May 27, 2020 -- Researchers have created a comprehensive map of cardiac neurons at the cellular scale that allows for gene expression data to be superimposed, giving insight into the functional roles of specific neuron clusters. The work was published in iScience on May 26.
An interdisciplinary group of researchers decided to develop a new approach to building cellular maps using the intrinsic cardiac nervous system -- intrinsic innervation of the heart that includes cardiac neurons called ganglia -- of rodents. Although the heart contains a significant population of neurons, they have not been previously mapped (in terms of extent, position, or distribution) in relation to the whole heart.
Molecular phenotypes of neurons had been shown to be an orderly spatial gradient of neuron types, but had never been mapped until now. This information is necessary to understand connectivity of neurons in the heart and how they are organized.
"Many cardiologists aren't even aware there are neurons in the heart, let alone that they are critical to heart health," said senior author James Schwaber, PhD, director of the Daniel Baugh Institute for Functional Genomics and Computational Biology at Thomas Jefferson University, in a statement. "By using this 3D reference space, we can build a comprehensive picture of the heart's structure which is foundational to address various health concerns."
A 3D model was developed by integrating whole-organ imaging, precise 3D neuroanatomical mapping, and molecular phenotyping. First, knife-edge scanning electron microscopy was deployed, in which a diamond knife is used to create thin slices throughout the length of the heart, which are subsequently imaged and sampled using 3Scan software. The images are used to develop the 3D reconstruction.
In parallel, laser capture microdissection is used to remove single neurons and precisely identify their location within the heart. Once collected, the data were fit into a 3D model that created a comprehensive picture of the heart's neural network.
The researchers were able to identify individual neurons in the cardiac nervous system and record each neuron's spatial positions in the heart at micron-scale resolution. Together, with the ability to understand the gene expression of individual neurons, the model represents a pipeline for cardiac molecular phenotype data acquisition.
The map revealed that neurons were compact and localized to a region on the superior and posterior aspects of both left and right atria. Using a "partial projection" software tool, the researchers were able to view, at higher resolution, the posterior cardiac structures where higher concentrations of neurons were found. This provided greater detail and a more coherent view of neurons in this region of the heart.
The laser capture microdissection technique produced over 23,000 data points from 151 samples of individual neurons. The researchers identified distinct molecular phenotypes along the base-to-apex axis of the heart, suggesting that such an organizational pattern may be the result of embryonic development. The gradient of phenotypes may follow chemical gradients and the researchers suggest that this could be tied to the biological function of the neurons.
The researchers also noted that the discovery of phenotypical spatial gradients will lead to connectomic studies that can associate phenotypes with cardiac targets and function.
"With the spatial mapping of the gene expression, we can begin to discuss the precise roles that these neurons play. Do separate clusters of the ICN [intrinsic cardiac nervous system] neurons have different functions, or do they work into an integrated way to influence heart health? Now we can address these questions in a way that wasn't possible before," said co-author Zixi Jack Cheng, PhD, a cardiovascular anatomist and physiologist from the University of Central Florida College of Medicine.
The framework has already generated several new projects across several labs that are working to understand the autonomic nervous system for other organs of the body. The new technique will help researchers work toward the larger goal of creating effective treatments utilizing neuromodulation.
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