Abstract
Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.
Highlights
Neuroscientists have long recognized the importance of neuronal structure in defining circuit function
Even though two-dimensional (2D) analyses of neuron morphology have remained in practice[4], the more complete and realistic three-dimensional (3D) reconstructions became the standard in the field[5]
Multichannel neuronal reconstruction Since the morphology of neuronal trees corresponds to the continuous bounds of their cytosolic membrane, the 3D location and thickness of each branch are typically traced from the image stack of the membrane label
Summary
Neuroscientists have long recognized the importance of neuronal structure in defining circuit function. In parallel to continuous improvements in labeling and imaging techniques, methods to trace axonal and dendritic branching evolved from pencil-on-paper to digital encoding of tree origins, bifurcations, and terminations[3]. Even though two-dimensional (2D) analyses of neuron morphology have remained in practice[4], the more complete and realistic three-dimensional (3D) reconstructions became the standard in the field[5]. As the number of scientific publications describing 3D digital tracings continued to increase, NeuroMorpho.Org emerged as a popular electronic repository to store, annotate, publicly share, and freely reuse these labor-intensive datasets[6]. The open availability of neuronal morphology digitally reconstructed in this form from a vast array of model systems, experimental preparations, anatomical regions, and cell types enabled a diverse array of secondary studies. Among the most flourishing applications are comparative morphometric analyses[8], electrophysiological simulations[9], large-scale biophysically-detailed modeling[10], and algorithmic generation of virtual neurons[11,12]
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