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Expression data of CFC1 overexpressed neuroblastoma cell line, NGP

ABSTRACT: We performed a microarray analysis on CFC1-expressing NGP cells. Expression data in CFC1- and mock-NGP cells were generated and compared. Differentially expressed genes (DEGs) were functionally annotated by a pathway analysis and Gene Set Enrichment Analysis (GSEA). Both analyses showed that genes involved in “differentiation”, “oncogene-induced senescence”, “stem cell proliferation” and “Activin signaling” pathways were significantly enriched in DEGs. “TGFbeta pathway” and “Nodal pathway” were also enriched in the GeneSpring GX pathway analysis, but were not significant in the GSEA analysis. This result suggests that the pathways involved in cell stemness and Activin signaling were enhanced by the expression of CFC1 in NB cells accompanied by transcriptional changes in their related gene members.

ORGANISM(S): Homo sapiens

PROVIDER: GSE90790 | GEO | 2017/12/01

SECONDARY ACCESSION(S): PRJNA355944

REPOSITORIES: GEO

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Project description:Background: Follistatin (FS) is an activin-binding glycoprotein, known for its antagonistic action on the TGF-M-NM-2 super-family of cytokines. It has ability to prevent muscle atrophy and to stimulate muscle growth through binding of myostatin (GDF8). FS does not cause these changes only by inhibition of GDF8 but this mechanism is still not well elucidated. In order to identify GDF8-dependent and GDF8-independent action of FS, we performed comprehensive transcriptomic analysis of differentiating C2C12 mouse myoblasts. Results: To evaluate influence of FS-288 (200ng/ml) on differentiating C2C12 myoblasts was used immunofluorescence analysis of MyHC protein expression in the culture and counting of the number and type of myotubes. Differentiating myoblasts treated with FS showed increase in number and size of myotubes. Microarray studies performed by us identified 30 genes oppositely regulated by FS and GDF8. Analyses of the transcriptomic profiles showed that FS has highly significant influence, associated with GDF8 binding, on the regulation of cell cycle, transport processes, cell communication, cell adhesion, cell motion, developmental processes and binding functions. The signalling pathways that may play a primary role in the myogenesis stimulation by FS are associated with TGF-M-NM-2, Delta-Notch and Wnt signalling pathways, integrin and actin cytoskeleton signalling and adipogenesis regulation. Changes in expression of Hgf, Sort1, Trdn, Stmn2, Hdac1 genes, significantly involved in the process of myoblasts differentiation were validated with Real-Time PCR method. Conclusions: Our results implicate which aspects of transcriptional regulation of myogenesis process by FS could have significant and protective influence. We have shown that FS activity is associated not only with GDF8 binding but also with inhibited expression of genes involved in TGF-M-NM-2 signalling and by regulating of genes associated with vesicle transport apoptosis signalling pathway, inflammatory pathways and adipogenesis expression. Our analysis showed that follistatin GDF8-independent on differentiating myotubes involves regulation of localization processes, cell-matrix adhesion, mRNA processing, apoptosis, fatty acid metabolic processes, SNAP receptor activity functions and partial regulation of transcription processes. These results give a new and complete image of the follistatin role in the muscle cell differentiation and indicate target genes for FS which in the future may be a good therapeutic approach. After scanning of hybridized microarrays, quantitation of slide images were performed using Feature Extraction Software 10.7.3.1 (Agilent) using default parameters. Normalized raw data was exported to GeneSpring GX 11.0.5 (Agilent, Santa Clara, CA). For identification of genes significantly altered in cells treated with FSl compared with untreated cells. T total detected entities were filtered by flags (present, marginal) and error (coefficient of variation: CV < 50.0 percent) to remove very low signal entities and to select reproducible signal values of entities among the replicated experiments, respectively. In statistical analysis, separated for experiment with myoblasts treated with FS(FS vs. CTRL 1-4) was used t-test unpaired (p < 0.05) with multiple testing correction: Benjamini-Hochberg <0.05, all significant changes over fold change 1.6 were selected. Analysis of GO, GSEA and signaling pathway was carried out using GeneSpring GX 12 (Agilent) and the DAVID, KEEG and PANTHER Classification System (p<0.01). In the analysis of signaling pathways using GeneSpring GX 12 (Agilent) and Ariadne Pathway Studio.

Project description:Technological advances in transcriptome sequencing of single cells provided an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cellular subpopulations, none of these comparative studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These systems and library preparation methods are representative for the wide variety of platforms, ranging from microfluids chips to droplet-based systems and from full transcript sequencing to 3’ end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identification of differentially expressed genes as robustly assessed by two statistical models, and the second is enrichment analysis of biological signals, which is independent of sample size or number of cells evaluated. Where relevant, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples to make the data more comparable. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP cells upon nutlin-3 treatment, pinpointing putative late-responders or resistant cells, hidden in bulk RNA-seq experiments.

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Expression data of CFC1 overexpressed neuroblastoma cell line, NGP

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