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Comprehensive single-cell atlas of the mouse retina.
Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity by characterizing cell types across tissues and species. While several mouse retinal scRNA-seq datasets exist, each dataset is either limited in cell numbers or focused on specific cell classes, thereby hindering comprehensive gene expression analysis across all retina types. To fill the gap, we generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse retinas, enriched for rare population cells via antibody-based magnetic cell sorting. Integrating this dataset with public datasets, we constructed the Mouse Retina Cell Atlas (MRCA) for wild-type mice, encompassing over 330,000 cells, characterizing 12 major classes and 138 cell types. The MRCA consolidates existing knowledge, identifies new cell types, and is publicly accessible via CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal, providing a user-friendly resource for the mouse retina research community.
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Comprehensive single-cell atlas of the mouse retina.
Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.
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A Single-Cell RNA Sequencing Profiles the Developmental Landscape of Arabidopsis Root.
Cells of eukaryotic multicellular organisms have inherent heterogeneity. Recent advances in single-cell gene expression studies enable us to explore transcriptional regulation in dynamic development processes and highly heterogeneous cell populations. In this study, using a high-throughput single-cell RNA-sequencing assay, we found that the cells in Arabidopsis root are highly heterogeneous in their transcriptomes. A total of 24 putative cell clusters and the cluster-specific marker genes were identified. The spatial distribution and temporal ordering of the individual cells at different developmental stages illustrate their hierarchical structures and enable the reconstruction of continuous differentiation trajectory of root development. Moreover, we found that each root cell cluster exhibits distinct patterns of ion assimilation and hormonal responses. Collectively, our study reveals a high degree of heterogeneity of root cells and identifies the expression signatures of intermediate states during root cell differentiation at single-cell resolution. We also established a web server (http://wanglab.sippe.ac.cn/rootatlas/) to facilitate the use of the datasets generated in this study.
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Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress.
Current single-cell RNA sequencing (scRNA-seq) methods with high cellular throughputs sacrifice full-transcript coverage and often sensitivity. Here we describe Smart-seq3xpress, which miniaturizes and streamlines the Smart-seq3 protocol to substantially reduce reagent use and increase cellular throughput. Smart-seq3xpress analysis of peripheral blood mononuclear cells resulted in a granular atlas complete with common and rare cell types. Compared with droplet-based single-cell RNA sequencing that sequences RNA ends, the additional full-transcript coverage revealed cell-type-associated isoform variation.
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Analysis and validation of prognostic markers for thyroid cancer by single-cell and bulk RNA sequencing.
BACKGROUND: Thyroid cancer represents the most prevalent malignancy within the endocrine system, exhibiting consistent increasing incidence rates over the years. We aim to utilize single-cell RNA sequencing (scRNA-seq) dataset to analyze key genes associated with prognosis in thyroid cancer. METHODS: Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to detect thyroid cell-related genes in the paratumor and tumor groups. Least absolute shrinkage and selection operator (LASSO) analysis was conducted on The Cancer Genome Atlas (TCGA) data to identify key genes and develop prognostic models. Immune infiltration analysis, correlation analysis, and differential expression analysis were used to explore the immunological features, correlation, and expression levels of the characterized genes. Western blot and immunohistochemical staining experiments were used to further validate the expression patterns of the characterized genes. RESULTS: A total of 3,681 differentially expressed genes (DEGs) were recognized. There were 157 genes derived from the key modules in the WGCNA analysis. By performing intersection and conducting LASSO analysis, a prognostic model was developed based on the expression levels of six signature genes (APOE, MDK, NPC2, FCGBP, GLUL, and DTX4). The model's applicability was confirmed using the Gene Expression Omnibus (GEO) validation set. Immune infiltration analysis revealed varying degrees of immune cell infiltration between the high- and low-risk groups, which were intimately associated with the characterized genes. Subsequent experiments confirmed that APOE, MDK, and NPC2 protein levels were significantly higher in the thyroid cancer group compared to the control group. CONCLUSIONS: APOE, MDK, NPC2, FCGBP, GLUL, and DTX4 may be key genes in thyroid cancer progression and serve as potential targets for disease treatment.
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Single-Cell Transcriptomic Atlas of Different Human Cardiac Arteries Identifies Cell Types Associated With Vascular Physiology.
[Figure: see text].
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FastRNA: An efficient solution for PCA of single-cell RNA-sequencing data based on a batch-accounting count model.
Almost always, the analysis of single-cell RNA-sequencing (scRNA-seq) data begins with the generation of the low dimensional embedding of the data by principal-component analysis (PCA). Because scRNA-seq data are count data, log transformation is routinely applied to correct skewness prior to PCA, which is often argued to have added bias to data. Alternatively, studies have proposed methods that directly assume a count model and use approximately normally distributed count residuals for PCA. Despite their theoretical advantage of directly modeling count data, these methods are extremely slow for large datasets. In fact, when the data size grows, even the standard log normalization becomes inefficient. Here, we present FastRNA, a highly efficient solution for PCA of scRNA-seq data based on a count model accounting for both batches and cell size factors. Although we assume the same general count model as previous methods, our method uses two orders of magnitude less time and memory than the other count-based methods and an order of magnitude less time and memory than the standard log normalization. This achievement results from our unique algebraic optimization that completely avoids the formation of the large dense residual matrix in memory. In addition, our method enjoys a benefit that the batch effects are eliminated from data prior to PCA. Generating a batch-accounted PC of an atlas-scale dataset with 2 million cells takes less than a minute and 1 GB memory with our method.
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Pan-cancer multi-omics dissection of podosome-related genes reveals their dual roles in tumor invasion and immune modulation.
BACKGROUND: Podosomes and their cancer-specific counterparts, invadopodia, are pivotal organelles facilitating extracellular matrix (ECM) degradation and tumor invasion. However, the pan-cancer landscape and clinical implications of podosome-related genes (PRGs) remain largely unexplored. METHODS: We conducted a systematic pan-cancer analysis of six core PRGs—MMP9, NCK1, RHOA, SH3PXD2A, SRC, and STIM1—across 33 tumor types utilizing multi-dimensional datasets from TCGA, GTEx, GDSC, TIDE, and CancerSEA. We integrated expression profiles with somatic mutations, DNA methylation, and copy number variations (CNV) to delineate their regulatory landscape. A podosome activity score was constructed via ssGSEA, and single-cell RNA-seq (scRNA-seq) data were leveraged to resolve cell-type specificity. We further evaluated associations with immune checkpoints, tumor-infiltrating lymphocytes (TILs), and immunotherapy response predictors. Finally, Colon Adenocarcinoma (COAD) was selected for integrative transcriptomic analysis and experimental validation of the hub gene MMP9. RESULTS: Our analysis revealed that PRGs are extensively dysregulated across tumor types, driven by distinct epigenetic and genomic alterations. Elevated podosome scores correlated with advanced tumor stage, poor prognosis, and an immunosuppressive microenvironment characterized by high TGFB1 expression and T-cell exclusion. Single-cell profiling identified that podosome activity was predominantly enriched in monocytes, endothelial cells, and mast cells, highlighting their role in immune remodeling and angiogenesis. Functionally, PRGs were strongly associated with epithelial–mesenchymal transition (EMT), metastasis, and drug sensitivity, particularly to targeted agents like dasatinib. In COAD, integrated network analysis and in vitro assays confirmed MMP9 as a central driver promoting cancer cell migration and invasion. CONCLUSIONS: This study provides the first comprehensive pan-cancer functional atlas of PRGs. Our findings elucidate the dual roles of PRGs in driving tumor invasion and modulating the immune microenvironment, nominating them as promising biomarkers and therapeutic targets for precision oncology.
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Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.
BACKGROUND: Endometriosis is often diagnosed late and presents significant challenges in clinical treatment. A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment. METHODS: This study utilized the Gene Expression Omnibus (GEO) public database and referenced single-cell RNA sequencing (scRNA-seq) atlases. The CIBERSORTx algorithm was applied to perform deconvolution on the samples and estimate the proportions of endometrial cell subtypes. A random forest model was constructed to predict the diagnosis of endometriosis. Additionally, immunohistochemical validation was performed on the marker genes of MUC5B+ epithelial cells and dStromal late mesenchymal cells, which showed high diagnostic contribution. RESULTS: Endometriosis consists of 5 major cell types, further classified into 52 distinct cell subtypes. Compared to healthy controls, these subtypes exhibited varying degrees of alterations, with MUC5B+ epithelial cells, dStromal late mesenchymal cells, and M2 macrophages showing an increasing trend. Enriched signaling pathways were primarily associated with epithelial-mesenchymal transition (EMT), cell migration, and inflammatory responses. A random forest model, based on cell-type proportions, has been shown to achieve excellent diagnostic performance (AUC = 0.932), with MUC5B+ epithelial cells identified as the top predictive feature. Immunohistochemical validation confirmed high expression of the marker genes MUC5B and TFF3. CONCLUSION: By integrating single-cell and bulk transcriptomics, we identified MUC5B+ epithelial cells and dStromal-late mesenchymal cells as dual drivers of fibrosis and inflammation in endometriosis. Our findings revealed that MUC5B+ epithelial cells may serve as the top factor for the diagnosis of endometriosis.
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Investigating prognostic features in high-grade serous ovarian cancer through gene regulatory network inference with single-cell transcriptomic profiles.
This study aimed to identify prognostic features in high-grade serous ovarian cancer (HGSOC) through the application of gene regulatory network (GRN) inference with single-cell RNA-sequencing (scRNA-seq) profiles. To achieve this goal, we developed a workflow comprising scRNA-seq analysis, metacell construction, GRN inference, and a binary classification task for prognosis prediction. We curated 118,173 cells from HGSOC patients in three conditions (Before-chemotherapy, After-chemotherapy, and control samples) from previous studies, and then constructed 1,211 metacells. GRN inference analysis revealed 312 regulons, each consisting of one transcription factor and its targeted features. For prognosis evaluation, we used bulk RNA-seq data covering 342 HGSOC patients from The Cancer Genome Atlas (TCGA) and defined a binary outcome of overall survival ≥ 2 years from initial diagnosis, with censored cases at last follow-up assigned to the appropriate class by observed time. We prioritized the features of the TCGA data based on regulon information and differentially expressed features extracted from the metacell data. Our results demonstrated that regulon-based prognostic features were more effective than differential expression-based features in both Before-chemotherapy and After-chemotherapy groups. Our framework can be generalized to other types of cancer when single-cell data for GRN inference and bulk RNA-seq data with clinical outcomes are available.