Understanding how a neural circuit executes a specific behavior requires information on many levels: cells types involved, strength and dynamics of particular connections, as well as the activity and plasticity of neurons underlying the behavior. Integrating information about the identity, function and connectivity of neural networks in intact 3D volumes is a fundamental aim in neuroscience. Recently, several tissue clearing techniques have emerged to enable the visualization and phenotyping of intact neural circuits, but most of these are not readily compatible with RNA labeling, and systematic methodologies to investigate RNAs present in such volumes have not been described. The ability to reliably detect RNAs is critical for many aspects of neuroscience research. RNA expression is routinely used to determine cell identity and plays a key role in defining different cell types in a neural circuit. Other RNAs are tightly regulated by activity and serve as markers for cells activated by a recent behavior. The ability to visualize activated cells in intact volume would be transformative for studies focused on identifying ensembles of neurons engaged during a particular behavior. Many non-coding RNAs are altered in neuropsychiatric disorders, but a global view of the spatial component to these changes is lacking. The goal of this proposal is to establish a tool broadly applicable to the research community that will enable scientists to label multiple RNA targets simultaneously in transparent brain tissue, allowing for broad molecular phenotyping in intact neural networks.
Complex behaviors involve the activity of many types of cells, distributed in networks that span multiple brain regions. This projects aims to combine cellular resolution gene expression analysis to classify cell type and report activity of individual cells while maintaining a global view of intact neuronal structures and long-range circuit projections. By simultaneously characterizing the identity, structure, and connectively of brain wide networks, this method will transform the way scientists study the role of these networks in behavior and how they are disrupted in disease.
| Wang, Xiao; Allen, William E; Wright, Matthew A et al. (2018) Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361: |
| Sylwestrak, Emily Lauren; Rajasethupathy, Priyamvada; Wright, Matthew Arnot et al. (2016) Multiplexed Intact-Tissue Transcriptional Analysis at Cellular Resolution. Cell 164:792-804 |
