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Single-cell characteristics and malignancy regulation of alpha-fetoprotein-producing gastric cancer.
OBJECTIVE: To characterize alpha-fetoprotein (AFP)-producing gastric cancer (AFPGC) at the single-cell level and to identify regulatory factors for AFP expression and malignancy. METHODS: ScRNA-seq was performed on two tumors collected from patients with AFPGC. InferCNV and sub-clustering were applied to identify typical AFPGC cells, followed by AddModuleScore, pathway enrichment, Pseudo-time, and Scenic analyses. Data from a gastric cancer (GC) cohort were collected for conjoint analysis. The analytical results were verified by cell experiments and immunohistochemistry. RESULTS: AFPGC cells are similar to hepatocytes in transcriptome and transcriptional regulation, with kinetic malignancy-related pathways, compared to the common malignant epithelium. In addition, compared to common GC cells, malignancy-related pathways, such as epithelial-mesenchymal transition (EMT) and angiogenesis, were upregulated in AFPGC. Mechanistically, Dickkopf-1 (DKK1) was found to be associated with AFP expression and malignant phenotype upon combining our scRNA-seq data with a public database, which was further verified by a series of in vitro experiments and immunohistochemistry. CONCLUSION: We demonstrated the single-cell characteristics of AFPGC and that DKK1 facilitates AFP expression and malignancy.
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Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD. METHODS: We applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells. RESULTS: The scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS's prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis. CONCLUSION: Our research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients.
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AMAR-seq: Automated Multimodal Sequencing of DNA Methylation, Chromatin Accessibility, and RNA Expression with Single-Cell Resolution.
Parallel single-cell multimodal sequencing is the most intuitive and precise tool for cellular status research. In this study, we propose AMAR-seq to automate methylation, chromatin accessibility, and RNA expression coanalysis with single-cell precision. We validated the accuracy and robustness of AMAR-seq in comparison with standard single-omics methods. The high gene detection rate and genome coverage of AMAR-seq enabled us to establish a genome-wide gene expression regulatory atlas and triple-omics landscape with single base resolution and implement single-cell copy number variation analysis. Applying AMAR-seq to investigate the process of mouse embryonic stem cell differentiation, we revealed the dynamic coupling of the epigenome and transcriptome, which may contribute to unraveling the molecular mechanisms of early embryonic development. Collectively, we propose AMAR-seq for the in-depth and accurate establishment of single-cell multiomics regulatory patterns in a cost-effective, efficient, and automated manner, paving the way for insightful dissection of complex life processes.
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A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing.
As part of the Human Cell Atlas Initiative, our goal is to generate single-cell transcriptomics (single-cell RNA sequencing [scRNA-seq], 86,708 cells) and regulatory (single-cell assay on transposase accessible chromatin sequencing [scATAC-seq], 59,830 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT). The FT contains 11 major cell types, and the ovary contains 6. The dominating cell type in the FT and ovary is the stromal cell, which expresses aging-associated genes. FT epithelial cells express multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB) and show active communication between fimbrial epithelial cells and ovarian stromal cells. Integrated single-cell transcriptomics and chromatin accessibility data show that the regulatory landscape of the fimbriae is different from other anatomic regions. Cell types with similar gene expression in the FT display transcriptional profiles. These findings allow us to disentangle the cellular makeup of the postmenopausal FT and ovary, advancing our knowledge of gynecologic diseases in menopause.
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Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-Seq.
Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas-Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs' expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.
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Macrophage-related Genes were Discovered by Applying Highdimensional Weighted Gene Co-expression Network Analysis as Biomarkers for Esophageal Squamous Cell Carcinoma.
INTRODUCTION: Esophageal squamous cell carcinoma (ESCC) lacks effective early diagnostic markers. High-dimensional WGCNA (hdWGCNA) enables the systematic identification of macrophage-related signatures from single-cell data, offering a novel approach to discovering potential biomarkers for ESCC. METHODS: This study analyzed bulk transcriptomic datasets and a single‑cell RNA‑seq dataset from the Gene Expression Omnibus (GEO) database. Specifically, the GSE53625 dataset (179 ESCC and 179 matched normal samples) was a training cohort, while GSE20347 (17 ESCC and 17 normal samples) was an independent validation set. The single‑cell dataset GSE196756 (3 ESCC and 3 adjacent normal tissues) was processed to construct a cellular atlas. The CellChat package was used to assess the communication between macrophages and other cell subpopulations. Macrophage‑associated co‑expression modules were identified by hdWGCNA. Differentially expressed genes (DEGs) were screened with the limma package, and the intersection of DEGs and hub genes from hdWGCNA was further refined via LASSO and SVM-RFE algorithms to obtain candidate genes. Functional enrichment, immune infiltration, drug prediction, and molecular docking were subsequently performed. In vitro experiments were carried out to validate the expression and functional role of candidate genes in ESCC cell lines. RESULTS: Four macrophage‑related gene models consisting of TGFBI, FTL, GPNMB, and APOE were created and validated. These genes were significantly overexpressed in ESCC tissues and exhibited high diagnostic accuracy, reaching an AUC value >0.8 in both the training and independent validation cohorts. In vitro functional assays confirmed that silencing TGFBI suppressed ESCC cell proliferation, migration, and invasion. Enrichment analysis linked these genes to neutrophil activation and apoptosis pathways, and immune infiltration analysis further revealed their correlation with immunosuppressive cell subsets. Drug prediction yielded 190 candidate compounds, and molecular docking suggested potential binding of retinoic acid, rosiglitazone, and GDC‑0941 to the corresponding targets in ESCC. DISCUSSION: In this study, four characteristic genes (TGFBI, FTL, GPNMB, and APOE) were identified for the diagnosis of ESCC, contributing to the improvement of early detection of the cancer. CONCLUSION: The present findings contributed to the pathogenesis research, detection, and management of ESCC.
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Deciphering the Divergent Gene Expression Landscapes of m6A/m5C/m1A Methylation Regulators in Hepatocellular Carcinoma Through Single-Cell and Bulk RNA Transcriptomic Analysis.
INTRODUCTION: RNA modifications mediated by the m6A, m1A, and m5C regulatory genes are crucial for the progression of malignancy. This study aimed to explore the expression of regulator genes for m6A/m5C/m1A methylation at the single-cell level and to validate their expression in cancerous and adjacent para-cancerous liver tissues of adult patients with HCC who underwent tumor resection. METHODS: The bulk sequencing from The Cancer Genome Atlas (TCGA) database and the single-cell RNA sequencing (scRNA-seq) data obtained from the Gene Expression Omnibus (GEO) database were used to identify the dysregulated m6A/m5C/m1A genes for hepatocellular carcinoma (HCC). A real-time polymerase chain reaction (real-time PCR) was used to measure the expression of dysregulated m6A/m5C/m1A genes in collected human HCC tissues and compared with adjacent para-cancerous liver tissues. Immune cell infiltration with these significantly expressed methylation-related genes was evaluated using Timer2.0. RESULTS: A discrepancy in m6A/m5C/m1A gene expression was observed between bulk sequencing and scRNA-seq. The clustered heatmap of the scRNA-seq-identified dysregulated m6A/m5C/m1A genes in TCGA cohort revealed heterogeneous expression of these methylation regulators within the cancer, whereas their expression in the adjacent liver tissues was more homogeneous. The real-time PCR validated the significant overexpression of DNMT1, NSUN5, TRMT6, IGF2BP1, and IGFBP3, which were identified using scRNA-seq, and IGFBP2, which was identified using bulk sequencing. These dysregulated methylation genes are mainly correlated with the infiltration of natural killer cells. DISCUSSION: This study suggests that cellular diversity inside tumors contributes to the discrepancy in the expression of methylation regulator genes between traditional bulk sequencing and scRNA-seq. This study identified five regulatory genes that will be the focus of further studies regarding the function of m6A/m5C/m1A in HCC.
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Single-cell transcriptomic atlas of human retina from Chinese donors reveals population-specific cellular diversity.
The human retina exhibits complex cellular heterogeneity which is critical for visual function, yet comprehensive ethnic-specific references are scarce in ophthalmic transcriptomics. The lack of single-cell RNA sequencing (scRNA-seq) data from Asian populations particularly Chinese donors imposes significant limitations in understanding population-specific retinal biology. We constructed the first comprehensive single-cell transcriptomic atlas of the human retina from Chinese donors, generated through high-throughput scRNA-seq of ∼290,000 viable cells obtained from 18 fresh retinal specimens (living donor and post-mortem specimens). Our multi-level analysis identified 10 distinct retinal cell types. Detailed subcluster analyses further revealed extensive heterogeneity, suggesting distinct subtypes within several cell populations such as 7 amacrine cell subtypes and 14 bipolar cell subtypes. Concurrently, we mapped their associated biological signaling pathways, functions, and mechanistic processes through systematic analysis of delineated subtype-specific molecular mechanisms. This analysis explained the critical involvement of these subpopulations in core biological processes including synaptic organization, neurotransmission, and phototransduction cascades, potentially governing retinal homeostatic regulation and disease mechanisms. Single-cell transcriptomic atlas of the human retina from Chinese donors describes a comprehensive cellular landscape, encompassing major cell types and subtypes including neuronal, glial, and immune populations. This ethnic-specific atlas provided an useful reference for understanding retinal development, cellular interaction and disease pathogenesis in Chinese populations, addressing a longstanding gap in retinal transcriptomic resources.
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Multitissue single-nucleus RNA-seq reveals cell type-specific regulatory patterns of alternative polyadenylation in pigs.
As an important posttranscriptional modification mechanism, alternative polyadenylation (APA) plays a crucial role in gene regulation and phenotypic diversity. Whereas extensive studies have explored the global APA landscape using bulk RNA-seq data, in-depth analyses of APA events at the single-cell level remain limited-particularly in farm animals. In this study, we construct a comprehensive APA atlas for 261 cell types across 19 porcine tissues based on single-nucleus RNA sequencing (snRNA-seq) data. This analysis reveals tissue- and cell type-specific patterns of APA. We find that many genes display a clear correlation between the average length of 3' untranslated regions (3' UTRs) and expression levels in various cell types, with most showing a negative correlation. Early cell types within the developmental lineage, such as spermatogonia and satellite cells, display longer 3' UTRs, especially for spermatogenesis, where 3' UTR lengths show significant decreasing trends along the differentiation trajectory. Notably, we find that variable 3' UTR lengths in the CD47 and GPD1 genes might be critical regulators during spermatogenesis and myogenesis, respectively, potentially through modulation of RNA-binding protein and miRNA binding sites. Furthermore, the SNP rs323354626, located in the 3' UTR of the CD47 gene, significantly impacts gene splicing and is strongly associated with reproductive phenotypes. Additionally, we observe that neuronal cells generally possess longer 3' UTRs-a pattern conserved across humans, mice, fruit flies, and pigs. Together, these findings enrich the single-cell atlas of pigs by adding a layer of posttranscriptional regulation to the existing gene expression data, highlighting the significant role of cell type-specific 3' UTR lengths in cell commitment and complex trait regulation.
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scAI-SNP: a method for inferring ancestry from single-cell data.
BACKGROUND: Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. METHODS: Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell dataset, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. RESULTS: Using diverse single-cell datasets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. DISCUSSION: Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s44330-025-00029-4.