DeTerm: Software for automatic detection of neuronal dendritic branch terminals via an artificial neural network

Inter-organ Wingless/Ror/Akt signaling regulates nutrient-dependent hyperarborization of somatosensory neurons

Nutrition in early life has profound effects on an organism, altering processes such as organogenesis. However, little is known about how specific nutrients affect neuronal development. Dendrites of class IV dendritic arborization neurons in Drosophila larvae become more complex when the larvae are reared on a low-yeast diet compared to a high-yeast diet. Our systematic search for key nutrients revealed that the neurons increase their dendritic terminal densities in response to a combined deficiency in vitamins, metal ions, and cholesterol. The deficiency of these nutrients upregulates Wingless in a closely located tissue, body wall muscle. Muscle-derived Wingless activates Akt in the neurons through the receptor tyrosine kinase Ror, which promotes the dendrite branching. In larval muscles, the expression of wingless is regulated not only in this key nutrient-dependent manner, but also by the JAK/STAT signaling pathway. Additionally, the low-yeast diet blunts neuronal light responsiveness and light avoidance behavior, which may help larvae optimize their survival strategies under low-nutritional conditions. Together, our studies illustrate how the availability of specific nutrients affects neuronal development through inter-organ signaling.

Studies of neurodegenerative diseases using Drosophila and the development of novel approaches for their analysis

The use of Drosophila in neurodegenerative disease research has contributed to the identification of modifier genes for the pathology. The basis for neurodegenerative disease occurrence in Drosophila is the conservation of genes across species and the ability to perform rapid genetic analysis using a compact brain. Genetic findings previously discovered in Drosophila can reveal molecular pathologies involved in human neurological diseases in later years. Disease models using Drosophila began to be generated during the development of genetic engineering. In recent years, results of reverse translational research using Drosophila have been reported. In this review, we discuss research on neurodegenerative diseases; moreover, we introduce various methods for quantifying neurodegeneration in Drosophila.

A photo-switchable assay system for dendrite degeneration and repair in Drosophila melanogaster

Neurodegeneration arising from aging, injury, or diseases has devastating health consequences. Whereas neuronal survival and axon degeneration have been studied extensively, much less is known about how neurodegeneration affects dendrites, in part due to the limited assay systems available. To develop an assay for dendrite degeneration and repair, we used photo-switchable caspase-3 (caspase-Light-Oxygen-Voltage-sensing [caspase-LOV]) in peripheral class 4 dendrite arborization (c4da) neurons to induce graded neurodegeneration by adjusting illumination duration during development and adulthood in Drosophila melanogaster. We found that both developing and mature c4da neurons were able to survive while sustaining mild neurodegeneration induced by moderate caspase-LOV activation. Further, we observed active dendrite addition and dendrite regeneration in developing and mature c4da neurons, respectively. Using this assay, we found that the mouse Wallerian degeneration slow (Wld^S) protein can protect c4da neurons from caspase-LOV-induced dendrite degeneration and cell death. Furthermore, our data show that Wld^S can reduce dendrite elimination without affecting dendrite addition. In summary, we successfully established a photo-switchable assay system in both developing and mature neurons and used Wld^S as a test case to study the mechanisms underlying dendrite regeneration and repair.

The branching code: A model of actin-driven dendrite arborization

The cytoskeleton is crucial for defining neuronal-type-specific dendrite morphologies. To explore how the complex interplay of actin-modulatory proteins (AMPs) can define neuronal types in vivo, we focused on the class III dendritic arborization (c3da) neuron of Drosophila larvae. Using computational modeling, we reveal that the main branches (MBs) of c3da neurons follow general models based on optimal wiring principles, while the actin-enriched short terminal branches (STBs) require an additional growth program. To clarify the cellular mechanisms that define this second step, we thus concentrated on STBs for an in-depth quantitative description of dendrite morphology and dynamics. Applying these methods systematically to mutants of six known and novel AMPs, we revealed the complementary roles of these individual AMPs in defining STB properties. Our data suggest that diverse dendrite arbors result from a combination of optimal-wiring-related growth and individualized growth programs that are neuron-type specific.

NeuriteNet: A convolutional neural network for assessing morphological parameters of neurite growth

BACKGROUND

During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro. New Method: NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure.

RESULTS

In this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurons cultured from control and genetically modified mouse strains. Comparison with Existing Methods: Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth.

CONCLUSIONS

NeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay.

Comparing Automated Morphology Quantification Software on Dendrites of Uninjured and Injured Drosophila Neurons

Dendrites shape inputs and integration of depolarization that controls neuronal activity in the nervous system. Neuron pathologies can damage dendrite architecture and cause abnormalities in morphologies after injury. Dendrite regeneration can be quantified by various parameters, including total dendrite length and number of dendrite branches using manual or automated image analysis approaches. However, manual quantification is tedious and time consuming and automated approaches are often trained using wildtype neurons, making them poorly suited for analysis of genetically manipulated or injured dendrite arbors. In this study, we tested how well automated image analysis software performed on class IV Drosophila neurons, which have several hundred individual dendrite branches. We applied each software to automatically quantify features of uninjured neurons and neurons that regenerated new dendrites after injury. Regenerated arbors exhibit defects across multiple features of dendrite morphology, which makes them challenging for automated pipelines to analyze. We compared the performances of three automated pipelines against manual quantification using Simple Neurite Tracer in ImageJ: one that is commercially available (Imaris) and two developed by independent research groups (DeTerm and Tireless Tracing Genie). Out of the three software tested, we determined that Imaris is the most efficient at reconstructing dendrite architecture, but does not accurately measure total dendrite length even after intensive manual editing. Imaris outperforms both DeTerm and Tireless Tracing Genie for counting dendrite branches, and is better able to recreate previous conclusions from this same dataset. This thorough comparison of strengths and weaknesses of each software demonstrates their utility for analyzing regenerated neuron phenotypes in future studies.