A computer-guided design tool to increase the efficiency of cellular conversions

• Published in: Nature communications 2021-03, Vol.12 (1), p.1659-1659, Article 1659
• Main Authors: Jung, Sascha, Appleton, Evan, Ali, Muhammad, Church, George M., del Sol, Antonio
• Format: Article
• Language: English
• Subjects: 13/100; 13/109; 13/31; 38/39; 38/91; 42/109; 631/114/2114; 631/114/2397; 631/114/2401; 631/532/2435; 631/553/2692; Cell differentiation; Cell Differentiation - genetics; Cell Line; Cellular Reprogramming; Computational Biology - methods; Computational efficiency; Computer applications; Conversion; Efficiency; Epigenetics; Epithelial cells; Epithelial Cells - metabolism; Gene Regulatory Networks; Gene therapy; Humanities and Social Sciences; Humans; Induced Pluripotent Stem Cells - metabolism; Integration; Mammary gland; Melanocytes; multidisciplinary; Natural killer cells; Overexpression; Protocol (computers); Science; Science (multidisciplinary); Software; Stochasticity; Transcription factors; Transplantation; Transposons
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-21801-4

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies. However, while transcription factor over-expression-based methods have shown great promise in generating cell types in vitro, they often endure low conversion efficiency. In this context, great effort has been devoted to increasing the efficiency of current protocols and the development of computational approaches can be of great help in this endeavor. Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency. Transcription factor over-expression-based cellular conversion methods often endure low conversion efficiency. Here the authors show how to increase conversion efficiency by combining a computational method for prioritizing more efficient TF combinations with a transposon-based genomic integration system for delivery.