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PubMed2025

CCDC137 knockdown suppresses bladder cancer progression by downregulating SCD.

BACKGROUND: The Coiled-coil domain-containing (CCDC) family, due to its unique protein structural domain and broad involvement in diverse biological processes, has emerged as a focus in oncology research. Nevertheless, its clinical significance and function in bladder cancer (BLCA) remain poorly defined. METHODS: Machine learning algorithms were employed to identify pivotal CCDC genes in the cancer genome atlas (TCGA), and a prognostic model was subsequently constructed. Multi-omics data encompassing pan-cancer cohorts, single-cell sequencing, and spatial transcriptomics were integrated to characterize the expression patterns and prognostic significance of Coiled-coil domain-containing 137 (CCDC137), a previously uncharacterized CCDC family member in BLCA. Tissue microarray confirmed CCDC137 abnormal expression in bladder carcinoma specimens. The effect of CCDC137 knockdown on BLCA progression was evaluated through CCK8 assay, clonogenic formation, wound healing, Transwell, and subcutaneous xenograft models. RNA sequencing, quantitative RT-PCR, and western blot were utilized to delineate its regulatory network. RESULTS: A prognostic model incorporating 10 CCDC genes was successfully established in the TCGA-BLCA cohort. Then, we found that CCDC137 exhibited pan-cancer overexpression and usually correlation with poor clinical outcomes. Immunohistochemistry further substantiated its dysregulation in bladder carcinoma. Integrated multi-omics analyses suggested associations between CCDC137 expression and a tumor immunosuppressive microenvironment. CCDC137 knockdown significantly suppressed bladder cancer cell proliferation and migratory capacity in vitro. Correspondingly, subcutaneous xenograft tumor growth was inhibited in vivo. Moreover, decreased expression of stearoyl-CoA desaturase (SCD), a key lipid metabolic enzyme, accompanied CCDC137 depletion. These findings collectively suggest a cancer-promoting role for CCDC137 in bladder carcinoma. CONCLUSIONS: This systematic investigation combining multi-omics bioinformatics analyses and experimental validation demonstrates the role of CCDC137 in bladder carcinoma progression, providing novel mechanistic insights into the pathogenesis of BLCA and offering a theoretical foundation for therapeutic targeting of CCDC137 in urothelial malignancies.

Urinary Bladder Neoplasms↗

PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗

PubMed2026

Multi-omics and experimental validation identify RAPGEF2 as a protective prognostic biomarker in clear cell renal cell carcinoma.

Kidney Renal Clear Cell Carcinoma (KIRC) is characterized by marked molecular heterogeneity and metabolic reprogramming, underscoring the need for reliable biomarkers for prognostic assessment and individualized treatment. RAPGEF2, a guanine nucleotide exchange factor has been implicated in cell adhesion and differentiation, but its role in KIRC remains unclear. In this study, we systematically evaluated the expression pattern, prognostic significance, genomic associations, biological function, and therapeutic relevance of RAPGEF2 in KIRC through integrated multi-omics analyses and experimental validation. Pan-cancer single-cell and Spatial transcriptomic analysis revealed heterogeneous RAPGEF2 expression across tumor types, with a relatively prominent signal in KIRC, where RAPGEF2 was mainly enriched in endothelial cells. Survival analyses in the TCGA-KIRC showed that high RAPGEF2 expression was significantly associated with favorable overall survival, disease-specific survival, and progression-free interval, and these findings were validated in independent ICGC_RECA-EU and E-MTAB-1980 cohorts. Multivariate Cox regression further confirmed RAPGEF2 as an independent protective prognostic factor. Immunohistochemistry in a tissue microarray cohort demonstrated that higher RAPGEF2 protein expression was associated with improved overall survival. Genomic analyses showed that low RAPGEF2 expression was related to higher mutational burden. Functional assays demonstrated that RAPGEF2 knockdown promoted KIRC progression. Enrichment analyses indicated that RAPGEF2 may be associated with metabolic pathway remodeling, while immunotherapy cohort analyses suggested its potential association with therapeutic benefit. Collectively, RAPGEF2 is identified as a protective prognostic biomarker and potential functional regulator in KIRC.

Biomarker↗

PubMed2026

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Machine Learning↗

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

Spatial habitat radiomics predicts tertiary lymphoid structure status and identifies an IDO1+ migratory dendritic cell axis in breast cancer.

BACKGROUND: Tertiary lymphoid structures (TLS) are spatially organized immune niches associated with therapeutic response and favorable outcomes in breast cancer (BC). However, TLS assessment currently relies on invasive tissue-based analyses, and the biological mechanisms underlying imaging-based TLS prediction remain poorly understood. METHODS: We developed and validated a spatial heterogeneity-based radiomic TLS signature (shTLS) using dynamic contrast-enhanced MRI to non-invasively predict TLS status across multicenter BC cohorts. Spatial habitat radiomics were used to capture intratumoral and peritumoral immune-related heterogeneity. Integrated multi-omics analyses, including transcriptomics, pathomics, genomics, single-cell RNA sequencing, immunohistochemistry, and multiplex immunofluorescence, were performed to biologically interpret shTLS-defined subgroups. Functional drug-sensitivity assays were conducted to assess therapeutic implications. RESULTS: The shTLS model achieved robust predictive performance across independent cohorts and molecular subtypes. High shTLS scores were associated with immune-inflamed tumors characterized by spatially clustered activated T cells and dendritic cells (DCs). In contrast, shTLS-low tumors exhibited an immunosuppressive spatial niche with peripheral accumulation of CD4+ PD-1+ T cells and plasma cells, increased immune-tumor separation, and enhanced inflammatory and immunoregulatory signaling. An indoleamine 2,3-dioxygenase 1 (IDO1)-associated immunoregulatory program was observed in the shTLS-low tumors, which appeared to be preferentially expressed by LAMP3+CCR7+ migratory DCs. Pharmacologic inhibition of IDO1 enhanced chemotherapy and CDK4/6 inhibitor sensitivity in vitro. CONCLUSION: This study establishes spatial radiomics as a non-invasive approach to decode TLS-associated immune ecosystems and supports the presence of an IDO1-associated immunosuppressive phenotype, providing biological insight and translational rationale for patient stratification and future combination strategies.

Humans↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

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↗

PubMed2026

Deciphering CD8+ T cell exhaustion in human cancers through single-cell and spatial transcriptomics.

Exhausted CD8+ T cells (Tex) within the tumor microenvironment (TME) represents a critical barrier limiting anti-tumor immune responses. Tex cells are characterized by upregulated inhibitory immune checkpoint receptors, reduced cytotoxicity, and functional heterogeneity. Their genomic features and regulatory networks remain poorly defined, and only a minority of patients respond to immune checkpoint blockade (ICB) therapy. Single-cell RNA sequencing (scRNA-seq), through high-resolution transcriptomic profiling, has revealed diverse Tex subpopulations, identified subpopulation-specific marker genes and regulatory pathways. Spatial transcriptomics has further mapped the spatial distribution of Tex and their interaction networks with immune cells, tumor cells, and stromal cells, elucidating the impact of spatial heterogeneity on Tex functionality. Current studies indicate that the exhausted state of Tex is dynamic and modifiable, with functional differences among subpopulations closely associated with tumor progression and therapeutic response. However, the genomic characteristics, epigenetic regulation, and spatial interaction mechanisms of Tex require further exploration. This review summarizes recent advances in high-resolution omics technologies for precisely dissecting Tex heterogeneity, functional features, and interactions with other cells. It emphasizes the central value of optimizing Tex-targeted tumor immunotherapy strategies, providing theoretical foundations and directional guidance for developing more effective anti-tumor immunotherapies.

Humans↗

PubMed2026

A regulatory network underlying idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease in which genetic susceptibility interacts with epithelial, immune, and mesenchymal remodeling. Although the chromosome 11p15.5 locus contains established IPF susceptibility signals near MUC5B and TOLLIP, the broader regulatory architecture of this region remains incompletely resolved. METHODS: We integrated IPF genome-wide association study summary statistics with methylation, expression, and protein quantitative trait loci using summary-data-based Mendelian randomization (SMR). SMR-prioritized candidates were evaluated in independent transcriptomic and methylation cohorts and further contextualized using microRNA, transcription-factor, protein-interaction, machine-learning, single-cell, and spatial transcriptomic analyses. Fibrosis-associated expression patterns were assessed in a bleomycin-induced pulmonary fibrosis rat model. RESULTS: The analyses recovered the established MUC5B and TOLLIP signals and prioritized BRSK2 as a comparatively underexplored candidate supported by eQTL-based SMR and independent molecular evidence. The BRSK2 pQTL association did not pass the HEIDI test and was therefore not interpreted as convergent protein-level genetic evidence. Network analyses linked BRSK2 to cell-cycle, metabolic-stress, and senescence-related programs, while cross-cohort machine learning prioritized FOXA2, CDC25B, and NFE2 as informative network features. Single-cell and spatial analyses localized BRSK2 preferentially to fibroblast and myofibroblast compartments and to regions with greater histological fibrosis severity. In fibrotic rat lungs, BRSK2 expression increased, whereas FOXA2 and CDC25B decreased at the transcript and protein levels. CONCLUSIONS: These findings refine the molecular landscape of the chromosome 11p15.5 IPF susceptibility locus and prioritize BRSK2 as a candidate component of an IPF-associated profibrotic fibroblast state. Its causal contribution, direct regulatory relationships, and therapeutic tractability require targeted mechanistic validation.

Idiopathic Pulmonary Fibrosis↗

PubMed2026

Impact of Genomic Mutations on the Transcriptional Pathways and Tumor Microenvironment Landscape of Localized Early Prostate Cancer.

BACKGROUND: The management of intermediate-risk early prostate cancer (PCa) is challenging due to the difficulty in distinguishing indolent from aggressive tumors. This study explores the association between genomic alterations and the tumor and its microenvironment (TME) and implications for disease progression. METHODS: We performed multi-omic profiling in a cohort of 53 localized PCa using targeted sequencing, transcriptional, and proteomic spatial profiling. RESULTS: Somatic mutations and copy number alterations in RB1 (21%), PTEN (18%), and TP53 (9%) were identified. Kaplan-Meier analysis revealed that alterations in the RB and Cell Cycle pathways, particularly aberrations in PTEN, TP53, or RB1, were associated with shorter biochemical recurrence-free survival (p < 0.001). Spatial proteomic analysis demonstrated a complex immune landscape in patients with mutations. The tumor compartment demonstrated higher expression of immune checkpoint markers, T-cell activation proteins, and proliferation markers; and a TME that is enriched with CD8 + T cells and antigen-presenting cells, but also with immunosuppressive M2 macrophages, suggesting adaptive immune resistance. CONCLUSIONS: Our analysis demonstrates that genomic alterations in PTEN, TP53, or RB1 are not only prognostic for poor outcomes but are also associated with a unique, immunologically complex TME in this Brazilian cohort.

Humans↗

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

Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.

Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.

Humans↗

PubMed2026

Spatial proximity sequencing maps developmental dynamics in the germinal center.

Spatial profiling of proteins and protein interactions facilitates understanding of cell functions within tissues and is essential for studies in signaling, immunity, and cancer. We present spatial proximity sequencing (Sprox-seq) for simultaneous profiling of surface proteins, protein complexes, and mRNAs, recording the tissue location of each molecule. Sprox-seq profiled 32 proteins, 528 pairwise interactions, and thousands of mRNAs with spatial resolution across human tonsils and germinal centers. Mapping tissue-wide protein interactions recapitulated RNA-defined tissue architecture but also revealed higher interaction complexity in the light zone. Protein-interaction trajectories uncovered a B cell state transition distinct from that inferred by RNA. Integrated protein-complex and mRNA analysis related spatially enriched complexes with mitotic pathways. Sprox-seq captured cell-cell interactions, such as B cell-follicular dendritic cell interactions mediated by the receptor complex VLA-4-VCAM1. Sprox-seq provides a spatially resolved multi-modal view of cell states and an integrated study of protein and cellular interactions across tissues.

Humans↗

PubMed2026

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n = 721), single-cell RNA-seq (n = 9), proteomic data (n = 49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗

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

Multi-omics analyses reveal DjTcf4 critical for proper timing of differentiation in planarian regeneration.

The blastema is key to forming complete tissues in regenerating Dugesia japonica (D. japonica). However, the dynamic changes in cellular compositions and transcription landscapes in blastema during regeneration are understudied. Here, through genome reannotation, 3D spatial transcriptome construction, single-cell RNA sequencing (scRNA-seq), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) analyses of changes in gene expression and chromatin structures, we delineate key transcription factors regulating the developmental trajectories of major cell clusters in the regenerating head. Importantly, we find that the T cell factor 4 (DjTcf4)-positive cells highly accumulate at wound areas, and its gene network is critical for the proper timing of development during regeneration in multiple progenitor cells. Depletion of DjTcf4 and its target genes leads to singular eye and/or dull tail phenotypes and delays regeneration. Taken together, we build multi-omics atlases in D. japonica and reveal the noncanonical function of the DjTcf4 network in developmental pattern formation, laying a foundation for studies of regeneration in D. japonica.

Animals↗

PubMed2025

Spatial Metabolomics Reveals the Role of Penicillic Acid in Cheese Rind Microbiome Disruption by a Spoilage Fungus.

Microbial interactions in cheese rinds influence community structure, food safety, and product quality. But the chemical mechanisms that mediate microbial interactions in cheeses and other fermented foods are generally not known. Here, we investigate how the spoilage mold Aspergillus westerdijkiae chemically inhibits beneficial cheese-rind bacteria using a combination of omics technologies. In cheese rind community and co-culture experiments, A. westerdijkiae strongly inhibited most cheese rind community members. In co-culture with Staphylococcus equorum, A. westerdijkiae strongly affected bacterial gene expression, including upregulation of a putative bceAB gene cluster that is associated with resistance to antimicrobial compounds in other bacteria. Mass spectrometry imaging (MSI) revealed spatially localized production of secondary metabolites, including penicillic acid and ochratoxin B at the fungal-bacterial interface. Integration of LC-MS/MS and genome annotations confirmed the presence of additional bioactive metabolites, such as notoamides and circumdatins. Fungal metabolic responses varied by bacterial partner, suggesting species-specific chemical strategies. Notably, penicillic acid levels increased 2.5-fold during interaction with Brachybacterium, and experiments with purified penicillic acid showed inhibition of a range of cheese rind bacteria. These findings show that A. westerdijkiae deploys a context-dependent arsenal of mycotoxins and other metabolites, disrupting microbial community assembly in cheese rinds.

Aspergillus westerdijkiae↗