BrainAligner is a new landmark-detection-based algorithm and software package for the large-scale automatic alignment of confocal images of Drosophila brains. We have used it, in combination with an optimized virtual target brain, consistent tissue preparation and imaging, and a library of GAL4 lines, to generate a pilot 3D atlas of neural expression patterns for Drosophila. We have also applied BrainAligner to our on-going FlyLight project that will produce an even higher resolution 3D digital map of the Drosophila brain. BrainAligner has robustly registered over 17,000 brain images of thousands of GAL4 lines within a few days, without any manual intervention during the alignment. The alignability of new samples, determined by their Qi scores, serves as an important quality control check. We are developing further methods to expand and query this resource, but it is already in use for anatomical and behavioral investigation of neural circuit principles.
Expression patterns generated by recombinase-based methods to label neurons of a common developmental lineage (MARCM26
) and images in which single neurons are labeled can be aligned with our GAL4 reference atlas to identify lines that have GAL4 expression in those cells, allowing investigation of their behavioral roles. Examination of different GAL4 expression patterns for proximity or overlap suggests which areas might be functionally connected. The Drosophila
brain is subdivided into large regions based on divisions in the synaptic neuropil caused by fiber tracts, glial sheaths, and cell bodies, but these anatomical regions may be further subdivided by gene expression patterns revealed by the GAL4 lines. When the GAL4 lines are aligned to a template brain upon which anatomical regions have been labeled, we can annotate the expression patterns using the VANO software27
in a faster and more uniform way. Alignment permits imaged-based searching, a significant improvement over keyword searching based on anatomical labels. Accurate alignment of images will also make it easier to correlate anatomy with behavioral consequences. Integration of aligned neuronal patterns with other genetic and physiological screening tools may be used to study different neuron types.
We have optimized BrainAligner to run on large datasets of GAL4 lines expressed in the adult fly brain and ventral nerve cord, but these are not the only type of data that can be aligned. Antibody expression patterns, in situs
, and protein-trap patterns28,29
are also suitable; if the same reference antibody is included, images from different sources can be aligned using BrainAligner. Although BrainAligner was developed using the nc82 pre-synaptic neuropil marker, we have also successfully aligned brains where the reference channel was generated by staining with rat anti-N-Cadherin antibody (see ). Other reference antibodies that label a more restricted area of the brain, such as anti-FasII, may also work with the algorithm. It is also possible to align any pair of brains directly, rather than aligning both to a common template.
BrainAligner can be used in many situations where the image data has different properties than the data presented in this study. The optic lobes of an adult Drosophila
brain shift in relation to the central brain and distort alignments. We developed an automated method to segregate the optic lobes from the central brain30
, which was then registered using BrainAligner. For the larval nervous system and the adult ventral nerve cord of Drosophila
, we detected and aligned the principal skeletons of these images13
, followed by BrainAligner registration. BrainAligner automatically detects the corresponding landmarks, but it permits using manually added landmarks to improve critical alignments or to optimize alignments in a particular brain region. Indeed the brains to be aligned may also be imaged using different magnification scales. Higher resolution images may have only a part of the brain in the field of view, complicating registration. In such a case, the user can manually supply as few as four to five markers using V3D software21
to generate a globally aligned brain, which can then be automatically aligned using BrainAligner.
Despite a number of successfully used image registration methods in other scenarios such as building the Allen mouse brain atlas10
, we have not found another automated image registration method that performs as well as BrainAligner on our large scale applications. Indeed, the key algorithm in BrainAligner, the RLM method, can be viewed as an optimized combination of several existing methods. It compares the results produced using different criteria and only uses results that agree with each other. BrainAligner is not limited to Drosophila
and could be applied to other image data such as mouse brains.