Abstract
Mapping neural circuits can be accomplished by labeling a small number of neural structures per brain, and then combining these structures across multiple brains. This sparse labeling method has been particularly effective in Drosophila melanogaster, where clonally related clusters of neurons derived from the same neural stem cell (neuroblast clones) are functionally related and morphologically highly stereotyped across animals. However identifying these neuroblast clones (approximately 180 per central brain hemisphere) manually remains challenging and time consuming. Here, we take advantage of the stereotyped nature of neural circuits in Drosophila to identify clones automatically, requiring manual annotation of only an initial, smaller set of images. Our procedure depends on registration of all images to a common template in conjunction with an image processing pipeline that accentuates and segments neural projections and cell bodies. We then measure how much information the presence of a cell body or projection at a particular location provides about the presence of each clone. This allows us to select a highly informative set of neuronal features as a template that can be used to detect the presence of clones in novel images. The approach is not limited to a specific labeling strategy and can be used to identify partial (e.g., individual neurons) as well as complete matches. Furthermore this approach could be generalized to studies of neural circuits in other organisms.
Highlights
How the nervous system processes sensory information and generates behavior critically depends on its underlying circuitry
We used cross-validation to test our procedure on a set of 350 images of male Drosophila brains
In this study, we have described how one can speed up the identification of Drosophila neuroblast clones in large datasets
Summary
How the nervous system processes sensory information and generates behavior critically depends on its underlying circuitry. Mapping this circuitry (and understanding its developmental origins) is a major challenge in neuroscience, and there are currently two distinct approaches to the problem. Dense reconstruction, involves labeling many neural structures in a single brain, and resolving these structures using electron microscopy (Briggman and Denk, 2006). The second approach, sparse labeling, involves labeling few neural structures in a single brain which can be subsequently resolved using light microscopy (e.g., Otsuna and Ito, 2006; Jefferis et al, 2007; Lin et al, 2007). By imaging many sparsely labeled brains, one can piece together the neural circuitry
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