Please note that we have stopped the regular imports of Gene Expression Omnibus (GEO) data into ArrayExpress. This may not be the latest version of this experiment.
E-GEOD-59865 - Cell type-specific requirements for iPSC reprogramming
Released on 11 September 2014, last updated on 12 September 2014
The differentiated state of somatic cells provides barriers for the efficient derivation of induced pluripotent stem cells (iPSCs). To address why some cell types reprogram more readily than others, we studied the effect of combined modulation of cellular signaling pathways. This revealed that inhibition of TGFβ together with activation of Wnt signaling in presence of ascorbic acid allows >80% of murine fibroblasts to acquire pluripotency after one week of reprogramming factor expression. In contrast, hepatic progenitors and blood progenitors predominantly required only TGFβ inhibition or canonical Wnt activation, respectively, to reprogram at efficiencies approaching 100%. Strikingly, blood progenitors reactivated endogenous pluripotency loci in a highly synchronous manner. We further demonstrate that expression of specific chromatin-modifying enzymes and reduced TGFβ/MAP kinase activity are intrinsic properties associated with the unique reprogramming response of these cells. Together, our observations define novel cell type-specific requirements for the rapid and synchronous reprogramming of somatic cells. For the study of reprogramming intermediates, cultures of reprogrammable MEFs at day 4 in presence of dox and compounds were harvested and stained with biotinylated anti-SSEA1 antibody (MC-480, eBioscience), followed by APC-conjugated Streptavidin and then anti-APC microbeads (Milteny Biotec) and enrichment using MACS separation columns (Milteny Biotec) according to manufacturer’s instructions. Total RNA extracted from SSEA1+ cells (>90% purity) with the RNeasy mini kit (QIAGEN) with a RIN value > 8 was subjected to transcriptional analyses with Affymetrix mouse genome 430 2.0 mRNA expression microarrays followed by bioinformatic analyses.
transcription profiling by array
Aristotelis Tsirigos, Bhishma Amlani, Matthias Stadtfeld, Simon E Vidal, Taotao Chen