Segmentation of center brains and optic lobes in 3D confocal images of adult fruit fly brains

High diversity in neuropeptide immunoreactivity patterns among three closely related species of Dinophilidae (Annelida)

Neuropeptides are conserved metazoan signaling molecules, and represent useful markers for comparative investigations on the morphology and function of the nervous system. However, little is known about the variation of neuropeptide expression patterns across closely related species in invertebrate groups other than insects. In this study, we compare the immunoreactivity patterns of 14 neuropeptides in three closely related microscopic dinophilid annelids (Dinophilus gyrociliatus, D. taeniatus and Trilobodrilus axi). The brains of all three species were found to consist of around 700 somata, surrounding a central neuropil with 3-5 ventral and 2-5 dorsal commissures. Neuropeptide immunoreactivity was detected in the brain, the ventral cords, stomatogastric nervous system, and additional nerves. Different neuropeptides are expressed in specific, non-overlapping cells in the brain in all three species. FMRFamide, MLD/pedal peptide, allatotropin, RNamide, excitatory peptide, and FVRIamide showed a broad localization within the brain, while calcitonin, SIFamide, vasotocin, RGWamide, DLamide, FLamide, FVamide, MIP, and serotonin were present in fewer cells in demarcated regions. The different markers did not reveal ganglionic subdivisions or physical compartmentalization in any of these microscopic brains. The non-overlapping expression of different neuropeptides may indicate that the regionalization in these uniform, small brains is realized by individual cells, rather than cell clusters, representing an alternative to the lobular organization observed in several macroscopic annelids. Furthermore, despite the similar gross brain morphology, we found an unexpectedly high variation in the expression patterns of neuropeptides across species. This suggests that neuropeptide expression evolves faster than morphology, representing a possible mechanism for the evolutionary divergence of behaviors.

Automated in situ brain imaging for mapping the Drosophila connectome

Mapping the connectome, a wiring diagram of the entire brain, requires large-scale imaging of numerous single neurons with diverse morphology. It is a formidable challenge to reassemble these neurons into a virtual brain and correlate their structural networks with neuronal activities, which are measured in different experiments to analyze the informational flow in the brain. Here, we report an in situ brain imaging technique called Fly Head Array Slice Tomography (FHAST), which permits the reconstruction of structural and functional data to generate an integrative connectome in Drosophila. Using FHAST, the head capsules of an array of flies can be opened with a single vibratome sectioning to expose the brains, replacing the painstaking and inconsistent brain dissection process. FHAST can reveal in situ brain neuroanatomy with minimal distortion to neuronal morphology and maintain intact neuronal connections to peripheral sensory organs. Most importantly, it enables the automated 3D imaging of 100 intact fly brains in each experiment. The established head model with in situ brain neuroanatomy allows functional data to be accurately registered and associated with 3D images of single neurons. These integrative data can then be shared, searched, visualized, and analyzed for understanding how brain-wide activities in different neurons within the same circuit function together to control complex behaviors.

A strategy for building neuroanatomy ontologies

MOTIVATION

Advancing our understanding of how nervous systems work will require the ability to store and annotate 3D anatomical datasets, recording morphology, partonomy and connectivity at multiple levels of granularity from subcellular to gross anatomy. It will also require the ability to integrate this data with other data-types including functional, genetic and electrophysiological data. The web ontology language OWL2 provides the means to solve many of these problems. Using it, one can rigorously define and relate classes of anatomical structure using multiple criteria. The resulting classes can be used to annotate datasets recording, for example, gene expression or electrophysiology. Reasoning software can be used to automate classification and error checking and to construct and answer sophisticated combinatorial queries. But for such queries to give consistent and biologically meaningful results, it is important that both classes and the terms (relations) used to relate them are carefully defined.

RESULTS

We formally define a set of relations for recording the spatial and connectivity relationships of neuron classes and brain regions in a broad range of species, from vertebrates to arthropods. We illustrate the utility of our approach via its application in the ontology that drives the Virtual Fly Brain web resource.

AVAILABILITY AND IMPLEMENTATION

The relations we define are available from http://purl.obolibrary.org/obo/ro.owl. They are used in the Drosophila anatomy ontology (http://purl.obolibrary.org/obo/fbbt/2011-09-06/), which drives the web resource http://www.virtualflybrain.org

BrainAligner: 3D registration atlases of Drosophila brains

Analyzing Drosophila neural expression patterns in thousands of 3D image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of 295 brains, we achieved a registration accuracy of 2µm on average, permitting assessment of stereotypy, potential connectivity, and functional mapping of the adult fruitfly brain. We used BrainAligner to generate an image pattern atlas of 2,954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.