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Deciphering the dynamic single-cell transcriptional landscape in the ocular surface ectoderm differentiation system.
The ocular surface ectoderm (OSE) is essential for the development of the ocular surface, yet the molecular mechanisms driving its differentiation are not fully understood. In this study, we used single-cell transcriptomic analysis to explore the dynamic cellular trajectories and regulatory networks during the in vitro differentiation of embryonic stem cells (ESCs) into the OSE lineage. We identified nine distinct cell subpopulations undergoing differentiation along three main developmental branches: neural crest, neuroectodermal, and surface ectodermal lineages. Key marker gene expression, transcription factor activity, and signaling pathway insights revealed stepwise transitions from undifferentiated ESCs to fate-specified cell types, including a PAX6 + TP63 + population indicative of OSE precursors. Comparative analysis with mouse embryonic development confirmed the model's accuracy in mimicking in vivo epiblast-to-surface ectoderm dynamics. By integrating temporal dynamics of transcription factor activation and cell-cell communication, we constructed a comprehensive molecular atlas of the differentiation pathway from ESCs to distinct ectodermal lineages. This study provides new insights into the cellular heterogeneity and regulatory mechanisms of OSE development, aiding the understanding of ocular surface biology and the design of cell-based therapies for ocular surface disorders.
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Deciphering mitochondrial dysfunction in keratoconus: Insights into ACSL4 from machine learning-based bulk and single-cell transcriptome analyses and experimental validation.
Keratoconus (KC) is a prevalent ectatic corneal disease and the leading cause of corneal transplantation globally. Despite evidence of mitochondrial abnormalities in KC, the underlying mechanisms remain unclear. Our aim was to investigate the role of mitochondrial dysfunction in this pathological condition. Based on transcriptomics datasets of KC, mitochondria-related differentially expressed genes (mDEGs) were identified and analyzed for potential functional pathways, protein-protein interaction (PPI), and gene regulatory networks. Hub genes were further screened and validated by multiple machine learning (ML) algorithms, followed by a comprehensive visualization of single-cell atlas and immune landscape. Additionally, bioinformatic results were validated through quantitative PCR, Western blot, and transcriptomics analysis in an in vitro KC model based on matrix stiffness using human stromal keratocytes. In total, 104 mDEGs were identified, enriched in pathways related to oxidative stress, apoptotic mitochondrial changes, ferroptosis, and inflammatory responses. Nine characteristic genes (CYP24A1, ACSL4, ACADL, HELZ2, AMT, DEPTOR, TUBA1A, TYMS, and ACSL5) were selected and validated using multiple ML models. Single cell sequencing data highlighted ACSL4 as the most promising biomarker, primarily expressed in corneal stromal cells (CSCs). Immune infiltration analysis revealed that ACSL4 was positively associated with monocytes and negatively correlated with eosinophils in KC. In cellular experiments, ACSL4 expression was significantly upregulated in response to decreased substrate stiffness, suggesting its critical role in KC development. These findings suggest a mitochondrial-related molecular mechanism implicated in KC pathogenesis. The identified pivotal biomarker ACSL4 provides a novel framework for future mechanistic and therapeutic studies of KC.
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Single Cell Transcriptomic Analysis Reveals Organ Specific Pericyte Markers and Identities.
Pericytes are mesenchymal-derived mural cells that wrap around capillaries and directly contact endothelial cells. Present throughout the body, including the cardiovascular system, pericytes are proposed to have multipotent cell-like properties and are involved in numerous biological processes, including regulation of vascular development, maturation, permeability, and homeostasis. Despite their physiological importance, the functional heterogeneity, differentiation process, and pathological roles of pericytes are not yet clearly understood, in part due to the inability to reliably distinguish them from other mural cell populations. Our study focused on identifying pericyte-specific markers by analyzing single-cell RNA sequencing data from tissue-specific mouse pericyte populations generated by the Tabula Muris Senis. We identified the mural cell cluster in murine lung, heart, kidney, and bladder that expressed either of two known pericyte markers, Cspg4 or Pdgfrb. We further defined pericytes as those cells that co-expressed both markers within this cluster. Single-cell differential expression gene analysis compared this subset with other clusters that identified potential pericyte marker candidates, including Kcnk3 (in the lung); Rgs4 (in the heart); Myh11 and Kcna5 (in the kidney); Pcp4l1 (in the bladder); and Higd1b (in lung and heart). In addition, we identified novel markers of tissue-specific pericytes and signaling pathways that may be involved in maintaining their identity. Moreover, the identified markers were further validated in Human Lung Cell Atlas and human heart single-cell RNAseq databases. Intriguingly, we found that markers of heart and lung pericytes in mice were conserved in human heart and lung pericytes. In this study, we, for the first time, identified specific pericyte markers among lung, heart, kidney, and bladder and reveal differentially expressed genes and functional relationships between mural cells.
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Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data.
How intrinsic gene-regulatory networks interact with a cell's spatial environment to define its identity remains poorly understood. We developed an approach to distinguish between intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell type mapping combined with a hidden Markov random field model. We applied this approach to dissect the cell-type- and spatial-domain-associated heterogeneity in the mouse visual cortex region. Our analysis identified distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNA sequencing data, we identified previously unknown spatially associated subpopulations, which were validated by comparison with anatomical structures and Allen Brain Atlas images.
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Cell-type brain-region specific changes in prefrontal cortex of a mouse model of alcohol dependence.
The prefrontal cortex is a crucial regulator of alcohol drinking, and dependence, and other behavioral phenotypes associated with AUD. Comprehensive identification of cell-type specific transcriptomic changes in alcohol dependence will improve our understanding of mechanisms underlying the excessive alcohol use associated with alcohol dependence and will refine targets for therapeutic development. We performed single nucleus RNA sequencing (snRNA-seq) and Visium spatial gene expression profiling on the medial prefrontal cortex (mPFC) obtained from C57BL/6 J mice exposed to the two-bottle choice-chronic intermittent ethanol (CIE) vapor exposure (2BC-CIE, defined as dependent group) paradigm which models phenotypes of alcohol dependence including escalation of alcohol drinking. Gene co-expression network analysis and differential expression analysis identified highly dysregulated co-expression networks in multiple cell types. Dysregulated modules and their hub genes suggest novel understudied targets for studying molecular mechanisms contributing to the alcohol dependence state. A subtype of inhibitory neurons was the most alcohol-sensitive cell type and contained a downregulated gene co-expression module; the hub gene for this module is Cpa6, a gene previously identified by GWAS to be associated with excessive alcohol consumption. We identified an astrocytic Gpc5 module significantly upregulated in the alcohol-dependent group. To our knowledge, there are no studies linking Cpa6 and Gpc5 to the alcohol-dependent phenotype. We also identified neuroinflammation related gene expression changes in multiple cell types, specifically enriched in microglia, further implicating neuroinflammation in the escalation of alcohol drinking. Here, we present a comprehensive atlas of cell-type specific alcohol dependence mediated gene expression changes in the mPFC and identify novel cell type-specific targets implicated in alcohol dependence.
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Transcriptomic analysis unveils bona fide molecular signatures of microglia under conditions of homeostasis and viral encephalitis.
Microglia serve as a front-line defense against neuroinvasive viral infection, however, determination of their actual transcriptional profiles under conditions of health and disease is challenging. Here, we used various experimental approaches to delineate the transcriptional landscape of microglia during viral infection. Intriguingly, multiple activation genes were found to be artificially induced in sorted microglia and we demonstrated that shear stress encountered during cell sorting was one of the key inducers. Post-hoc analysis revealed that publicly available large-scale single-cell RNA sequencing datasets were significantly tainted by aberrant signatures that are associated with cell sorting. By exploiting the ribosomal tagging approach, we developed a strategy to enrich microglia-specific transcripts by comparing immunoprecipitated RNA with total RNA. Such enriched transcripts were instrumental in defining bona fide signatures of microglia under conditions of health and virus infection. These unified microglial signatures may serve as a benchmark to retrospectively assess ex vivo artefacts from available atlases. Leveraging the microglial translatome, we found enrichment of genes implicated in T-cell activation and cytokine production during the course of VSV infection. These data linked microglia with T-cell re-stimulation and further underscored that microglia are involved in shaping antiviral T-cell responses in the brain. Collectively, our study defines the transcriptional landscape of microglia under steady state and during viral encephalitis and highlights cellular interactions between microglia and T cells that contribute to the control of virus dissemination.
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Single-Cell Transcriptomic Profiling of the O-Methyltransferase Characteristics of Flavonoid Biosynthesis in Ziziphus jujuba var. spinosa Fruit.
Sour jujube (Ziziphus jujuba var. spinosa) is a valuable medicinal and edible fruit owing to its health-promoting properties. Methoxylated flavonoids enhance their value by improving stability and bioavailability. However, the cellular basis of their biosynthesis remains unclear. We constructed a single-nucleus transcriptomic atlas of sour jujube fruit. Parenchyma cells were identified as the primary sites for flavonoid biosynthesis and their O-methylation, characterized by high expression of phenylpropanoid pathway genes and O-methyltransferases (OMTs). Among the 48 identified OMTs, four (ZjOMT1-4) exhibited high expression, phylogenetically clustered with known functional OMTs, and localized to the cytoplasm; they were further characterized by enzyme activity assays. Notably, ZjOMT1 demonstrates moderate substrate promiscuity, catalyzing the methylation of various substrates, including quercetin, quercitrin, and rutin. Our findings reveal the cellular architecture of flavonoid metabolism in sour jujube fruit, providing genetic resources for breeding varieties with enhanced flavonoid profiles to develop functional foods and nutraceuticals.
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Lipid Metabolism and Oxidative Stress in Uterine Corpus Endometrial Carcinoma: Prognostic and Immune Landscape Analysis.
OBJECTIVE: Uterine corpus endometrial carcinoma (UCEC) is a common gynecologic malignancy characterized by metabolic reprogramming and immune dysregulation. This study aimed to investigate the prognostic and diagnostic value of lipid metabolism- and oxidative stress-related genes (LMOSGs) in UCEC. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed to identify differentially expressed LMOSGs associated with prognosis using univariate Cox regression. Molecular subtypes were defined by non-negative matrix factorization (NMF) clustering. Functional differences between subtypes were evaluated through Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment, immune cell infiltration analysis, and tumor microenvironment scoring. Prognostic genes were further refined using Least Absolute Shrinkage and Selection Operator regression to construct a risk signature. Gene expression was validated by reverse transcription quantitative real-time polymerase chain reaction, the Human Protein Atlas, and single-cell RNA sequencing data. A diagnostic model was developed using 12 machine learning algorithms. In vitro functional assays were conducted to assess the effects of key LMOSGs on cell proliferation, migration, and invasion. RESULTS: Fifty-nine LMOSGs were identified as significantly associated with prognosis. NMF clustering classified UCEC into two molecular subtypes with distinct immune landscapes and survival outcomes: Cluster 1 exhibited higher immune infiltration and a favorable prognosis, whereas Cluster 2 showed immune suppression and poorer survival. A robust 10-gene prognostic signature (PHGDH, MAPT, KCNK9, EPHX2, LIPH, SLC8A1, CREB3L4, PDCD1, MYLIP, and ORMDL2) demonstrated strong predictive performance. Among these, EPHX2, SLC8A1, ORMDL2, and MYLIP showed superior diagnostic discrimination and were further validated functionally as key regulators of tumor progression. CONCLUSION: LMOSGs play critical roles in UCEC heterogeneity, prognosis, and immune modulation. These genes represent promising biomarkers and potential targets for personalized therapeutic strategies. TRIAL REGISTRATION: Not applicable.
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Mast cell marker gene signature in head and neck squamous cell carcinoma.
BACKGROUND: Mast cells can reshape the tumour immune microenvironment and greatly affect tumour occurrence and development. However, mast cell gene prognostic and predictive value in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study was conducted to identify and establish a prognostic mast cell gene signature (MCS) for assessing the prognosis and immunotherapy response of patients with HNSCC. METHODS: Mast cell marker genes in HNSCC were identified using single-cell RNA sequencing analysis. A dataset from The Cancer Genome Atlas was divided into a training cohort to construct the MCS model and a testing cohort to validate the model. Fluorescence in-situ hybridisation was used to evaluate the MCS model gene expression in tissue sections from patients with HNSCC who had been treated with programmed cell death-1 inhibitors and further validate the MCS. RESULTS: A prognostic MCS comprising nine genes (KIT, RAB32, CATSPER1, SMYD3, LINC00996, SOCS1, AP2M1, LAT, and HSP90B1) was generated by comprehensively analysing clinical features and 47 mast cell-related genes. The MCS effectively distinguished survival outcomes across the training, testing, and entire cohorts as an independent prognostic factor. Furthermore, we identified patients with favourable immune cell infiltration status and immunotherapy responses. Fluorescence in-situ hybridisation supported the MCS immunotherapy response of patients with HNSCC prediction, showing increased high-risk gene expression and reduced low-risk gene expression in immunotherapy-insensitive patients. CONCLUSIONS: Our MCS provides insight into the roles of mast cells in HNSCC prognosis and may have applications as an immunotherapy response predictive indicator in patients with HNSCC and a reference for immunotherapy decision-making.
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Multiomic characterization, immunological and prognostic potential of SMAD3 in pan-cancer and validation in LIHC.
SMAD3, a protein-coding gene, assumes a pivotal role within the transforming growth factor-beta (TGF-β) signaling pathway. Notably, aberrant SMAD3 expression has been linked to various malignancies. Nevertheless, an extensive examination of the comprehensive pan-cancer impact on SMAD3's diagnostic, prognostic, and immunological predictive utility has yet to be undertaken. Bioinformatics methods were employed to systematically investigate the potential carcinogenic impact of SMAD3. We extensively harnessed data from authoritative sources, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, Human Protein Atlas (HPA), UALCAN, and various other databases. Our study encompassed a comprehensive analysis of the following aspects: differential SMAD3 expression and its association with prognosis across diverse cancer types, gene mutations, immune cell infiltration, single-cell sequencing analysis, DNA methylation patterns, and drug sensitivity profiles. In vitro experiments were conducted with the primary objective of appraising both the expression profile and the precise functional attributes of SMAD3 within the milieu of Liver Hepatocellular Carcinoma (LIHC). Our findings revealed significant variations in SMAD3 expression between cancerous and adjacent normal tissues. High levels of SMAD3 expression were consistently associated with unfavorable prognoses across multiple cancer types,. Additionally, our analysis of SMAD3 methylation patterns in human cancers unveiled a favorable prognosis linked to elevated DNA methylation levels in pan-cancer. Furthermore, we identified positive associations between SMAD3 expression and RNAm6A methylation-related genes in the majority of cancers. Moreover, SMAD3 expression displayed substantial correlations with immune cell infiltration. Notably, immune checkpoint genes exhibited significant associations with SMAD3 expression across diverse cancers. Single-cell sequencing results elucidated the pan-cancer single-cell expression landscape of SMAD3. Within specific cancer subtypes, SMAD3 expression exhibited a noteworthy positive association with distinctive facets of malignancy. Finally, in our comprehensive analysis of drug sensitivity, we discerned a catalog of prospective therapeutic agents. In our comprehensive analysis across multiple cancer types, we observed a significant disparity in SMAD3 expression compared to normal tissues, and this findings suggest that SMAD3 holds promise as both a prognostic biomarker and a therapeutic target against various cancers. Difference displayed a noteworthy association with patient prognosis.