Search Results (32)

Background: Focal cortical dysplasia (FCD) is a prevalent cause of drug-resistant epilepsy, but a comprehensive understanding of its pathogenesis at a cellular resolution remains limited. Previous transcriptomic studies, often constrained by bulk tissue analysis, have been unable to dissect the cell-type-specific contributions to epileptogenesis. Methods: We performed scRNA-seq on cortical tissues from one surgical patient with FCD type II and one matched control. Cell clustering, annotation, and identification of differentially expressed genes (DEGs) were conducted using standard Seurat workflow. We focused on the molecular alterations in three major glial cell types: astrocytes, microglia, and oligodendrocytes. To functionally interpret the DEGs, we performed enrichment analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Results: Our profiling revealed a profoundly reconstituted cellular ecosystem in the FCD cortex. We found a marked expansion of microglia (65.57% vs. 47.02%; a ~39% relative increase) and astrocytes (10.98% vs. 4.11%; a ~167% relative increase), alongside a severe depletion of oligodendrocytes (8.12% vs. 30.63%; a ~73% relative decrease). Critically, a core set of 128 differentially expressed genes (DEGs) was shared across these glial populations, featuring consistent upregulation of RAC1 and downregulation of ATP5F1D, pointing to convergent pro-inflammatory and mitochondrial dysfunction pathways. Enrichment analyses further demonstrated a coordinated engagement of neuroinflammatory pathways, most notably IL-17 signaling. Subsequent cell–cell communication inference revealed a broad attenuation of intercellular signaling, with a 35% reduction in interaction numbers, indicating a breakdown of coordinated cellular crosstalk. Conclusions: This exploratory single-cell study provides preliminary evidence of a convergent glial pathology in FCD, characterized by shared molecular disruptions in inflammation and metabolism. Our findings highlight RAC1 and IL-17 signaling as potentially actionable pathways, warranting further investigation into their therapeutic potential for mitigating epileptogenesis in FCD. Full article

Objective: This study aims to identify clinically relevant lactylation-related biomarkers in chronic obstructive pulmonary disease (COPD) and investigate their potential mechanistic roles in COPD pathogenesis. Methods: Differentially expressed genes (DEGs) were identified from the GSE21359 dataset, followed by weighted gene co-expression network analysis (WGCNA) to detect COPD-associated modules. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine–recursive feature elimination (SVM–RFE) algorithms were applied to screen lactylation-related biomarkers, with diagnostic performance evaluated through the ROC curve. Candidates were validated in the GSE76925 dataset for expression and diagnostic robustness. Immune cell infiltration patterns were exhibited using EPIC deconvolution. Single-cell transcriptomics (from GSE173896) were processed via the ‘Seurat’ package encompassing quality control, dimensionality reduction, and cell type annotation. Cell-type-specific markers and intercellular communication networks were delineated using the ‘FindAllMarkers’ package and the ‘CellChat’ R package, respectively. In vitro validation was conducted using a cigarette smoke extract (CSE)-induced COPD model. Results: Integrated transcriptomic approaches and multi-algorithm screening (LASSO/Boruta/SVM–RFE) revealed carbonyl reductase 1 (CBR1) and peroxiredoxin 1 (PRDX1) as core COPD biomarkers enriched in oxidation–reduction and inflammatory pathways, with high diagnostic accuracy (AUC > 0.85). Immune profiling and scRNA-seq delineated macrophage and cancer-associated fibroblasts (CAFs) infiltration with oxidative-redox transcriptional dominance in COPD. CBR1 was significantly upregulated in T cells, neutrophils, and mast cells; and PRDX1 showed significant upregulation in endothelial, macrophage, and ciliated cells. Experimental validation in CSE-induced models confirmed significant upregulation of both biomarkers via transcription PCR (qRT-PCR) and immunofluorescence. Conclusions: CBR1 and PRDX1 are lactylation-associated diagnostic markers, with lactylation-driven redox imbalance implicated in COPD progression. Full article

Deposition of intramuscular fat (IM), also known as marbling, is the deciding factor of beef quality grade in the U.S. Defining molecular mechanisms underlying the differential deposition of adipose tissue in distinct anatomical areas in beef cattle is key to the development of strategies for marbling enhancement while limiting the accumulation of excessive subcutaneous adipose tissue (SAT). The objective of this exploratory study was to define the IM and SAT transcriptional heterogeneity at the whole tissue and single-nuclei levels in beef steers. Longissimus dorsi muscle samples (9–11th rib) were collected from two finished beef steers at harvest to dissect matched IM and adjacent SAT (backfat). Total RNA from IM and SAT was isolated and sequenced in an Illumina NovaSeq 6000. Nuclei from the same samples were isolated by dounce homogenization, libraries generated with 10× Genomics, and sequenced in an Illumina NovaSeq 6000, followed by analysis via Cell Ranger pipeline and Seurat in RStudio (v4.3.2) By the expression of signature marker genes, single-nuclei RNA sequencing (snRNAseq) analysis identified mature adipocytes (AD; ADIPOQ, LEP), adipose stromal and progenitor cells (ASPC; PDGFRA), endothelial cells (EC; VWF, PECAM1), smooth muscle cells (SMC; NOTCH3, MYL9) and immune cells (IMC; CD163, MRC1). We detected six cell clusters in SAT and nine in IM. Across IM and SAT, AD was the most abundant cell type, followed by ASPC, SMC, and IMC. In SAT, AD made up 50% of the cellular population, followed by ASPC (31%), EC (14%), IMC (1%), and SMC (4%). In IM depot, AD made up 23% of the cellular population, followed by ASPC at 19% of the population, EC at 28%, IMC at 7% and SMC at 12%. The abundance of ASPC and AD was lower in IM vs. SAT, while IMC was increased, suggesting a potential involvement of immune cells on IM deposition. Accordingly, both bulk RNAseq and snRNAseq analyses identified activated pathways of inflammation and metabolic function in IM. These results demonstrate distinct transcriptional cellular heterogeneity between SAT and IM depots in beef steers, which may underly the mechanisms by which fat deposits in each depot. The identification of depot-specific cell populations in IM and SAT via snRNAseq analysis has the potential to reveal target genes for the modulation of fat deposition in beef cattle. Full article

Hepatocellular carcinoma (HCC) presents significant intertumoral heterogeneity, complicating prognosis and treatment. To address this, we performed an integrated single-cell RNA-sequencing analysis of HCC specimens using Seurat and identified malignant cells via Infercnv. Through a systematic evaluation of 101 machine learning algorithms used in combination, we developed tumor-cell-specific gene signatures (TCSGs) that demonstrated strong predictive performance, with area under the curve (AUC) values ranging from 0.72 to 0.74 in independent validation cohorts. Risk stratification based on these signatures revealed distinct therapeutic vulnerabilities: high-risk patients showed increased sensitivity to sorafenib, while low-risk patients exhibited enhanced responses to immunotherapy and transarterial chemoembolization (TACE). Pharmacogenomic analysis with Oncopredict identified four chemotherapeutic agents, including sapitinib and dinaciclib, with risk-dependent efficacy patterns. Furthermore, CRISPR/Cas9-dependency screening prioritized SRSF7 as essential for HCC cell survival, a finding confirmed by the identification of protein-level overexpression in tumors via immunohistochemistry. This multi-omics framework bridges single-cell characterization to clinical decision-making, offering a clinically actionable prognostic system that can be used to optimize therapeutic selection in HCC management. Full article

Single-cell RNA sequencing (scRNA-seq) enables expression quantitative trait locus (eQTL) analysis at cellular resolution, offering new opportunities to uncover regulatory variants with cell-type-specific effects. However, existing tools are often limited in functionality, input compatibility, or scalability for sparse single-cell data. To address these challenges, we developed scQTLtools, a comprehensive R/Bioconductor package that facilitates end-to-end single-cell eQTL analysis, from preprocessing to visualization. The toolkit supports flexible input formats, including Seurat and SingleCellExperiment objects, handles both binary and three-class genotype encodings, and provides dedicated functions for gene expression normalization, SNP and gene filtering, eQTL mapping, and versatile result visualization. To accommodate diverse data characteristics, scQTLtools implements three statistical models—linear regression, Poisson regression, and zero-inflated negative binomial regression. We applied scQTLtools to scRNA-seq data from human acute myeloid leukemia and identified eQTLs with regulatory effects that varied across cell types. Visualization of SNP–gene pairs revealed both positive and negative associations between genotype and gene expression. These results demonstrate the ability of scQTLtools to uncover cell-type-specific regulatory variation that is often missed by bulk eQTL analyses. Currently, scQTLtools supports cis-eQTL mapping; future development will extend to include trans-eQTL detection. Overall, scQTLtools offers a robust, flexible, and user-friendly framework for dissecting genotype–expression relationships in heterogeneous cellular populations. Full article

Single-cell transcriptomics data are analyzed using two popular tools, Seurat and Scanpy. Multiple separate tools are used downstream of Seurat and Scanpy cell annotation to study cell differentiation and communication, including cell proportion difference analysis between conditions, pseudotime and trajectory analyses to study cell transition, and cell–cell communication analysis. To automate the integrative cell differentiation and communication analyses of single-cell RNA-seq data, we developed a single-cell RNA-seq downstream analysis pipeline called “scDown”. This R package includes cell proportion difference analysis, cell–cell communication analysis, pseudotime analysis, and RNA velocity analysis. Both Seurat and Scanpy annotated single-cell RNA-seq data are accepted in this pipeline. We applied scDown to a published dataset and identified a unique, previously undiscovered signature of neuronal inflammatory signaling associated with a rare genetic neurodevelopmental disorder. These findings were not identified with a simple implementation of Seurat differential gene expression analysis, illustrating the value of our pipeline in biological discovery. scDown can be broadly utilized in downstream analyses of scRNA-seq data, particularly in rare diseases. Full article

Background. Single-cell RNA sequencing (scRNA-seq) has transformed genomics by enabling the study of cellular heterogeneity. However, its high dimensionality, noise, and sparsity pose significant challenges for data analysis. Methods. We investigate the use of Hyperdimensional Computing (HDC), a brain-inspired computational framework recognized for its noise robustness and hardware efficiency, to tackle the challenges in scRNA-seq data analysis. We apply HDC to both supervised classification and unsupervised clustering tasks. Results. Our experiments demonstrate that HDC consistently outperforms established methods such as XGBoost, Seurat reference mapping, and scANVI in terms of noise tolerance and scalability. HDC achieves superior accuracy in classification tasks and maintains robust clustering performance across varying noise levels. Conclusions. These results highlight HDC as a promising framework for accurate and efficient single-cell data analysis. Its potential extends to other high-dimensional biological datasets including proteomics, epigenomics, and transcriptomics, with implications for advancing bioinformatics and personalized medicine. Full article

The visual system has served as an expeditious entry point for discerning the mechanism of action of many brain systems, spearheading multiple fields of neuroscience in the process. It has additionally launched the careers of countless scientists, as we have crafted new means to understand neuronal structures and their functions, leading to advances in many areas of the sciences. Indeed, one can readily mark the onset of the scientific examination of the visual system with the 1851 invention of the ophthalmoscope by Hermann von Helmholtz, and the trichromatic theory of color vision in 1802. The Young–Helmholtz understanding the red–green–blue nature of color vision became the foundation to understanding sensory system function that visual artists and also contemporary flat panel displays rely on. It is fascinating to realize that the paintings of Georges Seurat and an iPhone display share a commonality of this application of the trichromatic theory. While it was not until 1956 that the existence of cells responsive to three different ranges of wavelengths was proven with the work of Gunnar Svaetichin, this proof in many ways marked the advancement of tools to visualize at a microscopic level, a full century after the Young–Helmholtz theory was developed. Just a decade later, in 1966, the person widely considered as the founder of modern neuroscience, Stephen Kuffler, founded the Harvard neurobiology department. It was from Kuffler’s work with his post-doctoral students that many new fields of study were created and from whom many of the neuroscience programs across the US were founded. In terms of the visual system, Kuffler and his team were key in detailing areas of retinal neuroanatomy, neurochemistry, neurophysiology, and developmental neurobiology. This paper traces areas in visual system research that provide our understanding of the disparate areas of brain sciences. As such, there are six categories that are evaluated, each of which spawned work in multiple areas that have become mainstays in neuroscience. These range from fields that were dominant a half century ago to ones that have their origins in this decade. The commonality is that all of these owe their origin to Helmholtz and Kuffler, polymaths of the nineteenth and twentieth centuries. We will examine the impact of vision research across the following fields of neuroscience: sensory system function, neuroanatomy, neurochemistry, neurophysiology, developmental neurobiology, and neurological health and disease. Full article

Thoracic and abdominal aortic aneurysms (TAAs and AAAs, respectively) share morphological features but have distinct clinical and hereditary characteristics. Studies using bulk RNA comparisons revealed distinct patterns of gene expression in human TAA and AAA tissues. However, given the summative nature of bulk RNA studies, these findings represent the totality of gene expression without regards to the differences in cellular composition. Single-cell RNA sequencing provides an opportunity to interrogate cell-type-specific transcriptomes. Single cell RNA sequencing datasets from mouse TAA (GSE153534) and AAA (GSE164678 and GSE152583) with respective controls were obtained from the Gene Expression Omnibus. Bioinformatic analysis was performed with the Seurat 4, clusterProfiler, and Connectome software packages (V1.0.1). Immunostaining was performed with standard protocols. Within normal and aneurysmal aortae, three unique populations of cells that express smooth muscle cell (SMC) markers were identified (SMC1, SMC2, and SMCmod). A greater proportion of TAA SMCs clustered as a unique population, SMCmod, relative to the AAA SMCs (38% vs. 10–12%). These cells exhibited transcriptional features distinct from other SMCs, which were characterized by Igfbp2 and Tnfrsf11b expression. Genes upregulated in TAA SMCs were enriched for the Reactome terms “extracellular matrix organization” and “insulin-like growth factor (IGF) transport and uptake by IGF binding proteins (IGFBPs)”, indicating a role for Igfbp2 in TAA pathogenesis. Regulon analysis revealed transcription factors enriched in TAAs and AAAs. Validating these mouse bioinformatic findings, immunostaining demonstrated that both IGFBP2 and TNFRSF11B proteins increased in human TAAs compared to AAAs. These results highlight the unique cellular composition and transcriptional signature of SMCs in TAAs and AAAs. Future studies are needed to reveal the pathogenetic pathways of IGFBP2 and TNFRSF11B. Full article

Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research. Full article

Thyroid hormone (TH) plays an essential role in cell proliferation, differentiation, and metabolism. Experimental and clinical studies have shown a potential association between TH signaling and retinal degeneration. The suppression of TH signaling protects cone photoreceptors in mouse models of retinal degeneration, whereas excessive TH signaling induces cone degeneration, manifested as reduced light response and a loss of cones. This work investigates the genes/transcriptomic alterations that might be involved in TH-induced cone degeneration in mice using single-cell RNA sequencing (scRNAseq) analysis. One-month-old C57BL/6 mice received triiodothyronine (T3, 20 µg/mL in drinking water) for 4 weeks as a model of hyperthyroidism/excessive TH signaling. At the end of the experiments, retinal cells were dissociated, and cell viability was analyzed before being subjected to scRNAseq. The resulting data were analyzed using the Seurat package and visualized using the Loupe browser. Among 155,866 single cells, we identified 14 cell clusters, representing various retinal cell types, with rod and cone clusters comprising 76% and 4.1% of the total cell population, respectively. Cone cluster transcriptomes demonstrated the most alterations after the T3 treatment, with 450 differentially expressed genes (DEGs), accounting for 38.5% of the total DEGs. Statistically significant changes in the expression of genes in the cone cluster revealed that phototransduction and oxidative phosphorylation were impaired after the T3 treatment, along with mitochondrial dysfunction. A pathway analysis also showed the activation of the sensory neuronal/photoreceptor stress pathways after the T3 treatment. Specifically, the eukaryotic initiation factor-2 signaling pathway and the cAMP response element-binding protein signaling pathway were upregulated. Thus, excessive TH signaling substantially affects cones at the transcriptomic level. The findings from this work provide an insight into how excessive TH signaling induces cone degeneration. Full article

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function. Full article

The periodontium comprising periodontal ligament (PDL), gingiva, and epithelium play crucial roles in maintaining tooth integrity and function. Understanding tissue cellular composition and gene expression is crucial for illuminating periodontal pathophysiology. This study aimed to identify tissue-specific markers via scRNA-Seq. Primary human PDL, gingiva, and epithelium tissues (n = 7) were subjected to cell hashing and sorting. scRNA-Seq library preparation using 10× Genomics protocol and Illumina sequencing was conducted. The analysis was performed using Cellranger (v3.1.0), with downstream analysis via R packages Seurat (v5.0.1) and SCORPIUS (v1.0.9). Investigations identified eight distinct cellular clusters, revealing the ubiquitous presence of epithelial and gingival cells. PDL cells evolved in two clusters with numerical superiority. The other clusters showed varied predominance regarding gingival and epithelial cells or an equitable distribution of both. The cluster harboring most cells mainly consisted of PDL cells and was present in all donors. Some of the other clusters were also tissue-inherent, while the presence of others was environmentally influenced, revealing variability across donors. Two clusters exhibited genetic profiles associated with tissue development and cellular integrity, respectively, while all other clusters were distinguished by genes characteristic of immune responses. Developmental trajectory analysis uncovered that PDL cells may develop after epithelial and gingival cells, suggesting the inherent PDL cell-dominated cluster as a final developmental stage. This single-cell RNA sequencing study delineates the hierarchical organization of periodontal tissue development, identifies tissue-specific markers, and reveals the influence of environmental factors on cellular composition, advancing our understanding of periodontal biology and offering potential insights for therapeutic interventions. Full article

The relationship between psychological stress, altered skin immunity, and autophagy-related genes (ATGs) is currently unclear. Psoriasis is a chronic skin inflammation of unclear etiology that is characterized by persistence and recurrence. Immune dysregulation and emotional disturbances are recognized as significant risk factors. Emerging clinical evidence suggests a possible connection between anxiety disorders, heightened immune system activation, and altered skin immunity, offering a fresh perspective on the initiation of psoriasis. The aim of this study was to explore the potential shared biological mechanisms underlying the comorbidity of psoriasis and anxiety disorders. Psoriasis and anxiety disorders data were obtained from the GEO database. A list of 3254 ATGs was obtained from the public database. Differentially expressed genes (DEGs) were obtained by taking the intersection of DEGs between psoriasis and anxiety disorder samples and the list of ATGs. Five machine learning algorithms used screening hub genes. The ROC curve was performed to evaluate diagnostic performance. Then, GSEA, immune infiltration analysis, and network analysis were carried out. The Seurat and Monocle algorithms were used to depict T-cell evolution. Cellchat was used to infer the signaling pathway between keratinocytes and immune cells. Four key hub genes were identified as diagnostic genes related to psoriasis autophagy. Enrichment analysis showed that these genes are indeed related to T cells, autophagy, and immune regulation, and have good diagnostic efficacy validated. Using single-cell RNA sequencing analysis, we expanded our understanding of key cellular participants, including inflammatory keratinocytes and their interactions with immune cells. We found that the CASP7 gene is involved in the T-cell development process, and correlated with γδ T cells, warranting further investigation. We found that anxiety disorders are related to increased autophagy regulation, immune dysregulation, and inflammatory response, and are reflected in the onset and exacerbation of skin inflammation. The hub gene is involved in the process of immune signaling and immune regulation. The CASP7 gene, which is related with the development and differentiation of T cells, deserves further study. Potential biomarkers between psoriasis and anxiety disorders were identified, which are expected to aid in the prediction of disease diagnosis and the development of personalized treatments. Full article

The periodontal ligament (PDL) is a highly specialized fibrous tissue comprising heterogeneous cell populations of an intricate nature. These complexities, along with challenges due to cell culture, impede a comprehensive understanding of periodontal pathophysiology. This study aims to address this gap, employing single-cell RNA sequencing (scRNA-seq) technology to analyze the genetic intricacies of PDL both in vivo and in vitro. Primary human PDL samples (n = 7) were split for direct in vivo analysis and cell culture under serum-containing and serum-free conditions. Cell hashing and sorting, scRNA-seq library preparation using the 10x Genomics protocol, and Illumina sequencing were conducted. Primary analysis was performed using Cellranger, with downstream analysis via the R packages Seurat and SCORPIUS. Seven distinct PDL cell clusters were identified comprising different cellular subsets, each characterized by unique genetic profiles, with some showing donor-specific patterns in representation and distribution. Formation of these cellular clusters was influenced by culture conditions, particularly serum presence. Furthermore, certain cell populations were found to be inherent to the PDL tissue, while others exhibited variability across donors. This study elucidates specific genes and cell clusters within the PDL, revealing both inherent and context-driven subpopulations. The impact of culture conditions—notably the presence of serum—on cell cluster formation highlights the critical need for refining culture protocols, as comprehending these influences can drive the creation of superior culture systems vital for advancing research in PDL biology and regenerative therapies. These discoveries not only deepen our comprehension of PDL biology but also open avenues for future investigations into uncovering underlying mechanisms. Full article