scRegen

We are always looking to find inspirations to regain a more regenerative and youthful states in medicine so we can treat patients with disease and injury by optimizing their own healing abilities. One place to find that is to look for inspiration in the natural development of organs. Thus, we first reconstruct by looking into the existing dataset of a fully multiomic covered time course organogenesis Single cell dataset. Here, for example, we use lung as the primary focus where we will expand into other organs when data are more flushed out and possible. After the compilation, we reconstruct the gene regulatory network throughout the time course and look for key master regulators that mediates critical selfie changes throughout time. After that, We lock in between the lineage specific progenitors, in this case in lung, is the lung distal progenitors that are multipotent and also the mature eighty two cell that is the facultative stem cells that in injury context can have limited regenerative capacity. By analyzing the differences between the progenitors and the matured facultative stem cell groups, we find critical changes in gene regulatory network and dose master regulator and perform transcriptional factor interventions via virtual knockout or overexpression. By iterating all kinds of combo, we optimize for intervention combo that can maximize scoring that approach progenitor states the most. The package that has been involved here is a cell oracle, Scenic Plus, and also Pertformer. We also tested a currently popular way to reactivate and rejuvenate in vivo that has been, um, moving into clinical trials where a partial or interrupted reprogramming using, uh, Yamanaka factors or as it's known as the OSKM. To see if the exposure of those can have an effect in bringing mature eighty two cells back to a distal progenitor fade, and we benchmark that against the new combination that we can mine by the development reconstructed datasets, gene regulatory network predictions.