Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA168022 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:To explore the role of the mitochondrial Ca2+ uniporter (MCU) in exosomal microRNA(miRNA) sorting in MDA-MB-231 cells, we isolated miRNA from the exosomes derived from MCU-downregulated and control MDA-MB-231 cells. We then performed Next Generation Sequencing (NGS) and real-time quantitative polymerase chain reaction (qRT-PCR) to find and validate differetial expressed exosomal miRNAs that regulated by MCU.
2021-04-19 | GSE113411 | GEO
Project description:Real-time quantitative PCR analysis of human dendritic cells
| PRJNA140447 | ENA
Project description:Real-time quantitative PCR analysis of human T cells
| PRJNA348746 | ENA
Project description:1. Quantitative Proteomics: MDA-MB-231, MDA-MB-468, and MCF12A cells were treated with DMSO (vehicle control) or SU056 (novel small molecule drug candidate). Quantitative proteomics analysis was performed on cell lysates. 2. Cellular Thermal Shift Assay (CETSA): MDA-MB-231 cells were treated with DMSO or SU056 and incubated at different temperatures and protein differences in the resulting soluble and insoluble fractions were determined.3. Cellular Thermal Shift Assay (CETSA): MDA-MB-231 YBOX1 KD cells were treated with DMSO or SU056 and incubated at different temperatures and protein differences in the soluble fractions were determined.
2024-04-04 | PXD042380 | Pride
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived CTU-treated MDA-MB-231 cell's transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: Purified RNA of CTU-treated MDA-MB-231 cells (10 μM, 6 h) were sent to the Australian Genome Research Facility for RNA-Seq analysis. Differential expression analysis was performed by edgeR and pathway enrichment was performed using the Reactome database as a reference.qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: RNA-Seq profiling identified a total of 14,951 genes in MDA-MB-231 cells, and detected 382 differentially expressed genes greater than or equal to (≥) 2-fold after 10 μM CTU treatment at 6 h. Genes whose expression was changed significantly were identified using the false discovery rate (FDR), an adjusted P-value for multiple testing. The expression of 250 and 132 genes was significantly up-regulated and down-regulated, respectively, to ≥2-fold of control in CTU-treated cells (FDR<0.05). Secondary pathway analysis was performed using the Reactome database to identify the principal biological pathways that were altered in CTU-treated MDA-MB-231 cells. This analysis implicated several mechanisms, including disruption of the tumour microenvironment, activation of cellular stress signalling and activation of cell cycle arrest. ER stress via the UPR survival mechanism was a major dysregulated pathway in MDA-MB-231 cells that had been treated with CTU (10 μM, 6 h) compared with control (FDR= 0.0001). Conclusions: Our study represents the first detailed analysis of CTU-treated MDA-MB-231 cells (10 μM, 6 h) l transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2021-02-08 | GSE166312 | GEO
Project description:Real-time quantitative PCR analysis of human leukemic B-cells.
| PRJNA148223 | ENA
Project description:To provide preliminary insights into metabolic and lipidomic characteristics in radioresistant triple-negative breast cancer (TNBC) cells and suggest potential therapeutic targets, we performed a comprehensive metabolic and lipidomic profiling of radioresistant MDA-MB-231 (MDA-MB-231/RR) TNBC cells and their parental cells using gas chromatography-mass spectrometry and nano electrospray ionization-mass spectrometry, followed by multivariate statistical analysis. Buthionine sulfoximine (BSO) and radiation were co-treated to radioresistant TNBC cells. The level of glutathione (GSH) was significantly increased, and the levels of GSH synthesis-related metabolites, such as cysteine, glycine, and glutamine were also increased in MDA-MB-231/RR cells. In contrast, the level of lactic acid was significantly reduced. In addition, reactive oxygen species (ROS) level was decreased in MDA-MB-231/RR cells. In the lipidomic profiles of MDA-MB-231/RR cells, the levels of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were significantly increased, whereas those of most of the phosphatidylinositol species were significantly decreased. BSO sensitized MDA-MB-231/RR cells to radiotherapy, which resulted in decreased GSH level and increased ROS level and apoptosis. Radioresistant TNBC cells showed distinct metabolic and lipidomic characteristics compared to their parental cells. We suggested activated GSH, PC, and PE biosynthesis pathways as potential targets for treating radioresistant TNBC cells. Particularly, enhanced radiosensitivity was achieved by inhibition of GSH biosynthesis in MDA-MB-231/RR cells.
2021-09-22 | ST001920 | MetabolomicsWorkbench
Project description:Real-time quantitative PCR analysis of human astrocytomas
| PRJNA189282 | ENA
Project description:Real-time quantitative PCR analysis of spleen cells
| PRJNA191813 | ENA
Project description:Aurora Kinase B and ZAK interaction model
Equivalent of the stochastic model used in "Network pharmacology model predicts combined Aurora B and ZAK inhibition in MDA-MB-231 breast cancer cells" by Tang et. al. 2018.
The only difference is cell division and partitioning of the components, which are available in the original model for SGNS2.
2020-04-28 | BIOMD0000000940 | BioModels