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Gene expression patterns define the hepatocyte-like cells derived by different strategies

ABSTRACT: Gene expression patterns define the hepatocyte-like cells derived by different strategies

PROVIDER: PRJNA400163 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR5974290.fastq.gz	Fastqsanger.gz
	SRR5974300.fastq.gz	Fastqsanger.gz
	SRR5974291.fastq.gz	Fastqsanger.gz
	SRR5974301.fastq.gz	Fastqsanger.gz
	SRR5974292.fastq.gz	Fastqsanger.gz

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Gene expression patterns define the hepatocyte-like cells derived by different strategies [RNA-seq]

Project description:We performed genome-wide gene profiling in hiPSC-Hep and hiHep and compared gene expression patterns in these two types of hepatocyte-like cells.Genes involving in fat digestion and absorption and metabolism enzymes were induced in both hiHeps and hiPSC-Heps. There were also hepatic genes not expressed in either hiPSC-Heps or hiHeps, including cytochrome P450-based metabolism genes and coagulation complements. Interestingly, when genes were clustered based on their hepatic functions, we found that for each hepatic function there were always a few genes not fully induced in either hiPSC-Heps or hiHeps.

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Gene expression patterns define the hepatocyte-like cells derived by different strategies

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Temporal gene expression patterns (Next Generation Sequencing) in liver tumourigenesis of hepatocyte-specific deletion of shp2 and/or pten

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to reveal dynamics of liver tumourigenesis in different mouse model and identify some key regulators that control HCC initiation or progression. We also try to define a index based on transcriptome of samples to quantify tumor development stage. Methods: mRNA profiles of wild-type (WT), hepatocyte-specific shp2 deletion (Shp2−/−) mice (SKO), hepatocyte-specific pten deletion (Pten−/−) mice (PKO), and hepatocyte-specific shp2 and pten deletion mice (DKO) were generated by deep sequencing. The sequence reads that passed quality filters were mapped to Mouse genome using STAR, and mRNA profiles were obtained using cuffdiff. Results: quanlity control of mRNA profiles showed that the data captured key features of phenotypes. Significantly changed genes, pathways, biolgocial processes, ligand and receptor, epigenetic regulators et al of SKO, PKO, DKO mice at differnet age were obtaiend. Temporal gene expression patterns during liver tumorigenesis in SKO, PKO and DKO mice were obtained. Conclusions: Our study represents the first detailed analysis of temporal transcriptomes during liver tumourigenesis, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comprehensive investigations of expression profiles.

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