larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster

A quick and versatile protocol for the 3D visualization of transgene expression across the whole body of larval Drosophila

Larval Drosophila are used as a genetically accessible study case in many areas of biological research. Here we report a fast, robust and user-friendly procedure for the whole-body multi-fluorescence imaging of Drosophila larvae; the protocol has been optimized specifically for larvae by systematically tackling the pitfalls associated with clearing this small but cuticularized organism. Tests on various fluorescent proteins reveal that the recently introduced monomeric infrared fluorescent protein (mIFP) is particularly suitable for our approach. This approach comprises an effective, low-cost clearing protocol with minimal handling time and reduced toxicity in the reagents employed. It combines a success rate high enough to allow for small-scale screening approaches and a resolution sufficient for cellular-level analyses with light sheet and confocal microscopy. Given that publications and database documentations typically specify expression patterns of transgenic driver lines only within a given organ system of interest, the present procedure should be versatile enough to extend such documentation systematically to the whole body. As examples, the expression patterns of transgenic driver lines covering the majority of neurons, or subsets of chemosensory, central brain or motor neurons, are documented in the context of whole larval body volumes (using nsyb-Gal4, IR76b-Gal4, APL-Gal4 and mushroom body Kenyon cells, or OK371-Gal4, respectively). Notably, the presented protocol allows for triple-color fluorescence imaging with near-infrared, red and yellow fluorescent proteins.

Quantitative error prediction of medical image registration using regression forests

Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. The task of predicting registration error is demanding due to the lack of a ground truth in medical images. This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans. A random regression forest is utilized to predict the registration error locally. The forest is built with features related to the transformation model and features related to the dissimilarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans in two experiments: SPREAD (trained and tested on SPREAD) and inter-database (including three databases SPREAD, DIR-Lab-4DCT and DIR-Lab-COPDgene). The results show that the mean absolute errors of regression are 1.07  ±  1.86 and 1.76  ±  2.59 mm for the SPREAD and inter-database experiment, respectively. The overall accuracy of classification in three classes (correct, poor and wrong registration) is 90.7% and 75.4%, for SPREAD and inter-database respectively. The good performance of the proposed method enables important applications such as automatic quality control in large-scale image analysis.

Connectomics and function of a memory network: the mushroom body of larval Drosophila

The Drosophila larva is a relatively simple, 10 000-neuron study case for learning and memory with enticing analytical power, combining genetic tractability, the availability of robust behavioral assays, the opportunity for single-cell transgenic manipulation, and an emerging synaptic connectome of its complete central nervous system. Indeed, although the insect mushroom body is a much-studied memory network, the connectome revealed that more than half of the classes of connection within the mushroom body had escaped attention. The connectome also revealed circuitry that integrates, both within and across brain hemispheres, higher-order sensory input, intersecting valence signals, and output neurons that instruct behavior. Further, it was found that activating individual dopaminergic mushroom body input neurons can have a rewarding or a punishing effect on olfactory stimuli associated with it, depending on the relative timing of this activation, and that larvae form molecularly dissociable short-term, long-term, and amnesia-resistant memories. Together, the larval mushroom body is a suitable study case to achieve a nuanced account of molecular function in a behaviorally meaningful memory network.