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A single-cell transposable element atlas of human cell identity.
Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex biological systems. However, most sequencing studies overlook the contribution of transposable element (TE) expression to the transcriptome. The quantification of locus-specific TE expression in scRNA-seq experiments is challenging due to their repetitive sequence content and poorly characterized annotations. Here, we developed a computational tool for single-cell transposable element locus-level analysis of scRNA sequencing (Stellarscope) that reassigns multimapped reads to specific genomic loci using an expectation maximization algorithm. Using Stellarscope, we built an atlas of TE expression in human PBMCs. We found that locus-specific TEs delineate cell types and define cell subsets not identified by standard mRNA expression profiles. Altogether, this study provides comprehensive insights into the influence of TEs in human biology at the single-cell level.
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Cellular and molecular characterization of peripheral glia in the lung and other organs.
Peripheral glia are important regulators of diverse physiologic functions yet their molecular distinctions and locations in almost all visceral organs are not well-understood. We performed a systematic analysis of peripheral glia, focusing on the lung and leveraging single cell RNA sequencing (scRNA-seq) analysis to characterize their cellular and molecular features. Using in vivo lineage studies, we characterized the anatomic, cellular, and molecular features of the Sox10+ glial lineage of the mouse lung. Using high-resolution imaging, we quantified the distribution and cellular morphologies of myelinating, non-myelinating, satellite, and terminal glial cells with their intricate extensions along peripheral nerves, including terminals at specialized neurosensory structures within the lung. Spatial analysis of selectively expressed myelinating (periaxin/Prx, claudin 19/Cldn) or non-myelinating (sodium channel/Scn7a) glial cell genes identified by scRNA-seq analysis revealed molecularly distinct populations surrounding myelinated nerve fibers in the lung. To extend this analysis to primates and other organs, we extracted rare peripheral glial cells in whole organism scRNA-seq atlases of mouse lemur and human. Our cross-species data analysis and integration of scRNA-seq data of ~700 peripheral glial cells from mouse, mouse lemur, and human glial cells identified conserved gene expression of molecularly distinct peripheral glial cell populations. This foundational knowledge facilitates subsequent functional studies targeting molecularly distinct subsets of peripheral glia and integrating them into organ-specific disorders of autonomic dysregulation. In addition, our cross-species analysis identifying conserved gene expression patterns and glial networks in extrapulmonary organs provides a valuable resource for studying the functional role of peripheral glia in multiorgan human diseases.
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By integrating single-cell RNA sequencing and bulk RNA sequencing, plasma cells signature and tertiary lymphoid structures were verified to contribute to outcome in lung adenocarcinoma.
BACKGROUND: Tertiary lymphoid structures (TLS), consisting of T cell zones, B cell follicles, and germinal centers (GCs), are ectopic lymphoid tissue that form within non-lymphoid tissue. It has recently become a focus of attention. The TLS serve as an effective site for generating an anti-tumor inflammatory response by infiltrating immune cells, especially plasma cells. Thus, we aimed to explore the role of both TLS and plasma cells in influencing the prognosis of lung adenocarcinoma (LUAD). METHODS: Single-cell RNA sequencing (scRNA-seq) data were obtained from the Gene Expression Omnibus (GEO) database, and bulk RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Seurat R package was used to process scRNA-seq data and identify clusters by the marker genes with Kaplan-Meier (KM) curves plotted to predict the prognosis. Finally, hematoxylin and eosin (H&E) staining and multiplex immunofluorescence analysis were conducted to corroborate our suspicions. RESULTS: Seven clusters were identified in LUAD based on scRNA-seq data, with the number of B cells differing significantly between early and advanced cohorts. The plasma cells were also increased in advanced lung cancer (LC) and the number of TLS was significantly related to tumor stage. Then, via KM method, we confirmed that both plasma cells and TLS were associated with patient outcomes. Finally, H&E staining and multiplex immunofluorescence analysis verified the correlation between the two. CONCLUSIONS: Plasma cells and TLS can effectively predict the prognosis of LUAD. In the tumor microenvironment (TME) of advanced tumors, plasma cells might be in a state of functional exhaustion. Comprehensive characterization of TLS and corresponding B‑cell pathways may help to activate the function of plasma cells and provide new strategies for cancer treatment.
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Single-Cell RNA Sequencing Revealed That the Enrichment of TPI1+ Malignant Hepatocytes Was Linked to HCC Metastasis and Immunosuppressive Microenvironment.
BACKGROUND: Tumor metastasis is the leading cause of high mortality in hepatocellular carcinoma (HCC). The metastasis-related HCC microenvironment is characterized by high heterogeneity. Single-cell RNA sequencing (scRNA-seq) may aid in determining specific cell clusters involved in regulating the immune microenvironment of HCC. METHODS: The scRNA-seq data of 10 HCC samples were collected from the Gene Expression Omnibus (GEO) database GSE124395. Correlations between key gene expression and clinicopathological data were determined using public databases. HCC tissues and matched tumor-adjacent and normal tissue samples were obtained by surgical resection at Sichuan Cancer Hospital. Immune cell infiltration analysis was performed and verified by immunohistochemistry and immunofluorescent staining. RESULTS: Nine malignant hepatocyte clusters with different marker genes and biological functions were identified. C3_Hepatocyte-SERF2 and C6_Hepatocyte-IL13RA2 were mainly involved in the regulation of the immune microenvironment, which was also a significant pathway in regulating HCC metastasis. Key genes in malignant hepatocyte clusters that associated with HCC metastasis were further screened by LASSO regression analysis. TPI1, a key gene in C6_Hepatocyte-IL13RA2 and HCC metastasis, could participate in regulating the HCC immune microenvironment in The Cancer Genome Atlas (TCGA) and Tumor Immune Estimation Resource (TIMER) databases. Moreover, immunohistochemistry analysis demonstrated that TPI1 expression was positively correlated with HCC metastasis and poor prognosis, while negatively correlated with CD8+ T cell infiltration. The negative correlation between TPI1 expression and CD8+ T cell infiltration was further confirmed by immunofluorescence staining. CONCLUSION: In summary, a cluster of TPI1+ malignant hepatocytes was associated with the suppression of CD8+ T cell infiltration and HCC metastasis, providing novel insights into potential biomarkers for immunotherapy in HCC.
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Deciphering the role of NETosis-related signatures in the prognosis and immunotherapy of soft-tissue sarcoma using machine learning.
Background: Soft-tissue sarcomas (STSs) are a rare type of cancer, accounting for about 1% of all adult cancers. Treatments for STSs can be difficult to implement because of their diverse histological and molecular features, which lead to variations in tumor behavior and response to therapy. Despite the growing importance of NETosis in cancer diagnosis and treatment, researches on its role in STSs remain limited compared to other cancer types. Methods: The study thoroughly investigated NETosis-related genes (NRGs) in STSs using large cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine Recursive Feature Elimination (SVM-RFE) were employed for screening NRGs. Utilizing single-cell RNA-seq (scRNA-seq) dataset, we elucidated the expression profiles of NRGs within distinct cellular subpopulations. Several NRGs were validated by quantitative PCR (qPCR) and our proprietary sequencing data. To ascertain the impact of NRGs on the sarcoma phenotype, we conducted a series of in vitro experimental investigations. Employing unsupervised consensus clustering analysis, we established the NETosis clusters and respective NETosis subtypes. By analyzing DEGs between NETosis clusters, an NETosis scoring system was developed. Results: By comparing the outcomes obtained from LASSO regression analysis and SVM-RFE, 17 common NRGs were identified. The expression levels of the majority of NRGs exhibited notable dissimilarities between STS and normal tissues. The correlation with immune cell infiltration were demonstrated by the network comprising 17 NRGs. Patients within various NETosis clusters and subtypes exhibited different clinical and biological features. The prognostic and immune cell infiltration predictive capabilities of the scoring system were deemed efficient. Furthermore, the scoring system demonstrated potential for predicting immunotherapy response. Conclusion: The current study presents a systematic analysis of NETosis-related gene patterns in STS. The results of our study highlight the critical role NRGs play in tumor biology and the potential for personalized therapeutic approaches through the application of the NETosis score model in STS patients.
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EIF2Ss, a Novel c-Myc-Correlated Gene Family, is Associated with Poor Prognosis and Immune Infiltration in Pancreatic Adenocarcinoma.
BACKGROUND: Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor in urgent need of novel diagnostics, prognostic markers, and treatments. Eukaryotic translation initiation factor 2 subunits (EIF2Ss), comprising Eukaryotic translation initiation factor 2 subunit alpha (EIF2S1), Eukaryotic translation initiation factor 2 subunit beta (EIF2S2), and Eukaryotic translation initiation factor 2 subunit gamma (EIF2S3), is a family of eukaryotic initiation factors that participate in early protein synthesis and are crucial for tumor initiation and progression. However, the role of EIF2Ss in PAAD has yet to be reported. The aim of this study was therefore to analyze EIF2Ss in relation to the diagnosis, prognosis, and treatment of PAAD. METHODS: The cancer genome atlas (TCGA) database was used to investigate gene expression and patient survival. Gene alterations, immune cell infiltration, and immune checkpoints in PAAD were also evaluated. Univariate and multivariate analysis, nomograms, calibration curves, and Decision Curve Analysis (DCA) diagrams were used to develop and evaluate a prediction model for patient outcome. Single-cell RNA-seq (scRNA) analysis, functional enrichment, co-IP assay, mass spectrometry, and western blot were used to study the relationship between EIF2Ss and c-myc in PAAD. RESULTS: EIF2Ss are over-expressed in PAAD tissue and are associated with poor prognosis. The frequency of EIF2S1, EIF2S2, and EIF2S3 gene alteration in PAAD was 0.2%, 0.4%, and 0.2%, respectively. High EIF2Ss expression was associated with Th2 cell infiltration, whereas low expression was associated with pDC infiltration. Moreover, EIF2Ss expression was positively correlated with the expression of the NT5E, ULBP1, PVR, CD44, IL10RB, and CD276 checkpoints. A prediction model developed using EIF2Ss and important clinicopathologic features showed good predictive value for the overall survival of PAAD patients. ScRNA-Seq data showed that EIF2Ss was associated with enrichment for endothelial cells, fibroblasts, malignant cells, and ductal cells. EIF2Ss expression was also correlated with adipogenesis, interferon-alpha response, epithelial-mesenchymal transition, myc targets, G2M checkpoint, oxidative phosphorylation, and hypoxia. Functional enrichment analysis of EIF2Ss showed a close correlation with the myc pathway, and interactions between EIF2Ss and c-myc were confirmed by co-IP assay and mass spectrometry. Importantly, knockdown of c-myc decreased the expression of EIF2S1, EIF2S2, and EIF2S3 in PAAD cells. CONCLUSIONS: EIF2Ss were found to have significant clinical implications for the prognosis and treatment of PAAD. Inhibition of c-myc caused the downregulation of EIF2S1, EIF2S2, and EIF2S3 expression.
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Single cell RNA-sequencing identifies the effect of Normothermic ex vivo liver perfusion on liver-resident T cells.
BACKGROUND: Normothermic ex vivo liver perfusion (NEVLP) is an exciting strategy to preserve livers prior to transplant, however, the effects of NEVLP on the phenotype of tissue-resident immune cells is largely unknown. The presence of tissue-resident memory T cells (TRM) in the liver may protect against acute rejection and decrease allograft dysfunction. Therefore, we investigated the effects of NEVLP on liver TRMs and assessed the ability of anti-inflammatory cytokines to reduce TRM activation during NEVLP. METHODS: Rat livers underwent NEVLP with or without the addition of IL-10 and TGF-β. Naïve and cold storage livers served as controls. Following preservation, TRM T cell gene expression profiles were assessed through single cell RNA sequencing (scRNA-seq). Differential gene expression analysis was performed with Wilcoxon rank sum test to identify differentially expressed genes (DEGs) associated with a specific treatment group. Using the online Database for Annotation, Visualization and Integrated Discovery (DAVID), gene set enrichment was then conducted with Fisher's exact test on DEGs to highlight differentially regulated pathways and functional terms associated with treatment groups. RESULTS: Through scRNA-seq analysis, an atlas of liver-resident memory T cell subsets was created for all livers. TRM T cells could be identified in all livers, and through scRNA-seq, DEG was identified with Wilcoxon rank sum test at FDR < 0.05. Based on the gene set enrichment analysis of DEGs using Fisher's exact test, NEVLP is associated with downregulation of multiple gene enrichment pathways associated with surface proteins. Furthermore, NEVLP with anti-inflammatory cytokines was associated with down regulation of 52 genes in TRM T cells when compared to NEVLP alone (FDR <0.05), most of which are pro-inflammatory. CONCLUSION: This is the first study to create an atlas of liver TRM T cells in the rat liver undergoing NEVLP and demonstrate the effects of NEVLP on liver TRM T cells at the single cell gene expression level.
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Integrating single-cell regulatory atlas and multi-omics data for differential treatment response and multimodal predictive modeling in CDK 4/6 inhibitor-treated breast cancer.
INTRODUCTION: CDK4/6 inhibitors are cornerstone therapies for advanced HR+/HER2- breast cancer, yet treatment response heterogeneity remains a major clinical challenge. This study integrates single-cell regulatory landscapes with multi-omics data to decode resistance mechanisms and develop predictive biomarkers for CDK4/6 inhibitor response stratification. METHODS: Single-cell RNA-seq data (GSE158724, n=14 samples) and bulk multi-omics profiles (TCGA-BRCA, n=1,059; GSE186901, n=90) were analyzed. Gene regulatory networks were reconstructed using SCENIC to identify resistance-specific regulons. The Tumor Prognostic Regulon Index (TPRI) was derived from five prognostic transcription factors and validated in independent cohorts. Experimental validation including qPCR of core TFs was performed in patient-derived samples. Multimodal predictive models integrating TPRI, differentially expressed genes, and miRNAs were developed using logistic regression, with performance assessed via ROC/AUC analysis. RESULTS: We identified 86 resistance-associated regulons and established TPRI based on five prognostic TFs (ATF1, TEAD4, NFIL3, FOXO1, ETV3). TPRI significantly stratified patients into high/low-risk groups with differential overall survival and treatment response (Fisher's exact test P=0.0237). qPCR confirmed elevated expression of these TFs in resistant tumors (P<0.01). High-risk patients exhibited increased stemness indices (mRNAsi, P<2.2e-16) and mTOR pathway activation. The multimodal model (TPRI + top 30 DEGs + top 30 miRNAs) achieved superior prognostic accuracy (95%CI:0.6575-0.75). DISCUSSION: This study establishes TPRI as a novel biomarker for CDK4/6 inhibitor response prediction, validated through multi-omics integration and qPCR confirmation. The model provides actionable risk stratification, where high-risk patients may benefit from combinatorial mTOR-targeted therapies. Limitations include sample size constraints for methylation integration. Future studies should validate these findings in prospective clinical trials.
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Single-nucleus RNA-seq dissection of choroid plexus tumor cell heterogeneity.
The genomic, genetic and cellular events regulating the onset, growth and survival of rare, choroid plexus neoplasms remain poorly understood. Here, we examine the heterogeneity of human choroid plexus tumors by single-nucleus transcriptome analysis of 23,906 cells from four disease-free choroid plexus and eleven choroid plexus tumors. The resulting expression atlas profiles cellular and transcriptional diversity, copy number alterations, and cell-cell interaction networks in normal and cancerous choroid plexus. In choroid plexus tumor epithelial cells, we observe transcriptional changes that correlate with genome-wide methylation profiles. We further characterize tumor type-specific stromal microenvironments that include altered macrophage and mesenchymal cell states, as well as changes in extracellular matrix components. This first single-cell dataset resource from such scarce samples should be valuable for divising therapies against these little-studied neoplasms.
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A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications.
Recent advances in next-generation sequencing and computational technologies have enabled routine analysis of large-scale single-cell ribonucleic acid sequencing (scRNA-seq) data. However, scRNA-seq technologies have suffered from several technical challenges, including low mean expression levels in most genes and higher frequencies of missing data than bulk population sequencing technologies. Identifying functional gene sets and their regulatory networks that link specific cell types to human diseases and therapeutics from scRNA-seq profiles are daunting tasks. In this study, we developed a Component Overlapping Attribute Clustering (COAC) algorithm to perform the localized (cell subpopulation) gene co-expression network analysis from large-scale scRNA-seq profiles. Gene subnetworks that represent specific gene co-expression patterns are inferred from the components of a decomposed matrix of scRNA-seq profiles. We showed that single-cell gene subnetworks identified by COAC from multiple time points within cell phases can be used for cell type identification with high accuracy (83%). In addition, COAC-inferred subnetworks from melanoma patients' scRNA-seq profiles are highly correlated with survival rate from The Cancer Genome Atlas (TCGA). Moreover, the localized gene subnetworks identified by COAC from individual patients' scRNA-seq data can be used as pharmacogenomics biomarkers to predict drug responses (The area under the receiver operating characteristic curves ranges from 0.728 to 0.783) in cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database. In summary, COAC offers a powerful tool to identify potential network-based diagnostic and pharmacogenomics biomarkers from large-scale scRNA-seq profiles. COAC is freely available at https://github.com/ChengF-Lab/COAC.