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Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) represents a profound change in cell fate. Here, we show that combining ascorbic acid (AA) and 2i (MAP kinase and GSK inhibitors) increases the efficiency of reprogramming from fibroblasts and synergistically enhances conversion of partially reprogrammed intermediates to the iPSC state. AA and 2i induce differential transcriptional responses, each leading to the activation of specific pluripotency loci. A unique cohort of pluripotency genes including Esrrb require both stimuli for activation. Temporally, AA-dependent histone demethylase effects are important early, whereas Tet enzyme effects are required throughout the conversion. 2i function could partially be replaced by depletion of components of the epidermal growth factor (EGF) and insulin growth factor pathways, indicating that they act as barriers to reprogramming. Accordingly, reduction in the levels of the EGF receptor gene contributes to the activation of Esrrb. These results provide insight into the rewiring of the pluripotency network at the late stage of reprogramming.
Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.
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