Project description:Understanding the cellular origin and differentiation status of glioblastoma is critical to resolve the etiology of the disease. we profile 18 patient glioblastomas by single cell RNA sequencing (scRNAseq). From this, we uncovered two principal cell-of-origin relations. Each lineage displays unique directional differentiation trajectories and transcriptional cores from the naïve cell populations. Thus, glioblastoma is defined by robust cell lineage features which may provide insights into the cell origin of the diseases.
Project description:Understanding the cellular origin and differentiation status of glioblastoma is critical to resolve the etiology of the disease. we profile 18 patient glioblastomas by single cell RNA sequencing (scRNAseq). From this, we uncovered two principal cell-of-origin relations. Each lineage displays unique directional differentiation trajectories and transcriptional cores from the naïve cell populations. Thus, glioblastoma is defined by robust cell lineage features which may provide insights into the cell origin of the diseases.
Project description:Understanding the cellular origin and differentiation status of glioblastoma is critical to resolve the etiology of the disease. we profile control and genetically modified human brain perivasuclar fibroblasts by single cell RNA sequencing (scRNAseq). From this, we observed the potential tumorigenicity of brian perivascular fibroblasts.
Project description:Glioblastoma is the most common type of malignant brain tumor among adults. We used single-cell RNA sequencing (scRNA-seq) to analyze the diversity of glioblastoma cells.
Project description:Tumor microtubes (TMs) connect glioma cells to a network with considerable relevance for tumor progression and therapy resistance. The determination of TM-interconnectivity in individual tumors has been challenging and the impact on patient survival unresolved. Here, a connectivity signature from single-cell RNA-sequenced (scRNA-Seq) xenografted primary glioblastoma (GB) cells has been established using a dye uptake methodology, confirmed with recording of cellular calcium epochs and validated with clinical correlations. Astrocyte-like and mesenchymal-like GB cells have the highest connectivity signature scores in scRNA-sequenced patient-derived xenografts and patient samples. In large GB cohorts, network connectivity correlated with the mesenchymal subtype and dismal patient survival. CHI3L1 has been identified and validated as a robust molecular marker of connectivity with functional relevance. The connectivity signature allows novel insights into brain tumor biology, provides a proof-of-principle that tumor cell TM-connectivity is relevant for patients’ prognosis, and serves as a robust prognostic biomarker.
Project description:Plasmodium-specific CD4+ T cells from mice infected with Plasmodium chabaudi chabaudi AS parasites were recovered at Days 0, 7, and 28 to undergo processing and generate scRNA-seq dataset. At Day 28, mice were administered with either saline or artesunate (intermittent artesunate therapy - IAT). scRNA-seq dataset was analysed to investigate transcriptome dynamics of CD4+ T cells from effector to memory states.
Project description:Single-cell RNA-seq (scRNA-seq) can be used to characterize cellular heterogeneity in thousands of cells. The reconstruction of a gene network based on coexpression patterns is a fundamental task in scRNA-seq analyses, and the mutual exclusivity of gene expression can be critical to understand such heterogeneity. Here, we propose an approach for detecting communities from a gene network constructed on the basis of coexpression properties. The community-based comparison of multiple coexpression networks enables the identification of functionally related gene clusters that cannot be fully captured through differential gene expression-based analysis. We also developed a novel metric referred to as the exclusively expressed index (EEI) that identifies mutually exclusive gene pairs from sparse scRNA-seq data. EEI quantifies and ranks the exclusive expression levels of all gene pairs from binary expression patterns while maintaining robustness against a low sequencing depth. We applied our methods to glioblastoma scRNA-seq data and found that gene communities are partially conserved after serum stimulation despite a considerable number of differentially expressed genes. We also demonstrate that the identification of mutually exclusive gene sets with EEI can improve the sensitivity of capturing cellular heterogeneity. Our methods complement existing approaches and provide new biological insights from even a large sparse dataset in the single-cell analysis field.