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Regenerative medicine aims to develop methods for treating currently incurable diseases and pathological conditions. Its central idea is to leverage healthy cells to regenerate diseased or damaged cells, tissues or organs through the process of cellular conversion. The most common method to achieve this is by modulating the activity of specific transcription factors, which in turn, alters the transcriptional program of the cells resulting in their conversion to the desired target cell type. However, given the large number of protein-coding genes (∼19,000) and transcription factors (∼1,600) in humans and their complex interactions, it is challenging to identify the most suitable transcription factors for modulation.In this study, we leverage three existing computational tools, namely, TransSynW, PAGA and SIGNET, to develop an analysis workflow to facilitate identifying suitable transcription factors for achieving desired cellular conversion. The pipeline helps in generating hypotheses on suitable transcription factors for modulation and also in inferring their mechanistic basis. We used this pipeline on a sample dataset of human hindbrain neuroepithelial stem cells (hNES) and midbrain medial floorplate progenitor (hProgFPM) cells to generate hypothesis for converting the former to the latter cell type.The pipeline predicted the transcription factors for cellular conversion, highlighted their differential expression dynamics in starting and target cell types, and revealed their interactions and influence on the predicted gene regulatory network of the target cells. We believe these together can help researchers generate mechanistically founded hypotheses for achieving desired cellular conversions towards regenerative medicine. |
