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PubMed2003

Sputnik: a database platform for comparative plant genomics.

Two million plant ESTs, from 20 different plant species, and totalling more than one 1000 Mbp of DNA sequence, represents a formidable transcriptomic resource. Sputnik uses the potential of this sequence resource to fill some of the information gap in the un-sequenced plant genomes and to serve as the foundation for in silicio comparative plant genomics. The complexity of the individual EST collections has been reduced using optimised EST clustering techniques. Annotation of cluster sequences is performed by exploiting and transferring information from the comprehensive knowledgebase already produced for the completed model plant genome (Arabidopsis thaliana) and by performing additional state of-the-art sequence analyses relevant to today's plant biologist. Functional predictions, comparative analyses and associative annotations for 500 000 plant EST derived peptides make Sputnik (http://mips.gsf.de/proj/sputnik/) a valid platform for contemporary plant genomics.

Databases, Nucleic Acid↗
PubMed2002

The Gene Resource Locator: gene locus maps for transcriptome analysis.

Since the advent of the draft human genome sequence there has been growing interest in transcriptome analysis based on genomic data. The Gene Resource Locator (GRL) assembles gene maps that include information on gene-expression patterns, cis-elements in regulatory regions and alternatively spliced transcripts. The database was constructed using customized software, and currently contains 2.2 million alignments (exon-intron structures). The alignments have been annotated and integrated into a system that encompasses approximately 90 000 EST loci sharing common exons, 8091 alternatively spliced transcript groups, 10 801 expression-profile groups, 8066 candidate regulatory regions in full-length cDNAs, and 1 million SNP loci. We have used Flash technology to build a dynamic web viewer that facilitates browsing through the millions of alignments. All of the information is available through the World Wide Web at the Gene Resource Locator web site (http://grl.gi.k.u-tokyo.ac.jp).

Alternative Splicing↗
PubMed2026

Functional Annotation of the Major Histocompatibility Complex Locus.

The human major histocompatibility complex (MHC) locus has the greatest density of disease-associations in the human genome, including links to over 100 polygenic disorders. Its complex haplotype structure, rich gene density, and high degree of linkage disequilibrium combine to make deciphering the gene regulatory logic of the MHC locus extremely challenging. Employing complementary high-throughput CRISPR interference (CRISPRi) and activation (CRISPRa) epigenetic screens coupled with single-cell transcriptome profiling across three distinct human cell types, we identified hundreds of new connections between cis -regulatory elements (CREs) and their target genes in this locus. These CRE-gene links are largely cell type-specific and act as enhancers. Additionally, some CREs have complex features, including harboring both active and repressive histone marks, lacking chromatin accessibility, targeting multiple genes, or acting as silencers. Computational methods fail to predict a majority of these CRE-gene connections. These findings emphasize the potential for functional perturbation experiments to dissect complex loci and reveal shared and cell type-specific regulatory mechanisms relevant to genomics of complex diseases. Collectively, this study provides a unique resource for understanding the complex regulatory landscape within the MHC locus and supports the need for creating new models that encompass CRE-gene interactions, cell type-specific gene expression, and disease genetics in the noncoding genome.

Journal Article↗
PubMed2005

Benchmarking the CATMA microarray. A novel tool for Arabidopsis transcriptome analysis.

Transcript profiling is crucial to study biological systems, and various platforms have been implemented to survey mRNAs at the genome scale. We have assessed the performance of the CATMA microarray designed for Arabidopsis (Arabidopsis thaliana) transcriptome analysis and compared it with the Agilent and Affymetrix commercial platforms. The CATMA array consists of gene-specific sequence tags of 150 to 500 bp, the Agilent (Arabidopsis 2) array of 60mer oligonucleotides, and the Affymetrix gene chip (ATH1) of 25mer oligonucleotide sets. We have matched each probe repertoire with the Arabidopsis genome annotation (The Institute for Genomic Research release 5.0) and determined the correspondence between them. Array performance was analyzed by hybridization with labeled targets derived from eight RNA samples made of shoot total RNA spiked with a calibrated series of 14 control transcripts. CATMA arrays showed the largest dynamic range extending over three to four logs. Agilent and Affymetrix arrays displayed a narrower range, presumably because signal saturation occurred for transcripts at concentrations beyond 1,000 copies per cell. Sensitivity was comparable for all three platforms. For Affymetrix GeneChip data, the RMA software package outperformed Microarray Suite 5.0 for all investigated criteria, confirming that the information provided by the mismatch oligonucleotides has no added value. In addition, taking advantage of replicates in our dataset, we conducted a robust statistical analysis of the platform propensity to yield false positive and false negative differentially expressed genes, and all gave satisfactory results. The results establish the CATMA array as a mature alternative to the Affymetrix and Agilent platforms.

Arabidopsis↗
PubMed2003

Modification of the transcriptomic response to renal ischemia/reperfusion injury by lipoxin analog.

BACKGROUND: Lipoxins are lipoxygenase-derived eicosanoids with anti-inflammatory and proresolution bioactivities in vitro and in vivo. We have previously demonstrated that the stable synthetic LXA4 analog 15-epi-16-(FPhO)-LXA4-Me is renoprotective in murine renal ischemia/reperfusion injury, as gauged by lower serum creatinine, attenuated leukocyte infiltration, and reduced morphologic tubule injury. METHODS: We employed complementary oligonucleotide microarray and bioinformatic analyses to probe the transcriptomic events that underpin lipoxin renoprotection in this setting. RESULTS: Microarray-based analysis identified three broad categories of genes whose mRNA levels are altered in response to ischemia/reperfusion injury, including known genes previously implicated in the pathogenesis of ischemia/reperfusion injury [e.g., intercellular adhesion molecule-1 (ICAM-1), p21, KIM-1], known genes not previously associated with ischemia/reperfusion injury, and cDNAs representing yet uncharacterized genes. Characterization of expressed sequence tags (ESTs) displayed on microarrays represents a major challenge in studies of global gene expression. A bioinformatic annotation pipeline successfully annotated a large proportion of ESTs modulated during ischemia/reperfusion injury. The differential expression of a representative group of these ischemia/reperfusion injury-modulated genes was confirmed by real-time polymerase chain reaction. Prominent among the up-regulated genes were claudin-1, -3, and -7, and ADAM8. Interestingly, the former response was claudin-specific and was not observed with other claudins expressed by the kidney (e.g., claudin-8 and -6) or indeed with other components of the renal tight junctions (e.g., occludin and junctional adhesion molecule). Noteworthy among the down-regulated genes was a cluster of transport proteins (e.g., aquaporin-1) and the zinc metalloendopeptidase meprin-1 beta implicated in renal remodeling. CONCLUSION: Treatment with the lipoxin analog 15-epi-16-(FPhO)-LXA4-Me prior to injury modified the expression of many differentially expressed pathogenic mediators, including cytokines, growth factors, adhesion molecules, and proteases, suggesting a renoprotective action at the core of the pathophysiology of acute renal failure (ARF). Importantly, this lipoxin-modulated transcriptomic response included many genes expressed by renal parenchymal cells and was not merely a reflection of a reduced renal mRNA load resulting from attenuated leukocyte recruitment. The data presented herein suggest a framework for understanding drivers of kidney injury in ischemia/reperfusion and the molecular basis for renoprotection by lipoxins in this setting.

ADAM Proteins↗
PubMed2026

Genome-Wide Identification and Characterization of the TBL Gene Family and Temporal Expression Dynamics During Powdery Mildew Infection in Cucumber (Cucumis sativus).

Cell-wall polysaccharide O-acetylation contributes to cell-wall assembly, organ development, and plant-pathogen interactions, but the cucumber TBL gene family remains poorly characterized. Here, 37 CsTBL genes were identified genome-wide and analyzed using phylogenetic, syntenic, conserved-motif, gene-structure, promoter, protein-structure, Gene Ontology, and transcriptome approaches, followed by RT-qPCR analysis after powdery mildew inoculation. All CsTBL proteins contained the conserved GDS and DxxH motifs, whereas accessory motifs and predicted structural features varied among clades. Intraspecific analysis identified dispersed, WGD/segmental, and tandem duplication categories, and cross-species synteny was more extensive with melon than with Arabidopsis. Homology-derived annotations associated CsTBL genes with cell-wall polysaccharide metabolism, Golgi/endomembrane compartments, and O-acetyltransferase activity, including six genes assigned to xylan O-acetyltransferase-related annotations. Expression profiling revealed tissue- and developmental-stage-dependent patterns, whereas the publicly available powdery mildew RNA-seq dataset provided descriptive temporal expression profiles in Podosphaera xanthii-inoculated samples. Independent RT-qPCR analysis using time-matched mock controls revealed distinct post-inoculation responses among six selected genes. Relative to the corresponding mock controls, CsTBL2 was consistently repressed; CsTBL15 showed transient induction at 1 dpi followed by repression; CsTBL24 exhibited a biphasic response; CsTBL25 was induced at all sampled post-inoculation time points; CsTBL26 showed progressive induction; and CsTBL30 reached its highest observed expression level at 3 dpi. Integrated functional annotation and expression evidence highlighted CsTBL26 as a priority candidate for further functional characterization, while CsTBL24 and CsTBL25 represented fruit-associated candidates with distinct powdery mildew responses; CsTBL30 remained an additional strongly infection-responsive candidate. These findings provide an evolutionary and expression-based framework for the functional characterization of the cucumber TBL gene family.

O-acetylation↗
PubMed2002

Transcription profiling of renal cell carcinoma.

AIMS: Our aim was to prepare a comprehensive catalogue of the changes in gene expression accompanying the development and progression of renal cell carcinoma, and to correlate these with histo-pathological, cytogenetic and clinical findings. METHODS: mRNA samples from paired neoplastic and non-cancerous human kidney tissue were labeled and hybridized in duplicate against high-density cDNA arrays. Two array technologies were used: 31,500-element transcriptome-wide nylon arrays for hybridization with 37 radioactively labelled sample pairs, and 4200-element kidney- and cancer-specific glass microarrays for hybridization with 19 fluorescently labelled sample pairs. RESULTS: We identified more than 1700 cDNA clones that show differential transcription levels in kidney tumor tissue compared to normal kidney tissue. The functional classification of 389 annotated genes provided views of the changes in the activities of specific biological processes in renal cancer. Among the biological processes with a large proportion of up-regulated genes we found cell adhesion, signal transduction, and nucleotide metabolism. Down-regulated processes included small molecule transport, ion homeostasis, and oxygen and radical metabolism. Furthermore, we explored the feasibility of molecular diagnosis for renal cell tumors using cDNA microarrays on glass slides, investigating the association of transcription levels with tumor type, progression, and a putative prognostic variable. The experimental data is available from the GEO gene expression database (http://www.ncbi.nlm.nih.gov/geo; accession no. GSE3), and a comprehensive presentation of the results is available in the web supplement (http://www.dkfz-heidelberg.de/abt0840/whuber/rcc). CONCLUSION: Transcription profiling using high-density cDNA arrays is a powerful method with the potential to improve cancer diagnosis and prognosis. The identification and classification of differentially transcribed genes, as described in our study, is the beginning of a more complete understanding of kidney cancer.

Carcinoma, Renal Cell↗
PubMed2005

Development of a citrus genome-wide EST collection and cDNA microarray as resources for genomic studies.

A functional genomics project has been initiated to approach the molecular characterization of the main biological and agronomical traits of citrus. As a key part of this project, a citrus EST collection has been generated from 25 cDNA libraries covering different tissues, developmental stages and stress conditions. The collection includes a total of 22,635 high-quality ESTs, grouped in 11,836 putative unigenes, which represent at least one third of the estimated number of genes in the citrus genome. Functional annotation of unigenes which have Arabidopsis orthologues (68% of all unigenes) revealed gene representation in every major functional category, suggesting that a genome-wide EST collection was obtained. A Citrus clementina Hort. ex Tan. cv. Clemenules genomic library, that will contribute to further characterization of relevant genes, has also been constructed. To initiate the analysis of citrus transcriptome, we have developed a cDNA microarray containing 12,672 probes corresponding to 6875 putative unigenes of the collection. Technical characterization of the microarray showed high intra- and inter-array reproducibility, as well as a good range of sensitivity. We have also validated gene expression data achieved with this microarray through an independent technique such as RNA gel blot analysis.

Citrus↗
PubMed2002

Large-scale analysis of the human and mouse transcriptomes.

High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.

Animals↗
PubMed2026

Odon: an ultra-fast viewer for spatial proteomics.

MOTIVATION: Multiplexed spatial proteomics and spatial transcriptomics generate large, high-dimensional imaging datasets that are challenging to visualize efficiently, particularly at whole-slide and cohort scale. Visualization is an essential step for rapid detection of staining artefacts, such as protein aggregates or non-specific staining. RESULTS: Here, we present Odon, a native Rust desktop viewer designed for rapid, interactive exploration of multiplex imaging data on a standard laptop. Odon is primarily built around the OME-Zarr imaging format, and supports annotations via GeoJSON and GeoParquet, with secondary support for SpatialData, Xenium containers, and TIFF. Data can be stored locally or streamed directly from HTTP or S3-compatible object storage using viewport-driven tile loading. Odon incorporates a highly optimized rendering engine designed for viewport-driven tile loading and GPU-based compositing. In scripted benchmarks using synthetic multiplex OME-Zarr datasets, Odon showed lower peak memory use, lower affine-derived zoom-step error, and faster warm-start image loading than napari and QuPath under the tested conditions. Its GPU-based compositing pipeline also enables smooth rendering and interaction with >1 000 000 segmented cells. Odon further supports integrated visual analytics, including live thresholding and cell selection, and a mosaic mode for simultaneous viewing of hundreds of regions of interest in cohort and tissue microarray studies. Together, these features establish Odon as a high-performance platform for scalable visualization of spatial proteomics data. AVAILABILITY AND IMPLEMENTATION: Source code and compiled installers are available at https://github.com/alexcoulton/odon.

Proteomics↗
PubMed2005

Transcriptome profiling of adult zebrafish at the late stage of chronic tuberculosis due to Mycobacterium marinum infection.

The Mycobacterium marinum-zebrafish infection model was used in this study for analysis of a host transcriptome response to mycobacterium infection at the organismal level. RNA isolated from adult zebrafish that showed typical signs of fish tuberculosis due to a chronic progressive infection with M. marinum was compared with RNA from healthy fish in microarray analyses. Spotted oligonucleotide sets (designed by Sigma-Compugen and MWG) and Affymetrix GeneChips were used, in total comprising 45,465 zebrafish transcript annotations. Based on a detailed comparative analysis and quantitative reverse transcriptase-PCR analysis, we present a validated reference set of 159 genes whose regulation is strongly affected by mycobacterial infection in the three types of microarrays analyzed. Furthermore, we analyzed the separate datasets of the microarrays with special emphasis on the expression profiles of immune-related genes. Upregulated genes include many known components of the inflammatory response and several genes that have previously been implicated in the response to mycobacterial infections in cell cultures of other organisms. Different marker genes of the myeloid lineage that have been characterized in zebrafish also showed increased expression. Furthermore, the zebrafish homologs of many signal transduction genes with relationship to the immune response were induced by M. marinum infection. Future functional analysis of these genes may contribute to understanding the mechanisms of mycobacterial pathogenesis. Since a large group of genes linked to immune responses did not show altered expression in the infected animals, these results suggest specific responses in mycobacterium-induced disease.

Animals↗
PubMed2005

A method for automated detection of gene expression required for the establishment of a digital transcriptome-wide gene expression atlas.

Acquiring information about the expression of a gene in different cell populations and tissues can provide key insight into the function of the gene. A high-throughput in situ hybridization (ISH) method was recently developed for rapid and reproducible acquisition of gene expression patterns in serial tissue sections at cellular resolution. Characterizing and analysing expression patterns on thousands of sections requires efficient methods for locating cells and estimating the level of expression in each cell. Such cellular quantification is an essential step in both annotating and quantitatively comparing high-throughput ISH results. Here we describe a novel automated and efficient methodology for performing this quantification on postnatal mouse brain.

Animals↗
PubMed2025

Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.

The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.

Humans↗
PubMed2026

Identifying Co-Expressed lncRNAs Correlated With Traits of Interest in an Animal Model for Metabolic Diseases in Humans.

Nutrigenomics investigates how nutrients modulate gene expression. Among them, fatty acids (FA) play important roles in regulating gene transcription, while long non-coding RNAs (lncRNAs) may be associated with gene regulation and metabolic diseases. This study aimed to analyze the hepatic transcriptome of pigs, a species frequently used as a model for nutrigenomic studies, to identify novel lncRNAs and their potential target genes in response to diets containing different sources of FA. Seventy-two pigs were fed four diets supplemented with 1.5% soybean oil (control), 3% canola oil, 3% fish oil, and 3% soybean oil. RNA sequencing of liver samples was performed to identify novel lncRNAs. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify modules associated with phenotypic traits related to lipid metabolism and inflammation. Functional enrichment analyses were then conducted to annotate genes within these modules using Gene Ontology (GO) terms and to assess overlap with Quantitative Trait Loci (QTL). The results revealed 106 novel lncRNAs potentially regulating genes associated with lipid metabolism and immune responses in pigs fed diets with different FA sources. These findings enhance understanding of the regulatory role of lncRNAs in pigs and reinforce their relevance as models for human metabolic diseases.

Animals↗
PubMed2003

RIKEN Arabidopsis full-length (RAFL) cDNA and its applications for expression profiling under abiotic stress conditions.

Full-length cDNAs are essential for the correct annotation of genomic sequences and for the functional analysis of genes and their products. 155,144 RIKEN Arabidopsis full-length (RAFL) cDNA clones were isolated. The 3'-end expressed sequence tags (ESTs) of all 155,144 RAFL cDNAs were clustered into 14,668 non-redundant cDNA groups, about 60% of predicted genes. The sequence database of the RAFL cDNAs is useful for promoter analysis and the correct annotation of predicted transcription units and gene products. Recently, cDNA microarray analysis has been developed for quantitative analysis of global and simultaneous analysis of expression profiles. RAFL cDNA microarrays were prepared, containing independent full-length cDNA groups for analysing the expression profiles of genes under various stress- and hormone-treatment conditions and in various mutants and transgenic plants. In this review, recent progress on transcriptome analysis using the RAFL cDNA microarray is highlighted.

Arabidopsis↗
PubMed2025

Multi-omics analysis reveals distinct spatial compartmentalization of lung repair niches in pediatric ARDS.

BACKGROUND: Pediatric acute respiratory distress syndrome (PARDS), often triggered by viral infections, is a life-threatening condition. Despite its severity, children demonstrate significantly better survival rates and superior lung repair compared to adults. However, the mechanisms underlying this age-specific advantage remain incompletely understood. PATIENTS AND METHODS: We conducted a pilot multi-omics study of influenza-associated PARDS integrating single-cell RNA sequencing (scRNA-seq) of pediatric lung tissue and bronchoalveolar lavage fluid (BALF), spatial transcriptomics, and plasma proteomics. Analyses were harmonized with the Human Lung Cell Atlas (HLCA) reference, reanalysis of public pediatric PARDS airway scRNA-seq, and contextual comparisons to adult lethal COVID-19 lung. RESULTS: Tissue scRNA-seq and spatial data indicated outcome-linked divergence in PARDS. Survivor showed spatially restricted repair with preserved alveolar type II (AT2) cells, AT2-to-alveolar type I (AT1) differentiation signatures, and higher KRT17, whereas fatal case and adults exhibited diffuse immune activation with pro-fibrotic and pro-apoptotic signaling. In BALF, KRT17-positive airway stress–repair epithelial cells (hillock-like) increased from the acute to recovery phase, and plasma proteomics showed higher circulating KRT17 in survivors. HLCA-based label transfer strengthened cell-type definitions and enabled pediatric–adult comparisons suggesting biological and developmental differences; the adult lethal COVID-19 atlas provided a benchmark with attenuated epithelial repair and prominent collagen CTHRC1-pathologic fibroblasts. Fibroblast programs were regionally compartmentalized, with injury-enriched CTHRC1+ states versus alveolar fibroblasts in preserved areas, and showed stronger injury–homeostasis anti-correlation in fatalities. Myeloid remodeling included BALF transitions from FCN1-high inflammatory states toward FABP4-positive resident-like states, consistent with public pediatric datasets showing reduced inflammatory and interferon-stimulated gene (ISG) modules and severity-linked increases in aged neutrophils. CONCLUSIONS: This pilot multi-omics case series outlines putative pediatric lung repair niches in influenza-associated PARDS. KRT17-positive transitional epithelium, preserved AT2 differentiation, and restoration of resident-like macrophages may align with recovery, whereas diffuse immune activation and CTHRC1-enriched fibroblast programs may accompany worse outcomes. HLCA-guided annotations and adult benchmarks indicate possible age-related differences, warranting validation in larger multi-center cohorts.

Humans↗
PubMed2004

Gene expression profiles during beta cell maturation and after IL-1beta exposure reveal important roles of Pdx-1 and Nkx6.1 for IL-1beta sensitivity.

AIM/HYPOTHESIS: Maturation of the beta cells in the islets of Langerhans is dependent upon sequential activation of different transcription factors such as Pdx-1 and Nkx6.1. This maturation is associated with an acquired sensitivity to cytokines and may eventually lead to type 1 diabetes. The aims of this study were to characterise changes in mRNA expression during beta cell maturation as well as after interleukin-1beta (IL-1beta) exposure. METHODS: Transcriptome analyses were performed on two phenotypes characterised as a glucagon-producing pre-beta-cell phenotype (NHI-glu), which matures to an IL-1beta-sensitive insulin-producing beta cell phenotype (NHI-ins). Beta cell lines over-expressing Pdx-1 or Nkx6.1, respectively, were used for functional characterisation of acquired IL-1beta sensitivity. RESULTS: During beta cell maturation 98 fully annotated mRNAs changed expression levels. Of these, 50 were also changed after 24 h of IL-1beta exposure. In addition, 522 and 197 fully annotated mRNAs, not affected by maturation, also changed expression levels following IL-1beta exposure of the beta cell and the pre-beta-cell phenotype, respectively. Beta cell maturation was associated with an increased expression of Nkx6.1, whereas both Pdx-1 and Nkx6.1 expression were decreased following IL-1beta exposure. Over-expression of Nkx6.1 or Pdx-1 in cell lines resulted in a significantly increased sensitivity to IL-1beta. CONCLUSIONS/INTERPRETATION: These results suggest that the final beta cell maturation accompanied by increased IL-1beta sensitivity is, in part, dependent upon the expression of genes regulated by Pdx-1 and Nkx6.1. Future classification of the genes regulated by these transcription factors and changed during beta cell maturation should elucidate their role in the acquired sensitivity to IL-1beta and may be helpful in identifying new targets for intervention/prevention strategies.

Animals↗
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↗