiancestry genome-wide association and multiomics analyses elucidate spatiocellular features of multiple sclerosis genetics.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination disseminated in space and time. Here we performed a genome-wide association study (GWAS) using 688 MS cases and 205,199 controls from the Japanese population and identified significant associations in the major histocompatibility complex region and a population-specific risk variant in 11q24. Through cross-population GWAS meta-analyses using a total of 29,374 cases and 1,843,563 controls from 4 ancestral populations, we identified 22 novel susceptibility loci. Integration of GWAS and single-cell and single-nucleus RNA sequencing of peripheral blood mononuclear cells and subcortical lesions from patients with MS revealed enrichment of genetic risk factors for MS in CD4+ T helper cell lineage and regulatory T cells, as well as in endothelial cells. Furthermore, spatial transcriptomics of subcortical lesions demonstrated spatial and temporal heterogeneity in associations with MS genetic risk. Our study demonstrates the value of investigation of spatiocellular features of disease genetics across diverse populations and omics modalities.

Humans↗
PubMed2025

Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem.

Emerging spatial multiomics technologies provide an increasingly large amount of information content at multiple scales. However, it remains challenging to efficiently represent and harmonize diverse spatial datasets. Here we present Giotto Suite, a suite of modular packages that provides scalable and extensible end-to-end solutions for multiscale and multiomic data analysis, integration and visualization. At its core, Giotto Suite is centered around an innovative data framework, allowing the representation and integration of spatial omics data in a technology-agnostic manner. Giotto Suite integrates molecular, morphology, spatial and annotated feature information to create a responsive and flexible workflow, as demonstrated by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science in R, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive and multiscale ecosystem for spatial multiomic data analysis.

Genomics↗
PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗
PubMed2026

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects function. However, the reliability of current benchmarks of spatially aware clustering (SAC) methods is undermined by their narrow focus on Visium and brain tissue datasets and the incorrect interpretation of manual annotation as ground truth. Here we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration and metric evaluation, enabling rapid inclusion of new methods and datasets. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods and shows that anatomical labels commonly used as ground truths are often biased, error prone and unsuitable for benchmarking. Rather than ranking methods, we propose a consensus-guided workflow where descriptive spatial metrics highlight high-entropy regions of method disagreement, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual SAC methods and manual annotations, highlighting the need for iterative, expert-in-the-loop evaluation.

Benchmarking↗
PubMed2026

High-throughput identification of endogenous biomolecular condensates and phase-separating proteins.

Biomolecular condensates formed through liquid-liquid phase separation regulate cellular processes, and their dysregulation causes disease. Current methods for identifying endogenous phase-separating proteins have low throughput and cannot capture dynamic responses to stimuli. Here we present a protocol combining osmotic compression or transforming growth factor-β (TGF-β) treatment to induce condensation with sucrose density gradient centrifugation and quantitative mass spectrometry to enable systematic, high-throughput identification of endogenous condensates and phase-separating proteins. The method exploits the density changes that occur when phase-separating proteins undergo oligomerization during condensate formation. In H1975 cells, we identified over 1,500 phase-separating proteins under osmotic compression or TGF-β treatment; 538 of these candidates were not present in PhaSepDB, a database that compiles in vivo, in vitro and omics-derived proteins. The approach detects constitutive condensates and proteins that dynamically phase-separate in response to osmotic stress or TGF-β signaling. This protocol provides proteome-wide analysis of fractions of proteins having different densities and enables temporal resolution of phase-separation events. The procedure takes ~9 d and requires expertise in cell culture, biochemistry and mass spectrometry. This method enables systematic study of biomolecular condensates and disease-associated phase-separation mechanisms.

Phase Separation↗
PubMed2025

Temporal multiomics gene expression data of human embryonic stem cell-derived cardiomyocyte differentiation.

Human embryonic stem cells (hESCs) serve as a valuable in vitro model for studying early human developmental processes due to their ability to differentiate into all three germ layers. Here, we present a comprehensive multi-omics dataset generated by differentiating hESCs into cardiomyocytes via the mesodermal lineage, collecting samples at 10 distinct time points. We measured mRNA levels by mRNA sequencing (mRNA-seq), translation levels by ribosome profiling (Ribo-seq), and protein levels by quantitative mass spectrometry-based proteomics. Technical validation confirmed high quality and reproducibility across all datasets, with strong correlations between replicates. This extensive dataset provides critical insights into the complex regulatory mechanisms of cardiomyocyte differentiation and serves as a valuable resource for the research community, aiding in the exploration of mammalian development and gene regulation.

Humans↗
PubMed2026

A chromosome-level assembly of the alpine snow alga Chloromonas typhlos.

Chloromonas typhlos is a cosmopolitan alpine snow alga distributed across continents, and its blooming accelerates snow melting by decreasing the amount of snow albedo. To elucidate the genetic traits underlying the adaptation of C. typhlos to the alpine habitat, we combined PacBio sequencing and Hi-C to generate a high-quality chromosome-level genome assembly (contig N50: 1.29 Mb; scaffold N50: 7.23 Mb) with 31 chromosomes and a genome size of 200.86 Mb. Repetitive elements constituted 11.05% of the genome, and 16,133 protein-coding genes were predicted, of which 82% were functionally annotated. This study provides a set of omics resources both for snow algae and the genus Chloromonas.

Snow↗
PubMed2025

Genome-wide DNA methylation regulation analysis provides novel insights on post-radiation breast cancer.

Breast cancer (BC) is the most common malignancy with a poor prognosis. Radiotherapy is one of the leading traditional treatments for BC. However, radiotherapy-associated secondary diseases are severe issues for the treatment of BC. The present study integrated multi-omics data to investigate the molecular and epigenetic mechanisms involved in post-radiation BC. The differences in the expression of radiation-associated genes between post-radiation and pre-radiation BC samples were determined. Enrichment analysis revealed that these radiation-associated genes involved diverse biological functions and pathways in BC. Combining epigenetic data, we identified radiation-associated genes whose transcriptional changes might be associated with aberrant methylation. Then, we identified potential therapeutic targets and chemical drugs for post-radiation BC patient treatment by constructing a drug-target association network. Specifically, four radiation-associated genes (CD248, CCDC80, GADD45B, and MMP2) whose increased expression might be regulated by hypomethylation of the corresponding enhancer region were found to have excellent diagnostic effects and clinical prognostic value. Finally, we further used independent samples to verify CD248 expression and established a simple epigenetic regulatory model. In summary, this study provides novel insights for understanding the regulation of target genes mediated by DNA methylation and developing potential biomarkers for radiation-associated secondary diseases in BC.

Humans↗
PubMed2026

GFPT1 as a cross-ancestry validated target for degenerative spinal disease: genetic association in a Chinese cohort and functional characterization in zebrafish.

Degenerative spinal disease (DSD), including spinal stenosis and spondylosis, lacks effective pharmacological treatment. To identify druggable targets and assess cross-ancestry applicability, we integrate multi-omics analyses using Summary-data-based Mendelian Randomization (SMR), colocalization, and two-sample Mendelian randomization with European whole-blood, peripheral-blood, and CSF eQTL/pQTL datasets, followed by whole-genome sequencing (WGS) validation in a Chinese cohort. We identify 7 genes/proteins associated with spinal stenosis and 5 with spondylosis, with GFPT1, GPX1, and SERPINA1 shared by both. Two-sample MR further supports the causal associations of these targets with DSD. Phenome-wide MR prioritization selects GFPT1 and GPX1 as favorable candidates with no predicted adverse effects and potential beneficial effects on hypertension. In the Chinese cohort (67 lumbar spinal stenosis patients and 100 controls), WGS identifies 4 GFPT1 cis-eQTL loci (rs13016371, rs35392088, rs12997521, and rs13019789) associated with lumbar spinal stenosis risk; all risk alleles are linked to increased GFPT1 expression, and all 24 variant carriers show L4/L5 stenosis on imaging. Druggability analysis identifies IOX1 as the sole preclinical-stage compound targeting GFPT1, and molecular docking supports robust binding to GFPT1 (- 6.39 kcal/mol). Functional assays show that IOX1 directly inhibits GFPT1 enzymatic activity and induces fructose-6-phosphate accumulation. In zebrafish, IOX1 significantly rescues GFPT1-induced degenerative phenotypes. These findings establish GFPT1 as a cross-ancestry validated therapeutic target for DSD and nominate IOX1 as a promising disease-modifying candidate.

Animals↗
PubMed2026

Selection favors cost-ordered adaptive mutational paths in a stress-magnitude-dependent manner.

Chronic exposure to stress requires adaptive strategies beyond canonical regulatory mechanisms. Stress varies, both qualitatively and quantitatively, across physiological niches and exerts distinct selection pressures on colonizing bacteria. Bacteria employ diverse defense strategies to withstand various stressful conditions, yet how they tailor their responses to different magnitudes of the same stressor remains poorly understood. We used multiple adaptive laboratory evolution experiments of Escherichia coli across varying paraquat concentrations and genetic backgrounds to dissect adaptive strategies at different levels of stress. Integrating multi-omic analyses with a tailored genome-scale metabolic model-based parametrized cost calculations, we identify two fundamentally distinct tolerance mechanisms. Under low-paraquat stress, blocking the polyamine transporter that is reported to be hijacked for paraquat influx suffices to maintain optimal growth. In contrast, higher stress levels activate an energetically demanding program involving enhanced detoxification and efflux. The transport flux regulation establishes a primary defense layer, upon which metabolic repair systems provide additional fitness advantages. The stress magnitude-dependent differential engagement of previously reported paraquat tolerance approaches offers insights into the principles governing dynamic bacterial adaptation.

Journal Article↗
PubMed2025

De novo pyrimidine synthesis is a collateral metabolic vulnerability in NF2-deficient mesothelioma.

Pleural mesothelioma (PM) is one of the deadliest cancers, with limited therapeutic options due to its therapeutically intractable genome, which is characterized by the functional inactivation of tumor suppressor genes (TSGs) and high tumor heterogeneity, including diverse metabolic adaptations. However, the molecular mechanisms underlying these metabolic alterations remain poorly understood, particularly how TSG inactivation rewires tumor metabolism to drive tumorigenesis and create metabolic dependencies. Through integrated multi-omics analysis, we identify for the first time that NF2 loss of function defines a distinct PM subtype characterized by enhanced de novo pyrimidine synthesis, which NF2-deficient PM cells are critically dependent on for sustained proliferation in vitro and in vivo. Mechanistically, NF2 loss activates YAP, a downstream proto-oncogenic transcriptional coactivator in the Hippo signalling pathway, which in turn upregulates CAD and DHODH, key enzymes in the de novo pyrimidine biosynthesis pathway. Our findings provide novel insights into metabolic reprogramming in PM, revealing de novo pyrimidine synthesis as a synthetic lethal vulnerability in NF2-deficient tumors. This work highlights a potential therapeutic strategy for targeting NF2-deficient mesothelioma through metabolic intervention.

Pyrimidines↗