Boaz P. Levi

Adult mouse cortical cell taxonomy revealed by single cell transcriptomics

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type–specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

CORTECON: A Temporal Transcriptome Analysis of In Vitro Human Cerebral Cortex Development from Human Embryonic Stem Cells

Many neurological and psychiatric disorders affect the cerebral cortex, and a clearer understanding of the molecular processes underlying human corticogenesis will provide greater insight into such pathologies. To date, knowledge of gene expression changes accompanying corticogenesis is largely based on murine data. Here we present a searchable, comprehensive, temporal gene expression data set encompassing cerebral cortical development from human embryonic stem cells (hESCs). Using a modified differentiation protocol that yields neurons suggestive of prefrontal cortex, we identified sets of genes and long noncoding RNAs that significantly change during corticogenesis and those enriched for disease-associations. Numerous alternatively spliced genes with varying temporal patterns of expression are revealed, including TGIF1, involved in holoprosencephaly, and MARK1, involved in autism. We have created a database (http://cortecon.neuralsci.org/) that provides online, query-based access to changes in RNA expression and alternatively spliced transcripts during human cortical development.

Fixed single-cell transcriptomic characterization of human radial glial diversity

The diverse progenitors that give rise to the human neocortex have been difficult to characterize because progenitors, particularly radial glia (RG), are rare and are defined by a combination of intracellular markers, position and morphology. To circumvent these problems, we developed Fixed and Recovered Intact Single-cell RNA (FRISCR), a method for profiling the transcriptomes of individual fixed, stained and sorted cells. Using FRISCR, we profiled primary human RG that constitute only 1% of the midgestation cortex and classified them as ventricular zone−enriched RG (vRG) that express ANXA1 and CRYAB, and outer subventricular zone−localized RG (oRG) that express HOPX. Our study identified vRG and oRG markers and molecular profiles, an essential step for understanding human neocortical progenitor development. FRISCR allows targeted single-cell profiling of any tissues that lack live-cell markers.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001

Shared and distinct transcriptomic cell types across neocortical areas

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.Single-cell transcriptomics of more than 20,000 cells from two functionally distinct areas of the mouse neocortex identifies 133 transcriptomic types, and provides a foundation for understanding the diversity of cortical cell types.

Single-Cell Transcriptomic Characterization of Vertebrate Brain Composition, Development, and Function

A fundamental effort in neuroscience is to identify and characterize the building blocks of the central nervous system. Starting from the early days of the field, researchers have classified brain cells into types based on cellular morphology, electrical properties, connectivity patterns and molecular characteristics. Recent advances in molecular techniques, DNA sequencing, and computational power have enabled high-throughput molecular characterization of individual cells through the use of single-cell RNA-sequencing. This chapter reviews the general notion of cell types in the brain, and then outlines methods to select, isolate, and profile individual cells using single-cell RNA-sequencing. Also included is an overview of analysis methods to define putative types from single-cell RNA-sequencing data, and additional methods to link these data to other modalities in order to obtain a comprehensive picture of the basic components of the central nervous system.

Conserved cell types with divergent features between human and mouse cortex

Elucidating the cellular architecture of the human neocortex is central to understanding our cognitive abilities and susceptibility to disease. Here we applied single nucleus RNA-sequencing to perform a comprehensive analysis of cell types in the middle temporal gyrus of human cerebral cortex. We identify a highly diverse set of excitatory and inhibitory neuronal types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to a similar mouse cortex single cell RNA-sequencing dataset revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of human cell type properties. Despite this general conservation, we also find extensive differences between homologous human and mouse cell types, including dramatic alterations in proportions, laminar distributions, gene expression, and morphology. These species-specific features emphasize the importance of directly studying human brain.