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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↗

PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗

PubMed2026

Protocol to predict gene expression from transcriptomic data using PREDICT.

Linking DNA sequence variation to context-specific transcriptional programs is a critical challenge in regulatory genomics, especially for non-model organisms. Here, we present PREDICT, a modular Python package for discovering cis-regulatory elements and transcription factor binding motifs. We describe steps to identify enriched k-mers from differentially expressed genes, map them to known motifs, quantify their impact on gene expression, and visualize motif co-occurrences. PREDICT provides a robust, k-mer-based approach to uncover regulatory logic in diverse genomic systems. For complete details on the use and execution of this protocol, please refer to Yen et al. and Liu et al.1,2.

Gene Expression Profiling↗

PubMed2024

Genetic association between epilepsy and gliomas: Insights from Mendelian randomization and single-cell transcriptomic analyses.

BACKGROUND: Seizures are prevalent in glioma patients, especially in those with low-grade gliomas. The interaction between gliomas and epilepsy involves complex biological mechanisms that are not fully understood. METHODS: We collected Genome-Wide Association Study data for epilepsy and gliomas, performed differential expression analysis, and conducted Gene Ontology (GO) enrichment analysis on the identified genes. Single-cell RNA sequencing data (scRNA-seq) from GSE221534 dataset in Gene Expression Omnibus (GEO) were used to analyze cell-cell interactions within glioma samples from patients with and without epilepsy. RESULTS: Mendelian Randomization (MR) analysis revealed significant associations between genetic variants related to epilepsy and glioma risk, suggesting a potential causal relationship, especially in astrocytomas. Differential expression analysis identified epilepsy-related genes that were significantly upregulated in astrocytoma tissues compared to normal brain tissues. GO enrichment analysis indicated that these genes are involved in critical biological processes such as neurogenesis and cellular signaling. The scRNA-seq analysis showed, compared to non-epileptic samples, glioma stem cells, microglia, and NK cells are increased in the core regions of astrocytomas in epileptic patients. Additionally, intercellular communication between tumor cells and other non-tumor cells is markedly enhanced in astrocytoma samples from epileptic patients. CONCLUSION: This study provides evidence of a genetic association between epilepsy and gliomas and elucidates the biological mechanisms through which epilepsy may influence glioma progression.

Humans↗

PubMed2026

Dynamic transcriptomic landscape from bulk RNA-seq reveals critical mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 modules for deep second-degree burn wound healing.

Burn injuries constitute a significant global health challenge, with deep partial-thickness burns (deep second-degree) posing particular clinical concerns due to prolonged healing and high scarring risks stemming from reticular dermis damage. Current therapeutic strategies remain largely empirical, reflecting limited understanding of stage-specific regulatory mechanisms. This study systematically investigated the molecular basis of deep partial-thickness burn repair by establishing murine models and performing RNA-seq analysis across healing phases (0, 3, 7, 14 days post-burn, dpb). Integrated bioinformatics revealed pivotal ceRNA and PPI networks, identifying hif1a (hypoxia-responsive immunomodulator) and col1a1 (ECM remodeling hub) as nodal regulators. Mechanistically, mmu-miR-101a-3p and mmu-miR-181a-5p were validated as post-transcriptional repressors of col1a1 and hif1a, respectively. Our work pioneers the discovery of the mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 axes as master regulators of burn repair, offering novel therapeutic targets. The multi-omics dataset and molecular networks established herein provide a foundational resource for wound healing research.

MicroRNAs↗

PubMed2026

Single-cell spatial transcriptomic atlas of the mouse adrenal gland reveals sexual dimorphism in steroidogenic enzyme and hormone receptor expression.

The adrenal cortex shows sexual dimorphism in structure and function. We analysed adrenal glands from 7-week-old male and female BALB/c mice using Visium HD with Cellpose 3 segmentation, comprising 236,077 cells across eleven populations, including four cortical zones. Using curated marker-gene-based zonal annotation, we focused on steroidogenic enzymes and hormone receptors, complementing our companion study based on the same primary dataset. The X-zone was nearly absent in males but prominent in females. Females showed higher Hsd3b1 expression across cortical zones and higher Cyp11b1 expression in outer cortical compartments. The strongest sex difference involved Srd5a2, with markedly higher expression in male zona fasciculata (inner: 77.1% vs. 28.9%), independently supported by RNAscope and immunohistochemistry. Mc2r and Mrap showed discordant spatial distributions, with limited co-expression, suggesting potential MC2R-independent MRAP roles. Agtr1a dominated angiotensin II receptor expression in zona glomerulosa without major sex differences, providing a zone-resolved reference for adrenal sexual dimorphism.

Adrenal cortex↗

PubMed2026

Early oligodendrocyte dysfunction signature in Alzheimer's disease: Insights from DNA methylomics and transcriptomics.

Much research into the aetiology of Alzheimer's disease (AD) has focused on neuronal cell types, while studies on the contribution of glial cells, particularly oligodendrocytes (OLGs), are only starting to emerge. Altered brain DNA methylation, an epigenetic modification that provides the interplay between genetics and environmental cues to tightly regulate gene expression, is well documented in AD. Yet, cell-type-specific investigations remain limited. Here, we examine the role of DNA methylation and OLGs in AD, and how such changes may impact gene expression. We performed weighted-gene correlation network analysis (WGCNA) on multiple brain omics AD datasets across species: human DNA methylation data from 4 brain regions, human brain single-nuclei RNA sequencing data and mouse brain RNA sequencing data. We compared AD-associated network modules enriched for OLG genes across AD brain regions, as well as with other neurodegenerative disease DNA methylation datasets. We identified a DNA methylation signature associated with AD, enriched for OLGs, and preserved across brain regions representing early and late AD pathology stages. Genes within this signature showed altered expression in AD OLGs, confirming cell-type specificity and relevance to AD. This OLG signature was also preserved in transgenic mice with early Aβ pathology and in other neurodegenerative diseases without Aβ pathology. We reveal a consistent pattern of OLG dysfunction spanning early to late stages of AD, across DNA methylation and gene expression. Our findings highlight OLG-associated DNA methylation changes as important in AD pathogenesis, and possibly in other neurodegenerative diseases, opening new avenues for therapeutic development.

Alzheimer Disease↗

PubMed2025

Time-resolved transcriptomics of S. cerevisiae and S. pastorianus in response to plasma membrane stresses.

Yeasts are beneficial microorganisms for human society and are utilized for academic and industrial purposes. For academic purposes, S. cerevisiae is a well-investigated model for studying eukaryotic cellular processes. For industrial purposes, S. pastorianus, which has a hybrid genome of S. cerevisiae and S. eubayanus, has been served for lager beer production. During fermentation, S. pastorianus produces ~7% of EtOH, which induces plasma membrane (PM)/cell wall stress in yeast. Therefore, S. pastorianus may experience PM stress and adapt to the self-forming environment during fermentation. However, how yeast adapts to PM stress remains unclear. Here, we investigated the temporal cellular responses of S. cerevisiae and S. pastorianus during adaptation to PM stresses by time-resolved mRNA-seq analysis. Our data showed different transcriptional phenotypes between S. cerevisiae and S. pastorianus during adaptation. The results may reflect the distinct nature of the two yeasts that have evolved in different nutritional environments. The dataset presented here would provide a promising resource for studying the characteristic nature of these differentially domesticated yeasts upon PM stresses.

Cell Membrane↗

PubMed2026

From wild to domestic: Single-cell transcriptomic perspectives on hippocampal regulation and evolution.

How domestication shapes brain evolution remains an open question. In this study, we integrated single-nucleus RNA sequencing (snRNA-seq), population genomics, and machine learning to investigate the hippocampal evolution under domestication. Across-species comparisons revealed that hippocampal cell type profiles are largely conserved across vertebrate species, while supporting the presence of adult hippocampal neurogenesis in birds. We further found that domestication and selective breeding likely influence the cellular composition and molecular regulation of the hippocampus. Our findings provide cellular evidence supporting the hypothesis that domestication affects adult hippocampal neurogenesis. Additionally, we showed that genes associated with neural progenitor cells (NPC) states and cell-marker programs are enriched for signatures of selection. Many of these genes function as regulators of neurogenesis and pathways mediating stress and fear reduction. Specifically, we identified selection at the FKBP5 promoter that may influence its expression in the NPC lineage, potentially contributing to stress-response regulation during domestication. Collectively, these results suggest that domestication is associated with hippocampal remodeling as part of an adaptive response to human-managed environments. This study provides a cellular and genetic perspective on how domestication reshapes the brain and offers a basis for further investigation into the mechanisms of neural evolution within the context of microevolution.

Animals↗

PubMed2025

Identification of a prognostic signature consisting of three macrophage-related genes for glioblastoma based on bulk and single-cell transcriptomes analyses.

BACKGROUND: Tumor-associated macrophages have been implicated in the progression and treatment resistance of glioblastoma (GBM). This study aimed to identify macrophage-related genes associated with prognosis and therapeutic response in GBM. MATERIALS AND METHODS: Bulk RNA-seq data from 533 patients with GBM were downloaded from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Bioinformatic tools were used to detect the co-expression gene modules associated with the infiltration of immune cells, identify a prognostic macrophage-related gene signature, and explore their association with sensitivity to chemotherapeutic drugs and immune checkpoint blockade. Single-cell RNA-seq data and multiplexed immunofluorescence were used to validate ISG20 expression (a member of the identified gene signature) in macrophages. RESULTS: We detected gene modules associated with macrophages and identified a signature consisting of three macrophage-related genes (ISG20, PARP12 and IFIT5) in the discovery set (TCGA-GBM, n = 159), and validated its prognostic value in the validation set (CGGA-GBM, n = 374). This gene signature demonstrated favorable accuracy in predicting prognosis and resistance of immuno- and chemo-therapy. The co-expression of ISG20 and PD-1 in macrophages was verified by single-cell RNA-seq data and multiplex immunofluorescence. CONCLUSIONS: This study presents a macrophage-related gene signature to predict prognosis and therapeutic response in GBM. ISG20, PARP12 and IFIT5 are interferon-stimulated genes, and further investigations may provide new insights into the interplay between macrophages and interferon signaling in GBM.

Humans↗

PubMed2026

REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research.

SUMMARY: We introduce REACTOR, a computational tool designed to detect differential activity of transcriptional regulators and their target genes (regulons) in single-cell RNA-sequencing data. It expands the currently available framework for regulon analysis by introducing a robust statistical test to detect differential regulon activity between conditions, such as disease versus control, with multiple replicates. By contrasting different conditions, REACTOR enables identification of key condition- and cell type-specific regulons. To demonstrate the use of REACTOR, we illustrate its performance in a publicly available COVID-19 dataset. AVAILABILITY: REACTOR R-package together with an implementation vignette are available at https://www.github.com/elolab/REACTOR.

Regulon↗

PubMed2026

Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.

MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. RESULTS: We present a new approach, MetaFR, to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. AVAILABILITY AND IMPLEMENTATION: MetaFR is available under https://github.com/SchulzLab/MetaFR.

Single-Cell Analysis↗

PubMed2025

Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.

BACKGROUND: Cell clustering is an essential step in uncovering cellular architectures in single-cell RNA sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. RESULTS: Here, we develop a multiscale clustering (MSC) approach to construct a sparse cell-cell correlation network for unsupervised identification of de novo cell types and subtypes across multiple resolutions. Based upon simulated silver- and gold-standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods and reveals a biologically meaningful cell hierarchy to facilitate the discovery of novel disease-associated cell subtypes and mechanisms. CONCLUSIONS: We present MSC as a new single-cell multiscale clustering framework as a powerful tool for advancing discoveries in disease-associated cell populations using single-cell sequencing data.

Single-Cell Analysis↗

PubMed2025

Integrative Genomic, Transcriptomic and Epigenomic Analysis Reveals cis-regulatory Contributions to High-altitude Adaptation in Tibetan Pigs.

The Qinghai-Tibet Plateau, characterized by its extreme environmental conditions, presents significant challenges to life, making it an ideal region for studying adaptation and evolution. Tibetan pigs, known for their high genetic diversity and exceptional adaptability to high altitudes, serve as excellent models for investigating high-altitude adaptation. While previous studies have extensively identified genetic determinants associated with high-altitude adaptation, the molecular mechanisms, particularly cis-regulatory patterns, remain poorly understood. Here, we conducted a selective sweep analysis using 484 genomes from Chinese and Western pig breeds across various altitudes, revealing 38.56 Mb of genomic regions under selection in Tibetan pigs. Enrichment analysis identified the lung as the primary functional tissue involved in high-altitude adaptation, supported by tissue-specific transcriptional and regulatory patterns observed between Tibetan and Meishan pigs (low altitude). By integrating genomic, RNA-seq, ATAC-seq, and H3K27ac HiChIP data, we constructed comprehensive enhancer-promoter regulatory maps of candidate genes and pinpointed promising genetic determinants associated with high-altitude adaptation, including SNPs in EPAS1, KLF13, SPRED1, and CFD. These loci were predicted to influence chromatin accessibility and the interactions of regulatory elements, with altered binding strength of relevant transcription factors. Further in vitro experiments confirmed that these loci function as allele-specific enhancers, modulating the expression of target genes. Our findings elucidate the regulatory basis of high-altitude adaptation in Tibetan pigs and provide valuable insights for exploring hypoxia-related diseases in livestock and humans.

Animals↗

PubMed2026

Transcriptomic shift in ethanol and amino acid metabolic genes regulated by Med15 during alcoholic fermentation.

Organisms that thrive in extreme environments provide natural experiments in evolution, revealing the genetic regulators that orchestrate complex phenotypic change. Wine yeast (WY) are specialized strains that are adapted to survive in the wine making environment while producing high concentrations of ethanol. In addition to large genomic changes that differentiate WY from yeast used in other industries, SNP and polyglutamine tract polymorphism in the transcriptional regulator Med15 are associated with the fermentation efficiency and stress response phenotypes of WY. In this study, we investigated the transcriptional differences during wine fermentation in transgenic lab strain yeast having integrated WY MED15 alleles. Compared to the unmodified lab strain (MED15 LAB), the same strain in which the MED15 locus was replaced with a MED15 allele from yeast isolated from palm wine, the fermented sap of palm (oil, date, coconut) trees (MED15 WY23), exhibited enhanced expression of amino acid biosynthesis genes as well as stress resistance and metabolic adaptation genes. Our experimental data confirm the role of arginine in efficient fermentation and suggest that certain MED15 alleles alter the expression patterns of arginine pathway genes in some cases improving carbon flux under nitrogen stress. The global benefits conferred by natural polymorphisms in a single transcriptional regulator highlight Med15 as a target for engineering of strains devoted to various types of alcohol production.

Ethanol↗

PubMed2025

Comparative Embryology and Transcriptomics of Asellus infernus, an Isopod Crustacean From Sulfidic Groundwater.

Sulfidic caves are harsh and extreme environments characterized by limited oxygen, low pH, and the presence of hydrogen sulfide. Amazingly, animals can live in sulfidic caves, one such animal being Asellus infernus, a representative of the Asellus aquaticus species complex, originating from Movile Cave and from old wells that represent windows of access to a sulfidic groundwater ecosystem located in southeast Romania. Little previous work has been done on lab-reared populations of A. infernus as they have been historically difficult to raise in the lab. Here, we develop resources for A. infernus, examining questions of timing of morphological differences in cave versus surface individuals, whether the environment (lab-bred vs. wild-caught) influenced size characteristics, and the genes and pathways showing differential expression between cave and surface samples. We found that A. infernus did not develop pigmentation embryonically, and juveniles had increased body length and longer antenna II as compared to surface individuals. Furthermore, we found that some of these measures differed between wild-caught and lab-reared juveniles for a given population, indicating that environmental differences can also influence these size characteristics. In addition, differential expression between cave and surface samples and allele-specific expression studies within F1 hybrids identified multiple genes, including those involved in sulfide metabolism and phototransduction. Strikingly, molecular convergence of genes involved in sulfide detoxification was observed between A. infernus and previous work on a fish that lives in both cave and sulfidic environments, Poecilia mexicana. In sum, we were able to develop embryonic and genomic tools for A. infernus, a model for understanding cave adaptation and adaptation to sulfidic environments.

Animals↗

PubMed2026

Single-Cell Transcriptome-Wide Mendelian Randomization and Colocalization Uncover Potential Immunocytes-Related Therapeutic Targets for Obesity.

Weight-loss treatment is crucial for individuals with obesity to prevent various complications. The role of Immune cells in obesity has been recently recognized, whereas its translation into therapy requires identifying key target genes. We performed Mendelian randomization (MR) analysis to assess causal relationships between expression quantitative trait loci (eQTL) of 14 immune cells and obesity-related traits (obesity, body mass index and body fat percentage), and validated the results in colocalization analysis. For the putative causal genes identified by the MR and colocalization analyses, we conducted pathway enrichment, differential expressed gene (DEG) analysis and search of druggable evidence, and utilized a Tier system to prioritize drug targets for obesity. MR and colocalization evidence was observed for 1630 genes associated with one or more obesity-related traits, mainly expressed in CD4+ naive/central memory T cells and enriched in antigen processing and presentation pathways. Forty-one genes showed causal relationship with all three outcomes, among which 19 genes have not been reported for obesity previously. DEG analysis using single-cell RNA sequencing data of blood or adipose tissue indicated that the differential expression of UBE2Z in monocytes, ZCCHC7 in T cells, and FNBP4 in B cells between lean and obese individuals were consistent with the MR results. By searching drug-gene interaction databases, we found targeted drugs for PYGB and PRUNE1, and PYGB was the top gene ranked in the Tier system. This study provides evidence for the involvement of immune cells in obesity, and the potential cell-specific, immune-related targets for obesity treatment.

Obesity↗

PubMed2025

Transcriptomic and enzymological evidence for plastid peptidoglycan synthesis in the gymnosperm Picea abies.

It is understood that a cyanobacterium was the progenitor of plastids and that the biosynthesis of cell wall peptidoglycan was lost during chloroplast evolution. However, accumulated data, especially from the moss Physcomitrium patens, suggest that peptidoglycan remains essential for plastid division in some land plants. A fundamental set of peptidoglycan biosynthesis (Mur) genes has been identified in the genomes of these land plants, while many angiosperms no longer encode some core Mur genes, including a bifunctional penicillin-binding protein (PBP). Ten incomplete Mur genes were previously identified in the genome of the gymnosperm Picea abies but these could be pseudogenes or encode proteins that have been repurposed. For instance, mutant albino maize and Arabidopsis seedlings possess a defective UDP-N-acetylmuramoyl-l-alanyl-d-glutamate--2,6-diaminopimelate ligase (MurE), an intact MurE ligase being essential for peptidoglycan synthesis. In this study, we isolated a full set of cDNAs for peptidoglycan biosynthesis from P. abies. GFP fusion proteins with either P. abies (Pa)MurE or PaPBP were detected in chloroplasts. Cross-species complementation assays with PaMurE in Arabidopsis albino MurE mutants and Physcomitrium MurE chloroplast division mutants showed that the gymnosperm MurE completely rescued both mutant phenotypes. Enzymatic assay of recombinant PaMurE proteins revealed they catalyze the same reaction performed by their bacterial MurE homologs. Moreover, the expression of the PaPbp cDNA partially rescued the giant chloroplast phenotype in the moss Pbp knockout line. These results are consistent with the operation of a functional Mur gene set in the Norway spruce genome.

Peptidoglycan↗