The intrinsic cardiac nervous system (ICN) has been regarded as the "little brain" of the heart and is implicated to have a major role in the autonomic regulation of chronotropy, inotropy, and dromotropy. There has been an increasing interest in studying the neurons of the ICN as a region of interest for developing targeted therapeutics in the modulation of heart disease. Previous studies have characterized the intrinsic cardiac neurons through sectioned tissue in immunohistochemical studies, whole-mount heart preparations, and macroscopic inflation studies. However, these techniques show a limited perspective on the location of the intrinsic cardiac neurons with respect to the entire heart. To characterize a holistic distribution of the intrinsic cardiac neurons in the context of the whole heart, we have developed a scalable data acquisition workflow that consists of a set of tools and techniques that enable novel whole-organ imaging, precise 3D neuroanatomical mapping, and molecular phenotyping of the intrinsic cardiac neurons. A Knife Edge Scanning Microscope (KESM) was used for high-throughput whole-heart imaging of 9 rat hearts to assemble image stacks consisting of a range of 1400-2580 sections, where each section was 5 µm thick. The precise annotation of intrinsic cardiac neurons, delineation of cardiac anatomy on each section, and visualization of the spatial distribution of mapped neurons in 3D heart reconstructions was performed through the Tissue Mapper software. Laser capture microdissection was performed on 151 individual neurons for RNA sequence analysis, where the location of each cell was marked on the section they were lifted. The spatial location of each RNA sequenced neuron was mounted onto an interactive heart scaffold to visualize the molecular heterogeneity of the ICN in a 3D environment. For the first time, we have established a comprehensive large-scale atlas of the rat ICN at a single-cell resolution.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1086 |
| Date | 01 January 2020 |
| Creators | Leung, Clara |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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