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A Single-Cell Transcriptome Atlas of the Mouse Glomerulus.
Background Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. However, to what extent cellular heterogeneity exists within healthy glomerular cell populations remains unknown.Methods We used nanodroplet-based highly parallel transcriptional profiling to characterize the cellular content of purified wild-type mouse glomeruli.Results Unsupervised clustering of nearly 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells that can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry images obtained from the Human Protein Atlas. Subclustering of endothelial cells revealed a subset of endothelium that expressed marker genes related to endothelial proliferation. By comparison, the podocyte population appeared more homogeneous but contained three smaller, previously unknown subpopulations.Conclusions Our study comprehensively characterized gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.
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Single-cell RNA sequencing identifies distinct transcriptomic signatures between PMA/ionomycin- and αCD3/αCD28-activated primary human T cells.
Immunologists have activated T cells in vitro using various stimulation methods, including phorbol myristate acetate (PMA)/ionomycin and αCD3/αCD28 agonistic antibodies. PMA stimulates protein kinase C, activating nuclear factor-κB, and ionomycin increases intracellular calcium levels, resulting in activation of nuclear factor of activated T cell. In contrast, αCD3/αCD28 agonistic antibodies activate T cells through ZAP-70, which phosphorylates linker for activation of T cell and SH2-domain-containing leukocyte protein of 76 kD. However, despite the use of these two different in vitro T cell activation methods for decades, the differential effects of chemical-based and antibody-based activation of primary human T cells have not yet been comprehensively described. Using single-cell RNA sequencing (scRNA-seq) technologies to analyze gene expression unbiasedly at the single-cell level, we compared the transcriptomic profiles of the non-physiological and physiological activation methods on human peripheral blood mononuclear cell-derived T cells from four independent donors. Remarkable transcriptomic differences in the expression of cytokines and their respective receptors were identified. We also identified activated CD4 T cell subsets (CD55+) enriched specifically by PMA/ionomycin activation. We believe this activated human T cell transcriptome atlas derived from two different activation methods will enhance our understanding, highlight the optimal use of these two in vitro T cell activation assays, and be applied as a reference standard when analyzing activated specific disease-originated T cells through scRNA-seq.
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Single-cell RNA sequencing via endoscopic ultrasound-guided fine-needle biopsy for pancreatic tumors uncovered an aggressive subclone with a poor prognosis.
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful tool which can unveil regulatory connections between genes and cells. However, due to the high demand for tissue quality, most scRNA-seq for pancreatic cancer is carried out by surgical specimen. This study (NCT05767697) aims to gain practical experience in carrying out scRNA-seq using endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB). MATERIALS AND METHODS: With a within-subjects design, two punctures from the same lesion, with a negative pressure of 5 ml (suction group) and without applying suction (non-suction group), were evaluated. Each biopsy sample was separated into three parts: white tissue, red material, and buffer for living cell counting. scRNA-seq was carried out according to the manufacturer's protocol. The pancreatic adenocarcinoma dataset in The Cancer Genome Atlas (TCGA) was used as external validation. RESULTS: A total of 20 patients with 40 specimens were enrolled. The suction group achieved a success rate of 80% (16/20), which was significantly higher than the 10% (2/20) success rate in the non-suction group (P < 0.001). scRNA-seq data were generated for four patients, including two early stage and two late stage. Overall, 15 major cell subtypes, including 4 cancer subclones, were identified across early and late stages. Analysis of differentially expressed genes identified an aggressive subclone highlighted by ubiquitin-conjugating enzyme E2 C (UBE2C) high expression, commonly in late stage. The TCGA dataset also demonstrated that UBE2C high-expression pancreatic cancer had a poor prognosis. CONCLUSIONS: EUS-FNB with a negative pressure of 5 ml is feasible for scRNA-seq in clinical practice. A UBE2C high-expression subclone correlates with a poor prognosis, potentially becoming a new therapeutic target in future studies.
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Single-cell transcriptomic atlas of different endometriosis indicating that an interaction between endometriosis-associated mesothelial cells (EAMCs) and ectopic stromal cells may influence progesterone resistance.
BACKGROUND: Endometriosis is a hormone-dependent disease, which can usually be divided into peritoneal endometriosis (PEM), deep-infiltrating endometriosis (DIE) and ovarian endometriosis (OEM). Although the three pathologic types are essentially the same disease, they differ in pathological manifestations, molecular features, pain symptoms and hormonal responsiveness. However, there is limited literature focusing on the differences among these types. In this study, we employed single-cell RNA sequencing (scRNA-seq) to profile the transcriptome of each type using surgical biopsy samples obtained from six patients. We aimed to explore and elucidate the variations among these different types of endometriosis. RESULTS: We identified five major cell types and 44 subpopulations, including the presence of mesothelial cells in all pathological types, including OEM. Furthermore, we characterised the variations in cell types across different pathological types by employing enrichment analysis to assess functions and pathways. Notably, our findings reveal distinct levels of epithelial-mesenchymal transition (EMT) processes experienced by mesothelial cells within the microenvironment of endometriotic lesions. Through ligand-receptor analysis and referencing relevant literature, we propose that mesothelial cells exert an influence on progesterone resistance in stromal cells through intercellular communication mediated by the FN1-AKT pathway. CONCLUSIONS: Our study comprehensively characterises the heterogeneity of the different pathologic types of endometriosis and offers valuable insights into the underlying mechanisms contributing to variations in progesterone resistance across the three subtypes. KEY POINTS: Single-cell RNA (ScRNA) atlas across types of endometriosis is established. Mesothelial cells are founded in ovarian endometriosis. Endometriosis-associated mesothelial cells (EAMCs) experience various level of epithelial-mesenchymal transition (EMT) process in different subtypes. EAMCs may exert an influence on progesterone resistance in stromal cells through intercellular communication mediated by the FN1-AKT pathway.
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Integrated Analysis of Single-cell and Bulk RNA-Sequencing Identifies a Signature Based on Cancer-related Fibroblast Marker Genes to Predict Prognosis and Therapy Response in Lung Adenocarcinoma.
Cancer-related fibroblasts (CAFs), crucial in the tumor microenvironment, significantly influence tumorigenesis and extracellular matrix shaping. This study aimed to analyze the expression of CAF marker genes in lung adenocarcinoma (LUAD) and create a prognostic signature. We included 716 LUAD patients from different cohorts, conducting a comprehensive analysis of single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database, identifying 227 CAF marker genes. Using the Cancer Genome Atlas (TCGA) LUAD cohort, we developed a 3-gene prognostic signature, categorizing patients into high-risk and low-risk groups. The signature's predictive capability was validated across clinical subgroups and GEO cohorts. It was determined as an independent prognostic factor via univariate and multivariate analyses, leading to the construction of a nomogram for clinical prognosis prediction. Immune profile analysis indicated that high-risk patients exhibited immunosuppression and immune cell infiltration, while the tumor immune dysfunction and exclusion score suggested higher immunotherapy sensitivity in the low-risk group. In addition, high-risk patients showed greater sensitivity to several first-line chemotherapeutic drugs. The expression of hub genes was validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and the Human Protein Atlas (HPA). In conclusion, this study presented a novel prognostic signature for LUAD patients based on CAF marker genes, demonstrating strong predictive power for prognosis and treatment response.
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Prognostic genes in hepatocellular carcinoma and their function: an analysis integrating high-throughput-based spatial transcriptomics and single-cell RNA-sequencing.
BACKGROUND: Identifying novel prognostic indicators for patients with hepatocellular carcinoma (HCC) is critical to improving patient outcomes. Mapping the spatial composition of HCC tumors remains challenging. Therefore, this study aimed to identify prognostic genes and assess their role in HCC by integrating spatial transcriptome sequencing (ST-seq) and single-cell RNA sequencing (scRNA-seq). METHODS: HCC-related datasets, including The Cancer Genome Atlas (TCGA)-HCC, International Cancer Genome Consortium (ICGC)-HCC, GSE149614, and GSE203612, were examined in this study. Single-cell and spatial transcriptome analyses were first conducted and followed by cell communication analysis. Target cell types with differential proportions and higher enrichment were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was implemented to select prognostic genes and construct a risk model. Subsequently, a nomogram incorporating independent prognostic factors was developed, and immune microenvironment and immunotherapy response were analyzed. Finally, pseudotime analysis was performed to determine the developmental trajectory of the target cell types. RESULTS: Among hepatocytes, T/natural killer (NK) cells, B cells, myeloid cells, fibroblasts, and endothelial cells, hepatocytes and T/NK cells were identified as target cell types. ST analysis delineated seven cell subclusters (C0-C6). Cell communication analysis indicated that hepatocytes interact more strongly with other cell types compared to T/NK cells. The number of interactions between C4 and other cell subclusters was higher than the interaction intensity. Of the seven prognostic genes, ADH4, IGFBP3, and LCAT were downregulated in HCC, while AKR1B10, GAGE2A, MAGEA6, and UCHL1 were upregulated. The patients deemed to have a higher risk according to the model had shorter survival. The nomogram that was created by combining the seven prognostic genes had a good ability to forecast the patients' probability of survival. In the immunoinfiltration analysis, UCHL1 expression had the highest positive correlation with T/NK cell abundance. Moreover, in silico analysis suggested a potential association between high-risk status and reduced sensitivity to immunotherapy. Pseudotime analysis indicated that the expression of UCHL1, GAGE2A, MAGEA6, ADH4, and LCAT displayed an inverted U shape during the differentiation of hepatocytes, with the expression of UCHL1 was elevated at the end of T/NK cell differentiation. CONCLUSIONS: This study identified hepatocytes, T/NK cells, and seven prognostic genes in HCC, constructed a risk model and nomogram capable of predicting patient prognosis, and revealed through computational prediction that patients in the high-risk group might exhibit a poor response to immunotherapy, thereby providing new potential tools for the clinical treatment of HCC.
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Comprehensive Analysis of the Expression and Prognostic Significance of NPAS1 in Patients with Colorectal Adenocarcinoma Based on Bioinformatics.
BACKGROUND: Neuronal PAS domain protein 1 (NPAS1) is a protein-coding gene expressed mainly in the central nervous system and plays a key role in nervous system development. The expression and prognostic value of NPAS1 in colorectal adenocarcinoma (COAD) are unknown. METHODS: The expression and clinicopathological data of COAD from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. NPAS1 gene expression in colon cancer tissues was validated by Western blotting and immunohistochemical staining. Function and immune infiltration were established using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single sample Gene Set Enrichment Analysis (ssGSEA). Methylation levels were analyzed utilizing the UALCAN database. NPAS1-related mutations were analyzed using cBioPortal. Single-cell and tissue-specific expression of NPAS1 was examined using the Human Protein Atlas. Protein-protein interactions and transcription factor networks of NPAS1-associated genes were analyzed with STRING and Network Analyst. RESULTS: Our study found that the expression level of NPAS1 was higher in normal tissues compared to COAD tissues, higher NPAS1 expression was associated with better survival outcomes. The bioinformatics analysis confirmed that NPAS1 co-expressed genes were linked to diverse signalling pathways and cellular functions. NPAS1 is correlated with CD56bright, cytotoxic and NK cells, and negatively correlated with helper T cells and Tcm cells. The DNA methylation level of NPAS1 in tumor tissues was elevated compared to normal tissues. We analyzed the mutation characteristics of NPAS1, single-cell expression profiling of NPAS1, and protein-protein interactions involving genes associated with NPAS1. These discoveries offered perspectives on the etiology, identification, and management of COAD. CONCLUSION: This study revealed a significant decrease of NPAS1 expression in COAD tissues, exhibiting associations with the clinical stage, prognosis, immune infiltration, and DNA methylation of COAD.
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An integrated analysis of human myeloid cells identifies gaps in in vitro models of in vivo biology.
The Stemformatics myeloid atlas is an integrated transcriptome atlas of human macrophages and dendritic cells that systematically compares freshly isolated tissue-resident, cultured, and pluripotent stem cell-derived myeloid cells. Three classes of tissue-resident macrophage were identified: Kupffer cells and microglia; monocyte-associated; and tumor-associated macrophages. Culture had a major impact on all primary cell phenotypes. Pluripotent stem cell-derived macrophages were characterized by atypical expression of collagen and a highly efferocytotic phenotype. Myeloid subsets, and phenotypes associated with derivation, were reproducible across experimental series including data projected from single-cell studies, demonstrating that the atlas provides a robust reference for myeloid phenotypes. Implementation in Stemformatics.org allows users to visualize patterns of sample grouping or gene expression for user-selected conditions and supports temporary upload of your own microarray or RNA sequencing samples, including single-cell data, to benchmark against the atlas.
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Integrative Multi-Omics and Single-Cell Analysis Prioritizes YWHAB Within Ubiquitination-Related Transcriptional Programs in Endometrial Carcinoma.
BACKGROUND: Ubiquitination-related transcriptional programs may help characterize epithelial heterogeneity in endometrial carcinoma (EC). We aimed to identify candidates associated with these programs and epithelial plasticity. METHODS: We integrated a 43,613-cell atlas from five endometrioid EC samples and two normal endometrial samples with bulk transcriptomic, genetic-association and protein-expression data. Five scoring methods defined a consensus expression score, with HIGH and LOW quartiles defined within the Epithelial + EMT_like lineage. Differential expression and high-dimensional weighted gene co-expression network analysis (hdWGCNA) identified candidates for exploratory Mendelian randomization (MR) and survival-based prioritization among 541 patients from The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) cohort. Repeated nested cross-validation and cross-model feature support were used to evaluate internal performance and candidate stability. Pseudobulk, independently curated ubiquitin-proteasome system (UPS) gene sets and scSurv input masking provided sensitivity analyses. RESULTS: The lineage-restricted screen identified 444 candidates, including 412 detectable in TCGA. GeneCards and curated Reactome and KEGG UCell scores were positively correlated (ρ = 0.519 and 0.579). YWHAB ranked fourth among 412 genes in the top-10% workflow analysis and fifth in the top-20% sensitivity analysis. Among the 12 nominal MR-associated genes, it had the highest machine-learning priority. Its single-nucleotide polymorphism (SNP) MR estimate was nominal (odds ratio (OR) = 1.732, p = 0.0488) and did not survive multiple-testing correction. Higher bulk YWHAB expression was associated with poorer overall survival (HR = 2.188, 95% CI 1.559-3.072). Western blotting showed higher YWHAB protein expression in EC than controls (nine versus nine tissues, p = 0.0027), consistent with descriptive Human Protein Atlas (HPA) staining. Single-cell analyses associated YWHAB with RNA processing, proteostasis-related expression and epithelial-state heterogeneity. CONCLUSIONS: Lineage-restricted discovery and cross-model prioritization identify YWHAB as an exploratory candidate associated with ubiquitination-related transcriptional programs in EC. Protein-expression evidence supports its further investigation, while causal function and clinical prognostic utility require independent validation.
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NKX2‑1 copy number alterations are associated with oncogenic, immunological and prognostic remodeling in non‑small cell lung cancer.
NK2 homeobox 1 (NKX2-1) copy number alterations (CNAs) are frequently observed in lung cancer. However, little is known about the complete landscape of focal alterations in NKX2-1 copy number (CN), their clinical significance and their therapeutic implications in non-small cell lung cancer (NSCLC). The correlations between NKX2-1 expression and EGFR driver mutations and programmed death ligand 1 (PD-L1) co-expression were studied using immunohistochemistry and PCR from the tumors of recruited Filipino patients (n=45). Clinical features of NSCLC with NKX2-1 CNAs were resolved at the tumor and clonal levels using the molecular profiles of patients with lung adenocarcinoma and lung squamous cell carcinoma from The Cancer Genome Atlas (n=1,130), and deconvoluted single-cell RNA-seq data from the Bivona project (n=1,654), respectively. Despite a significant and positive correlation between expression and CN (r=0.264; P<0.001), NKX2-1 CNAs exerted a stronger influence on the combined EGFR and PD-L1 status of NSCLC tumors than expression. NKX2-1 CN gain was prognostic of favorable survival (P=0.018) and a better response to targeted therapy. NKX2-1 CN loss predicted a worse survival (P=0.041). Mutational architecture in the Y-chromosome differentiated the two prognostic groups. There were 19,941 synonymous mutations and 1,408 genome-wide CN perturbations associated with NKX2-1 CNAs. Tumors with NKX2-1 CN gain expressed lymphocyte markers more heterogeneously than those with CN loss. Higher expression of tumor-infiltrating lymphocyte gene signatures in CN gain was prognostic of longer disease-free survival (P=0.005). Tumors with NKX2-1 CN gain had higher B-cell (P<0.001) and total T-cell estimates (P=0.003). NKX2-1 CN loss was associated with immunologically colder tumors due to higher M2 macrophage infiltrates (P=0.011) and higher expression of immune checkpoint proteins, CD274 (P=0.025), VTCN1 (P<0.001) and LGALS9 (P=0.002). In conclusion, NKX2-1 CNAs are associated with tumors that exhibit clinically diverse characteristics, and with unique oncogenic, immunological and prognostic signatures.