Stereologic cell counting has revolutionized the field of neuroscience over the last 20 years, and numerous studies highlighting the impact of stereologic cell counting in basic neuroscience and pharmaceutical and biotechnology research continue to be published. A single study, however, may take months since investigators must decide by visual inspection whether or not to count each cell according to three-dimensional (3D) stereologic counting rules within thousands of microscopic fields-of-view. Because of this reliance on manual inspection, stereologic cell counting has remained very labor intensive and time-consuming to perform. As a result, faster automated, though less accurate non-stereologic cell detection approaches have remained in widespread use despite their known disadvantages and biased results. In response to this challenge, this project aims to develop an innovative microscope system for fully-automated stereologic cell counting (""""""""FASTCOUNT""""""""), building upon technology implemented in the FARSIGHT toolkit for automated 3D cell detection created by our collaborator, Dr. Badri Roysam and his team, and combining it with technology developed for our Stereo Investigator(R) software (used in approximately 1,000 laboratories worldwide). This project is fully in line with the NIMH program titled Lab to Marketplace: Tools for Brain and Behavioral Research. FASTCOUNT will allow investigators, for the first time, to perform automated stereologic cell counting. The benefit for the neuroscience research community - and society in general - will be increased research throughput (i.e., at least ten times faster) in basc neuroscience and pharmaceutical and biotechnology research. Increased throughput will make possible new kinds of studies currently not feasible due to the labor intensive nature of the existing methods, thus leading to new discoveries. Our project will be implemented in two phases. During Phase I, we will create a prototype FASTCOUNT application incorporating cell detection, learning, and editing. We will then benchmark its performance in automated 3D cell detection on tissue sections for various combinations of species, tissue processing, cell staining/labeling, imaging, and image pre-processing. During Phase II, we will (i) improve and optimize the algorithms for automated stereologic 3D cell detection used in FASTCOUNT, (ii) develop a Software Development Kit as interface between FASTCOUNT and third party software applications to allow development of new automated 3D cell detection methods, (iii) develop novel image data management functionality for automated stereologic cell counting on tissue specimens processed with recent tissue clearing procedures such as CLARITY, and (iv) develop recommended operating procedures for FASTCOUNT addressing tissue preparation, labeling, imaging, and pre-processing. Together with experienced academic collaboration partners, we will perform extensive product validation studies of FASTCOUNT throughout development to demonstrate its superiority over both manual stereologic cell counting and biased profile counting.
This project will create a much needed innovative system for performing high-throughput, accurate, automated stereologic 3D cell counting, thereby increasing the pace of research and enabling the design of new studies not currently feasible with existing methodologies. Because stereologic cell counting is currently a very time consuming method, increasing the throughput is critical for the development of new treatments for Alzheimer's, Parkinson's, and many other neurological conditions and diseases. This project embraces the goals of the NIMH program Lab to Marketplace: Tools for Brain and Behavioral Research by leveraging a collaboration with academic researchers in automated 3D cell detection (Dr. Badri Roysam at the University of Houston, Texas), to translate a technology for brain research from the academic research sector to the marketplace, and further developing this technology into a high impact, robust, and user-friendly commercial product.
|Schmitz, Christoph; Eastwood, Brian S; Tappan, Susan J et al. (2014) Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting. Front Neuroanat 8:27|