AUTHOR=Mays Kasie C. , Haiman Justin H. , Janušonis Skirmantas TITLE=An experimental platform for stochastic analyses of single serotonergic fibers in the mouse brain JOURNAL=Frontiers in Neuroscience VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1241919 DOI=10.3389/fnins.2023.1241919 ISSN=1662-453X ABSTRACT=

The self-organization of the serotonergic matrix, a massive axon meshwork in all vertebrate brains, is driven by the structural and dynamical properties of its constitutive elements. Each of these elements, a single serotonergic axon (fiber), has a unique trajectory and can be supported by a soma that executes one of the many available transcriptional programs. This “individuality” of serotonergic neurons necessitates the development of specialized methods for single-fiber analyses, both at the experimental and theoretical levels. We developed an integrated platform that facilitates experimental isolation of single serotonergic fibers in brain tissue, including regions with high fiber densities, and demonstrated the potential of their quantitative analyses based on stochastic modeling. Single fibers were visualized using two transgenic mouse models, one of which is the first implementation of the Brainbow toolbox in this system. The trajectories of serotonergic fibers were automatically traced in the three spatial dimensions with a novel algorithm, and their properties were captured with a single parameter associated with the directional von Mises-Fisher probability distribution. The system represents an end-to-end workflow that can be imported into various studies, including those investigating serotonergic dysfunction in brain disorders. It also supports new research directions inspired by single-fiber analyses in the serotonergic matrix, including supercomputing simulations and modeling in physics.