Unraveling the complexity of the mammalian brain is one of the most challenging problems in biology today. A major goal of neuroscience is to understand how circuits of neurons and non-neuronal cells process sensory information, generate movement, and subserve memory, emotion and cognition. Elucidating the properties of neural circuits requires an understanding of the cell types that comprise these circuits and their roles in processing and integrating information. However, since the description of diverse neuronal cell types over a century ago by Ramon y Cajal, we have barely scratched the surface of understanding the diversity of cell types in the brain and how each individual cell type contributes to nervous system function. Current approaches for classifying neurons rely upon features including the differential expression of small numbers of genes, cell morphology, anatomical location, physiology, and connectivity - important descriptive properties that nonetheless are insufficient to fully describe or predict the vast number of different cell types that comprise the mammalian brain. Here we propose a suite of technologies for identifying and classifying the myriad cell types present in the brain. Our method will be developed using layer 5 pyramidal cells from mouse somatosensory cortex as a model system. First, we will exploit the latest developments in DNA sequencing technologies to characterize gene expression profiles on single layer 5 neurons at high throughput. This information will be used to classify individual cells based on their transcriptome """"""""fingerprints."""""""" Second, genes found to define newly discovered neuronal subtypes will be used to gain genetic access to these cells using Cas9/CRISPR-mediated genome engineering to create transgenic reporter lines. Development of this technology promises to open a pipeline for the rapid generation of multigenic mouse reporter strains in which specific neuronal subtypes are uniquely labeled by combinations of tagged genes. Third, we will use these genetically engineered mice to confirm that our taxonomy represents distinct functional properties of the classified neurons. Our approach can ultimately be scaled up to generate a complete census of cell types in the brain, a critically needed resource for dissecting nervous system function with modern investigative tools.
The human brain contains billions of neurons which in turn are thought to comprise hundreds if not thousands of distinct cell types, each tailored for a specific functional role in the processing of sensory information, generation of motor output, and for providing the biological basis of memory, behavior and consciousness. Current technologies so far have been unable to provide a detailed census of the myriad neuronal cell types in the mammalian brain, information that is critical for providing tools for unraveling and probing the computational logic of the brain. This BRAIN Initiative application proposes a suite of cutting edge technologies - including single cell transcriptome profiling and genome engineering - that can be developed and scaled up to catalog all of the neuronal cell types in the mammalian brain.
|Kramer, Daniel J; Risso, Davide; Kosillo, Polina et al. (2018) Combinatorial Expression of Grp and Neurod6 Defines Dopamine Neuron Populations with Distinct Projection Patterns and Disease Vulnerability. eNeuro 5:|
|Risso, Davide; Purvis, Liam; Fletcher, Russell B et al. (2018) clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets. PLoS Comput Biol 14:e1006378|
|Martin-Gayo, Enrique; Cole, Michael B; Kolb, Kellie E et al. (2018) A Reproducibility-Based Computational Framework Identifies an Inducible, Enhanced Antiviral State in Dendritic Cells from HIV-1 Elite Controllers. Genome Biol 19:10|
|Van den Berge, Koen; Perraudeau, Fanny; Soneson, Charlotte et al. (2018) Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol 19:24|
|Street, Kelly; Risso, Davide; Fletcher, Russell B et al. (2018) Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19:477|
|Fletcher, Russell B; Das, Diya; Ngai, John (2018) Creating Lineage Trajectory Maps Via Integration of Single-Cell RNA-Sequencing and Lineage Tracing: Integrating transgenic lineage tracing and single-cell RNA-sequencing is a robust approach for mapping developmental lineage trajectories and cell fate cha Bioessays 40:e1800056|
|Risso, Davide; Perraudeau, Fanny; Gribkova, Svetlana et al. (2018) A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun 9:284|
|Ecker, Joseph R; Geschwind, Daniel H; Kriegstein, Arnold R et al. (2017) The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas. Neuron 96:542-557|
|Choi, Yong Jin; Lin, Chao-Po; Risso, Davide et al. (2017) Deficiency of microRNA miR-34a expands cell fate potential in pluripotent stem cells. Science 355:|
|Vallejos, Catalina A; Risso, Davide; Scialdone, Antonio et al. (2017) Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat Methods 14:565-571|
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