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Applications of DNA tiling arrays for whole-genome analysis.

DNA microarrays are a well-established technology for measuring gene expression levels. Microarrays designed for this purpose use relatively few probes for each gene and are biased toward known and predicted gene structures. Recently, high-density oligonucleotide-based whole-genome microarrays have emerged as a preferred platform for genomic analysis beyond simple gene expression profiling. Potential uses for such whole-genome arrays include empirical annotation of the transcriptome, chromatin-immunoprecipitation-chip studies, analysis of alternative splicing, characterization of the methylome (the methylation state of the genome), polymorphism discovery and genotyping, comparative genome hybridization, and genome resequencing. Here we review different whole-genome microarray designs and applications of this technology to obtain a wide variety of genomic scale information.

Animals↗

GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.

SUMMARY: While current Gene Regulatory Network (GRN) databases provide comprehensive reference maps of potential interactions between transcription factors and target genes, they do not specify which regulatory interactions are active within specific biological contexts. This limitation is particularly critical in cancer, where transcriptional programs are inherently tissue-specific. To address this gap, we developed GRNContext, an interactive web platform designed for the visualization, exploration, and comparative analysis of gene regulatory networks contextualized across 33 cancer types from The Cancer Genome Atlas (TCGA). Our approach uses the TFLink human reference GRN as a starting point and integrates TCGA transcriptomic profiles to infer cancer-specific regulatory activity. Regulatory relevance was assessed using complementary machine learning and statistical methods, which were unified into a consensus score to prioritize and filter the most relevant candidate regulators for each target gene. By providing both curated context-specific GRNs and a user-friendly platform, GRNContext constitutes a comprehensive and accessible resource that supports mechanistic investigations, hypothesis generation, and translational research focused on transcriptional regulation in cancer. AVAILABILITY AND IMPLEMENTATION: GRNContext is supported by all major browsers and freely available on the web at https://apps.cienciavida.org/grncontext. It is implemented as a client-server web application featuring a FastAPI backend and a React frontend utilizing Cytoscape.js for interactive network visualization, all containerized via Docker for cross-platform compatibility.

Humans↗

DNA microarrays in vaccine research.

DNA microarrays represent the technology platform for a wide variety of analytical methods built around the detection of sequence-specific nucleic acid hybridization. They allow the analysis of complex biological systems on the basis of the genome and transcriptome with high simplicity, efficiency, sensitivity and specificity. This positions DNA microarrays as ideal tools for novel approaches in biomedical research and explains the tremendous pace at which they are being applied to an enormous variety of systems and projects. Recently, vaccine researchers began to use DNA microarrays for the identification of vaccine candidates against bacterial, parasitic and viral pathogens, as well as tumors. Here, DNA microarrays as a technology platform in these areas of vaccine research are reviewed and their limitations, as well as their potential for future applications in vaccinology, critically evaluated.

Bacterial Vaccines↗

Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

Tumor heterogeneity-encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts-is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques-such as deep-learning classifiers and graph-based models-with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified ∼380 ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine.

Humans↗

Methods and platforms for the quantification of splice variants' expression.

The relatively limited number of human protein encoding genes highlights the importance of the diversity generated at the level of the mRNA transcripts. As alternative RNA splicing plays a key role in mediating this diversity, it becomes critical to develop the tools and platforms that will deliver quantitative information on the specific expression levels associated with splice isoforms. This chapter describes the constraints generated by this global transcriptome analysis and the state-of-the-art techniques and products available to the scientific community.

Alternative Splicing↗

Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.

Treatment resistance (TR) in major depressive disorder (MDD) affects a substantial minority of patients and is hard to recognize early, delaying intensified care. The Esketamine multi-omic biomarker evaluation in MDD (EMBER-MDD) is a non-interventional, investigator-initiated, in-vitro study within the EU Psych-STRATA programme, analyzing biospecimens collected in the randomized INTENSIFY study and the mirror OBS-TR cohort after participants complete treatment. EMBER-MDD aims to discover individual-omic and integrated multi-omic (hypothesis-free) biomarkers and signatures associated with TR risk, and molecular correlates of clinical response to esketamine nasal spray versus treatment as usual (TAU). Biomaterials will derive from approximately 420 adults with MDD (estimated n = 210 esketamine; n = 210 TAU) and include whole blood, RNA-stabilized whole blood, plasma and serum, sampled at baseline and, when feasible, during and after treatment (up to ~ 5,040 aliquots stored at - 80 °C). Genomics will use baseline DNA genotyping on Illumina Infinium GSA v3.0+MD arrays; epigenomics will profile genome-wide DNA methylation across time points using MethylationEPIC v2.0; transcriptomics will employ mRNA-seq (NovaSeq X/ X Plus); and proteomics/ metabolomics will be generated using high-throughput Olink and/ or Biocrates platforms. Each layer will undergo state-of-the-art preprocessing and analyses (e.g., GWAS/ PRS, EWAS, differential expression, WGCNA, pathway and network analyses), followed by integrative strategies including QTL mapping (meQTL/ eQTL/ pQTL/ mQTL) and intermediate-fusion machine learning with nested cross-validation, explainable AI (SHAP/ LIME) and treatment-effect modelling. All outputs are research-only and will not support individual efficacy, tolerability, or clinical decision-making. The study will deliver robust biosignatures and mechanistic hypotheses to guide future validation and inform stratified, molecularly guided intervention strategies in subsequent prospective trials. Trial registration number: 2023-506617-21-00 and 2025-178-f-S.

Humans↗

The transcriptional landscape of the mammalian genome.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

3' Untranslated Regions↗

Comparative Multiomics Analysis of Cerebral Organoid-Derived Exosomes during Organoid Maturation.

Cerebral organoids derived from human pluripotent stem cells recapitulate key features of early brain development and provide a physiologically relevant model for neurogenesis. Exosomes secreted by these organoids carry bioactive cargo and offer a noninvasive means to monitor maturation and intercellular communication. We performed comprehensive multiomics profiling of exosomes collected from cerebral organoids at defined developmental stages to evaluate their utility as biomarkers of neuronal differentiation. Metabolomic analysis revealed a progressive decline in amino acids, including glutamic acid, consistent with increased metabolic demand during neurogenesis. Lipidomic and neurosteroid profiling showed dynamic increases in phosphatidylethanolamine and pregnenolone, reflecting synaptic membrane formation and signaling. Transcriptomic and proteomic analyses identified stage-specific neurodevelopmental signatures, with key markers mirroring those of parent organoids. Collectively, cerebral organoid-derived exosomes faithfully reflect organoid maturation and provide a robust platform for tracking in vitro brain development.

Humans↗

Defining the human macula transcriptome and candidate retinal disease genes using EyeSAGE.

PURPOSE: To develop large-scale, high-throughput annotation of the human macula transcriptome and to identify and prioritize candidate genes for inherited retinal dystrophies, based on ocular-expression profiles using serial analysis of gene expression (SAGE). METHODS: Two human retina and two retinal pigment epithelium (RPE)/choroid SAGE libraries made from matched macula or midperipheral retina and adjacent RPE/choroid of morphologically normal 28- to 66-year-old donors and a human central retina longSAGE library made from 41- to 66-year-old donors were generated. Their transcription profiles were entered into a relational database, EyeSAGE, including microarray expression profiles of retina and publicly available normal human tissue SAGE libraries. EyeSAGE was used to identify retina- and RPE-specific and -associated genes, and candidate genes for retina and RPE disease loci. Differential and/or cell-type specific expression was validated by quantitative and single-cell RT-PCR. RESULTS: Cone photoreceptor-associated gene expression was elevated in the macula transcription profiles. Analysis of the longSAGE retina tags enhanced tag-to-gene mapping and revealed alternatively spliced genes. Analysis of candidate gene expression tables for the identified Bardet-Biedl syndrome disease gene (BBS5) in the BBS5 disease region table yielded BBS5 as the top candidate. Compelling candidates for inherited retina diseases were identified. CONCLUSIONS: The EyeSAGE database, combining three different gene-profiling platforms including the authors' multidonor-derived retina/RPE SAGE libraries and existing single-donor retina/RPE libraries, is a powerful resource for definition of the retina and RPE transcriptomes. It can be used to identify retina-specific genes, including alternatively spliced transcripts and to prioritize candidate genes within mapped retinal disease regions.

Adult↗

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↗

Volumetric DNA microscopy for mapping spatial transcriptomes in three dimensions.

The architecture and function of biological systems are inherently three-dimensional, yet most existing spatial transcriptomic technologies remain restricted to thin tissue sections, limiting their capacity to resolve cellular organization and microenvironments within intact tissue volumes. To address this limitation, we developed volumetric DNA microscopy, a scalable, optics-free approach for spatial transcriptome profiling directly within intact biological specimens. The method encodes spatial information into DNA molecules that form a dense intermolecular network in situ, enabling the reconstruction of three-dimensional spatial relationships through short-read sequencing and computational analysis. Here we detail the complete workflow including in situ cDNA synthesis, spatial encoding through DNA nanoball formation, dual-scale proximity bridging between neighboring nanoballs and spatial reconstruction via geodesic spectral embedding. Sequencing libraries can be generated within 7-8 d by a competent graduate-level molecular biologist, followed by standardized downstream computational analysis. Because the workflow requires only routine molecular biology reagents and a benchtop sequencer, volumetric DNA microscopy provides a versatile platform for exploring genetic and morphological features in intact tissues.

Spatial Transcriptomics↗

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Cancer is a complex disease affecting various organs and is a major cause of death worldwide. During cancer initiation, disease progression, and tumor metastasis, various genomic and proteomic alterations are observed. Recent technological advances have led to the generation of large amounts of molecular data, including genomics and transcriptomics. These large-scale datasets can be utilized to analyze and identify sub-class-specific cancer biomarkers and targets. However, there is a need for the development of user-friendly tools for large-scale data analysis, disseminating the analyzed data in a visualizable format to cancer researchers with no programming skills. We developed UALCAN, a comprehensive platform that allows users to integrate disparate data to better understand the genes, proteins, and pathways perturbed in cancer and make discoveries of potential biomarkers and targets. In the current study, we describe the development of the UALCAN Mobile application (app) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project to evaluate protein-coding gene expression based on various stratifications, including stage, grade, race, gender, and molecular-subtypes across over 30 types of cancers. In addition, the UALCAN mobile provides data analysis options for epigenetic changes due to DNA promoter methylation and Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteomic data. The app provides access to large cancer molecular datasets on the go. To find changes in the expression of causative genes and proteins and to identify biomarkers and therapeutic targets, UALCAN mobile app will be extremely valuable. The "UALCAN Mobile" app is free to use and can be downloaded from both the iOS/Apple and the Android Play Store and has been downloaded over 100 times in each of iOS and android app stores.

app↗

Functional genomic research of alpha 1-adrenoceptors.

The Human Genome Project is now almost completed, and we are about to move into the post-genome sequence era of functional genomics. The advent of genome science has markedly changed the way life science research including pharmacological study is conducted; thus, systematic and integrated 'genome-wide' survey is feasible. The stream of 'Genome-->Transcriptome--> Proteomics' is logical and, in each aspect, approaches for functional genomics are now pursued at a high pace. We have recently developed a standardized technical platform (in various levels, such as transcription, cell and whole animal levels, etc.), and applied these techniques to the study of functional genomics of G-protein-coupled receptors, particularly alpha1-adrenoceptors as a model. Combining the genome information and technology, future pharmacological studies would become the genome-based search and research.

Animals↗

Ferroptosis in Oral Cancer: Mechanistic Insights and Clinical Prospects.

Ferroptosis, an iron-dependent form of regulated cell death characterized by lipid peroxidation, has emerged as a pivotal vulnerability in oral squamous cell carcinoma (OSCC). This review provides an overview of ferroptosis mechanisms and their implications for OSCC pathobiology and therapy. OSCC cells exhibit heightened reliance on anti-ferroptotic defenses such as GPX4, SLC7A11, FSP1, and Nrf2, and disrupting these pathways suppresses tumor growth and restores sensitivity to chemotherapy, radiotherapy, and immunotherapy. Genetic and epigenetic regulators, including p53, PER1, circ_0000140, and STARD4-AS1, critically modulate ferroptotic sensitivity, while metabolic enzymes such as ACSL4, LPCAT3, and TPI1 link ferroptosis to cellular plasticity and resistance. Preclinical studies highlight the promise of small-molecule inhibitors, repurposed agents (e.g., sorafenib, artesunate, trifluoperazine), natural compounds (e.g., piperlongumine, Evodia lepta, quercetin), and nanomedicine platforms for targeted ferroptosis induction. We further address ferroptosis within the tumor microenvironment, highlighting its immunogenic and context-dependent dual roles, and summarize genomic and transcriptomic evidence linking ferroptosis-related genes to patient prognosis. Beyond cancer, ferroptosis also contributes to non-malignant oral diseases, including pulpitis, periodontitis, and infection-associated inflammation, where inhibitors may protect tissues. Despite these advances, clinical translation is constrained by the lack of safe ferroptosis inducers and validated biomarkers. Future research should focus on developing pharmacologically viable GPX4 inhibitors, refining biomarker-driven patient stratification, and designing multimodal regimens that combine ferroptosis induction with standard therapies while preserving immune and tissue integrity. Ferroptosis therefore represents both a mechanistic framework and a translational opportunity to reshape oral oncology and broader oral disease management.

Humans↗

Characterization of METTL3/14-mediated m6A modification in human transcriptome using Nanopore direct RNA sequencing.

Post-transcriptional RNA modifications modulate diverse aspects of RNA metabolism. N6-methyladenosine (m6A), one of the most abundant internal RNA modifications, is deposited by the core methyltransferase complex, METTL3 and METTL14. Oxford Nanopore Technologies (ONT) platform permits direct, single RNA molecule sequencing while preserving native modifications. However, without rigorous benchmarking, the accuracy and reproducibility of modification detection remain uncertain. Here, we leveraged ONT to comprehensively profile bona fide m6A modifications in cellular RNAs at single-nucleotide resolution by integrating two direct RNA sequencing chemistries (RNA002 and RNA004) with the m6Anet and Dorado modification-detection models. We independently depleted METTL3 and METTL14 in human cells and rigorously validated modification calls through several assays and independent orthogonal methods (GLORI and miCLIP). We find that Dorado detected a higher number of m6A events and enabled simultaneous detection of other RNA modifications (5-methylcytosine, pseudouridine, and inosine). Pairing Dorado with an in vitro transcribed, unmodified control under stringent filtering, we provide compelling evidence supporting a global reduction in m6A sites and stoichiometry within coding sequences and across genes, particularly in highly modified genes and sites, and at consensus DRACH motifs. We report a differential and complex regulation of modified transcripts, accompanied by a global reduction in poly(A) tail length. Notably, METTL3 and METTL14 depletion produced distinct transcript-specific effects, supporting non-redundant roles within the m6A writer complex. Together, our study illustrates a notable advancement of ONT capabilities and establishes a robust transcriptome-wide framework for RNA modification detection, thereby laying the groundwork for exploring the contribution of METTL3/METTL14 to cellular functions and disease.

Humans↗

WormBase as an integrated platform for the C. elegans ORFeome.

The ORFeome project has validated and corrected a large number of predicted gene models in the nematode C. elegans, and has provided an enormous resource for proteome-scale studies. To make the resource useful to the research and teaching community, it needs to be integrated with other large-scale data sets, including the C. elegans genome, cell lineage, neurological wiring diagram, transcriptome, and gene expression map. This integration is also critical because the ORFeome data sets, like other 'omics' data sets, have significant false-positive and false-negative rates, and comparison to related data is necessary to make confidence judgments in any given data point. WormBase, the central data repository for information about C. elegans and related nematodes, provides such a platform for integration. In this report, we will describe how C. elegans ORFeome data are deposited in the database, how they are used to correct gene models, how they are integrated and displayed in the context of other data sets at the WormBase Web site, and how WormBase establishes connection with the reagent-based resources at the ORFeome project Web site.

Animals↗

Transcriptomic Profiling of Canine Testicular Leydig Cell Tumors Uncovers Key Upregulated Gene Pathways.

Total RNA was isolated from sections of healthy testes and Leydig cell tumors of mixed-breed dogs using TMA Master II device. The RNA-seq libraries were sequenced on the Illumina platform. Following differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were applied with quality control obtained using FastQC and Trimmomatic. This analysis revealed 1500 transcripts, including 928 upregulated and 168 downregulated genes. The results demonstrated that a significant proportion of these differentially expressed genes are directly involved in the control of sex steroid production (CYP11A1, STAR, and 3β-HSD3B1) or tube formation, angiogenesis, and extracellular matrix remodeling in interstitial cells (ESM1, FGG, and VEGFA). Moreover, we identified the upregulation of transcripts responsible for neurotransmitter or neuroendocrine signaling (SLC6A4, GRIN2C, GABRB3) and cholesterol metabolism and its regulation (GPX3, MSMO1, DHCR24). These genes were strongly associated with the phosphatidylinositol-3-kinase (PI3K)-Protein Kinase B (Akt) cascade and extracellular matrix interactions, features shared with various malignancies. Alterations in estrogen and relaxin signaling appear to be distinctive, understudied mechanisms specific to canine Leydig cell tumors. Concurrently, downregulated genes (e.g., DMRTC2, SEMA3C, ALOX12) were linked with cell differentiation, signaling and immunoregulatory pathway suppression involved in tumorigenesis. A complex transcriptomic profile of canine Leydig cell tumors was developed, revealing a conserved oncogenic core shared in some aspects with human malignancies alongside unique species-specific alterations. Findings seem to be useful for identifying novel diagnostic biomarkers and targeted therapies in veterinary oncology, establishing canine reproductive tissues as a valuable comparative biomedical model for research in human.

Leydig cell tumor↗

MitoScribe single-cell molecular recorder logs graded signaling dynamics into mitochondrial DNA.

Genetically encoded DNA recorders convert transient biological events into stable genomic mutations, offering a means to reconstruct past cellular states. However, current approaches to log historical events by modifying genomic DNA have limited capacity to record the magnitude of biological signals within individual cells. Here, we introduce MitoScribe, a mitochondrial DNA (mtDNA)-based recording platform that uses mtDNA base editors (DdCBEs) to write graded biological signals into mtDNA as neutral, single-nucleotide substitutions at a defined site. Taking advantage of the hundreds to thousands of mitochondrial genome copies per cell, we demonstrate MitoScribe enables reproducible, highly sensitive, non-destructive, durable, and high-throughput measurements of molecular signals, including hypoxia, NF-κB activity, BMP and Wnt signaling. We show multiple modes of operation, including multiplexed recordings of two independent signals, and coincidence detection of temporally overlapping signals. Coupling MitoScribe with single-cell RNA sequencing and mitochondrial transcript enrichment, we further reconstruct signaling dynamics at the single-cell transcriptome level. Applying this approach during the directed differentiation of human induced pluripotent stem cells (iPSCs) toward mesoderm, we show that early heterogeneity in response to a differentiation cue predicts the later cell state. Together, MitoScribe provides a scalable platform for high-resolution molecular recording in complex cellular contexts.

Journal Article↗