To understand the brain, we need a parts list of its cell types. The list will need to integrate molecular, functional and morphological data, but of these, molecular classification is best suited for comprehensive categorization and the only approach that can lead directly to genetically accessing the types; such access is essential in order to mark and manipulate neurons and to allow rigorous comparison of neurons from normal and diseased brains. We will apply the emerging method of single-cell transcriptional profiling (scRNA-seq) to this task. We will first rigorously compare and optimize cutting- edge methods for cell isolation, transcriptional profiling, and computational analysis to establish an efficientand effective pipeline for categorization. Then, we will apply our suite of methods to two brain regions - mouse retina and zebrafish habenula - that differ in several ways but share key features: they are accessible and compact and it is feasible to map their cell types comprehensively. In each case, we will perform unbiased and exhaustive profiling of 1,000's of neurons, to ensure that even rare classes of neurons are included in the survey. We will validate gene modules obtained from profiling by in situ hybridization, integrate them with structural and functional data, and provide standardized and comprehensive maps of cell type. Finally, we will apply what we have learned to a larger region, the mouse habenula. Profiling and classification in this structure will not only provide a stringent test of our ability to scale up our methods, bu also allow us to ask two important and interesting questions: to what extent cell types are conserved across species (zebrafish vs. mouse habenula) and to what extent cell types are conserved across regions (mouse retina vs. habenula). Together, insights, methods and reagents obtained in this work will provide an essential toolkit for tackling the whole brain.
To achieve the goals of the BRAIN initiative and to translate them into novel diagnostic and therapeutic advances, it is necessary to classify the cells of the brain so they can be characterized and so that critical differences between normal and pathological tissue can be detected. We propose to apply to this task a suite of methods based on the emerging technology of single cell transcriptomic analysis. We will optimize methods, show that they can be used for comprehensive classification of neurons in two very different regions (mouse retina and zebrafish habenula), and, finally, apply them to mouse habenula to test their general applicability.
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