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Regulating the transcriptomes that mediate the conversion of fibroblasts to various nervous system neural cell types
Author(s) -
Khazaei Niusha,
RastegarPouyani Shima,
O'Toole Nicholas,
Wee Ping,
Mohammadnia Abdulshakour,
Yaqubi Moein
Publication year - 2018
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.26221
Subject(s) - reprogramming , biology , neural cell , transcriptome , neural stem cell , cell type , gene silencing , microbiology and biotechnology , regulator , gene expression , regulation of gene expression , cell , gene , genetics , stem cell
Our understanding of the mechanism of cell fate transition during the direct reprogramming of fibroblasts into various central nervous system (CNS) neural cell types has been limited by the lack of a comprehensive analysis on generated cells, independently and in comparison with other CNS neural cell types. Here, we applied an integrative approach on 18 independent high throughput expression data sets to gain insight into the regulation of the transcriptome during the conversion of fibroblasts into induced neural stem cells, induced neurons (iNs), induced astrocytes, and induced oligodendrocyte progenitor cells (iOPCs). We found common down‐regulated genes to be mostly related to fibroblast‐specific functions, and suggest their potential as markers for screening of the silencing of the fibroblast‐specific program. For example, Tagln was significantly down‐regulated across all considered data sets. In addition, we identified specific profiles of up‐regulated genes for each CNS neural cell types, which could be potential markers for maturation and efficiency screenings. Furthermore, we identified the main TFs involved in the regulation of the gene expression program during direct reprogramming. For example, in the generation of iNs from fibroblasts, the Rest TF was the main regulator of this reprogramming. In summary, our computational approach for meta‐analyzing independent expression data sets provides significant details regarding the molecular mechanisms underlying the regulation of the gene expression program, and also suggests potentially useful candidate genes for screening down‐regulation of fibroblast gene expression profile, maturation, and efficiency, as well as candidate TFs for increasing the efficiency of the reprogramming process.