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Single-cell eQTL mapping reveals cell-type-specific genetic regulation in lung cancer.
Genome-wide association studies (GWASs) have identified over 50 lung cancer risk loci; however, the precise cellular context of these genetic mechanisms remains unclear due to limitations in bulk tissue expression quantitative trait locus (eQTL) analyses. Here, we present the largest single-cell eQTL (sc-eQTL) atlas of human lung tissue to date, profiling 222 donors using multiplexed single-cell RNA sequencing (scRNA-seq). We identified 4,341 independent eQTLs across 17 cell types, with over 60% of sc-eQTLs and 51% of eGenes being cell-type specific, and fewer than 52% were detectable in paired bulk datasets. Integration with GWASs for non-small cell lung cancer highlighted epithelial and immune cells as key contributors to genetic susceptibility, identifying 28 candidate genes within known risk loci and 24 in novel regions. Notably, 47% of established non-small cell lung cancer (NSCLC) susceptibility loci exhibited cell-type-specific pleiotropic genetic regulation. This study provides a valuable resource of lung sc-eQTLs and illuminates how genetic variation modulates gene expression in a cell-type-specific fashion, contributing to lung cancer susceptibility.
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Integrative analysis of bulk and single-cell RNA sequencing reveals PHLDA3 as a key regulator of Hypoxic TAMs and prognostic biomarker in HNSC.
BACKGROUND: Head and neck squamous cell carcinoma (HNSC) is a highly aggressive malignancy characterized by a complex tumor microenvironment (TME). Despite advances in immunotherapy, the immunosuppressive microenvironment driven by metabolic reprogramming remains a critical factor contributing to treatment failure. The prognostic value of PHLDA3, a target gene of the p53 family, in HNSC and its specific mechanisms in shaping TME heterogeneity remain ill-defined. METHODS: This study integrated large-scale transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, alongside single-cell RNA sequencing (scRNA-seq) data. A prognostic nomogram incorporating PHLDA3 and clinical characteristics was constructed using multivariate Cox regression analysis. Biological functions were assessed utilizing Weighted Gene Co-expression Network Analysis (WGCNA), Gene Set Variation Analysis (GSVA), and the drug sensitivity prediction algorithm, oncoPredict. At the single-cell resolution, algorithms including Seurat, CellChat, scMetabolism, and Monocle 2 were employed to comprehensively characterize cell type annotation, intercellular communication, metabolic activity scoring, and pseudotime differentiation trajectories. RESULTS: PHLDA3 was significantly upregulated in HNSC tissues and strongly correlated with poor overall survival (OS). The PHLDA3-based nomogram demonstrated superior calibration and discrimination capabilities in both the training and validation cohorts. Genomic analysis revealed that high PHLDA3 expression was associated with a lower Tumor Mutation Burden (TMB) but an increased sensitivity to the PLK1 inhibitor, BI-2536. Single-cell analysis further unveiled that PHLDA3 expression was not ubiquitous; rather, it was specifically enriched in tumor-associated macrophage (TAM) subsets characterized by hypoxic and lipid metabolic signatures (Hypoxic TAMs and Lipid TAMs). These subpopulations exhibited transcriptomic signatures indicative of enhanced glycolysis and orchestrated immunosuppressive communication networks within the TME, predominantly via the SPP1-CD44 and MIF-CD74 signaling axes. Ultimately, through its specific enrichment in these metabolically reprogrammed TAM subsets, PHLDA3 intimately participates in remodeling an immunosuppressive TME, thereby facilitating tumor progression and immune evasion. CONCLUSIONS: PHLDA3 serves as a robust prognostic biomarker for HNSC. Its elevated expression is indicative not only of a low TMB status but also of a critical association with metabolically reprogrammed SPP1+ TAM subpopulations. By potentially sustaining the survival and function of these specific TAMs under hypoxic conditions, PHLDA3 drives the formation of an immunosuppressive microenvironment, highlighting a promising therapeutic target for combined metabolic-immune strategies in HNSC.
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RNA Sequencing of Whole Blood Defines the Signature of High Intensity Exercise at Altitude in Elite Speed Skaters.
Although high altitude training has been increasingly popular among endurance athletes, the molecular and cellular bases of this adaptation remain poorly understood. We aimed to define the underlying physiological changes and screen for potential biomarkers of adaptation using transcriptional profiling of whole blood. Seven elite female speed skaters were profiled on the 18th day of high-altitude adaptation. Whole blood RNA-seq before and after an intense 1 h skating bout was used to measure gene expression changes associated with exercise. In order to identify the genes specifically regulated at high altitudes, we have leveraged the data from eight previously published microarray datasets studying blood expression changes after exercise at sea level. Using cell type-specific signatures, we were able to deconvolute changes of cell type abundance from individual gene expression changes. Among these were PHOSPHO1, with a known role in erythropoiesis, and MARC1 with a role in endogenic NO metabolism. We find that platelet and erythrocyte counts uniquely respond to altitude exercise, while changes in neutrophils represent a more generic marker of intense exercise. Publicly available data from both single cell atlases and exercise-related blood profiling dramatically increases the value of whole blood RNA-seq for the dynamic evaluation of physiological changes in an athlete's body.
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Comprehensive analysis to identify the influences of SARS-CoV-2 infections to inflammatory bowel disease.
BACKGROUND: Coronavirus disease 2019 (COVID-19) and inflammatory bowel disease (IBD) are both caused by a disordered immune response and have direct and profound impacts on health care services. In this study, we implemented transcriptomic and single-cell analysis to detect common molecular and cellular intersections between COVID-19 and IBD that help understand the linkage of COVID-19 to the IBD patients. METHODS: Four RNA-sequencing datasets (GSE147507, GSE126124, GSE9686 and GSE36807) from Gene Expression Omnibus (GEO) database are extracted to detect mutual differentially expressed genes (DEGs) for IBD patients with the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to find shared pathways, candidate drugs, hub genes and regulatory networks. Two single-cell RNA sequencing (scRNA-eq) datasets (GSE150728, PRJCA003980) are used to analyze the immune characteristics of hub genes and the proportion of immune cell types, so as to find common immune responses between COVID-19 and IBD. RESULTS: A total of 121 common DEGs were identified among four RNA-seq datasets, and were all involved in the functional enrichment analysis related to inflammation and immune response. Transcription factors-DEGs interactions, miRNAs-DEGs coregulatory networks, and protein-drug interactions were identified based on these datasets. Protein-protein interactions (PPIs) was built and 59 hub genes were identified. Moreover, scRNA-seq of peripheral blood monocyte cells (PBMCs) from COVID-19 patients revealed a significant increase in the proportion of CD14+ monocytes, in which 38 of 59 hub genes were highly enriched. These genes, encoding inflammatory cytokines, were also highly expressed in inflammatory macrophages (IMacrophage) of intestinal tissues of IBD patients. CONCLUSIONS: We conclude that COVID-19 may promote the progression of IBD through cytokine storms. The candidate drugs and DEGs-regulated networks may suggest effective therapeutic methods for both COVID-19 and IBD.
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A single-cell analysis of thymopoiesis and thymic iNKT cell development in pigs.
Many aspects of the porcine immune system remain poorly characterized, which poses a barrier to improving swine health and utilizing pigs as preclinical models. Here, we employ single-cell RNA sequencing (scRNA-seq) to create a cell atlas of the early-adolescent pig thymus. Our data show conserved features as well as species-specific differences in cell states and cell types compared with human thymocytes. We also describe several unconventional T cell types with gene expression profiles associated with innate effector functions. This includes a cell census of more than 11,000 differentiating invariant natural killer T (iNKT) cells, which reveals that the functional diversity of pig iNKT cells differs substantially from the iNKT0/1/2/17 subset differentiation paradigm established in mice. Our data characterize key differentiation events in porcine thymopoiesis and iNKT cell maturation and provide important insights into pig T cell development.
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Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain.
The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.
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Prednisone Reprograms the Transcriptional Immune Cell Landscape in CNS Autoimmune Disease.
Glucocorticoids (GCs) are widely used immunosuppressive drugs for autoimmune diseases, although considerable gaps exist between current knowledge of the mechanisms of GCs and their conclusive immune-regulatory effects. Here we generated a single-cell transcriptional immune cell atlas based on prednisone-treated or untreated experimental autoimmune uveitis (EAU) mice. Immune cells were globally activated in EAU, and prednisone partially reversed this effect in terms of cell composition, gene expression, transcription factor regulation, and cell-cell communication. Prednisone exerted considerable rescue effects on T and B cells and increased the proportion of neutrophils. Besides commonly regulated transcriptional factors (Fosb, Jun, Jund), several genes were only regulated in certain cell types (e.g. Cxcr4 and Bhlhe40 in T cells), suggesting cell-type-dependent immunosuppressive properties of GC. These findings provide new insights into the mechanisms behind the properties and cell-specific effects of GCs and can potentially benefit immunoregulatory therapy development.
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Integrated single-cell and bulk RNA-seq analysis reveals an NK-cell-related immune landscape and a prognostic signature for hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by high heterogeneity and poor prognosis. Despite the importance of natural killer (NK) cells in innate immunity, their role in the HCC microenvironment remains unclear. This study aims to develop an NK-cell-related signature to optimize prognostic prediction and personalized therapy. METHODS: Single-cell RNA sequencing (scRNA-seq) identified candidate genes. Differential expression and univariate Cox analyses were performed on an integrated cohort comprising The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) and the Gene Expression Omnibus (GEO) dataset GSE76427. To ensure data consistency, technical batch effects were eliminated using the ComBat algorithm. The integrated cohort was randomly assigned to training and testing sets (1:1). A prognostic model was constructed via Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression (seed = 2) and validated in an independent ICGC-LIRI-JP cohort. Survival differences and model accuracy were evaluated using Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm and the Tumor Immune Dysfunction and Exclusion (TIDE) database were applied to characterize immune cell infiltration and predict immunotherapy response, respectively. RESULTS: From 790 initial candidates, 18 prognosis-related DEGs associated with NK-cell-related cellular programs were identified, which classified HCC into two distinct subtypes. A three-gene prognostic model (AP1S3, RPL23, and HM13) was established, showing high predictive accuracy in both the internal testing set and an independent external ICGC-LIRI-JP cohort. High-risk patients exhibited significantly poorer survival and specific clinical profiles. Notably, the risk score served as an independent prognostic factor, and a combined nomogram offered superior predictive power. Further characterization of the tumor microenvironment revealed that a high-risk score correlated with a remodeled stroma and a distinct potential for immunotherapy response. Additionally, high-risk patients showed increased sensitivity to targeted agents, such as sorafenib. Finally, the expression patterns of these signature genes were validated via RT-qPCR and immunohistochemistry (IHC), further supporting their role as key components of the identified prognostic landscape. CONCLUSION: The established risk score model, reflecting an NK-cell-related immune landscape, effectively predicts HCC prognosis and provides an integrated molecular landscape for optimizing personalized interventions.
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Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis.
BACKGROUND: Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively. MATERIALS AND METHODS: LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay. RESULTS: A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues. CONCLUSION: Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.
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Mapping single-cell atlases throughout Metazoa unravels cell type evolution.
Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.