Transcriptomics

Dataset Information

0

Footprint-free human fetal foreskin derived iPSCs: a tool for modeling hepatogenesis associated gene regulatory networks


ABSTRACT: Induced pluripotent stem cells (iPSCs) are similar to embryonic stem cells and can be generated from somatic cells. We have generated episomal plasmid-based and integration-free iPSCs (E-iPSCs) from human fetal foreskin fibroblast cells (HFF1). E-iPSCs were fully characterized and their transcriptomes are more similar to that of hESCs (R2 = 0.9363) in comparison to viral-derived HFF1-iPSCs (R2 = 0.8176). We used an E-iPSC-line to model hepatogenesis in vitro. The differentiation of iPSCs into hepatocyte-like cells (HLCs) is a three-step process, from the undifferentiated E-iPSC to definitive endoderm (DE), hepatic endoderm (HE) and ultimately HLCs. The HLCs were characterized biochemically, i.e. glycogen storage, ICG uptake and release, UREA and bile acid production, as well as CYP3A4 activity. Ultra-structure analysis by electron microscopy revealed the presence of lipid and glycogen storage, tight junctions and bile canaliculi- all typical features of hepatocytes. Furthermore, the transcriptome of undifferentiated E-iPSC, DE, HE and HLCs were compared to that of fetal liver and primary human hepatocytes (PHH). K-means clustering identified 100 clusters which include developmental stage-specific groups of genes, e.g. OCT4 expression at the undifferentiated stage, SOX17 marking the DE stage, DLK and HNF6 the HE stage, HNF4a and Albumin is specific to HLCs, fetal liver and adult liver (PHH) stage. The lack of viral DNA integrations in these E-iPSCs endow them superior to viral-derived iPSCs for (i) modeling gene regulatory networks associated with hepatogenesis and gastrulation in general, (ii) toxicology studies and (iii) drug screening platforms.

ORGANISM(S): Homo sapiens

PROVIDER: GSE66282 | GEO | 2017/09/06

SECONDARY ACCESSION(S): PRJNA276409

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2018-01-16 | GSE100447 | GEO
2011-01-20 | E-GEOD-25744 | biostudies-arrayexpress
2014-11-12 | GSE61287 | GEO
| PRJNA260672 | ENA
2020-07-27 | GSE140732 | GEO
2021-02-18 | GSE155771 | GEO
2012-07-03 | E-GEOD-34943 | biostudies-arrayexpress
2012-03-01 | E-GEOD-31725 | biostudies-arrayexpress
2012-03-01 | GSE31725 | GEO
| EGAS00001002676 | EGA