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Single-cell RNA-seq analyses show that long non-coding RNAs are conspicuously expressed in Schistosoma mansoni gamete and tegument progenitor cell populations.
Schistosoma mansoni is a flatworm that causes schistosomiasis, a neglected tropical disease that affects over 200 million people worldwide. New therapeutic targets are needed with only one drug available for treatment and no vaccine. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with low or no protein-coding potential. In other organisms, they have been shown as involved with reproduction, stem cell maintenance and drug resistance, and they tend to exhibit tissue-specific expression patterns. S. mansoni expresses thousands of lncRNA genes; however, the cell type expression patterns of lncRNAs in the parasite remain uncharacterized. Here, we have re-analyzed publicly available single-cell RNA-sequencing (scRNA-seq) data obtained from adult S. mansoni to identify the lncRNAs signature of adult schistosome cell types. A total of 8023 lncRNAs (79% of all lncRNAs) were detected. Analyses of the lncRNAs expression profiles in the cells using statistically stringent criteria were performed to identify 74 lncRNA gene markers of cell clusters. Male gamete and tegument progenitor lineages clusters contained most of the cluster-specific lncRNA markers. We also identified lncRNA markers of specific neural clusters. Whole-mount in situ hybridization (WISH) and double fluorescence in situ hybridization were used to validate the cluster-specific expression of 13 out of 16 selected lncRNA genes (81%) in the male and female adult parasite tissues; for one of these 16 gene loci, probes for two different lncRNA isoforms were used, which showed differential isoform expression in testis and ovary. An atlas of the expression profiles across the cell clusters of all lncRNAs detected in our analysis is available as a public website resource (http://verjolab.usp.br:8081). The results presented here give strong support to a tissue-specific expression and to a regulated expression program of lncRNAs in S. mansoni. This will be the basis for further exploration of lncRNA genes as potential therapeutic targets.
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A multimodal zebrafish developmental atlas reveals the state-transition dynamics of late-vertebrate pluripotent axial progenitors.
Elucidating organismal developmental processes requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by reconstructions of cellular trajectories. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate-mapping experiments. To demonstrate the versatility of our multimodal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of neuro-mesodermal progenitors (NMPs) and the origins of a joint kidney-hemangioblast progenitor population.
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Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing.
BACKGROUND: This study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq). METHODS: Bulk RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases. 10x scRNA-seq data for GBM were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). A non-negative matrix decomposition (NMF) algorithm was used to identify the different subtypes based on DEGs, and multivariate cox regression analysis to model the prognosis. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects, and enriched pathways were investigated between different risk groups. RESULTS: The analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying Fisher's exact test, ECs were identified as the most important cell type. The NMF algorithm identified two clusters with different prognostic and immunological features based on DEGs. We finally built a prognostic model based on the expression levels of four key genes. Higher risk scores were significantly associated with poorer survival outcomes, low mutation rates in IDH genes, and upregulation of immune checkpoints such as PD-L1 and CD276. CONCLUSION: We built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work.
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Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing.
OBJECTIVE: Ovarian cancer is the deadliest gynaecological cancer globally. In our study, we aimed to analyze specific cell subpopulations and marker genes among ovarian cancer cells by single-cell RNA sequencing (RNA-seq). METHODS: Single-cell RNA-seq data of 66 high-grade serous ovarian cancer cells were employed from the Gene Expression Omnibus (GEO). Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, principal component analysis (PCA) and cell clustering were performed. The marker genes in different cell clusters were detected. A total of 568 ovarian cancer samples and 8 normal ovarian samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were identified according to ∣log2fold change (FC) | >1 and adjusted p value <0.05. To explore potential biological processes and pathways, functional enrichment analyses were performed. Furthermore, survival analyses of differentially expressed marker genes were performed. RESULTS: After normalization, 6000 highly variable genes were identified across the single cells. The cells were divided into 3 cell populations, including G1, G2M, and S cell cycles. A total of 1,124 differentially expressed genes were identified in ovarian cancer samples. These differentially expressed genes were enriched in several pathways associated with cancer, such as metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. Furthermore, marker genes, STAT1, ANP32E, GPRC5A, and EGFL6, were highly expressed in ovarian cancer, while PMP22, FBXO21, and CYB5R3 were lowly expressed in ovarian cancer. These marker genes were positively associated with prognosis of ovarian cancer. CONCLUSION: Our findings revealed specific cell subpopulations and marker genes in ovarian cancer using single-cell RNA-seq, which provided a novel insight into the heterogeneity of ovarian cancer.
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ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses.
Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in 'big data' management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.
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Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus.
BACKGROUND: Hepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses. METHODS: scRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC. RESULTS: These analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. In vitro experiments were used to partially validate the biological prediction results. CONCLUSION: These results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.
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Single-cell analysis of shared signatures and transcriptional diversity during zebrafish development.
During development, animals generate distinct cell populations with specific identities, functions, and morphologies. We mapped transcriptionally distinct populations across 489,686 cells from 62 stages during wild-type zebrafish embryogenesis and early larval development (3-120 h post-fertilization). Using these data, we identified the limited catalog of gene expression programs reused across multiple tissues and their cell-type-specific adaptations. We also determined the duration each transcriptional state is present during development and identify unexpected long-term cycling populations. Focused clustering and transcriptional trajectory analyses of non-skeletal muscle and endoderm identified transcriptional profiles and candidate transcriptional regulators of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and recently discovered best4+ cells. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.
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Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals distinct cancer-associated fibroblasts in head and neck squamous cell carcinoma.
BACKGROUND: The heterogeneity of cancer-associated fibroblasts (CAFs) in head and neck squamous cell carcinoma (HNSCC) has been widely acknowledged, but has not yet been elucidated. The potential roles and clinical relevance of CAFs subclusters in HNSCC progression remain obscure. METHODS: In this study, we combined single-cell and bulk tissue transcriptome profiles of HNSCC with clinical data from The Cancer Genome Atlas (TCGA). The Seurat package was used to perform single-cell RNA-seq analysis to distinguish distinct CAFs subtypes. Prognostic relevance of several CAFs markers was assessed and functional analysis was also performed. RESULTS: We identified eight CAFs subclusters; of these, seven showed enhanced expression levels in HNSCC tumor tissues compared to normal tissue, and three (clusters 0, 3, and 4) were associated with poorer overall survival. Further functional analysis revealed that cluster 0 was characterized by myofibroblasts with high alpha smooth muscle actin (aSMA) expression and enrichment in smooth muscle contraction. The cluster 3 exhibited expression of extracellular matrix (ECM)-related genes and was enriched in epithelial-mesenchymal transition (EMT)-related gene sets. Cluster 4 expressed high levels of the major histocompatibility complex (MHC) class II family, which was characterized as antigen-presenting CAFs. CONCLUSIONS: We determined CAFs heterogeneity in HNSCC. 8 CAFs subclusters were recognized and 3 of which were prognosis related. The 3 CAFs subclusters showed distinct phenotypes enriched in myofibroblast function, ECM remodeling and antigen-presenting function respectively.
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Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis.
Scaling scRNA-seq to profile millions of cells is crucial for constructing high-resolution maps of transcriptional manifolds. Current analysis strategies, in particular dimensionality reduction and two-phase clustering, offer only limited scaling and sensitivity to define such manifolds. We introduce Metacell-2, a recursive divide-and-conquer algorithm allowing efficient decomposition of scRNA-seq datasets of any size into small and cohesive groups of cells called metacells. Metacell-2 improves outlier cell detection and rare cell type identification, as shown with human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline.
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Single-cell RNA landscape of the osteoimmunology microenvironment in periodontitis.
Single-cell RNA sequencing (scRNA-seq) enables specific profiling of cell populations at single-cell resolution. The osteoimmunology microenvironment in the occurrence and development of periodontitis remains poorly understood at the single-cell level. In this study, we used single-cell transcriptomics to comprehensively reveal the complexities of the molecular components and differences with counterparts residing in periodontal tissues. Methods: We performed scRNA-seq to identify 51248 single cells from healthy controls (n=4), patients with severe chronic periodontitis (n=5), and patients with severe chronic periodontitis after initial periodontal therapy within 1 month (n=3). Uniform manifold approximation and projection (UMAP) were further conducted to explore the cellular composition of periodontal tissues. Pseudotime cell trajectory and RNA velocity analysis, combined with gene enrichment analysis were used to reveal the molecular pathways underlying cell fate decisions. CellPhoneDB were performed to identify ligand-receptor pairs among the major cell types in the osteoimmunology microenvironment of periodontal tissues. Results: A cell atlas of the osteoimmunology microenvironment in periodontal tissues was characterized and included ten major cell types, such as fibroblasts, monocytic cells, endothelial cells, and T and B cells. The enrichment of TNFRSF21+ fibroblasts with high expression of CXCL1, CXCL2, CXCL5, CXCL6, CXCL13, and IL24 was detected in patients with periodontitis compared to healthy individuals. The fractions of CD55+ mesenchymal stem cells (MSCs), APOE+ pre-osteoblasts (pre-OBs), and IBSP+ osteoblasts decreased significantly in response to initial periodontal therapy. In addition, CXCL12+ MSC-like pericytes could convert their identity into a pre-OB state during inflammatory responses even after initial periodontal therapy confirmed by single-cell trajectory. Moreover, we portrayed the distinct subtypes of monocytic cells and abundant endothelial cells significantly involved in the immune response. The heterogeneity of T and B cells in periodontal tissues was characterized. Finally, we mapped osteoblast/osteoclast differentiation mediators to their source cell populations by identifying ligand-receptor pairs and highlighted the effects of Ephrin-Eph signaling on bone regeneration after initial periodontal therapy. Conclusions: Our analyses uncovered striking spatiotemporal dynamics in gene expression, population composition, and cell-cell interactions during periodontitis progression. These findings provide insights into the cellular and molecular underpinning of periodontal bone regeneration.