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Identification of Cell Subpopulations and Interactive Signaling Pathways From a Single-Cell RNA Sequencing Dataset in Osteosarcoma: A Comprehensive Bioinformatics Analysis.

ABSTRACT: Osteosarcoma is a type of highly aggressive bone tumor arising from primitive cells of mesenchymal origin in adults and is associated with a high rate of tumor relapse. However, there is an urgent need to clarify the molecular mechanisms underlying osteosarcoma development. The present study performed integrated bioinformatics analysis in a single-cell RNA sequencing dataset and explored the potential interactive signaling pathways associated with osteosarcoma development. Single-cell transcriptomic analysis of osteosarcoma tissues was performed by using the Seurat R package, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes was performed by using the clusterProfiler R package, and the cell-cell interaction analysis was performed by using the CellPhoneDB package. Our results showed that 11 clustered cell types were identified across 11 osteosarcoma tissues, with cell types including "osteoblastic", "myeloid", "osteoblastic_proli", "osteoclast", and "tumor-infiltrating lymphocytes (TILs)" as the main types. The DEGs between different cell types from primary, metastatic, and recurrent osteosarcomas were mainly enriched in the GO terms including "negative regulation of hydrolase activity", "regulation of peptidase activity", "regulation of binding", "negative regulation of proteolysis", and "negative regulation of peptidase activity" and in the KEGG pathways including "transcriptional misregulation in cancer", "cellular senescence", "apoptosis", "FoxO signaling pathway", "cell cycle", "NF-kappa B signaling pathway", "p53 signaling pathway", "pentose phosphate pathway", and "protein export". For the cell-cell communication network analysis, the different interaction profiles between cell types were detected among primary, metastatic, and recurrent osteosarcomas. Further exploration of the KEGG pathway revealed that these ligand/receptor interactions may be associated with the NF-κB signaling pathway and its interacted mediators. In conclusion, the present study for the first time explored the scRNA-seq dataset in osteosarcoma, and our results revealed the 11 clustered cell types and demonstrated the novel cell-cell interactions among different cell types in primary, metastatic, and recurrent osteosarcomas. The NF-κB signaling pathway may play a key role in regulating the TME of osteosarcoma. The present study may provide new insights into understanding the molecular mechanisms of osteosarcoma pathophysiology.

SUBMITTER: Wu R

PROVIDER: S-EPMC9066489 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Detrimental health consequences from exposure to space radiation are a major concern for long-duration human exploration missions to the Moon or Mars. Cellular responses to radiation are expected to be heterogeneous for space radiation exposure, where only high-energy protons and other particles traverse a fraction of the cells. Therefore, assessing DNA damage and DNA damage response in individual cells is crucial in understanding the mechanisms by which cells respond to different particle types and energies in space. In this project, we identified a cell-specific signature for radiation response by using single-cell transcriptomics of human lymphocyte subpopulations. We investigated gene expression in individual human T lymphocytes 3 h after ex vivo exposure to 2-Gy gamma rays while using the single-cell sequencing technique (10X Genomics). In the process, RNA was isolated from ~700 irradiated and ~700 non-irradiated control cells, and then sequenced with ~50 k reads/cell. RNA in each of the cells was distinctively barcoded prior to extraction to allow for quantification for individual cells. Principal component and clustering analysis of the unique molecular identifier (UMI) counts classified the cells into three groups or sub-types, which correspond to CD4+, naïve, and CD8+/NK cells. Gene expression changes after radiation exposure were evaluated using negative binomial regression. On average, BBC3, PCNA, and other TP53 related genes that are known to respond to radiation in human T cells showed increased activation. While most of the TP53 responsive genes were upregulated in all groups of cells, the expressions of IRF1, STAT1, and BATF were only upregulated in the CD4+ and naïve groups, but were unchanged in the CD8+/NK group, which suggests that the interferon-gamma pathway does not respond to radiation in CD8+/NK cells. Thus, single-cell RNA sequencing technique was useful for simultaneously identifying the expression of a set of genes in individual cells and T lymphocyte subpopulation after gamma radiation exposure. The degree of dependence of UMI counts between pairs of upregulated genes was also evaluated to construct a similarity matrix for cluster analysis. The cluster analysis identified a group of TP53-responsive genes and a group of genes that are involved in the interferon gamma pathway, which demonstrate the potential of this method for identifying previously unknown groups of genes with similar expression patterns.

Project description: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.

Project description:BackgroundOne of the main causes of low back pain is intervertebral disc degeneration (IDD). Annulus fibrosus (AF) is important for the integrity and functions of the intervertebral disc (IVD). However, the resident functional cell components such as progenitors and vascularization-associated cells in AF are yet to be fully identified.PurposeIdentification of functional AF cell subpopulations including resident progenitors and vascularization-associated cells.MethodsIn this study, the single-cell RNA sequencing data of rat IVDs from a public database were analyzed using Seurat for cell clustering, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analysis, StemID for stem cell identification, Monocle and RNA velocity for pseudotime differentiation trajectory validation, single-cell regulatory network inference and clustering (SCENIC) for gene regulatory network (GRN) analysis, and CellChat for cell-cell interaction analysis. Immunostaining on normal and degenerated rat IVDs, as well as human AF, was used for validations.ResultsFrom the data analysis, seven AF cell clusters were identified, including two newly discovered functional clusters, the Grem1 + subpopulation and the Lum + subpopulation. The Grem1 + subpopulation had progenitor characteristics, while the Lum + subpopulation was associated with vascularization during IDD. The GRN analysis showed that Sox9 and Id1 were among the key regulators in the Grem1 + subpopulation, and Nr2f2 and Creb5 could be responsible for the vascularization function in the Lum + subpopulation. Cell-cell interaction analysis revealed highly regulated cellular communications between these cells, and multiple signaling networks including PDGF and MIF signaling pathways were involved in the interactions.ConclusionsOur results revealed two new functional AF cell subpopulations, with stemness and vascularization induction potential, respectively.The translational potential of this articleThese findings complement our knowledge about IVDs, especially the AF, and in return provide potential cell source and regulation targets for IDD treatment and tissue repair. The existence of the cell subpopulations was also validated in human AF, which strengthen the clinical relevance of the findings.

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Identification of Cell Subpopulations and Interactive Signaling Pathways From a Single-Cell RNA Sequencing Dataset in Osteosarcoma: A Comprehensive Bioinformatics Analysis.

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