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Transcriptomic Mapping of Human Parotid Gland at Single-Cell Resolution.
As the largest salivary gland in oral cavity, the parotid gland plays an important role in initial digesting and lubricating food. The abnormal secretory function of the parotid gland can lead to dental caries and oral mucosal inflammation. In recent years, single-cell RNA sequencing (scRNA-seq) has been used to explore the heterogeneity and diversity of cells in various organs and tissues. However, the transcription profile of the human parotid gland at single-cell resolution has not been reported yet. In this study, we constructed the cell atlas of human parotid gland using the 10× Genomics platform. Characteristic gene analysis identified the biological functions of serous acinar cell populations in secreting digestive enzymes and antibacterial proteins. We revealed the specificity and similarity of the parotid gland compared to other digestive glands through comparative analyses of other published scRNA-seq data sets. We also identified the cell-specific expression of hub genes for Sjögren syndrome in the human parotid gland by integrating the results of genome-wide association studies and bulk RNA-seq, which highlighted the importance of immune cell dysfunction in parotid Sjögren syndrome pathogenesis.
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Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing.
BACKGROUND: Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. METHODS: We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. RESULTS: We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. CONCLUSIONS: By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.
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JOINTLY: interpretable joint clustering of single-cell transcriptomes.
Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of cell types at homeostasis, during development and in disease. However, this is a challenging task, as biological effects can be masked by technical variation. Here, we present JOINTLY, an algorithm enabling joint clustering of sxRNA-seq datasets across batches. JOINTLY performs on par or better than state-of-the-art batch integration methods in clustering tasks and outperforms other intrinsically interpretable methods. We demonstrate that JOINTLY is robust against over-correction while retaining subtle cell state differences between biological conditions and highlight how the interpretation of JOINTLY can be used to annotate cell types and identify active signalling programs across cell types and pseudo-time. Finally, we use JOINTLY to construct a reference atlas of white adipose tissue (WATLAS), an expandable and comprehensive community resource, in which we describe four adipocyte subpopulations and map compositional changes in obesity and between depots.
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Unveiling osmoregulation and immunological adaptations in Eleutheronema tetradactylum gills through high-throughput single-cell transcriptome sequencing.
The fourfinger threadfin fish (Eleutheronema tetradactylum) is an economically significant species renowned for its ability to adapt to varying salinity environments, with gills serving as their primary organs for osmoregulation and immune defense. Previous studies focused on tissue and morphological levels, whereas ignored the cellular heterogeneity and the crucial gene information related to core cell subsets within E. tetradactylum gills. In this study, we utilized high-throughput single-cell RNA sequencing (scRNA-seq) to analyze the gills of E. tetradactylum, characterizing 16 distinct cell types and identifying unique gene markers and enriched functions associated within each cell type. Additionally, we subdivided ionocyte cells into four distinct subpopulations for the first time in E. tetradactylum gills. By employing weighted gene co-expression network analysis (WGCNA), we further investigated the cellular heterogeneity and specific response mechanisms to salinity fluctuant. Our findings revealed the intricate osmoregulation and immune functions of gill cells, highlighting their crucial roles in maintaining homeostasis and adapting to fluctuating salinity levels. This comprehensive cell-type atlas provides valuable insights into the species adaptive strategies, contributing to the conservation and management of this commercially significant fish as well as other euryhaline species.
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Integrative single-cell and bulk transcriptomics identify an ALK-associated three-gene signature predicting neuroblastoma outcomes.
BACKGROUND: Activating anaplastic lymphoma kinase (ALK) mutations define a clinically relevant subset of neuroblastoma (NB), yet ALK-linked transcriptional programs with robust prognostic value remain insufficiently characterized. This study aimed to identify ALK-associated transcriptional features across single-cell and bulk datasets and to develop a reproducible prognostic signature for NB. METHODS: We integrated a public single-cell atlas of high-risk NB from CELL×GENE with bulk RNA sequencing (RNA-seq) cohorts and NB cell-line datasets. Differential expression between ALK-mutant and ALK-wild-type samples was analyzed in (I) pseudobulk malignant cells from the single-cell dataset, (II) the Kids First (Maris) cohort, and (III) GSE89413 NB cell lines, followed by direction-consistent intersection. A prognostic model was developed in GSE62564 using Cox regression and evaluated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) curves. External validation was performed in GSE181559 and TARGET-NBL. Biological significance was further explored using preranked gene set enrichment analysis (GSEA). RESULTS: Three concordant ALK-associated genes, DLK1, RTL1, and ISLR2, were identified across all three analytical contexts and were consistently upregulated in ALK-mutant samples. A three-gene risk score significantly stratified overall survival and event-free survival in GSE62564 and remained predictive in GSE181559 and TARGET-NBL without model re-fitting. High-risk tumors were enriched for translational and ribosome biogenesis pathways, whereas low-risk tumors were enriched for adaptive immune activation programs. CONCLUSIONS: Cross-context transcriptomic integration identified a minimal ALK-associated three-gene signature that reproducibly predicts NB outcomes and reflects distinct underlying biological states, supporting its potential value for risk stratification and hypothesis-driven therapeutic development.
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Comparative Single-Cell Transcriptomic Landscape Reveals the Regulatory Mechanisms of Lactation during Selective Breeding in Asian Water Buffalo.
Characterizing the cell type-specific transcriptome is crucial for understanding the cellular and molecular regulatory mechanisms underlying adaptive evolution and complex phenotypes. Here, single-cell/nucleus RNA sequencing (sc/snRNA-seq) is used to construct a cell transcriptomic atlas of 397,011 cells, representing 57 cell types, from 12 tissues in river and swamp buffalo, which exhibit significant divergence in milk production. Differential expression analyses identify metabolic and secretory tissues (i.e., liver, mammary gland, and pituitary) and cell types (e.g., hepatocytes, luminal cells, somatotropes, and lactotropes) that mediate the divergence of milk production. Lactotrope-specific downregulation of TRHDE in river buffalo is associated with high milk production. Integrative analyses of sc/snRNA-seq data with genomic data in buffalo and cattle reveal key cell types (e.g., luminal cells and excitatory neurons) and genes (e.g., RPL13 and LALBA) associated with milk production. Ultimately, the Buffalo Cell Atlas (http://bovomicshub.com) will serve as a valuable resource for advancing buffalo genetics and genomics research, enabling cross-species comparative transcriptome studies and providing deeper insights into the regulation of milk synthesis and secretion.
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Web-based gene expression analysis-paving the way to decode healthy and diseased ocular tissue.
BACKGROUND: Gene expression analysis using RNA sequencing has helped to improve the understanding of many diseases. Databases, such as the Gene Expression Omnibus database of the National Center for Biotechnology Information provide RNA sequencing raw data from various diseased tissue types but their analysis requires advanced bioinformatics skills. Therefore, specific ocular databases provide the transcriptional profiles of different ocular tissues and in addition enable intuitive web-based data analysis. OBJECTIVE: The aim of this narrative review is to provide an overview of ocular transcriptome databases and to compare them with the Human Eye Transcriptome Atlas newly established in Freiburg. METHODS: PubMed literature search. RESULTS: A total of nine ocular transcriptome databases focusing on different aspects were identified. The iSyTE and Express platforms specialize in gene expression during lens and retinal development in mice, whereas retina.tigem.it, Eye in a Disk, and Spectacle focus on selected ocular tissues such as the retina. Spectacle, UCSC Cell Browser and Single Cell Portal allow intuitive exploration of single cell RNA sequencing data derived from retinal, choroid, cornea, iris, trabecular meshwork and sclera specimens. The microarray profiles of a variety of healthy ocular tissues are included in the Ocular Tissue Database. The Human Eye Transcriptome Atlas provides the largest collection of different ocular tissue types, contains the highest number of ocular diseases and is characterized by a high level of quality achieved by methodological consistency. CONCLUSION: Ocular transcriptome databases provide comprehensive and intuitive insights into the transcriptional profiles of a variety of healthy and diseased ocular tissues. Thus, they improve our understanding of the underlying molecular mediators, support hypothesis generation and help in the search for new diagnostic and therapeutic targets for various ocular diseases.
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Integrative analysis deciphers the heterogeneity of cancer-associated fibroblast and implications on clinical outcomes in ovarian cancers.
Accumulating evidence has recognized that cancer-associated fibroblasts (CAFs) are major players in the desmoplastic stroma of ovarian cancer, modulating tumor progression and therapeutic response. However, it is unclear regarding the signatures of CAFs could be utilized to predict the clinical outcomes of ovarian cancer patients. To fill in this gap, we explored the intratumoral compartment of ovarian cancer by analyzing the single-cell RNA-sequencing (scRNA-seq) datasets of ovarian carcinoma samples, and identified two distinct CAFs (tumor-promoting CAF_c1 subtype and myofibroblasts-like CAF_c2 subtype). The clinical significance of CAF subtypes was further validated in The Cancer Genomics Atlas (TCGA) database and other independent immunotherapy response datasets, and the results revealed that the patients with a higher expression of CAF_c1 signatures had a worse prognosis and showed a tendency of resistance to immunotherapy. This work uncovered the signatures of the CAF_c1 subtype that could serve as a novel prognostic indicator and predictive marker for immunotherapy.
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Single-cell expression and immune infiltration analysis of polyamine metabolism in breast cancer.
Breast cancer is one of the most threatening women health diseases worldwide and its molecular heterogeneity offers a range of response to therapy. The role of polyamine metabolism is receiving increasing attention. Polyamine metabolism not only plays an important role in the occurrence and development of breast cancer, but also interacts with tumor immune microenvironment. In this work, we applied single-cell RNA-sequencing (scRNA-seq) and systems immunological approaches to interrogate immune cell infiltration gene-to-gene co-expressions in the bulk tumor transcriptomes of breast cancer. We acquired breast cancer sample data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), evaluated the infiltration status of 22 immune cell types using CIBERSORTx tool, respectively. By leveraging the Retrospective Breast sample of various technologies including gene expression and methylation, we identified 46 breast cancer proliferation-associated co-expression modules using weighted gene coexpression network analysis (WGCNA) approach along with machine learning models which in turn delineated single cell level expressions features that these selected module possessed. We observed substantial cellular heterogeneity in the breast cancer microenvironment, where lineage-specific gene expression patterns were highly associated with tumor progression. Moreover, we also identified the gene modules correlated with immune cell infiltration level that could function as regulators in response to tumors for immune therapy. Moreover, risk scores were correlated with immune cell function in different patient groups defined by high- and low-risk. The findings of this study shed a new light upon molecular classification prognostic assessment and personalized treatment in breast cancer.
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The Human Microglia Atlas (HuMicA) unravels changes in disease-associated microglia subsets across neurodegenerative conditions.
Dysregulated microglia activation, leading to neuroinflammation, is crucial in neurodegenerative disease development and progression. We constructed an atlas of human brain immune cells by integrating nineteen single-nucleus RNA-seq and single-cell RNA-seq datasets from multiple neurodegenerative conditions, comprising 241 samples from patients with Alzheimer's disease, autism spectrum disorder, epilepsy, multiple sclerosis, Lewy body diseases, COVID-19, and healthy controls. The integrated Human Microglia Atlas (HuMicA) included 90,716 nuclei/cells and revealed nine populations distributed across all conditions. We identified four subtypes of disease-associated microglia and disease-inflammatory macrophages, recently described in mice, and shown here to be prevalent in human tissue. The high versatility of microglia is evident through changes in subset distribution across various pathologies, suggesting their contribution in shaping pathological phenotypes. A GPNMB-high subpopulation was expanded in AD and MS. In situ hybridization corroborated this increase in AD, opening the question on the relevance of this population in other pathologies.