UC Santa Barbara

The current understanding of monoaminergic neurotransmission relies upon the ability to image and analyze the components of these systems. Yet, the efforts to understand these systems are inhibited from a combination of the unique properties of axons that release monoamines and limitations in imaging technologies. Axons from monoaminergic cells can be thin (~ 1 μm in diameter) and cannot be viewed directly, requiring both fluorescent labeling and high-resolution imaging. Adding further complications are that the axons travel the entirety of the brain, appear to intertwine freely with each other, and are often difficult to track individually. Here, we successfully designed an experimental process for monoaminergic axon research that endows the ability for axon differentiation with the use of Brainbow AAV and Cre technologies. These approaches were validated with a new algorithm that can reliably trace individual axons in various brain regions. To complement these methods and provide information on axon trajectories in the natural 3D-space, we also designed a process for high-resolution light-sheet microscopy using tissue clearing technologies for imaging of axons without sectioning tissue combined with machine learning algorithms for automated tracing. These methods allow for unprecedented access to individual axon trajectories and support their modeling as paths of spatial stochastic processes. This project focused on the serotonin system, however, these methods can be extended and applied to research using other monoamines. This is the first research on serotonin or monoamines in general with the capability of individual axon discrimination and can help answer many previously elusive questions within this field of research.