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Results for “transcriptomic profiling”

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PubMed2026

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify transcriptional and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around silent genes prior to their expression in response to injury. These results provide evidence for a latent activated stem cell state in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Animals↗

PubMed2000

Large-scale profiling of the Arabidopsis transcriptome.

Explore the source record for details and available documents.

Arabidopsis↗

PubMed2026

Spatial multiomics in biomedical research: advances beyond transcriptomics.

Coordinated changes in gene expression, epigenetic regulation, protein and metabolic activities together drive disease progression and determine clinical outcomes. While spatially resolved transcriptomics has been widely adopted across biomedical fields, it offers an incomplete picture limited to transcriptomic levels. Here, we survey the latest developments in spatial multiomics technologies, with particular emphasis on platforms that extend beyond conventional transcriptomics and profile genomics, epigenomics, proteomics, or metabolomics within intact tissues. These approaches are rapidly becoming commercialized, and here we highlight major technical breakthroughs, enhanced sample compatibility, emerging applications, and computational tools for data analysis. This Review aims to equip researchers with a clear understanding of the current technological landscape and to accelerate the adoption of spatial multiomics methods in biomedical research.

Humans↗

PubMed2026

Effects of rumen fluid transplantation on longissimus dorsi muscle development in Xizang sheep: An association analysis based on transcriptomic and serum metabolomic profiles.

This study aimed to investigate the effects of rumen fluid transplantation (RFT) on the growth and development of the longissimus dorsi muscle in female Xizang sheep. After RFT, muscle lightness differed significantly between the two groups, with the LDC group showing significantly higher lightness than the LDT group. In contrast, no significant differences were observed between groups in other muscle phenotypic traits, including drip loss, pH, cooking loss, shear force, redness, and yellowness. Antioxidant-related indices (SOD, GSH-PX, MDA, CAT, and T-AOC) also showed no significant differences between groups. Histological analysis revealed that muscle fiber length, width, and density were significantly greater in the experimental group than in the control group. Transcriptomic analysis identified 515 differentially expressed genes (DEGs), of which 419 were downregulated. KEGG analysis indicated that genes involved in muscle development-related pathways, such as cell adhesion and the PI3K-Akt signaling pathway, were predominantly downregulated. Key serum metabolites (L-kynurenine, IPA, allantoin, and propionylcarnitine) showed highly significant positive correlations with muscle fiber growth indices. In contrast, metabolites such as l-carnitine, acetylcarnitine, and citrulline were negatively correlated with muscle fiber growth, but positively correlated with the expression of muscle structure-related genes (COL11A1 and EFNA5) and with meat lightness. Overall, this study provides new insights into the potential molecular basis by which RFT influences muscle growth and development. However, the mechanisms by which RFT affects muscle development and meat quality-related traits remain unclear and warrant further investigation.

Animals↗

PubMed2026

Preliminary Exploration on Melatonin-Mediated Protective Effects in Intracranial Aneurysms: Transcriptomic, Proteomic, and Metabolomic Profiling of Cerebral Vascular Tissues Combined with in vivo Animal Experiments.

BACKGROUND: Intracranial aneurysm (IA) is a life-threatening cerebrovascular disease with unclear molecular mechanisms and limited drug treatment. Our previous research has shown that melatonin (MLT) has potential protective effects in IA, but its mechanism remains unclear. The purpose of this study is to explore the pathological mechanism of IA and the therapeutic mechanism of MLT by integrating transcriptomic, proteomic and metabolomic analyses. METHODS: In this study, mouse models of IA were successfully established by combining elastase injection with angiotensin II infusion. C57BL/6 mice were divided into control, IA model, IA model+MLT, and IA model+nimodipine groups. The pathological conditions were evaluated by hematoxylin-eosin (HE) staining, TUNEL staining, and scanning electron microscopy. Transcriptomic (n=3 for each group), proteomic (n=3 for each group), and metabolomic (n=6 for each group) analyses were performed based on cerebral vascular tissue samples. The screening thresholds for differentially expressed genes and differentially expressed proteins were P <0.05 and fold change >1.5 and fold change <0.667. The screening criteria for differential metabolites were variable importance for the projection (VIP)> 1.0, fold change >1.2 and fold change <0.833, and P <0.05. RESULTS: MLT alleviated brain tissue damage, vascular endothelial damage, structural disruption, and apoptosis in IA mice. Transcriptomic, proteomic and metabolomic analyses identified numerous differential molecules. Functional annotation revealed that these molecules may be involved in biological pathways and processes such as immune inflammation, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress and metabolic pathways, thereby regulating the occurrence and development of IA or mediating the therapeutic effects of MLT. Furthermore, transcriptomic and proteomic analyses also suggest that there may be extensive post-transcriptional, translational and post-translational regulatory events in the progression of IA and the therapeutic effects of MLT. Integrated transcriptomic and proteomic analyses suggest that Npy may be a key molecule in regulating IA progression and mediating MLT therapeutic effects, and its potential value is further supported by our immunohistochemical validation results. CONCLUSION: Multi-omics integrative analysis preliminarily revealed that the potential mechanisms of MLT may involve the regulation of inflammatory response, vascular remodeling, extracellular matrix remodeling, neuropeptide activity, oxidative stress, metabolic pathways, and post-transcriptional/translational regulation.

Animals↗

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↗

PubMed2000

Expression profile viewer (ExProView): a software tool for transcriptome analysis.

A software tool, Expression Profile Viewer (ExProView), for analysis of gene expression profiles derived from expressed sequence tags (ESTs) and SAGE (serial analysis of gene expression) is presented. The software visualizes a complete set of classified transcript data in a two-dimensional array of dots, a "virtual chip," in which each dot represents a known gene as characterized in the transcript databases Expressed Gene Anatomy Database or UniGene. The virtual chip display can be changed between representations of different conceptual systems for gene/protein classification and grouping. Four alternative projections are currently available: (i) cellular role, (ii) subcellular compartment, (iii) chromosome localization, and (iv) total UniGene display. However, the chip can be adapted to any other desired layout. By selecting dots, further information about the represented genes is obtained from the local database and WWW links. The software thus provides a visualization of global mRNA expression at the descriptive level and guides in the exploration of patterns of functional expression, while maintaining direct access to detailed information on each individual gene. To evaluate the software, public EST and SAGE gene expression data obtained from the Cancer Genome Anatomy Project at the National Center for Biotechnology Information were analyzed and visualized. A demonstration of the software is available at http://www.biochem.kth. se/exproview/.

Data Display↗

PubMed2027

Systematic Analysis of Tumor Microenvironment Using IOBR.

The Immuno-Oncology Biological Research (IOBR) package is an R-based analysis tool for exploring the tumor microenvironment (TME) and its influence on anti-tumor immunity. Built for high-throughput data-spanning both transcriptomic and genomic profiles-IOBR integrates six analytical modules, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand-receptor interaction analysis, genome-TME interaction assessment, and visualization. In this chapter, we walk through a multi-omics workflow using example datasets, illustrating data preparation, distribution analyses, result interpretation, and graphical output. IOBR is open source and is available at https://github.com/IOBR/IOBR and a detailed GitBook ( https://iobr.github.io/book/ ) offers a complete manual and analysis guide for each function.

Tumor Microenvironment↗

PubMed2026

Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome.

Rett syndrome (RTT) is an X-linked neurological disorder caused by MECP2 mutations, creating distinct cellular environments in females (mosaic) versus males (nonmosaic). Despite female patients representing most cases, how mosaicism contributes molecularly to RTT pathogenesis, particularly in presymptomatic stages, remains poorly understood. To address this question, we profiled hippocampal transcriptomes of young female and male RTT mice using bulk and single-nucleus RNA sequencing. We identified a core disease signature of consistently dysregulated genes only in MeCP2- cells across RTT models. Moreover, we uncovered non-cell autonomous effects exclusively in female MeCP2+ excitatory neurons, suggesting that these circuits are more vulnerable early in the mosaic RTT environment. The single-nuclei data also revealed an underappreciated MeCP2- interneuron subtype that had the most transcriptional dysregulation in both male and female RTT hippocampi. Together, these data highlight the different effects of MeCP2 loss on excitatory and inhibitory circuits between the mosaic and nonmosaic environments in early RTT pathogenesis.

Rett Syndrome↗

PubMed2025

Venomics of the Arabian saw-scaled viper (Echis coloratus) through transcriptome-guided proteomics and in vitro functional profiling.

The Arabian saw-scaled viper (Echis coloratus) is among the snakes of highest medical relevance in the Middle East and North Africa. However, to date, its venom has been investigated in a very limited number of studies, and much remains unknown regarding its compositional and functional properties. By integrating proteotranscriptomics with bioactivity profiling, we present a comprehensive transcriptome-level catalogue of E. coloratus venom components and their associated biological activities. Our analysis identified 183 venom components belonging to 17 distinct protein families. Relative toxin abundances revealed that 92% of the venom proteome is composed of C-type lectin and C-type lectin-related protein (CTL), L-amino acid oxidase (LAAO), phospholipase A2 (PLA2), snake venom serine protease (SVSP), and snake venom metalloproteinase (SVMP), with CTL and PLA2 alone accounting for 73% of the total composition. Bioassays targeting key aspects of viperid envenomation demonstrated potent protease and PLA2 activity in a concentration-dependent manner. In contrast, Factor Xa-like, plasmin-like, and haemolytic activities were negligible. Marked cytotoxicity was observed at the highest concentration tested (i.e., 25 μg/ml) in the mammalian cell lines MDCK II and Calu-3, whereas cytotoxic effects were minimal at lower concentrations. These findings highlight the complexity and potency of E. coloratus venom, and provide a valuable foundation for improving our understanding of envenomation caused by this species.

Animals↗

PubMed2026

A critical appraisal of base-resolution m6A profiling techniques.

N6-methyladenosine (m6A) is the most prevalent internal modification in eukaryotic mRNA, influencing RNA fate and gene regulation. Early antibody-based approaches enabled transcriptome-wide profiling but lacked resolution and quantitative accuracy. Newer approaches now achieve base-resolution m6A detection using improved crosslinking, chemical or enzymatic conversion, and single-molecule sequencing. Antibody-free methods provide quantitative stoichiometry from minimal input, while nanopore direct RNA sequencing offers real-time, single-molecule readouts across entire transcriptomes. Collectively, these methods form a versatile toolkit that integrates global mapping with precise site-level analysis, advancing knowledge of context-dependent m6A regulation in physiology and disease. This review compares their principles, strengths, and limitations to guide method selection and highlight how next-generation epitranscriptomic tools are paving the way for clinical and therapeutic applications.

Humans↗

PubMed2025

Prior vaccination prevents overactivation of innate immune responses during COVID-19 breakthrough infection.

At this stage in the COVID-19 pandemic, most infections are "breakthrough" infections that occur in individuals with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure. To refine long-term vaccine strategies against emerging variants, we examined both innate and adaptive immunity in breakthrough infections. We performed single-cell transcriptomic, proteomic, and functional profiling of primary and breakthrough infections to compare immune responses from unvaccinated and vaccinated individuals during the SARS-CoV-2 Delta wave. Breakthrough infections were characterized by a less activated transcriptomic profile in monocytes and natural killer cells, with induction of pathways limiting monocyte migratory potential and natural killer cell proliferation. Furthermore, we observed a female-specific increase in transcriptomic and proteomic activation of multiple innate immune cell subsets during breakthrough infections. These insights suggest that prior SARS-CoV-2 vaccination prevents overactivation of innate immune responses during breakthrough infections with discernible sex-specific patterns and underscore the potential of harnessing vaccines in mitigating pathologic immune responses resulting from overactivation.

Immunity, Innate↗

PubMed2025

Understanding the sources of performance in deep drug response models reveals insights and improvements.

MOTIVATION: Anti-cancer drug response prediction (DRP) using cancer cell lines (CLs) is crucial in stratified medicine and drug discovery. Recently, new deep learning models for DRP have improved performance over their predecessors. However, different models use different input data types and architectures making it hard to find the source of these improvements. Here we consider published DRP models that report state-of-the-art performance predicting continuous response values. These models take chemical structures of drugs and omics profiles of CLs as input. RESULTS: By experimenting with these models and comparing with our simple baselines, we show that no performance comes from drug features, instead, performance is due to the transcriptomics CL profiles. Furthermore, we show that, depending on the testing type, much of the current reported performance is a property of the training target values. We address these limitations by creating BinaryET and BinaryCB that predict binary drug response values, guided by the hypothesis that this reduces the noise in the drug efficacy data. Thus, better aligning them with biochemistry that can be learnt from the input data. BinaryCB leverages a chemical foundation model, while BinaryET is trained from scratch using a transformer-type architecture. We show that these models learn useful chemical drug features, which is the first time this has been demonstrated for multiple testing types to our knowledge. We further show binarizing the drug response values causes the models to learn useful chemical drug features. We also show that BinaryET improves performance over BinaryCB, and the published models that report state-of-the-art performance. AVAILABILITY AND IMPLEMENTATION: Code is available from https://github.com/Nik-BB/Understanding_DRP_models.

Humans↗

PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗

PubMed2026

Multi-omics integration uncovers epigenetic control of metabolic reprogramming in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the absence of estrogen, progesterone, and HER2 receptors, limiting effective targeted therapies. Increasing evidence suggests that metabolic reprogramming, a hallmark of TNBC progression, is driven by underlying epigenetic mechanisms such as DNA methylation. The represented study performed an integrative analysis of transcriptomic (RNA-seq) and methylome data to uncover the metabolic-epigenetic interplay in TNBC. Differential gene expression analysis using DESeq2 revealed significant dysregulation of key metabolic genes, including upregulation of genes encoding glycolytic and serine biosynthesis enzymes and downregulation of metabolic tumor suppressors. Genome-wide methylation profiling identified extensive cytosine-phosphate-guanine (CpG) hypermethylation events associated with transcriptional repression, particularly in promoter regions. Integrative analysis pinpointed a subset of metabolism-related genes exhibiting both differential expression and methylation, such as FBP1, RASSF1A, and PHGDH. Pathway enrichment analysis highlighted aberrations in glycolysis/gluconeogenesis, fatty acid metabolism, and one-carbon pathways (adjusted p < 0.01). Importantly, TNBC patients with hypermethylated metabolic gene signatures displayed significantly shorter overall survival (log-rank p < 0.05). These findings reveal that DNA methylation-driven metabolic dysregulation contributes to TNBC aggressiveness and may provide novel biomarkers and therapeutic targets at the metabolic-epigenetic interface.

Humans↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2025

Spatial Genomic Approaches to Investigate HOX Genes in Mouse Brain Tissues.

Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.

Animals↗

PubMed2024

Transcriptomic analysis reveals key molecular signatures across recovery phases of hemorrhagic fever with renal syndrome.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS), a life-threatening zoonosis caused by hantavirus, poses significant mortality risks and lacks specific treatments. This study aimed to delineate the transcriptomic alterations during the recovery phases of HFRS. METHODS: RNA sequencing was employed to analyze the transcriptomic alterations in peripheral blood mononuclear cells from HFRS patients across the oliguric phase (OP), diuretic phase (DP), and convalescent phase (CP). Twelve differentially expressed genes (DEGs) were validated using quantitative real-time PCR in larger sample sets. RESULTS: Our analysis revealed pronounced transcriptomic differences between DP and OP, with 38 DEGs showing consistent expression changes across all three phases. Notably, immune checkpoint genes like CD83 and NR4A1 demonstrated a monotonic increase, in contrast to a monotonic decrease observed in antiviral and immunomodulatory genes, including IFI27 and RNASE2. Furthermore, this research elucidates a sustained attenuation of immune responses across three phases, alongside an upregulation of pathways related to tissue repair and regeneration. CONCLUSION: Our research reveals the transcriptomic shifts during the recovery phases of HFRS, illuminating key genes and pathways that may serve as biomarkers for disease progression and recovery.

Hemorrhagic Fever with Renal Syndrome↗