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Aging Atlas: a multi-omics database for aging biology.
Organismal aging is driven by interconnected molecular changes encompassing internal and extracellular factors. Combinational analysis of high-throughput 'multi-omics' datasets (gathering information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and pharmacogenomics), at either populational or single-cell levels, can provide a multi-dimensional, integrated profile of the heterogeneous aging process with unprecedented throughput and detail. These new strategies allow for the exploration of the molecular profile and regulatory status of gene expression during aging, and in turn, facilitate the development of new aging interventions. With a continually growing volume of valuable aging-related data, it is necessary to establish an open and integrated database to support a wide spectrum of aging research. The Aging Atlas database aims to provide a wide range of life science researchers with valuable resources that allow access to a large-scale of gene expression and regulation datasets created by various high-throughput omics technologies. The current implementation includes five modules: transcriptomics (RNA-seq), single-cell transcriptomics (scRNA-seq), epigenomics (ChIP-seq), proteomics (protein-protein interaction), and pharmacogenomics (geroprotective compounds). Aging Atlas provides user-friendly functionalities to explore age-related changes in gene expression, as well as raw data download services. Aging Atlas is freely available at https://bigd.big.ac.cn/aging/index.
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Downregulation of F11R in Colorectal Cancer and Tumour Progression.
OBJECTIVE: To evaluate the prognostic value and expression profile of F11R (junctional adhesion molecule-A, JAM-A) in colorectal cancer (CRC) and to elucidate its functional role, particularly in epithelial-mesenchymal transition (EMT) and Rap1 signalling. STUDY DESIGN: Integrated bioinformatic and experimental study. Place and Duration of the Study: Department of Gastroenterology, Affiliated Hospital of Kunming University of Science and Technology, the First People's Hospital of Yunnan Province, Kunming, China, from 1st August 2021 to 30th June 2023. METHODOLOGY: F11R-associated genes were analysed using The Cancer Genome Atlas (TCGA) and the Human Protein Atlas (HPA). Protein expression was assessed by immunohistochemistry, and cell-type localisation was determined by single-cell RNA-seq. Functional enrichment was performed to explore related pathways. CRC cell proliferation, migration, invasion, and apoptosis were examined with CCK-8, Transwell, scratch, and flow cytometry assays, while EMT-related proteins were evaluated by western blotting. RESULTS: F11R expression was significantly reduced in CRC compared with normal tissues. IHC revealed cytoplasmic and membranous localisation, and single-cell data showed enrichment in endothelial and epithelial cells. Enrichment analysis implicated F11R in T-cell receptor signalling, cadherin binding, and tight-junction pathways. Expression correlated with CD4⁺ Th1-like cells, effector and resting Tregs, and effector-memory T cells. Silencing F11R enhanced proliferation, migration, and invasion; reduced apoptosis; and promoted EMT, evidenced by decreased ZO-1 and E-cadherin, increased N-cadherin and vimentin, and Rap1 activation. CONCLUSION: F11R regulates EMT and Rap1 signalling, thereby influencing CRC metastasis. Its reduced expression is associated with unfavourable outcomes and tumour progression. Cell type-specific enrichment in endothelial and epithelial cells, along with links to immune subsets, highlights F11R as a potential prognostic biomarker in CRC. KEY WORDS: Colorectal cancer, F11R, Tumour progression, Prognostic potential, Immune infiltration.
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Decoding the universal human chromatin landscape through teratoma-based profiling.
Teratoma formation is key for evaluating differentiation of human pluripotent stem cells into embryonic germ layers and serves as a model for understanding stem cell differentiation and developmental processes. Its potential for insights into epigenome and transcriptome profiling is significant. This study integrates the analysis of the epigenome and transcriptome of hESC-generated teratomas, comparing transcriptomes between hESCs and teratomas. It employs cell type-specific expression patterns from single-cell data to deconvolve RNA-Seq data and identify cell types within teratomas. Our results provide a catalog of activating and repressive histone modifications, while also elucidating distinctive features of chromatin states. Construction of an epigenetic signature matrix enabled the quantification of diverse cell populations in teratomas and enhanced the ability to unravel the epigenetic landscape in heterogeneous tissue contexts. This study also includes a single cell multiome atlas of expression (scRNA-Seq) and chromatin accessibility (scATAC-Seq) of human teratomas, further revealing the complexity of these tissues. A histology-based digital staining tool further complemented the annotation of cell types in teratomas, enhancing our understanding of their cellular composition. This research is a valuable resource for examining teratoma epigenomic and transcriptomic landscapes and serves as a model for epigenetic data comparison.
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Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration.
Genome-wide association studies (GWAS) have identified genetic variants associated with age-related macular degeneration (AMD), one of the leading causes of blindness in the elderly. However, it has been challenging to identify the cell types associated with AMD given the genetic complexity of the disease. Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the first single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures. Heterogeneity is observed within macroglia, suggesting that human retinal glia are more diverse than previously thought. Finally, GWAS-based enrichment analysis identifies glia, vascular cells, and cone photoreceptors to be associated with the risk of AMD. These data provide a detailed analysis of the human retina, and show how scRNA-seq can provide insight into cell types involved in complex, inflammatory genetic diseases.
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scDGCN: A Graph-Based Deep Clustering Framework for Single-Cell Transcriptomics via Neighborhood-Enhanced Context Representation Learning.
Self-supervised and context-aware representation learning offers a powerful strategy for extracting robust features from high-dimensional, sparse, and noisy single-cell data. Here, we present scDGCN, a graph-based deep clustering framework for single-cell RNA sequencing (scRNA-seq) analysis. scDGCN models intercellular similarity as a graph and leverages local neighborhood context to guide expression reconstruction, thereby learning robust cell representations. The method integrates a network enhancement module to denoise the cell similarity graph, a neighborhood-enhanced autoencoder that reconstructs gene expression using topological neighborhood context, and a graph neural network to preserve structural semantics among cells. By embedding each cell's expression profile within its neighbors, scDGCN captures subtle transcriptional variation and identifies cell subtypes without labeled references. Across ten scRNA-seq datasets from diverse human and mouse tissues, scDGCN achieves competitive adjusted Rand index (ARI), normalized mutual information (NMI), and clustering accuracy (ACC), supporting unsupervised identification of cellular subpopulations. These results demonstrate that combining graph denoising, neighborhood-enhanced context reconstruction, and graph-structured representation learning improves unsupervised single-cell clustering and facilitates high-resolution cell atlas construction.
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Single-cell sequencing uncovers the mechanistic role of DAPK1 in glioma and its diagnostic and prognostic implications.
BACKGROUND: We conducted an investigation into the characteristics of single-cell differentiation data in gliomas, with a focus on developing DAPK1-based prognostic markers to predict patient outcomes. Dysregulated expression of DAPK1 has been associated with the invasive behavior of various malignancies, including gliomas. However, the precise role and underlying mechanisms of DAPK1 in gliomas remain inadequately understood. METHODS: We performed analyses on RNA-seq and microarray datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to single-cell RNA sequencing (scRNA-seq) data from glioma patients available in GEO. Utilizing the Seurat R package, we identified gene clusters associated with survival from the scRNA-seq data. Prognostic models were developed using LASSO and stepwise regression algorithms. Furthermore, we assessed the predictive potential of these genes within the immune microenvironment and their relevance in immunotherapy contexts. RESULTS: Our scRNA-seq data analysis revealed 32 distinct cell clusters corresponding to 10 cell types. Through dimensionality reduction and clustering, we identified three glial cell subpopulations based on their differentiation trajectories. DAPK1, serving as a marker gene for the terminal subpopulation, exhibited an association with poor prognosis. CONCLUSIONS: DAPK1-based prognostic models show promise for accurately predicting outcomes in glioblastoma and glioma. An in-depth examination of DAPK1's specific mechanisms in glioblastoma could elucidate its role in immunotherapy response. Targeting the DAPK1 gene may offer therapeutic benefits for glioma patients.
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Copper Metabolism-Related Genes as Biomarkers in Colon Adenoma and Cancer.
PURPOSE: To elucidate the role of copper (Cu) metabolism in the progression of colon adenoma (CA) to colorectal cancer (CRC) and to identify potential biomarkers and therapeutic targets through comprehensive bioinformatics analysis. PATIENTS AND METHODS: Datasets associated with colon adenoma were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between CA samples and normal controls (NC) were intersected with genes related to copper metabolism (CMRGs) and DEGs between CRC and CA. Five machine-learning algorithms were employed to identify biomarkers. The degree of immune infiltration was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), and the expression profiles of these biomarkers across various cell types were further characterized using single-cell RNA sequencing (scRNA-seq). The expression levels of the identified genes were validated using quantitative polymerase chain reaction (qPCR) and data from the Human Protein Atlas (HPA) database. RESULTS: Five biomarkers were identified: ZEB1, ABCA1, SLC24A3, CAV1, and FLNA. Functional enrichment analysis revealed significant pathway alterations in the low-expression groups of CAV1 (eg, phagosome pathway) and FLNA (eg, ribosome pathway). Significant differences in the infiltration abundance of macrophages and mast cells were observed between CA and NC. scRNA-seq analysis demonstrated that these biomarkers were expressed in fibroblasts, lymphocytes, goblet cells, B cells, and macrophages. The consistency of gene expression between patient samples and public datasets was confirmed through qPCR and HPA data. CONCLUSION: This study explores the role of copper metabolism in colon adenoma progression using bioinformatics. Five genes (ZEB1,ABCA1, SLC24A3, CAV1, FLNA) were identified as potential biomarkers. These genes correlate with immune infiltration and may serve as diagnostic and therapeutic targets. Further clinical validation is needed.
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Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation.
A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.
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Single-cell transcriptomic analysis of flowering regulation and vernalization in Chinese cabbage shoot apex.
In Chinese cabbage development the interplay between shoot apex activity and vernalization is pivotal for flowering timing. The intricate relationship between various cell types in the shoot apex meristem and their roles in regulating flowering gene expression in Chinese cabbage is not yet fully understood. A thorough analysis of single-cell types in the Chinese cabbage shoot apex and their influence on flowering genes and vernalization is essential for deeper insight. Our study first established a single-cell transcriptomic atlas of Chinese cabbage after 25 days of non-vernalization. Analyzing 19 602 single cells, we differentiated them into 15 distinct cell clusters using established marker genes. We found that key genes in shoot apex development and flowering were primarily present in shoot meristematic cells (SMCs), companion cells (CCs), and mesophyll cells (MCs). MADS-box protein FLOWERING LOCUS C 2 (BrFLC2), a gene suppressing flowering, was observed in CCs, mirroring patterns found in Arabidopsis. By mapping developmental trajectories of SMCs, CCs, and MCs, we elucidated the evolutionary pathways of crucial genes in shoot apex development and flowering. The creation of a single-cell transcriptional atlas of the Chinese cabbage shoot apex under vernalization revealed distinct alterations in the expression of known flowering genes, such as VERNALIZATION INSENSITIVE 3 (VIN3), VERNALIZATION 1 (VRN1), VERNALIZATION 2 (VRN2), BrFLC, and FLOWERING LOCUS T (FT), which varied by cell type. Our study underscores the transformative impact of single-cell RNA sequencing (scRNA-seq) for unraveling the complex differentiation and vernalization processes in the Chinese cabbage shoot apex. These insights are pivotal for enhancing breeding strategies and cultivation management of this vital vegetable.
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Transcriptomic Mapping of Human Parotid Gland at Single-Cell Resolution.
As the largest salivary gland in oral cavity, the parotid gland plays an important role in initial digesting and lubricating food. The abnormal secretory function of the parotid gland can lead to dental caries and oral mucosal inflammation. In recent years, single-cell RNA sequencing (scRNA-seq) has been used to explore the heterogeneity and diversity of cells in various organs and tissues. However, the transcription profile of the human parotid gland at single-cell resolution has not been reported yet. In this study, we constructed the cell atlas of human parotid gland using the 10× Genomics platform. Characteristic gene analysis identified the biological functions of serous acinar cell populations in secreting digestive enzymes and antibacterial proteins. We revealed the specificity and similarity of the parotid gland compared to other digestive glands through comparative analyses of other published scRNA-seq data sets. We also identified the cell-specific expression of hub genes for Sjögren syndrome in the human parotid gland by integrating the results of genome-wide association studies and bulk RNA-seq, which highlighted the importance of immune cell dysfunction in parotid Sjögren syndrome pathogenesis.