Revista de investigación y terapia con células madre

Abstracto

Exploring the Neural Reprogrammome using Bioinformatics Approaches

Thileepan Sekaran, Rajkumar P. Thummer and Frank Edenhofer

Recent studies demonstrate that mammalian cells can be artificially reprogrammed by ectopic expression of transcription factors in an unforeseen straightforward manner. Patient-derived reprogrammed cells hold great potential for biomedical applications such as cell replacement therapy, drug toxicity studies and disease modeling. Somatic cells such as fibroblasts can be dedifferentiated into so-called induced pluripotent stem cells (iPSCs) that are similar to embryonic stem cells (ESCs) by overexpression of Oct4, Sox2, Klf4 and cMyc. However, clinical applications using iPSCs carry the risk of tumor formation due to incomplete differentiation. More recently, it has been demonstrated that transcription factor-driven reprogramming enables direct conversion of fibroblasts into neurons, cardiomyocytes, hepatocytes as well as neural progenitors. Various groups elaborated protocols for the direct conversion of fibroblasts into multipotent induced neural stem cells (iNSCs) using different combinations of transcription factors and media conditions. These studies have shown that iNSCs exhibit morphology, gene expression and self-renewing capacity similar to NSCs derived from primary tissue. Moreover, these iNSCs differentiated into neurons, astrocytes and oligodendrocytes indicating multipotency of these cells. Here, we compare the gene expression profile of reprogrammed cells reported in these studies to determine the similarity in expression profile between the generated iNSCs using bioinformatics approaches. We provide a general workflow that can be applied to evaluate the status of reprogrammed cell populations. Using hierarchical clustering analysis and principal component analysis (PCA), we show that iNSCs resemble more closely. On the other hand, iNSCs are relatively similar to 4F iNSC (late) of the study as judged by hierarchical clustering analysis. Our study demonstrates that bioinformatics approaches are particularly valuable to robustly assess the transcriptional status of reprogrammed cells and to anticipate their cellular functionality.