Progress in neural recording is critical to understanding the brain and developing treatments for brain disorders. Current neural recordings can, at best, capture a few hundred interacting neurons. The number of recorded neurons is relatively small because current neural recording devices, such as electrodes, amplifiers, lasers, and cameras, are macroscopic. The objective of our research is to create neural recorders at the molecular scale, by writing neural activities onto DNA, like a molecular ticker tape. The device will consist of an engineered DNAP polymerase that can be cheaply synthesized and easily delivered to neurons, where it will write the temporal dynamics of activity of each neuron onto local DNA molecules, which can later be analyzed via increasingly cheap genome sequencing technologies. The long term goal of our research is to enable a paradigm shift, making recording instrumentation-free, easy to use, and scalable to arbitrary numbers of neurons. We will obtain the nanoscale recording device using three pipelines: (1) Polymerase design pipeline. We will search through different DNA polymerases to find a polymerase that makes many replication mistakes when ion concentrations increase, and thus when neurons are active. We will use directed protein engineering to add ion-sensitive domains. Lastly we will use high throughput protein directed evolution, to produce a polymerase with desirable properties. (2) Template design pipeline. We will design and deliver an engineered DNA template to the cell to be copied. We will utilize transfection, which is feasible but might not be convenient in some neuroscientific experiments, moving later towards viral template delivery methods, which may be simpler. (3) Statistics pipeline. The resulting DNA sequences need to be converted back into signals of neurobiological meaning. Such conversion needs to be precise, robust to various problems such as biological polymerase noise, and error-correcting. The approach is innovative, because it reinvents the concept of recording using molecular engineering to produce a device that is orders of magnitude smaller and arguably more versatile than comparable devices. The proposed research is significant, because it allows a whole range of new electrophysiological experiments. The approach will complement other emerging approaches that promise to lead to large dataset based neuroscience, e.g. connectomics. The resulting technique will be easyto- use and inexpensive, yet will promise to allow recording simultaneously from potentially arbitrary numbers of neurons, with temporal precision comparable to existing state-of-the-art calcium imaging. It promises massively increased amounts of neural data and entirely new approaches to asking deep questions about the way the brain works and how to cure disease of the brain.
The proposed research is relevant to public health because it will result in a tool, a molecular neural activity recorder, that will enable substantial progress n wide areas of neuroscience, both basic and clinical. Hence, the proposed research is relevant to the part of NIH's mission that pertains to foster fundamental creative discoveries, innovative research strategies, and their applications as a basis for ultimately protecting and improving health.
|Zhang, Yu Shrike; Chang, Jae-Byum; Alvarez, Mario MoisÃ©s et al. (2016) Hybrid Microscopy: Enabling Inexpensive High-Performance Imaging through Combined Physical and Optical Magnifications. Sci Rep 6:22691|
|Glaser, Joshua I; Kording, Konrad P (2016) The Development and Analysis of Integrated Neuroscience Data. Front Comput Neurosci 10:11|
|Shipman, Seth L; Nivala, Jeff; Macklis, Jeffrey D et al. (2016) Molecular recordings by directed CRISPR spacer acquisition. Science 353:aaf1175|
|Chen, Fei; Wassie, Asmamaw T; Cote, Allison J et al. (2016) Nanoscale imaging of RNA with expansion microscopy. Nat Methods 13:679-84|
|Adamala, Katarzyna P; Martin-Alarcon, Daniel A; Boyden, Edward S (2016) Programmable RNA-binding protein composed of repeats of a single modular unit. Proc Natl Acad Sci U S A 113:E2579-88|
|Marblestone, Adam H; Wayne, Greg; Kording, Konrad P (2016) Toward an Integration of Deep Learning and Neuroscience. Front Comput Neurosci 10:94|
|Kodandaramaiah, Suhasa B; Holst, Gregory L; Wickersham, Ian R et al. (2016) Assembly and operation of the autopatcher for automated intracellular neural recording in vivo. Nat Protoc 11:634-54|
|Harrison, Reid R; Kolb, Ilya; Kodandaramaiah, Suhasa B et al. (2015) Microchip amplifier for in vitro, in vivo, and automated whole cell patch-clamp recording. J Neurophysiol 113:1275-82|
|Chen, Fei; Tillberg, Paul W; Boyden, Edward S (2015) Optical imaging. Expansion microscopy. Science 347:543-8|
|Glaser, Joshua I; Zamft, Bradley M; Church, George M et al. (2015) Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization. PLoS One 10:e0131593|
Showing the most recent 10 out of 11 publications