The cells of the brain exhibit a diversity of expressed genes, morphologies, and electrophysiological properties, and have come to be grouped into "cell types" that are distinguished by one or more of these characteristics. However, there is no one-to-one correspondence between cell type-defining expressed genes, morphological characteristics, and electrophysiological properties and no unified taxonomy of brain cells. Furthermore, cells routinely change their expressed genes, morphologies, and electrophysiological properties, as a result of development, plasticity, or disease, raising the question of how to categorize cell types as they change their states as a result of experience. Accordingly, we propose to develop a powerful, easy-to-use tool that enables the integrative phenotyping of cells of the brain - namely, a robot that can acquire simultaneously the gene expression patterns, morphologies, and electrophysiological properties of single cells in brain tissue, in an automated fashion. Recently, two of our labs developed a prototype "autopatching" robot that enables automated whole-cell patch clamp recording of neurons in living mouse brain, significantly increasing the efficiency of this highly challenging task. In a multidisciplinary tea, we propose to augment this robot, coupling it to transcriptional and morphological analysis strategies, yielding a platform for the comprehensive characterization of single cells in intact tissues. We will develop variants of the robot and its algorithms to enable it to patch in brain slices, including in an image guided fashion (Aim 1), to extract transcriptomic information (Aim 2), and to perform morphological fills (Aim 3) and gene delivery to cells (Aim 5). We will also create massively parallel autopatching robots (Aim 4). We will autopatch hundreds to thousands of single cells from different cortical regions of mice (Aim 6), in vivo as well as in slices, both broadly surveying cells, as well as targeting specific fluorescently labeled neural populations. We will create visualization software to help with analysis of the integrated cell profiles that emerge, aiming to estimate the dimensionality of "cell type space", characterize cell- to-cell heterogeneity, and discover optimal cell type markers for molecular targeting. Our goal is to create a powerful, easy-to-use toolbox that makes fundamentally new kinds of science possible, converting the critical tasks of categorizing cell types, and characterizing cell states, into routne, simple tasks. As our goal is to develop a toolbox which will have very broad applicability, we are focusing our innovation not only on power, but ease of use, aiming to enable fields across biology to characterize normal and diseased organ states at the single cell level. We will distribute all tools, methods, and datasets as freely as possible, and teach others to use these technologies. As many diseases affect different cells to different extents, we will seek to commercialize our work to enable diagnostic or therapeutic tools that directly improve human health.
Our project will generate a new kind of robot, as well as relevant methods of use, that enable biologists and clinicians to automatically assess the gene expression profile, shape, and electrical properties of individual cells embedded in intact tissues such as the brain. By enabling the automated characterization of cells in complex organ systems, our technology will empower scientists across biology to map the cell types present in organ systems (e.g., brain circuits) in disease states, enabling new mechanistic understandings of disease, and enabling new molecular drug targets to be identified. Our robot will also enable new kinds of biopsy analysis and diagnostic, helping empower personalized medicine in arenas ranging from epilepsy to cancer, to utilize information about cellular diversity in disease states towards patient care optimization.
|Prevedel, Robert; Yoon, Young-Gyu; Hoffmann, Maximilian et al. (2014) Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nat Methods 11:727-30|
|Glaser, Joshua I; Zamft, Bradley M; Marblestone, Adam H et al. (2013) Statistical analysis of molecular signal recording. PLoS Comput Biol 9:e1003145|