Mapping the Developing Human Neocortex by Massively Parallel Single Cell Analysis

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 DESCRIPTION (provided by applicant): This proposal seeks to create a single cell resolution map of the developing human neocortex. We propose to determine the number of different subtypes of neural stem and progenitor cells that generate the cerebral cortex, and then follow the developmental trajectories of the newborn neurons they produce to obtain an understanding of the diversity of cortical neurons that will ultimately form the adult cortex. We plan a novel approach to this problem by integrating surveys of single cell gene expression and physiology in human cortical cells from multiple brain regions at a series of developmental stages. In collaboration with Fluidigm Corporation, we have developed innovative strategies for massively parallel profiling of molecular and physiological properties of primary human cortical cells using microfluidic technologies, cellular barcoding, and timelapse microscopy. We now propose to conduct an integrated survey of human fetal cortical cells in prefrontal, motor, and visual cortex to classify cell types and lineages. Our work will shed light on the developmental origins of cell diversity in the human cortex by addressing three specific aims. First, we will use unbiased cell type classification to provide a realistic estimate of the number of defined progenitor and immature cell types in specific brain regions. We hypothesize that progenitor diversity influences the development of structural and connectivity differences in cortical areas, but the relationship between the diversity of progenitor cells and adult neurons has been difficult to study in the human brain. We propose to sequence >100,000 single cells collected from specific lamina of the developing cortex and determine single cell gene expression using cellular barcodes for efficient low-coverage mRNA sequencing. By analyzing genes, microRNAs, and chromatin states, we anticipate to be able to distinguish discrete populations of progenitor and postmitotic cells. Second, we hypothesize that a combined understanding of physiological and molecular properties will improve cell type classification and reveal molecular factors most predictive of functional maturation. To this end we will use a novel high-throughput screen to measure physiological responses to a range of ligands and neurotransmitters in single cells captured directly on microfluidic chips. The cells will then be lysed, and mRNA reverse-transcribed, amplified, and sequenced. This approach to profiling and classification of single cells will integrate information on molecular properties with physiological responses across anatomical locations. Finally, we will classify cells as belonging to specific lineage trajectories, in additin to discrete categories, using cellular resolution maps of development. We will validate predicted functional and molecular lineages using timelapse microscopy and electrophysiology in cultured primary human cells and slices. This approach will further integrate the molecular and physiological identity of cells from distinct cortical areas with their lineage identity and will provide clues to the determinants that specify neuronal subtypes and connectivity patterns in the maturing human cortex.