Over-expression of just a few transcription factors (TFs) can transform a fully differentiated cell into a state of induced pluripotency (iPS). Even more astoundingly, a similar strategy can perform cellular transdifferentiation, i.e. direct conversion of a cell from one type to another. However, the selection of TFs to overexpress or repress to induce transdifferentiation is typically found unsystematically through a combination of biological intuition and extensive trial-and-error required for each combination of source and target cell types. This approach does not scale to transdifferentiation of any cell type into any other cell type, which would be desirable not only for regenerative medicine but also to answer fundamental questions about the biology of cellular identity.
A premise for an unbiased, quantitative approach to designing TF transdifferentiation cocktails is the knowledge of the impact of each of the TFs on gene expression with a single-cell resolution. Generating such a resource is now possible thanks to the advances in single-cell sequencing and pooled CRISPR gene editing, which enable coupling gene editing with transcriptome readout. At the same time, the analysis and data-driven prediction for single-cell transcriptomic datasets is currently being revolutionized by deep learning. This is due to the ability to scale the learning process to very large datasets and to extract informative data representations directly from the data.
Considering the above, we formulated the following objectives: Objective 1: Development of deep-learning models for CRISPR-engineered cells; Objective 2: Computational modeling of transdifferentiation using the deep-learning models; Objective 3: Experimental validation of fibroblast transdifferentiation into hematopoietic cells.
