A research group comprising members from the Keio University School of Medicine, National Center for Child Health and Development, Kazusa DNA Research Institute, Xcoo, and DNA Chip Research announced on July 17 that it has successfully captured and identified the transcription factors (TFs) that influence the differentiation of stem cells through the use of human embryonic stem cells (human ES cells).
Along with the release of these research findings on public databases and the Keio University website, human ES cells that are capable of genetic induction will be provided to researchers. These data and material resources are among the largest in scale across existing analyses of the features of the human genome, and they are expected to be useful in stem cell research and for elucidating the functions of TFs.
ES cells are cells that differentiate into the cells of various organs when transplanted into blastocysts, and are known as pluripotent stem cells along with induced pluripotent stem cells (iPS cells). Although clinical studies have been carried out for particular cells and tissues, the factors that control the differentiation of these cells into the target cells are often elusive, resulting in this technique being applicable only in the generation of certain target cells.
In addition, although TFs have been found to play a major role in the outcome of cell differentiation in previous studies in mice, large-scale systematic studies have not been performed on humans, and the casual relations between TFs and the fate of cells remain unclear.
This research created a human ES cell line which contains a gene that can be controlled with the use of drugs. It successfully captured 26,998 microscopic images of 714 genes as well as the gene expression data of 511 genes after 48 hours have elapsed.
The analysis of this data reveals that most genes in the human genome are controlled by these 511 genes, and it was found that the expression of genes in heterochromatin for which induction was previously challenging can in fact be induced by specific TFs.
Upon comparing this data with the gene expression data of cells in human organs (obtained from public databases), the group was able to identify TFs capable of inducing cells that are highly similar to those in various human organs.
The biological big data obtained in this study provides useful insights for transcription factors on the same platform, and is suitable for further numerical analysis using artificial intelligence. The elucidation of the complex network structure of TFs in the future is expected to catalyze the development of technologies capable of generating the desired cell lineages from human stem cells such as iPS cells.