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Found: 4 898
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Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes.
Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here, we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in Drosophila starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study. With over 44,000 nuclei and 6000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, in situ hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function in vivo.
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Identification of immunotherapy biomarkers for improving the clinical outcome of homologous recombination deficiency patients with lung adenocarcinoma.
Homologous recombination deficiency (HRD) is a common molecular signature of genomic instability and has been shown to be a biomarker for targeted therapies. However, there is a lack of studies on the role of HRD changes in lung adenocarcinoma (LUAD) transcriptomics. HRD scores were determined using single nucleotide polymorphism (SNP) array data from LUAD patients from The Cancer Genome Atlas (TCGA) database. Transcriptional data from patients with different scores were analyzed to identify biomarkers associated with HRD. Candidate biomarkers were validated using Gene Expression Omnibus (GEO)-sourced datasets and an immunotherapy cohort. According to the bulk transcriptome and clinical characteristics of 912 LUAD patients and Single-cell RNA-seq of 9 LUAD patients from TCGA and GEO databases, we observed increased MS4A6A expression in HRD tumors; high MS4A6A expression predicted improved survival outcomes. Furthermore, a comprehensive analysis of the tumor immune microenvironment (TIME) revealed a positive correlation between MS4A6A expression and neoantigen loading and immune cell infiltration. Additionally, the immunotherapy cohort confirmed the possibility of using MS4A6A as a biomarker. Collectively, we suggest that MS4A6A is associated with HRD and provide a new perspective toward identifying promising biomarkers for immunotherapy.
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Bulk and single cell RNA sequencing data reveal lactylation-related gene signatures for prognosis and immunity in cervical cancer.
Previous studies indicate that lactylation, a post-translational modification, may play an important role in the progression of cervical cancer (CC). However, the comprehensive roles of lactylation in influencing the tumor microenvironment, immune landscape, and prognosis of CC have yet to be fully elucidated. The bulk RNA-seq and single-cell RNA-seq datasets of CC patients were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. A total of 630 genes associated with lactylation activity were identified using AUCell algorithm, differential expression, and correlation analyses. Subsequently, a prognostic risk model comprising 17 genes was constructed through univariate Cox and LASSO analyses, which accurately predicted the prognosis of CC patients and was validated in an independent dataset. The nomogram, including risk scores and N staging, outperformed other clinical parameters. The high-risk group was positively related to the glycolysis pathway. Furthermore, bioinformatics analyses further indicated that the high-risk group was associated with a poorer prognosis, pro-tumorigenic pathways, and immunosuppression, and was sensitive to ulixertinib, dasatinib, nutlin-3a, and trametinib. Mendelian randomization analysis suggested that ITGA5 may be causally associated with an increased risk of CC, with caution in causal inference. Additionally, the expressions of several risk genes were validated using real-time qPCR on tissue samples from six CC patients. In conclusion, the lactylation-related gene risk model could accurately and independently predict the prognosis of CC patients, providing insights into therapeutic strategies for CC patients. These bioinformatics-based findings are exploratory and warrant further validation in preclinical and clinical settings.
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A single-parasite transcriptional atlas of Toxoplasma Gondii reveals novel control of antigen expression.
Toxoplasma gondii, a protozoan parasite, undergoes a complex and poorly understood developmental process that is critical for establishing a chronic infection in its intermediate hosts. Here, we applied single-cell RNA-sequencing (scRNA-seq) on >5,400 Toxoplasma in both tachyzoite and bradyzoite stages using three widely studied strains to construct a comprehensive atlas of cell-cycle and asexual development, revealing hidden states and transcriptional factors associated with each developmental stage. Analysis of SAG1-related sequence (SRS) antigenic repertoire reveals a highly heterogeneous, sporadic expression pattern unexplained by measurement noise, cell cycle, or asexual development. Furthermore, we identified AP2IX-1 as a transcription factor that controls the switching from the ubiquitous SAG1 to rare surface antigens not previously observed in tachyzoites. In addition, comparative analysis between Toxoplasma and Plasmodium scRNA-seq results reveals concerted expression of gene sets, despite fundamental differences in cell division. Lastly, we built an interactive data-browser for visualization of our atlas resource.
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Single-cell sequencing reveals MYC targeting gene MAD2L1 is associated with prostate cancer bone metastasis tumor dormancy.
BACKGROUND: Among malignant tumors, bone metastasis is frequently associated with prostate cancer which is seen in about 80% of patients. During cancer treatments, some tumor cells switch to a "dormant mode" to help tumor cells avoid attack from the immune system and anti-tumor therapies. In this dormant mode, tumor cells can be resuscitated, causing cancer to reoccur. The generally accepted explanation for this phenomenon is that the tumor cells have spread to the bone marrow before treatment and are dormant in the bone marrow. However, the key mechanism for inducing and maintaining the dormancy of these prostate cancer disseminated tumor cells in the bone marrow is still unclear. Therefore, studying the dormancy mechanism of tumor cells in bone metastasis is of great significance for the treatment and the prevention of recurrence of prostate cancer. METHODS: We obtained single-cell RNA-seq data of tumors from mouse models of prostate cancer bone metastasis mouse model numbered (GSE147150) from the GEO database, and obtained RNA-seq expression data and clinical information from The Cancer Genome Atlas Program (TCGA) of prostate cancer patients from the USCS Xena database. Screening of differential genes and annotation of GO functions were performed separately. Subsequently, the screened differential genes were compared and analyzed with 50 classic Hallmark signaling pathways, and the prognosis analysis of prostate cancer patients in TCGA data was performed to discover the key genes of the dormant mechanism of tumor cells in bone metastasis, and obtain new biomarkers that can be used to predict the prognosis of patients. RESULTS: A total of 378 differentially expressed genes were screened, of which 293 were significantly up-regulated and 85 were significantly down-regulated. Among them, the up-regulated genes were mainly related to the immune response, and the down-regulated genes were mainly related to the cell cycle. Through GSVA (Gene set variation analysis), it is found that there are differences in a total of 3 signal pathways: COMPLEMENT, MYC_TARGETS_V1 and MYC_TARGETS_V2. By comparing and analyzing the significantly down-regulated genes in dormant tumor cells with MYC_TARGETS_V1, MYC_TARGETS_V2, three significantly down-regulated genes were obtained: Ccna2, Mad2L1 and Plk1. CONCLUSION: In summary, our findings indicate that the MYC targeting gene Mad2L1 is potentially related to the dormancy mechanism of prostate cancer. At the same time, Mad2L1, a gene associated with dormant prostate cancer cells, may be used as a biomarker for prognostic survival.
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Lactate metabolism gene signature predicts prognosis in breast cancer using bulk and single cell sequencing data.
OBJECTIVE: Lactate metabolism, a key feature of the tumor microenvironment, plays a crucial role in cancer progression and immune suppression. However, the prognostic implication of lactate metabolism-related genes in breast cancer (BC) is not fully understood. This study aimed to explore key lactate metabolism-related genes related to the prognosis and immune cell clusters in BC. METHODS: Based on The Cancer Genome Atlas (TCGA) RNA-seq data, lactate-related differentially expressed genes in BC were identified from lactate gene sets from MSigDB. A prognostic risk model was built through univariate and LASSO Cox regression analyses and validated in an independent Gene Expression Omnibus (GEO) cohort. Based on single-cell RNA-seq datasets from GEO database, immune infiltration was assessed via CIBERSORT, MCP-counter, and ssGSEA. Single-cell data analysis using Seurat, Monocle3, and CellChat elucidated cellular heterogeneity, developmental trajectories, and cell-cell communication. RESULTS: A 15-gene lactate-related prognostic signature was established. High-risk scores correlated significantly with poorer overall survival, advanced pathological stage, and lymph node metastasis. Single-cell analysis revealed nine major cell types. Cancer-associated fibroblasts promoted ECM remodeling via COL1A1, while tumor epithelial cells engaged in immunosuppressive interactions. Key molecules such as the lactate transporters MCT1/4 and CD147 axis were associated with poor prognosis. Immune regulatory pathways involving macrophage migration inhibitory factor-CD74 and MDK-SDC4 were linked to tumor migration and immune evasion, representing potential therapeutic targets. CONCLUSION: This study established a robust lactate metabolism-related gene signature for prognostic stratification in BC and highlighted its link to an immunosuppressive microenvironment, offering novel insights for developing targeted therapeutic strategies.
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Single-cell RNA-Seq analysis identifies a putative epithelial stem cell population in human primary prostate cells in monolayer and organoid culture conditions.
Human primary prostate epithelial (PrE) cells represent patient-derived in vitro models and are traditionally grown as a monolayer in two-dimensional culture. It has been recently demonstrated that expansion of primary cells into three-dimensional prostatic organoids better mimics prostate epithelial glands by recapitulating epithelial differentiation and cell polarity. Here, we sought to identify cell populations present in monolayer PrE cells and organoid culture, grown from the same patient, using single-cell RNA-sequencing. Single-cell RNA-sequencing is a powerful tool to analyze transcriptome profiles of thousands of individual cells simultaneously, creating an in-depth atlas of cell populations within a sample. Organoids consisted of six distinct cell clusters (populations) of intermediate differentiation compared to only three clusters in the monolayer prostate epithelial cells. Integrated analysis of the datasets allowed for direct comparison of the monolayer and organoid samples and identified 10 clusters, including a distinct putative prostate stem cell population that was high in Keratin 13 (KRT13), Lymphocyte Antigen 6D (LY6D), and Prostate Stem Cell Antigen (PSCA). Many of the genes within the clusters were validated through RT-qPCR and immunofluorescence in PrE samples from 5 additional patients. KRT13+ cells were observed in discrete areas of the parent tissue and organoids. Pathway analyses and lack of EdU incorporation corroborated a stem-like phenotype based on the gene expression and quiescent state of the KRT13+ cluster. Other clusters within the samples were similar to epithelial populations reported within patient prostate tissues. In summary, these data show that the epithelial stem population is preserved in PrE cultures, with organoids uniquely expanding intermediate cell types not present in monolayer culture.
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A stony coral cell atlas illuminates the molecular and cellular basis of coral symbiosis, calcification, and immunity.
Stony corals are colonial cnidarians that sustain the most biodiverse marine ecosystems on Earth: coral reefs. Despite their ecological importance, little is known about the cell types and molecular pathways that underpin the biology of reef-building corals. Using single-cell RNA sequencing, we define over 40 cell types across the life cycle of Stylophora pistillata. We discover specialized immune cells, and we uncover the developmental gene expression dynamics of calcium-carbonate skeleton formation. By simultaneously measuring the transcriptomes of coral cells and the algae within them, we characterize the metabolic programs involved in symbiosis in both partners. We also trace the evolution of these coral cell specializations by phylogenetic integration of multiple cnidarian cell type atlases. Overall, this study reveals the molecular and cellular basis of stony coral biology.
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Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic references.
Single-cell RNA-sequencing (scRNA-seq) is an indispensable tool for characterizing cellular diversity and generating hypotheses throughout biology. Droplet-based scRNA-seq datasets often lack expression data for genes that can be detected with other methods. Here we show that the observed sensitivity deficits stem from three sources: (1) poor annotation of 3' gene ends; (2) issues with intronic read incorporation; and (3) gene overlap-derived read loss. We show that missing gene expression data can be recovered by optimizing the reference transcriptome for scRNA-seq through recovering false intergenic reads, implementing a hybrid pre-mRNA mapping strategy and resolving gene overlaps. We demonstrate, with a diverse collection of mouse and human tissue data, that reference optimization can substantially improve cellular profiling resolution and reveal missing cell types and marker genes. Our findings argue that transcriptomic references need to be optimized for scRNA-seq analysis and warrant a reanalysis of previously published datasets and cell atlases.
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scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning.
Single-cell multiomics data continues to grow at an unprecedented pace. Although several methods have demonstrated promising results in integrating several data modalities from the same tissue, the complexity and scale of data compositions present in cell atlases still pose a challenge. Here, we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semisupervised framework and uses a neural network to simultaneously train labeled and unlabeled data, allowing label transfer and joint visualization in an integrative framework. Using atlas data as well as multimodal datasets generated with ASAP-seq and CITE-seq, we demonstrate that scJoint is computationally efficient and consistently achieves substantially higher cell-type label accuracy than existing methods while providing meaningful joint visualizations. Thus, scJoint overcomes the heterogeneity of different data modalities to enable a more comprehensive understanding of cellular phenotypes.