Dawen Cai - US grants

? DESCRIPTION: During development, stem/progenitor cells replicate and differentiate into many lineages, which give rise to precise number and subtypes of cells. Defects in lineage development can cause severe developmental diseases. Currently, the state-of-the-art lineage analysis uses mosaic labeling techniques to study one or a few lineages at a time to avoid ambiguity. While the small number of highlighted cells can be investigated extensively, complications in the unlabeled adjacent lineages are hidden from analysis. The ability of unambiguously labeling large number of lineages in situ is highly desired, since it is extremely exhausting, if not impossible to use the available tools to study the precise spatial-temporal relationship of all related lineages in one animal. We propose to develop a two-photon compatible multispectral and subcellular-coding system (MACS), which permits unambiguous labeling of large number of cell lineages in the same animal. We will validate MACS by mapping all of the ~100 neural lineages in single Drosophila central brain and depict the developmental processes of all Drosophila embryo neural lineages precisely in space and time. If success, MACS can be easily adapt to other transgenic animal models, including fish, mouse and rat. MACS will create new opportunities in lineage studies, such as investigating lineage variations among individuals, and between hypomorphic alleles or different sex; as well as cell non-autonomous effects of gene mutations in stem/progenitor cells.

Nanoscale-resolution reconstructions of the complete architectures of neurons offer the potential to revolutionize the study of neuronal circuitry in normal and diseased brains but this promise is far from realized because the larger neuroscience community cannot access the technologies that provide nanoscale reconstructions over large volumes and the computational infrastructure required to analyze them. As a result, neuroanatomy at the nanoscale is restricted to one-off studies of smaller volumes, rarely extending past small volume reconstructions in single animals (n<=1), and accessible only to labs that can afford the extraordinary time commitment, labor, and expense required to reconstruct even simple neuronal circuits. Here we propose a novel approach: rather than developing more and more sophisticated hardware for nanoscale reconstructions of the brain, we propose to develop a toolbox of accessible molecular, chemical, and computational approaches for accurate nanoscale reconstructions of large volumes of the brain targeting fluorescence microscopy and the conventional confocal microscope ? two of the most widely accessed tools in the neuroscience arsenal. By providing an accessible pipeline to reconstructing the natural shape and space between brain cells found in the living brain over wide range of experimental conditions, we will help deliver access to comprehensive nanoscale neuroanatomy to the broader neuroscience community.

ABSTRACT Depicting the specific neuronal identity and connectivity underlying particular brain function remains a central goal for neuroscience. For over a century, neuroanatomy has continued to play critical roles in referencing a neuron's synaptic contact, dendritic morphology and axonal projection to its connectivity. The advances of genetic probes, optical imaging modalities and computer technologies permit monitoring and manipulating neuronal activity in living animals with unprecedented precision and scale. In addition, the forefront of ?-omics? study begins to discriminate the molecular diversity of the heterogenous population neurons in the same brain region. The identification of unique molecular markers further enables creating novel transgenic models to interrogate precise subsets of neurons. Despite the tremendous success in applying these revolutionary technologies in studying systems and behavior neuroscience, there currently lacks a unified experimental paradigm to directly link activity, connection and molecular information of the exact same neurons in a functional circuit at the single cell/single synapse resolution. The ability to do so will remove the ambiguity in current attempts to correlate different attributes of the ?same? neuronal populations sampled from different animals. More importantly, the ability to do so will tremendously improve our efficiency and accuracy in differentiating specific neuronal populations that correlate with distinct circuit functions in the same brain region. Here, we demonstrate the feasibility of coMAAP, a multimodal experimental paradigm that allows correlative optical mapping of activity, anatomy and molecular-identity of the same neurons in the same animal. Importantly, coMAAP can be implemented using standard instruments. While combining with specialized imaging modalities it can achieve unprecedented resolution and scale. The goals of our proposal are to optimize and validate the coMAAP experimental paradigm, and to utilize coMAAP to depict the heterogenous neuronal populations that are arousal activated in the mouse ventral tegmental area (VTA).

Abstract Neural circuits composed of interconnected neurons with distinct properties lay the physical foundation of any brain function. Identifying connections between individual neurons is central to understand how information is processed and propagated in the brain. While emerging high throughput light microscopy technologies are highly promising in allowing whole brain scale imaging at the single cell level, optical resolution limitation prevents their use in differentiating densely labeled neuronal processes in the same brain. In addition, computational tools for automatically extracting morphological information from intermingled neurons with high accuracy are still lacking. Our team will concurrently develop novel genetic tools for neuronal labeling, super-resolution imaging, and automated neuronal tracing for high-throughput circuit reconstruction. We will apply these tools to obtain densely reconstructed inhibitory microcircuits in the mouse cortex. These tools will be readily applicable for studying other long-standing questions in neuroscience and the resources generated by this project will be useful for future computational tool development.