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[…] Images were obtained on a Zeiss LSM510 Meta confocal laser-scanning microscope (Carl Zeiss, Oberkochen, Germany) using a 63× 1.4 NA PlanApo lens at an optical zoom of 3×. Images were centered on the glomerulus in question and the Z-boundaries set according to the confines of the synaptic label (Brp-Short-mStraw or Dα7-GFP). System gain was set to minimize saturation but maintain a high signal-to-noise ratio. Selected raw image stacks are available at image stacks for quantification were first deconvolved using AutoQuant X3 software (Media Cybernetics, Rockville, MD) on a custom-built computer (Digital Storm, Fremont, CA). Specifically, the images were deconvolved using 10 iterations with settings of medium noise, and a blind point spread function. Following deconvolution, images were visually inspected to ensure that striping, ringing, or discontinuity artifacts were not introduced. Further, each image was visually examined to ensure that the borders of puncta were sharpened by the deconvolution and that the membrane staining remained continuous. Following, images were imported into Imaris 7.4.1 (Bitplane AG, South Windsor, CT) for quantification. Though deconvolution improved the visual quality of the images, it was dispensable for accurate quantification of Brp-Short or Dα7 puncta.The ‘Spots’ function was used to quantify Brp-Short or Dα7 puncta in Imaris. A region of interest was drawn around the glomerulus being quantified. For individually labeled glomeruli, the standard Imaris box was sufficient to mark the region of interest. Where multiple glomeruli were labeled and a single glomerulus was quantified, a freehand region of interest was drawn in each section using the neuropil staining as a guideline and a 3D region extrapolated by Imaris. The spot size (puncta diameter) was set at 0.6 μm for Brp-Short and 0.8 μm for Dα7, as determined by direct measurement of images and previous studies (). Background subtraction was selected. Following automatic detection of puncta, the threshold was manually adjusted (usually within 5% among different samples) with visual inspection to ensure that all puncta were identified, minimal background staining or noise was recognized, and that individual puncta were not double-counted. In all, the resultant count was recorded as the number of puncta. The accuracy was assessed by comparing puncta detection figures to glomeruli that were counted by hand following 3D projection. In all cases, there was less than 5% difference, supporting the accuracy of the semi-automatic quantification method. In male Or67d-GAL4 samples that did not express the synaptic label (normally have ∼1500 puncta), but were processed identically using primary and secondary antibodies, the number of puncta recognized by this method was <30, suggesting that the ‘false positive’ rate was low. To ensure that user-introduced noise in threshold adjustment did not introduce significant bias, each image was quantified a second time, at a later time, without access to the prior quantification. If the difference exceeded 5%, the sample was discarded. Further, if the averages of an entire genotype were significantly different from each other (as determined by student's t test), the entire cohort was discarded and the experiment repeated.For the nearest neighbor distance (NND) analysis, the ‘DistMin’ was calculated in Imaris using the ‘Spots to Spots Closest Distance XTension’ plug-in for Matlab. Cumulative frequency histograms were compiled in Prism 6.04 (GraphPad Software, San Diego, CA). As each puncta is ∼0.6 μm in diameter, the minimum NND should be 0.6. Indeed, few measurements were seen below 0.6. Thus, ‘clustering’ was defined as puncta with an NND of less than 25% of the diameter. The ‘% Cluster’ was calculated by expressing the number of puncta with an NND value ≤0.75 divided by the total number of puncta.To obtain neurite volume based on mCD8-GFP or mtdT labeling, the ‘Surfaces’ function was used. Regions of interest were drawn as above. Background subtraction was set to 3 μm and the smoothing to 0.2 μm; these settings optimally reflected actual neurite labeling. Automatic detection with a 10e5 threshold was then used to remove background, detecting only the glomerulus in question. For smaller objects (MARCM clones), a 10e1 threshold was used and only the specific objects that directly corresponded to mCD8-GFP labeling were selected and counted. Volume was then calculated by Imaris. Density was then expressed as the number of Brp-Short puncta divided by the neurite volume. For images involving a neurite co-stain, the Brp-Short-mStraw channel was masked in Imaris so that only the puncta within the neurite were identified and counted (not background puncta resulting from secondary antibodies, or image noise). Images were processed using Photoshop CS4 (Adobe, San Jose, CA) and levels or contrast adjusted identically for all cases of comparison. For cases of immunofluorescence comparison, brains were stained in parallel, imaged on the same slide in the same session, and processed identically. Statistical and graphical analyses were completed in Excel (Microsoft, Redmond, WA), Prism 6.04 (GraphPad Software, San Diego, CA), and Matlab (Mathworks, Natick, MA). […]

Pipeline specifications

Software tools AutoQuant, Imaris
Application Laser scanning microscopy
Organisms Drosophila melanogaster
Chemicals Acetylcholine