NGN2 induces diverse neuron types from human pluripotency

Human neurons engineered from induced pluripotent stem cells (iPSCs) through neurogenin 2 (NGN2) overexpression are widely used to study neuronal differentiation mechanisms and to model neurological diseases. However, the differentiation paths and heterogeneity of emerged neurons have not been fully explored. Here, we used single-cell transcriptomics to dissect the cell states that emerge during NGN2 overexpression across a time course from pluripotency to neuron functional maturation. We find a substantial molecular heterogeneity in the neuron types generated, with at least two populations that express genes associated with neurons of the peripheral nervous system. Neuron heterogeneity is observed across multiple iPSC clones and lines from different individuals. We find that neuron fate acquisition is sensitive to NGN2 expression level and the duration of NGN2-forced expression. Our data reveal that NGN2 dosage can regulate neuron fate acquisition, and that NGN2-iN heterogeneity can confound results that are sensitive to neuron type.

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NGN2-iNs are molecularly heterogeneous

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NGN2-iNs subtypes have signatures of central and peripheral nervous system

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Neural fate acquisition is sensitive to the level and duration of NGN2 expression

SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species

Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.

Assessment of engineered cells using CellNet and RNA-seq

CellNet is a computational platform designed to assess cell populations engineered by either directed differentiation of pluripotent stem cells (PSCs) or direct conversion, and to suggest specific hypotheses to improve cell fate engineering protocols. CellNet takes as input gene expression data and compares them with large data sets of normal expression profiles compiled from public sources, in regard to the extent to which cell- and tissue-specific gene regulatory networks are established. CellNet was originally designed to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types. Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet platform applicable to, for example, other species or additional cell and tissue types. Once the raw data have been preprocessed, running CellNet takes only several minutes, whereas the time required to create a completely new CellNet is several hours.

Computational Tools for Stem Cell Biology

For over half a century, the field of developmental biology has leveraged computation to explore mechanisms of developmental processes. More recently, computational approaches have been critical in the translation of high throughput data into knowledge of both developmental and stem cell biology. In the past several years, a new subdiscipline of computational stem cell biology has emerged that synthesizes the modeling of systems-level aspects of stem cells with high-throughput molecular data. In this review, we provide an overview of this new field and pay particular attention to the impact that single cell transcriptomics is expected to have on our understanding of development and our ability to engineer cell fate.