Adaptive and background-aware GAL4 expression enhancement of co-registered confocal microscopy images

GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions

Automatic Segmentation of Drosophila Neural Compartments Using GAL4 Expression Data Reveals Novel Visual Pathways.

Identifying distinct anatomical structures within the brain and developing genetic tools to target them are fundamental steps for understanding brain function. We hypothesize that enhancer expression patterns can be used to automatically identify functional units such as neuropils and fiber tracts. We used two recent, genome-scale Drosophila GAL4 libraries and associated confocal image datasets to segment large brain regions into smaller subvolumes. Our results (available at https://strawlab.org/braincode) support this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. The basis for the structural assignment is clustering of voxels based on patterns of enhancer expression. These initial clusters are agglomerated to make hierarchical predictions of structure. We applied the algorithm to central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 11 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates

Cellular and behavioral functions of fruitless isoforms in Drosophila courtship

Background: Male-specific products of the fruitless (fru) gene control the development and function of neuronal circuits that underlie male-specific behaviors in Drosophila, including courtship. Alternative splicing generates at least three distinct Fru isoforms, each containing a different zinc-finger domain. Here, we examine the expression and function of each of these isoforms.Results: We show that most fru(+) cells express all three isoforms, yet each isoform has a distinct function in the elaboration of sexually dimorphic circuitry and behavior. The strongest impairment in courtship behavior is observed in fru(C) mutants, which fail to copulate, lack sine song, and do not generate courtship song in the absence of visual stimuli. Cellular dimorphisms in the fru circuit are dependent on Fru(C) rather than other single Fru isoforms. Removal of Fru(C) from the neuronal classes vAB3 or aSP4 leads to cell-autonomous feminization of arborizations and loss of courtship in the dark.Conclusions: These data map specific aspects of courtship behavior to the level of single fru isoforms and fru(+) cell types an important step toward elucidating the chain of causality from gene to circuit to behavior