We presented the Neurite Identification Tool (NIT) for the quantitative measurement of similarity between a pair of neurite traces in the same reference space. We built a reference library of traced and annotated secondary axon tracts (SATs) labeled with anti-Neurotactin at the 3rd instar developmental stage of Drosophila
larva, which is publically available at http://t2.ini.uzh.ch/nit/
. We applied the tool for the semiautomatic annotation of SATs in wild-type, and extended and refined the secondary lineage nomenclature by Pereanu and Hartenstein, 2006
. We quantified the accuracy of the automatically suggested annotations, and we found it to be between 93% and 99%. We tested the automatic annotation in mutant 3rd instar brains, and in brains labeled with markers other than anti-Neurotactin, and found the accuracy to be very high. Remarckably, we were able to use NIT for the discovery of primary neurons associated with SATs, and for the identification of SATs in the adult brain. Upon recognition of a few fiduciary points for 3D registration, NIT significantly decreases the time it takes for identifying a neuron or a lineage. Therefore, our tool may provide the means to link digital atlases of neurons and neuronal lineages across developmental stages of Drosophila
, from late embryo to the adult.
5.1 The accuracy of SAT identification
There exist differences in the accuracy with which individual lineages could be identified with their proper cognate in the reference database. Shorter SATs are likelier to share their trajectory with other SATs, and are less reliably identified. Lineages BLD1-4, BLAd2-3, and BLAl, present relatively short SATs that project into the compact transverse superior fascicle. Beyond small differences in their relative position, their distinctive feature is the position of the cell body cluster. Consequently, SATs in the lineage groups BLD and BLA are among the hardest to annotate by hand, and among the least well discriminated by the median Euclidean distance parameter ().
The second type of errors in SAT identification occurs in cases where multiple lineages share, for substantial parts of their trajectories, a compact fascicle, before diverging from each other to form more discrete terminal segments. This happens for the CM lineage group, all of which share the longitudinal central or longitudinal superior medial fascicle. SATs of the CM groups are also hard to annotate by hand in neurotactin-labeled brains, given 1) the elevated number of SATs per lineage, and 2) the high density of SATs from multiple lineages near the future location of the central complex. The generation of GFP-labeled flip-out clones was essential for the manual annotation of CM lineages.
The ‘sister lineages’ represent a third set of instances with problematic manual and algorithmic identification. The prime example are DAMd2 and DAMd3, which the trained classifier cannot discriminate from each other, presenting results with numerous false positives. These lineages are particularly hard to annotate by hand, and their annotation may remain ambiguous until sufficient GFP-labeled flip-out clones indicate individual characteristics.
5.2 Stereotypy and secondary axon tracts
The concept of morphological stereotypy may be defined as the degree of variability of a structure across individuals as measured under a given set of conditions. Our SAT semiautomatic annotation approach was made possible by the observed strong stereotypy of neuronal components in Drosophila
and insect brains in general. In particular, neuroblasts (Technau, 2008
) and neuronal cell bodies (Hiesinger et al., 2006
), primary neuronal lineages (Nassif et al., 1998
), secondary neuronal lineages (Ito and Hotta, 1992
; Ito et al., 1997
; Pereanu and Hartenstein, 2006
), olfactory neurons (Jefferis et al., 2007
), and neuropile compartments (Younossi-Hartenstein et al., 2003
; Jenett et al., 2006
) have been described as highly stereotypical in their position, dimensions and numbers.
Stereotypy, as defined, is a function of measurement conditions. Measuring stereotypy to generate consensus SAT traces, or to identify the same SAT in different brains, requires non-linear three-dimensional registration, which eliminates global and local differences in brain size and shape. These differences originated in artifactual deformations induced by sample preparation, in the exact developmental time at which each larva was fixed and dissected, and in phenotypical differences among individuals. Under the condition of equal coordinate space, corresponding SATs from different wild-type individuals are very similar, yet not identical. We have observed a non-homogeneous distribution of the residual variability (), defined as the remaining variability between two structures after bringing them into approximately the same coordinate space.
The proximal segment of a SAT, located close to the cell bodies, is generally more variable than the rest, in agreement with reports on relatively high variability in the location of the neuroblasts and neuronal cell bodies (Pereanu and Hartenstein, 2006
). Neuroblasts and their attached cell body clusters can vary up to about the diameter of a lineage (approximately 10–12 μm). By contrast, we find a much lower variability of the middle and distal segments of the SAT, which indicates a stricter regulation of axon pathfinding and positioning within the neuropile ().
5.3 Identification and quantification of mutant secondary lineages
We report a high accuracy in the recognition of SATs at 3rd instar larva. We tested the robustness of our method by attempting the identification of SATs in mutant, severely deformed glia-less brains. Our method correctly identified numerous secondary axon tracts, indicating a preservation of relative positions of SATs within the neuropile despite lacking glial cells. The difficulty of manually annotating SATs in mutants is reduced to determining the position of fiduciary points, then trace and compare a SAT to all SATs in the annotated library. The systematic application of our method to the SATs of glia-less brains highlighted some SATs without conclusive identification, suggesting these lineages were strongly affected, in accordance to their close association with glial sheets (Spindler et al., 2009
). The sequence analysis approach employed by our method indicates the presence of tract segment deletions (such as the missing terminal segment in BAmas1/2, ), or complete misrouting ().
This robustness of NIT will facilitate studies of mutant phenotypes. Current methods for quantifying phenotypic changes in the brain were mostly qualitative and restricted to parts where changes are most obvious, like the mushroom body (Heisenberg et al., 1985
; Heisenberg, 1998
). Quantitative mutant analysis can now be extended to all central brain lineages.
5.4 Bootstraping digital atlases of different developmental stages, species, and imaging modalities
How far can 3D registration be pushed to obtain valuable suggestions on the identity of a SAT? Beyond individuals, our approach enables identification of primary and secondary lineages across developmental time, nerve cord segments, and species (). Easily identifiable lineages, such as those forming the mushroom body, antennal lobe, or central complex, have been identified across all insect taxa (Boyan and Williams, 2007
; Strausfeld et al., 2009
). With appropriate fiduciary points, NIT could relate lineages from different species to those of Drosophila
Efforts are underway to use the same techniques by which lineages were reconstructed in the larva to follow neuronal lineage differentiation throughout pupal stages into the adult. Our exploratory data () indicates that the brain, despite undergoing massive growth following arborization of secondary lineages, does not change substantially in the relative position of internal components. However, certain changes occur, being non-trivial to identify adult lineages using their larval instances. For example, certain SATs grow massively; more generally, the clustering of somata changes as the brain cortex expands and simultaneously becomes thinner (Larsen et al., 2009
). As a result, proximal SAT segments change in direction. For best accuracy, and in order to detect newly developed or lost SATs in the pupal period, we envision multiple digital atlases of SATs for several pupal stages and the adult brain. Our quantitative analysis of similarities between SATs will be invaluable in establishing lineage identity through time.
5.5 The use of NIT for the semi-automated mapping of neurons
Systematic approaches to obtain markers for eventually every cell of the brain and ventral nerve cord have been initiated for the Drosophila
brain (Pfeiffer et al., 2008
). Such markers are crucial for the usage of Drosophila
as a model for neural function, development or pathology.
Neurons of the vertebrate brain are defined topologically in relationship to rich spatial frameworks of reference, composed of compartments (e.g. ‘lateral geniculate nucleus’) and tracts (e.g. ‘olivocerebellar tract’). Neurons in invertebrate brains have been classified based on cell body position (‘dorsomedial group’) or special attributes (‘giant fiber neuron’; ‘optic lobe pioneers’), lacking detailed reference frameworks. This non-systematic classification is insufficient for the comparison of different sets of neurons. We propose neuronal lineages as a high-resolution topological framework for single neuron identification.
With NIT, traced low-order branch segments of labeled neurons may be used for the assignment of neurons to their enclosing lineages with high reliability, providing them with a genetic address. The lineage represents an envelope; the knowledge we have about the envelope will overlap to a large extent with the individual neuron enclosed in it.