Automated quantification of synaptic fluorescence in C. elegans

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at the synaptic level. The Active Zone (AZ) is the region at the presynaptic membrane that mediates neurotransmitter release and is composed of a dense collection of scaffold proteins. AZs of Drosophila melanogaster (Drosophila) photoreceptors undergo molecular remodeling after prolonged exposure to natural ambient light. Thus the level of neuronal activity can rearrange the molecular composition of the AZ and contribute to the regulation of the functional output. Starting from the light exposure set-up preparation to the immunohistochemistry, this protocol details how to quantify the number, the spatial distribution, and the delocalization level of synaptic molecules at AZs in Drosophila photoreceptors. Using image analysis software, clusters of the GFP-fused AZ component Bruchpilot were identified for each R8 photoreceptor (R8) axon terminal. Detected Bruchpilot spots were automatically assigned to individual R8 axons. To calculate the distribution of spot frequency along the axon, we implemented a customized software plugin. Each axon's start-point and end-point were manually defined and the position of each Bruchpilot spot was projected onto the connecting line between start and end-point. Besides the number of Bruchpilot clusters, we also quantified the delocalization level of Bruchpilot-GFP within the clusters. These measurements reflect in detail the spatially resolved synaptic dynamics in a single neuron under different environmental conditions to stimuli.

Using Multiple Phenotype Assays and Epistasis Testing to Enhance the Reliability of RNAi Screening and Identify Regulators of Muscle Protein Degradation

RNAi is a convenient, widely used tool for screening for genes of interest. We have recently used this technology to screen roughly 750 candidate genes, in C. elegans, for potential roles in regulating muscle protein degradation in vivo. To maximize confidence and assess reproducibility, we have only used previously validated RNAi constructs and have included time courses and replicates. To maximize mechanistic understanding, we have examined multiple sub-cellular phenotypes in multiple compartments in muscle. We have also tested knockdowns of putative regulators of degradation in the context of mutations or drugs that were previously shown to inhibit protein degradation by diverse mechanisms. Here we discuss how assaying multiple phenotypes, multiplexing RNAi screens with use of mutations and drugs, and use of bioinformatics can provide more data on rates of potential false positives and negatives as well as more mechanistic insight than simple RNAi screening.