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A human adipose tissue cell-type transcriptome atlas.
The importance of defining cell-type-specific genes is well acknowledged. Technological advances facilitate high-resolution sequencing of single cells, but practical challenges remain. Adipose tissue is composed primarily of adipocytes, large buoyant cells requiring extensive, artefact-generating processing for separation and analysis. Thus, adipocyte data are frequently absent from single-cell RNA sequencing (scRNA-seq) datasets, despite being the primary functional cell type. Here, we decipher cell-type-enriched transcriptomes from unfractionated human adipose tissue RNA-seq data. We profile all major constituent cell types, using 527 visceral adipose tissue (VAT) or 646 subcutaneous adipose tissue (SAT) samples, identifying over 2,300 cell-type-enriched transcripts. Sex-subset analysis uncovers a panel of male-only cell-type-enriched genes. By resolving expression profiles of genes differentially expressed between SAT and VAT, we identify mesothelial cells as the primary driver of this variation. This study provides an accessible method to profile cell-type-enriched transcriptomes using bulk RNA-seq, generating a roadmap for adipose tissue biology.
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A glycosylation-related gene signature predicts prognosis, immune microenvironment infiltration, and drug sensitivity in glioma.
Glioma represents the most common primary cancer of the central nervous system in adults. Glycosylation is a prevalent post-translational modification that occurs in eukaryotic cells, leading to a wide array of modifications on proteins. We obtained the clinical information, bulk RNA-seq data, and single-cell RNA sequencing (scRNA-seq) from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Gene Expression Omnibus (GEO), and Repository of Molecular Brain Neoplasia Data (Rembrandt) databases. RNA sequencing data for normal brain tissues were accessed from the Genotype-Tissue Expression (GTEx) database. Then, the glycosylation genes that were differentially expressed were identified and further subjected to variable selection using a least absolute shrinkage and selection operator (LASSO)-regularized Cox model. We further conducted enrichment analysis, qPCR, nomogram, and single-cell transcriptome to detect the glycosylation signature. Drug sensitivity analysis was also conducted. A five-gene glycosylation signature (CHPF2, PYGL, GALNT13, EXT2, and COLGALT2) classified patients into low- or high-risk groups. Survival analysis, qPCR, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group. Furthermore, GSEA and immune infiltration analysis indicated that the glycosylation signature has the potential to predict the immune response in glioma. In addition, four drugs (crizotinib, lapatinib, nilotinib, and topotecan) showed different responses between the two risk groups. Glioma cells had been classified into seven lines based on single-cell expression profiles. The five-gene glycosylation signature can accurately predict the prognosis of glioma and may offer additional guidance for immunotherapy.
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Characterizing the landscape of cervical squamous cell carcinoma immune microenvironment by integrating the single-cell transcriptomics and RNA-Seq.
BACKGROUND: Cervical squamous cell carcinoma (CSCC), caused by the infection of high-risk human papillomavirus, is one of the most common malignancies in women worldwide. METHODS: RNA expression data, including those from the Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression databases, were used to identify the expression of RNAs in normal and tumor tissue. Correlation analysis was performed to identify the immune-related long noncoding RNAs (IRLs) and hypoxia-related genes (IRHs) that can influence the activity of the immune system. Prognosis models of immune-related RNAs (IRRs) were used to construct a coexpression network of the immune system. We identified the role of IRRs in immunotherapy by correlation analysis with immune checkpoint genes (ICGs). We then validated the expression data by integrating two single-cell sequencing data sets of CSCC to identify the key immune features. RESULTS: In total, six immune-related gene (IRG), four IRL, and five IRH signatures that can significantly influence the characteristics of the tumor immune microenvironment (TIME) were selected using machine learning methods. The expression level of ICGs was significantly upregulated in GZMB+ CD8+ T-cells and tumor-associated macrophages (TAMs) in tumor tissues. TGFBI+ TAMs are a kind of blood-derived monocyte-derived M0-like TAM linked to hypoxia and a poor prognosis. IFI30+ M1-like TAMs participate in the process of immune-regulation and showed a role in the promotion of CD8+ T-cells and Type 1 T helper (Th1)/Th2 cells in the coexpression network, together with several IRLs, IRGs, and ICGs. CONCLUSIONS: CD16+ monocyte-derived IFI30+ TAMs participated in our coexpression network to regulate the TIME, showing the potential to be a novel immunotherapy target. The enrichment of M0-like TAMs was associated with a worse prognosis in the high-risk score group with IRH signatures. Remarkably, M0-like TAMs in tumor tissues overexpressed TGFBI and were associated with several well-known tumor-proliferation pathways.
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Analysis of single-cell RNA-sequencing data identifies a hypoxic tumor subpopulation associated with poor prognosis in triple-negative breast cancer.
Triple-negative breast cancer (TNBC) is an aggressive subtype of mammary carcinoma characterized by low expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Along with the rapid development of the single-cell RNA-sequencing (scRNA-seq) technology, the heterogeneity within the tumor microenvironment (TME) could be studied at a higher resolution level, facilitating an exploration of the mechanisms leading to poor prognosis during tumor progression. In previous studies, hypoxia was considered as an intrinsic characteristic of TME in solid tumors, which would activate downstream signaling pathways associated with angiogenesis and metastasis. Moreover, hypoxia-related genes (HRGs) based risk score models demonstrated nice performance in predicting the prognosis of TNBC patients. However, it is essential to further investigate the heterogeneity within hypoxic TME, such as intercellular communications. In the present study, utilizing single-sample Gene Set Enrichment Analysis (ssGSEA) and cell-cell communication analysis on the scRNA-seq data retrieved from Gene Expression Omnibus (GEO) database with accession number GSM4476488, we identified four tumor subpopulations with diverse functions, particularly a hypoxia-related one. Furthermore, results of cell-cell communication analysis revealed the dominant role of the hypoxic tumor subpopulation in angiogenesis- and metastasis-related signaling pathways as a signal sender. Consequently, regard the TNBC cohorts acquired from The Cancer Genome Atlas (TCGA) and GEO as train set and test set respectively, we constructed a risk score model with reliable capacity for the prediction of overall survival (OS), where ARTN and L1CAM were identified as risk factors promoting angiogenesis and metastasis of tumors. The expression of ARTN and L1CAM were further analyzed through tumor immune estimation resource (TIMER) platform. In conclusion, these two marker genes of the hypoxic tumor subpopulation played vital roles in tumor development, indicating poor prognosis in TNBC patients.
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Toward the generation of a laser-capture microdissection-based human hair follicle transcriptome atlas.
Characterizing the transcriptome of defined human hair follicle (HF) compartments remains a fundamental hair research challenge. In this study, we have used laser-capture microdissection coupled with RNA sequencing to reveal gene expression profiles of 8 selected compartments of human anagen VI HFs. Principal component analysis identified a high degree of similarity in the distinct transcriptional profiles of each examined compartment as well as a sex-specific clustering. To validate our transcriptomic analysis, we demonstrated that well-known signature genes map to the correct HF compartment. Comparison with published microarray and single-cell RNA-sequencing data on human anagen VI HFs or selected cell populations showed that we were able to corroborate significant elements of their transcriptomic signatures, confirming the accuracy of our experimental pipeline. Furthermore, we identified, to our knowledge, previously unreported compartment-specific markers and confirmed the expression of selected ones, namely PAPPA2 for the dermal papilla, FOXM1 for the germinative hair matrix, APOD for the connective tissue sheath, and EHF for the bulge outer root sheath, by in situ hybridization. Finally, we conducted a CellChat analysis to uncover intercompartmental communication patterns and generated an-as yet unavailable-transcriptomic atlas of distinct human anagen VI HF compartments, which can be utilized to develop compartment-specific therapeutic interventions for the management of human HF disorders.
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Single-Cell RNA Sequencing of Human Corpus Cavernosum Reveals Cellular Heterogeneity Landscapes in Erectile Dysfunction.
PURPOSE: To assess the diverse cell populations of human corpus cavernosum in patients with severe erectile dysfunction (ED) at the single-cell level. METHODS: Penile tissues collected from three patients were subjected to single-cell RNA sequencing using the BD Rhapsody™ platform. Common bioinformatics tools were used to analyze cellular heterogeneity and gene expression profiles from generated raw data, including the packages Seurat, Monocle, and CellPhoneDB. RESULTS: Disease-related heterogeneity of cell types was determined in the cavernous tissue such as endothelial cells (ECs), smooth muscle cells, fibroblasts, and immune cells. Reclustering analysis of ECs identified an arteriole ECs subcluster and another one with gene signatures of fibroblasts. The proportion of fibroblasts was higher than the other cell populations and had the most significant cellular heterogeneity, in which a distinct subcluster co-expressed endothelial markers. The transition trajectory of differentiation from smooth muscle cells into fibroblasts was depicted using the pseudotime analysis, suggesting that the expansion of corpus cavernosum is possibly compromised as a result of fibrosis. Cell-cell communications among ECs, smooth muscle cells, fibroblasts, and macrophages were robust, which indicated that inflammation may also have a crucial role in the development of ED. CONCLUSIONS: Our study has demonstrated a comprehensive single-cell atlas of cellular components in human corpus cavernosum of ED, providing in-depth insights into the pathogenesis. Future research is warranted to explore disease-specific alterations for individualized treatment of ED.
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Single-Cell RNA Sequencing Reveals the Role of Epithelial Cell Marker Genes in Predicting the Prognosis of Colorectal Cancer Patients.
Single-cell RNA sequencing (scRNA-seq) is increasingly used in studies on gastrointestinal cancers. This study investigated the prognostic value of epithelial cell-associated biomarkers in colorectal cancer (CRC) using scRNA-seq data. We downloaded and analysed scRNA-seq data from four CRC samples from the Gene Expression Omnibus (GEO), and we identified marker genes of malignant epithelial cells (MECs) using CRC transcriptome and clinical data downloaded from The Cancer Genome Atlas (TCGA) and GEO as training and validation cohorts, respectively. In the TCGA training cohort, weighted gene correlation network analysis, univariate Cox proportional hazard model (Cox) analysis, and least absolute shrinkage and selection operator regression analysis were performed on the marker genes of MEC subsets to identify a signature of nine prognostic MEC-related genes (MECRGs) and calculate a risk score based on the signature. CRC patients were divided into high- and low-risk groups according to the median risk score. We found that the MECRG risk score was significantly correlated with the clinical features and overall survival of CRC patients, and that CRC patients in the high-risk group showed a significantly shorter survival time. The univariate and multivariate Cox regression analyses showed that the MECRG risk score can serve as an independent prognostic factor for CRC patients. Gene set enrichment analysis revealed that the MECRG signature genes are involved in fatty acid metabolism, p53 signalling, and other pathways. To increase the clinical application value, we constructed a MECRG nomogram by combining the MECRG risk score with other independent prognostic factors. The validity of the nomogram is based on receiver operating characteristics and calibration curves. The MECRG signature and nomogram models were well validated in the GEO dataset. In conclusion, we established an epithelial cell marker gene-based risk assessment model based on scRNA-seq analysis of CRC samples for predicting the prognosis of CRC patients.
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Deciphering the tumor ecosystem dynamics undergoing immunochemotherapy therapy across multiple cancer types unveils the immunosuppressive role of S100A4 in fibroblasts by promoting PD-L1 expression in tumor cells.
BACKGROUND: Neoadjuvant therapy (NAT) has transformed cancer treatment by improving surgical outcomes and survival rates, yet resistance mechanisms across multiple cancer types remain unclear. This study aimed to decipher tumor ecosystem dynamics during NAT using cross-cancer single-cell sequencing data, focusing on identifying key mediators of immunosuppression and treatment resistance. METHODS: Single-cell RNA-sequencing (scRNA-seq) datasets from five solid tumors (esophageal squamous cell carcinoma, esophagogastric junction carcinoma, colorectal cancer, cervical cancer, and triple-negative breast cancer) were integrated. The data from these five cancer types underwent a rigorous process to standardize cell types across all datasets. Cell-cell communication analysis, Meta-Programs (MPs) via non-negative matrix factorization, and functional enrichment were performed. Immunohistochemistry (IHC) and Western blot validated S100A4 expression and PD-L1 induction in vitro. RESULTS: We constructed a single-cell map across cancer types and systematically characterized dynamic changes in tumor cells and diverse microenvironmental cell populations following neoadjuvant therapy, along with thier gene expression and pathway alterations. Our findings highlight that crosstalk between cancer-associated fibroblasts (CAFs) and tumor cells represents a critical determinant of neoadjuvant therapy resistance. Fibroblasts underwent significant state transitions post-treatment, marked by hypoxia-associated gene upregulation (e.g., S100A4) and immunosuppressive pathways. Meta-Programs (MPs) analysis identified a hypoxia-driven fibroblast state (MP5) containing S100A4 that correlated with treatment resistance. In vitro experiments, S100A4 co-localized with α-SMA + fibroblasts and directly induced PD-L1 expression in tumor cells, linking CAFs secreted S100A4 to immunosuppressive PD-L1 upregulation. CONCLUSION: This cross-cancer single-cell atlas reveals S100A4, secreted by CAFs, as a conserved mediator of PD-L1 upregulation in tumor cells, driving immunosuppression and resistance to nICT. The atlas and mechanistic findings provide a rationale for targeting S100A4 to enhance treatment efficacy, pending validation in larger cohorts and mechanistic studies. This resource also supports the development of personalized, cross-cancer neoadjuvant strategies.
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Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing.
With the rapid advancement of single-cell RNA-sequencing (scRNA-seq) technology, many data-preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene network reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on Human Cell Atlas bone marrow data with respect to their impacts on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data-preprocessing steps due to oversmoothing of the raw data. We proposed a model-agnostic noise-regularization method that can effectively eliminate the correlation artifacts. The noise-regularized gene-gene correlations were further used to reconstruct a gene co-expression network and successfully revealed several known immune cell modules.
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Prognostic and therapeutic implications of PSMD14 in hepatocellular carcinoma: an integrative analysis of transcriptomic and single-cell profiles.
BACKGROUND: Liver hepatocellular carcinoma (LIHC) represents a common and aggressive malignancy characterized by an unfavorable clinical outcome, highlighting an urgent need to discover reliable prognostic indicators and novel therapeutic targets. The present investigation centers on the JAMM family genes, with a specific emphasis on PSMD14, to delineate their functional contributions in the context of LIHC. METHODS: We utilized an integrated analytical framework, incorporating bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing (scRNA-seq) datasets, to systematically investigate the expression profiles, prognostic significance, and underlying functional roles of JAMM family deubiquitinases in LIHC. A prognostic risk score model was developed through least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis. To elucidate the biological functions and signaling pathways linked to PSMD14, we performed comprehensive functional enrichment analyses, encompassing Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The landscape of immune cell infiltration within the tumor microenvironment was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA). Furthermore, scRNA-seq data were leveraged to dissect cellular heterogeneity and examine the expression patterns of PSMD14 across distinct cell populations. Finally, immunohistochemical staining was applied to validate PSMD14 protein expression levels in clinical tissue samples. RESULTS: Our findings demonstrated that PSMD14 and EIF3H were markedly upregulated in LIHC tissues, with PSMD14 emerging as a critical prognostic indicator. A risk stratification model constructed from JAMM family genes effectively categorized patients into distinct high- and low-risk cohorts, where the high-risk group displayed significantly shorter overall survival. The dysregulated expression of PSMD14 is partly attributable to copy number alterations and DNA methylation patterns, showing associations with multiple DNA methyltransferases. Functional enrichment analyses indicated that elevated PSMD14 expression is involved in metabolic pathways and oncogenic signaling cascades. Moreover, PSMD14 expression was linked to an immunosuppressive tumor microenvironment, exhibiting a positive correlation with Th2 cells and an inverse relationship with cytotoxic immune cells. scRNA-seq validated the predominant expression of PSMD14 in malignant epithelial cells. A nomogram incorporating PSMD14 expression and clinical variables was developed to predict patient survival probabilities at 1 and 5 years. Additionally, immunohistochemical analysis of 55 clinical samples revealed that PSMD14 protein levels were substantially higher in LIHC tissues compared to adjacent non-tumorous tissues. CONCLUSIONS: These results indicate that PSMD14 may serve as a promising prognostic biomarker and therapeutic target for LIHC, highlighting the need for further exploration of its underlying molecular mechanisms and therapeutic applications.