STEM CELL BIOLOGY AND REGENERATIVE TRANSCRIPTOMICS

Abstract

 There is increasingly the reliance on a fusion of stem cell biology and transcriptomics to find out how the cells and tissues heal themselves, and how they can be induced to be reprogrammed using regenerative medicine.  This experiment adopts a mixed design, whereby a thorough high-throughput transcriptome profiling, practical functional regenerative assays, and single-cell resolution will be used to explore how stem-cell derived lineages may be regenerated.  Mesenchymal, neuronal, and epithelial has been generated when scientists converted pluripotent stem cells to other cells. Then they placed the cells in circumstances that appeared to be harmed, such as an experience of oxidative pressure and the activation of inflammation.  The results of bulk RNA-Seq and single-cell RNA-Seq experiments demonstrated that key regenerative markers such as FGF2, IGF1, VEGF-A, and BMP7 were also being activated in all cases. This was highly associated with increased rates of wound healing and migration.  Tables revealed expression stability, differentiation scores and regeneration index between various lineages and stress circumstances. Tables 6 and 9 revealed genes that had high expression levels and which were linked with superior regenerative phenotypes.  The figures provided the evidence of how the co-expression varies with time, how the changes in transcriptome are specific to individual lineages, and how regeneration capabilities are dispersed, using line, scatter, hybrid, and pie charts.  In case, hybrid plots (Figures 4 and 12) presented an excellent job in demonstrating the relationship between gene expression, differentiation and regeneration efficacy in more than one manner.  The statistical approach of developing PLS regression to generate statistical models demonstrated that transcriptome factors could efficiently foretell functional regeneration outcomes.  As regulated, the outcomes demonstrate that regenerative transcriptome statuses are characterized by lineage-sensitive gene sets. One can use these signatures both as a predictor and a medicine.  This finding provides us with a paradigm scaleable and repeatable that links molecular discoveries to their phenotypical consequences. That will enable us to develop more specific regenerative technology through stem cell technology.