Automating Array Production for High-resolution Three-dimensional Proteomic Imagi

DESCRIPTION (provided by applicant): Understanding the structure and function of the brain is an outstanding scientific problem, and neurodegenerative diseases are among the most expensive, disruptive, and least well treated of human maladies, arguably because they are not well understood. Array Tomography is a new method for tissue imaging with resolution in all three dimensions sufficient to resolve individual synapses and provide quantitative characterization of multiple (currently ~40) molecular constituents, throughout large samples. Array Tomography imaging data enables the description of neural networks in the context of the three-dimensional tissue architecture. We believe such data will enable researchers to begin to comprehend the proper functionof neural circuits and, importantly, to begin to understand how it is that the various neurodegenerative processes present and progress. This prospect is alluring, and yet, due to the complexity of the method and the cost of establishing it in the lab of an individual investigator, Array Tomography has been used in relatively few studies following the first publication in 2007 (Micheva and Smith, 2007). Aratome has been established to offer Array Tomography as a service to the research community. We believethis is the best way to bring the power of the method to bear on problems in neuroscience, cancer research, medical diagnostics and therapeutics development. To that end, in this application we propose to industrialize the most fragile aspects of array production, which will reduce the cost of producing high quality data and allow an offering at a commercially viable price. PUBLIC HEALTH RELEVANCE: Neurodegenerative diseases, including Alzheimer's and Parkinson's, are becoming increasingly prevalent as the population ages. Efforts to develop treatments for these diseases have been hampered by a lack of basic understanding of the disease processes, due in part to the lack of technologies that provide big picture information regarding the disease progression. We propose to develop a technology for high-resolution proteomic analysis of the brain to better understand the pathologies, enabling the development of more effective treatments in the future.