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Single cell sequencing analysis of respiratory syncytial virus-infected pediatric and adult human nose organoids reveals age differences, proliferative diversity and identifies novel cellular tropism.
Respiratory syncytial virus (RSV) is a leading cause of infant death across the globe. Age is a significant factor that contributes to the severity of infection in young children. RSV primarily infects the ciliated cells of the airway epithelium, induces mucus hypersecretion, and impaired mucociliary clearance. Better understanding of RSV infection at the cellular level is needed for the development of effective therapeutic interventions. To investigate the age difference and comprehensively understand gene signatures associated with RSV infection, we performed single-cell transcriptomic analysis of adult and pediatric human nose organoids (HNOs) infected with RSV. Our analysis revealed a significant difference in transcriptomic signature associated with cellular differentiation and proliferative pathways between the adult and pediatric HNOs. Moreover, we found a distinct innate immune response to RSV infection, with pediatric HNO revealing a lower and dysregulated response. Through sub-clustering analysis of the ciliated cell population, we identified the primary ciliary cell as a novel and prominent susceptible ciliary cell type to RSV infection. Intriguingly and unexpectedly, we found that in the pediatric more than in the adult, HNO RSV infects other novel airway cells, including basal cells, and ionocytes/tuft cells, as demonstrated by increased RSV-gene counts and induction of interferon-related pathways. Together, our study provides the first HNO cell atlas dissecting the heterogeneity of RSV infection in airway epithelium between adult versus pediatric HNOs and identifies novel cell types that are susceptible to RSV infection, which altogether provides a key resource for research on RSV pathogenesis, therapeutics and vaccines.
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Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics.
BACKGROUND: Accumulating evidence indicates that elevated polyamine levels are closely linked to tumor initiation and progression. However, the precise role of polyamine metabolism in hepatocellular carcinoma (HCC) remains poorly understood. METHODS: We conducted differential expression analysis on bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify 65 polyamine metabolism-related genes. By employing unsupervised consensus clustering, AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network analysis (WGCNA), we identified polyamine metabolism-related genes at both the bulk RNA-seq and single-cell RNA-seq (scRNA-seq) levels. Utilizing 101 machine learning algorithms, we constructed a polyamine metabolism-related signature (PMRS) and validated its predictive power across training, testing, and external validation cohorts. Additionally, we developed a prognostic nomogram model by integrating PMRS with clinical variables. To explore immune treatment sensitivity, we assessed tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, mutation frequency, and immune checkpoint genes expression. Immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, RT-qPCR experiments were conducted to validate the expression of key genes. RESULTS: Using 101 machine learning algorithms, we established a polyamine metabolism-related signature comprising 9 genes, which exhibited strong prognostic value for HCC patients. Further analysis revealed significant differences in clinical features, biological functions, mutation profiles, and immune cell infiltration between high-risk and low-risk groups. Notably, TIDE analysis and immune phenotype scoring (IPS) demonstrated distinct immune treatment sensitivities between the two risk groups. RT-qPCR validation confirmed that these 9 genes were highly expressed in normal cells but significantly downregulated in tumor cells. CONCLUSIONS: Our study developed a polyamine metabolism-based prognostic risk signature for HCC, which may provide valuable insights for personalized treatment strategies in HCC patients.
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A single cell atlas of the mouse seminal vesicle.
During mammalian reproduction, sperm are delivered to the female reproductive tract bathed in a complex medium known as seminal fluid, which plays key roles in signaling to the female reproductive tract and in nourishing sperm for their onwards journey. Along with minor contributions from the prostate and the epididymis, the majority of seminal fluid is produced by a somewhat understudied organ known as the seminal vesicle. Here, we report the first single-cell RNA-seq atlas of the mouse seminal vesicle, generated using tissues obtained from 23 mice of varying ages, exposed to a range of dietary challenges. We define the transcriptome of the secretory cells in this tissue, identifying a relatively homogeneous population of the epithelial cells which are responsible for producing the majority of seminal fluid. We also define the immune cell populations-including large populations of macrophages, dendritic cells, T cells, and NKT cells-which have the potential to play roles in producing the various immune mediators present in seminal plasma. Together, our data provide a resource for understanding the composition of an understudied reproductive tissue, with potential implications for paternal control of offspring development and metabolism.
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Transcriptome analysis reveals high tumor heterogeneity with respect to re-activation of stemness and proliferation programs.
Significant alterations in signaling pathways and transcriptional regulatory programs together represent major hallmarks of many cancers. These, among all, include the reactivation of stemness, which is registered by the expression of pathways that are active in the embryonic stem cells (ESCs). Here, we assembled gene sets that reflect the stemness and proliferation signatures and used them to analyze a large panel of RNA-seq data from The Cancer Genome Atlas (TCGA) Consortium in order to specifically assess the expression of stemness-related and proliferation-related genes across a collection of different tumor types. We introduced a metric that captures the collective similarity of the expression profile of a tumor to that of ESCs, which showed that stemness and proliferation signatures vary greatly between different tumor types. We also observed a high degree of intertumoral heterogeneity in the expression of stemness- and proliferation-related genes, which was associated with increased hazard ratios in a fraction of tumors and mirrored by high intratumoral heterogeneity and a remarkable stemness capacity in metastatic lesions across cancer cells in single cell RNA-seq datasets. Taken together, these results indicate that the expression of stemness signatures is highly heterogeneous and cannot be used as a universal determinant of cancer. This calls into question the universal validity of diagnostic tests that are based on stem cell markers.
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A time-resolved multi-omics atlas of transcriptional regulation in response to high-altitude hypoxia across whole-body tissues.
High-altitude hypoxia acclimatization requires whole-body physiological regulation in highland immigrants, but the underlying genetic mechanism has not been clarified. Here we use sheep as an animal model for low-to-high altitude translocation. We generate multi-omics data including whole-genome sequences, time-resolved bulk RNA-Seq, ATAC-Seq and single-cell RNA-Seq from multiple tissues as well as phenotypic data from 20 bio-indicators. We characterize transcriptional changes of all genes in each tissue, and examine multi-tissue temporal dynamics and transcriptional interactions among genes. Particularly, we identify critical functional genes regulating the short response to hypoxia in each tissue (e.g., PARG in the cerebellum and HMOX1 in the colon). We further identify TAD-constrained cis-regulatory elements, which suppress the transcriptional activity of most genes under hypoxia. Phenotypic and transcriptional evidence indicate that antenatal hypoxia could improve hypoxia tolerance in offspring. Furthermore, we provide time-series expression data of candidate genes associated with human mountain sickness (e.g., BMPR2) and high-altitude adaptation (e.g., HIF1A). Our study provides valuable resources and insights for future hypoxia-related studies in mammals.
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Integrating multiple key molecules in uveal melanoma to uncover metastatic and immune microenvironment-related gene signatures.
AIM: To identify metastasis-associated prognostic genes and construct a robust molecular signature for survival prediction in uveal melanoma (UVM) patients. METHODS: Transcriptomic data and clinical information from 80 UVM patients in the Cancer Genome Atlas (TCGA)-UVM cohort and an external Gene Expression Omnibus (GEO) microarray dataset (GSE73652; 8 non-metastatic vs 5 metastatic cases) were analyzed to identify differentially expressed genes (DEGs). Functional enrichment, protein-protein interaction (PPI) network construction, and survival analyses identified seven metastasis- and prognosis-related genes. Their expression was further examined using public single-cell RNA-seq data (GSE139829; 11 tumors). Experimental validation was performed in UVM cell lines (92.1, OMM1, MEL270) and adult retinal pigment epithelial (ARPE-19) cells using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting to confirm transcriptomic trends. A LASSO Cox model was applied to construct a metastasis-related risk Score signature. Tumor immune microenvironment characteristics were evaluated via single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE. Somatic mutation and copy number variation (CNV) profiles were also examined. RESULTS: Seven key genes (UBE2T, KIF20A, DLGAP5, KLC3, TPX2, UBE2C, AURKA) were significantly associated with overall survival and used to construct a metastasis-related riskScore signature, which effectively stratified patients into high- and low-risk groups and served as an independent prognostic factor. qRT-PCR and Western blot results confirmed that the expression levels of selected key genes in UVM cell lines showed significant differences compared to ARPE-19 cells, which were largely consistent with the transcriptomic findings. The high-risk group exhibited reduced immune infiltration and stromal activity. Single-cell analysis revealed these genes were predominantly expressed in a tumor cell cluster characterized by BAP1 loss and high metastatic potential. Mutation and CNV analyses further supported the relevance of these genes to UVM progression. CONCLUSION: This study establishes and validates a seven-gene signature associated with metastasis and prognosis in UVM. The findings provide a framework for understanding molecular determinants of tumor progression and immune microenvironment alterations, and may offer guidance for future mechanistic studies and therapeutic exploration.
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Using a pan-cancer atlas to investigate tumour associated macrophages as regulators of immunotherapy response.
The paradigm for macrophage characterization has evolved from the simple M1/M2 dichotomy to a more complex model that encompasses the broad spectrum of macrophage phenotypic diversity, due to differences in ontogeny and/or local stimuli. We currently lack an in-depth pan-cancer single cell RNA-seq (scRNAseq) atlas of tumour-associated macrophages (TAMs) that fully captures this complexity. In addition, an increased understanding of macrophage diversity could help to explain the variable responses of cancer patients to immunotherapy. Our atlas includes well established macrophage subsets as well as a number of additional ones. We associate macrophage composition with tumour phenotype and show macrophage subsets can vary between primary and metastatic tumours growing in sites like the liver. We also examine macrophage-T cell functional cross talk and identify two subsets of TAMs associated with T cell activation. Analysis of TAM signatures in a large cohort of immune checkpoint inhibitor-treated patients (CPI1000 + ) identify multiple TAM subsets associated with response, including the presence of a subset of TAMs that upregulate collagen-related genes. Finally, we demonstrate the utility of our data as a resource and reference atlas for mapping of novel macrophage datasets using projection. Overall, these advances represent an important step in both macrophage classification and overcoming resistance to immunotherapies in cancer.
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A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain.
BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia. This neurodegenerative disorder is associated with neuronal death and gliosis heavily impacting the cerebral cortex. AD has a substantial but heterogeneous genetic component, presenting both Mendelian and complex genetic architectures. Using bulk RNA-seq from the parietal lobes and deconvolution methods, we previously reported that brains exhibiting different AD genetic architecture exhibit different cellular proportions. Here, we sought to directly investigate AD brain changes in cell proportion and gene expression using single-cell resolution. METHODS: We generated unsorted single-nuclei RNA sequencing data from brain tissue. We leveraged the tissue donated from a carrier of a Mendelian genetic mutation, PSEN1 p.A79V, and two family members who suffer from sporadic AD, but do not carry any autosomal mutations. We evaluated alternative alignment approaches to maximize the titer of reads, genes, and cells with high quality. In addition, we employed distinct clustering strategies to determine the best approach to identify cell clusters that reveal neuronal and glial cell types and avoid artifacts such as sample and batch effects. We propose an approach to cluster cells that reduces biases and enable further analyses. RESULTS: We identified distinct types of neurons, both excitatory and inhibitory, and glial cells, including astrocytes, oligodendrocytes, and microglia, among others. In particular, we identified a reduced proportion of excitatory neurons in the Mendelian mutation carrier, but a similar distribution of inhibitory neurons. Furthermore, we investigated whether single-nuclei RNA-seq from the human brains recapitulate the expression profile of disease-associated microglia (DAM) discovered in mouse models. We also determined that when analyzing human single-nuclei data, it is critical to control for biases introduced by donor-specific expression profiles. CONCLUSION: We propose a collection of best practices to generate a highly detailed molecular cell atlas of highly informative frozen tissue stored in brain banks. Importantly, we have developed a new web application to make this unique single-nuclei molecular atlas publicly available.
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Landscape and the immune patterns of cuproptosis in oral squamous cell carcinoma.
BACKGROUND: Oral squamous cell carcinoma is an increasingly prevalent cancer type characterized by high incidence and mortality rates. Its early detection is challenging, primarily because of the absence of early molecular markers. Cuproptosis is a novel regulatory mechanism of cell death with implications in various cancers. In this study, we aimed to study cuproptosis-related genes in oral squamous cell carcinoma to identify their prognostic value. METHODS: By analyzing genomic, bulk RNA-seq, and single-cell RNA-seq data, we investigated 13 cuproptosis-related genes in The Cancer Genome Atlas-Oral Squamous Cell Carcinoma dataset and Gene Expression Omnibus repository (GSE172577). RESULTS: ATP7A, ATP7B, and DLST were the most frequently mutated genes, with nine of our studied genes associated with overall survival. Single-cell analysis was conducted to identify cuproptosis-related tumor cells in oral squamous cell carcinoma, which revealed two distinct patterns based on the expression of cuproptosis-related genes. These patterns exhibit differences in genetic alterations and tumor immune microenvironment. Finally, we developed a cuproptosis index using a random forest algorithm based on cuproptosis pattern-related genes in which higher levels were linked to poorer prognosis. CONCLUSION: Our findings provide valuable insights into the mechanisms underlying oral squamous cell carcinoma-associated cuproptosis.
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Impact of Single-Cell RNA Reference Selection for the Deconvolution of Breast Cancer Spatial Transcriptomics Datasets.
Spot-based spatial transcriptomics (ST) allows for unbiased gene expression analysis within tissue architecture, overcoming the limitations of single-cell RNA sequencing (scRNA-seq) by preserving spatial context. However, the high spatial resolution in ST leads to cellular heterogeneity within spots, requiring computational deconvolution to infer cellular compositions. While scRNA-seq serves as a key reference for deconvolution, the impact of reference composition on its accuracy is still unclear. In this study, we systematically evaluate the impact of reference selection for cellular deconvolution and provide helpful guidelines for researchers. Pseudospots mimicking 55 μm Visium spots were generated from spatial transcriptomics data to evaluate global and cell type-specific deconvolution in primary (Xenium) and metastatic (MERFISH) breast cancer samples. Focusing on state-of-the-art deconvolution tools Cell2location and RCTD, we assess the influence of varying reference sizes, cell type distributions, and reference-ST pairings, as well as the usage of large breast cancer and cross-cancer atlases. Our findings demonstrate that even small references can yield accurate deconvolution, with RCTD and Cell2location exhibiting similar results. Spatial domains of prominent cell types like cancer and stromal cells were detected, although their contributions were systematically under- or over-estimated. Additionally, Reference-ST sample matching enhances accuracy compared to the usage of cross-patient references, while large diverse breast cancer atlases also provided reliable results. This study concluded that reference selection had modest effects on RCTD and Cell2location, but matching samples and large atlases stabilize outcomes. Nonetheless, performance varies between cell types and annotation levels, and deconvolution results should always be interpreted with caution.