An unbiased template of the Drosophila brain and ventral nerve cord

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individuals neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals...

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Bibliographic Details
Published inbioRxiv
Main Authors Bogovic, John A, Otsuna, Hideo, Heinrich, Larissa, Ito, Masayoshi, Jeter, Jennifer, Meissner, Geoffrey W, Nern, Aljoscha, Colonell, Jennifer, Oz Malkesman, Ito, Kei, Saalfeld, Stephan
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 21.12.2019
Subjects
Brain mapping
Gene mapping
Insects
Nervous system
Neuroimaging
Ventral nerve cord
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Summary:The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individuals neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time. Footnotes * We extended evaluation with the registration tool elastix and show that this offers a 10x speedup over CMTK at comparable quality. We developed and describe a new format for transformations that offers >10x compression at minimal loss of precision over existing formats. We developed and describe new open source software to convert between transformation formats including our own and to apply and compare the registrations to image data and geometric data. We added and discussed another metric for local deformation. We added more qualitative examples in the supplement.
DOI:10.1101/376384