Generation of induced pluripotent stem cells (iPSCs) by somatic cell reprogramming involves global epigenetic remodeling1. While several proteins are known to regulate chromatin marks associated with the distinct epigenetic states of cells before and after reprogramming2,3, the role of specific chromatin modifying enzymes in reprogramming remains to be determined. To address how chromatin-modifying proteins influence reprogramming, we used shRNAs to target genes in DNA and histone methylation pathways, and have identified positive and negative modulators of iPSC generation. While inhibition of the core components of the polycomb repressive complex 1 and 2, including the histone 3 lysine 27 methyltransferase Ezh2, reduced reprogramming efficiency, suppression of SUV39H1, YY1, and Dot1L enhanced reprogramming. Specifically, inhibition of the H3K79 histone methyltransferase Dot1L by shRNA or a small molecule accelerated reprogramming, significantly increased the yield of iPSC colonies, and substituted for Klf4 and c-Myc. Inhibition of Dot1L early in the reprogramming process is associated with a marked increase in two alternative factors, Nanog and Lin28, which play essential functional roles in the enhancement of reprogramming. Genome-wide analysis of H3K79me2 distribution revealed that fibroblast-specific genes associated with the epithelial to mesenchymal transition lose H3K79me2 in the initial phases of reprogramming. Dot1L inhibition facilitates the loss of this mark from genes that are fated to be repressed in the pluripotent state. These findings implicate specific chromatin-modifying enzymes as barriers to or facilitators of reprogramming, and demonstrate how modulation of chromatin-modifying enzymes can be exploited to more efficiently generate iPSCs with fewer exogenous transcription factors.

Visual Abstract

Our results indicate convergence of transcriptome studies of schizophrenia and bipolar disorder on changes in cortical astrocytes and fast-spiking parvalbumin interneurons, providing a unified interpretation of numerous studies. We suggest that these changes can be attributed to alterations in the relative abundance of the cells and are important for understanding the pathophysiology of the disorders.

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.