Efficient stimulation of retinal regeneration from Müller glia in adult mice using combinations of proneural bHLH transcription factors

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

Single-cell gene expression studies promise to unveil rare cell types and cryptic states in development and disease through a stunningly high-resolution view of gene regulation. However, measurements from single-cell RNA-Seq are highly variable, frustrating efforts to assay how expression differs between cells. We introduce Census, an algorithm available through our single-cell analysis toolkit Monocle 2, which converts relative RNA-Seq expression levels into relative transcript counts without the need for experimental spike-in controls. We show that analyzing changes in relative transcript counts leads to dramatic improvements in accuracy compared to normalized read counts and enables new statistical tests for identifying developmentally regulated genes. We explore the power of Census through reanalysis of single-cell studies in several developmental and disease contexts. Census counts can be analyzed with widely used regression techniques to reveal changes in cell fate-dependent gene expression, splicing patterns, and allelic imbalances, demonstrating that Census enables robust single-cell analysis at multiple layers of gene regulation.

Loss of neurons after brain injury and in neurodegenerative disease is often accompanied by reactive gliosis and scarring, which are difficult to reverse with existing treatment approaches. Here, we show that reactive glial cells in the cortex of stab-injured or Alzheimer's disease (AD) model mice can be directly reprogrammed into functional neurons in vivo using retroviral expression of a single neural transcription factor, NeuroD1. Following expression of NeuroD1, astrocytes were reprogrammed into glutamatergic neurons, while NG2 cells were reprogrammed into glutamatergic and GABAergic neurons. Cortical slice recordings revealed both spontaneous and evoked synaptic responses in NeuroD1-converted neurons, suggesting that they integrated into local neural circuits. NeuroD1 expression was also able to reprogram cultured human cortical astrocytes into functional neurons. Our studies therefore suggest that direct reprogramming of reactive glial cells into functional neurons in vivo could provide an alternative approach for repair of injured or diseased brain. Copyright © 2014 Elsevier Inc. All rights reserved.