This study was financially supported by the Air Force Office of Scientific Research/European Office of Aerospace Research and Development AFOSR/EOARD (grant FA9550-15-1-0068, to D.G.S.). We thank Dr. Primo&#382; Pirih for many helpful discussions and Kehan Satu, Hein Leertouwer, and Oscar Rinc&#243;n Carde&#241;o for assistance.

The protocol is in accordance with the University&#39;s insect care guidelines.
1. Preparation of a housefly,&#160;&#160;Musca domestica
<ol>
	<li>Collect the fly from the laboratory-reared population. Place the fly in the brass holder (Figure 1).
	<ol>
		<li>Cut 6 mm from the upper part of the restraining tube (see Table of Materials). The new upper part of the tube has an external diameter of 4 mm and an internal diameter of 2.5 mm (Figure 1A). Place the live fly inside the tube, seal the tube with cotton to prevent damaging the fly, and push the fly such that the head protrudes from the tube and its body is restrained (Figure 1B). Immobilize the head with beeswax such that the eyes remain uncovered (Figure 1C-E).</li>
		<li>Cut the tube again such that the tube length is 10 mm (Figure 1C). Place the plastic tube with the fly in the brass holder, such that one eye of the fly is pointing upward when the holder is resting on a tabletop (Figure 1D,E).</li>
	</ol>
	</li>
	<li>Adjust the orientation of the tube such that with the goniometer elevation at 0&#176; (i.e., the azimuth stage is in a horizontal position), the vertical illumination beam of the microscope is perpendicular to the eye surface in a central region, between ventral and dorsal, and between anterior and posterior edges of the eye, so that the whole eye can be scanned within the range of azimuth and elevation allowed by the setup.</li>
</ol>
2. Alignment of the goniometer&#39;s rotating azimuth axis with the microscope optical axis
<ol>
	<li>Mount an alignment pin on the azimuth rotation stage so that the x-y position of the tip can be adjusted to coincide with the azimuth axis on the motorized stage. While viewing with the microscope, equipped with a 5x objective, focus on the tip using the z-axis joystick (Figure 2).</li>
	<li>Align the x-y adjustment of the azimuth axis with the microscope&#39;s optical axis and ensure that the elevation and azimuth rotary axes are pre-aligned with the centered pin, using the x- and y-axis joysticks.</li>
	<li>Manipulate the azimuth and elevation joysticks to check whether the pin is centered with respect to both degrees of freedom. When well-centered, the pin tip remains in, approximately, the same position during azimuth and elevation rotations.</li>
</ol>
3. Alignment of the fly eye with the motorized stages
<ol>
	<li>With the elevation stage at 0&#176;, mount the fly and its holder on the azimuth stage. Observe the fly&#39;s eye with the microscope.</li>
	<li>With the illumination LED on, adjust the horizontal position of the fly so that the center of the pseudopupil is aligned with the microscope. Adjust the vertical position of the fly by using the rotating screw of the holder (Figure 1D), so that the deep pseudopupil (DPP; Figure 3)19,20,21 is brought into focus at the level of the elevation axis.</li>
	<li>Align the DPP with respect to the azimuth and elevation axes by centering it in the field of view (see Figure 2). Use the magnets glued to the bottom of the fly holder to affix it firmly to an iron plate mounted on the azimuth stage, while permitting manual sliding adjustments.
	<ol>
		<li>Switch the view to the digital camera mounted at the microscope. Run the software initialization of the GRACE system, which includes initializing the motor controllers and the Arduino LED controller (Figure 4). Therefore, open MATLAB R2020a or higher version. Run the MATLAB script Initialize_All_Systems (Supplementary File 1).</li>
	</ol>
	</li>
	<li>Confirm whether the fly&#39;s pseudopupil (Figure 3B,C) is at the center of the projected image on the computer screen.</li>
</ol>
4. Autofocusing and autocentering
<ol>
	<li>Bring the focus to the level of the corneal pseudopupil (CPP; Figure 3B)19,20,21 manually by using the z-axis joystick.</li>
	<li>Run the autofocusing algorithm (Supplementary File 1, script AF) to attain a sharp image at the cornea level. Check by returning the focus to the DPP level by adjusting the motorized z-axis stage. Store the distance between the DPP and CPP (in motor steps).</li>
	<li>Fine-tune the pseudopupil centering by running the autocentering algorithm (Supplementary File 1, script AC). Bring the focus back to the CPP level.</li>
	<li>Re-run the autofocusing algorithm. Zero the motorized stages at their current positions (X,Y,Z,E,A) = (0,0,0,0,0), where E is elevation and A is azimuth.</li>
	<li>Run the scanning algorithm (Supplementary File 1, script Scan_Begin), which samples eye images along trajectories in 5&#176; steps, while performing the autocentering and autofocusing algorithms.</li>
	<li>At the conclusion of the sampling, turn off the LED Controller, and the motor controllers.</li>
	<li>Process the images by applying the image processing algorithms (Supplementary File 1, script ImProcFacets).</li>
</ol>

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The authors have no conflicts of interest to report.

The spatial distribution of the visual axes of housefly eyes can be charted using the pseudopupil phenomenon of compound eyes and the reflection changes caused by the light-dependent pupil mechanism. Therefore, an investigated fly is mounted in a goniometric system, which allows inspection of the local facet pattern with a microscope setup equipped with a digital camera, all under computer control. Image analysis yields eye maps. An essential difficulty encountered is that without careful positioning of the eye at the be...

The compactness of insect visual systems and the agility of their owners, demonstrating highly developed visual information processing, have intrigued people from both scientific and non-scientific backgrounds. Insect compound eyes have been recognized as powerful optical devices enabling acute and versatile visual capacities1,2. Flies, for instance, are well-known for their fast responses to moving objects, and bees are famous for possessing color vision and polarization vision2.
The compound eyes of arthropods consist of numerous anatomically similar units, the ommatidia, each of which is capped by a facet lens. In Diptera (flies), the assembly of facet lenses, known collectively as the cornea, often approximates a hemisphere. Each ommatidium samples incident light from a small solid angle with half-width on the order of 1&#730;. The ommatidia of the two eyes together sample approximately the full solid angle, but the visual axes of the ommatidia are not evenly distributed. Certain eye areas have a high density of visual axes, which creates a region of high spatial acuity, colloquially called a fovea. The remaining part of the eye then has a coarser spatial resolution3,4,5,6,7,8,9.
A quantitative analysis of the optical organization of the compound eyes is crucial for detailed studies of the neural processing of visual information. Studies of the neural networks of an insect&#39;s brain10 often require knowledge of the spatial distribution of the ommatidial axes. Furthermore, compound eyes have inspired several technical innovations. Many initiatives to produce bio-inspired artificial eyes have been built on existing quantitative studies of real compound eyes11,12,13. For instance, a semiconductor-based sensor with high-spatial resolution was designed based on the model of insect compound eyes11,14,15,16,17. However, the devices developed so far have not implemented the actual characteristics of existing insect eyes. Accurate representations of insect compound eyes and their spatial organization will require detailed and reliable data from natural eyes, which is not extensively available.
The main reason for the paucity of data is the extreme tediousness of the available procedures for charting the eyes&#39; spatial characteristics. This has motivated attempts to establish a more automated eye mapping procedure. In a first attempt at automated analyses of insect compound eyes, Douglass and Wehling18 developed a scanning procedure for mapping facet sizes in the cornea and demonstrated its feasibility for a few fly species. Here we extend their approach by developing methods for not only scanning the facets of the cornea but also assessing the visual axes of the ommatidia to which the facets belong. We present the case of housefly eyes to exemplify the procedures involved.
The experimental setup for scanning insect eyes is: partly optical, i.e., a microscope with camera and illumination optics; partly mechanical, i.e., a goniometer system for rotating the investigated insect; and partially computational, i.e., use of software drivers for the instruments and programs for executing measurements and analyses. The developed methods encompass a range of computational procedures, from capturing images, choosing camera channels, and setting image processing thresholds to recognizing individual facet locations via bright spots of light reflected from their convex surfaces. Fourier transform methods were crucial in the image analysis, both for detecting individual facets and for analyzing the facet patterns.
The paper is structured as follows. We first introduce the experimental setup and the pseudopupil phenomenon-the optical marker used to identify the visual axes of the photoreceptors in living eyes19,20,21. Subsequently, the algorithms used in the scanning procedure and image analysis are outlined.

<table><tbody><tr><td>Digital Camera</td><td>PointGrey</td><td>BFLY-U3-23S6C-C</td><td>Acquision of amplified images and digital communication with PC</td></tr><tr><td>High power star LED</td><td>Velleman</td><td>LH3WW</td><td>Light source for observation and imaging the compound eye</td></tr><tr><td>Holder for the investigated fly</td><td>University of Groningen</td><td></td><td>Different designs were manufactured by the university workshop</td></tr><tr><td>Linear motor</td><td>ELERO</td><td>ELERO Junior 1, version C</td><td>Actuates the upper microscope up and down. (Load 300N, Stroke speed 15mm/s, nominal current 1.2A)</td></tr><tr><td>Low temperature melting wax</td><td>various</td><td></td><td>The low-temperature melting point wax serves to immobilize the fly and fix it to the holder</td></tr><tr><td>Microscope</td><td>Zeiss</td><td></td><td>Any alternative microscope brand will do; the preferred objective is a 5x</td></tr><tr><td>Motor and LED Controller</td><td>University of Groningen</td><td>Z-o1</td><td>Designed and built by the University of Groningen and based on Arduino and Adafruit technologies.</td></tr><tr><td>Motorized Stage</td><td>Standa (Vilnius, Lithuania)</td><td>8MT175-50XYZ-8MR191-28</td><td>A 6 axis motorized stage modified to have 5 degrees of freedom.</td></tr><tr><td>Optical components</td><td>LINUS</td><td></td><td>Several diagrams and lenses forming an epi-illumination system (see Stavenga, Journal of Experimental Biology 205, 1077-1085, 2002)</td></tr><tr><td>PC running MATLAB</td><td>University of Groningen</td><td></td><td>The PC is able to process the images of the PointGrey camera, control the LED intensity, and send control commants to the motor cotrollers of the system</td></tr><tr><td>Power Supply (36V, 3.34A)</td><td>Standa (Vilnius, Lithuania)</td><td>PUP120-17</td><td>Dedicated power supply for the STANDA motor controllers</td></tr><tr><td>Soldering iron</td><td>various</td><td></td><td>Used for melting the wax</td></tr><tr><td>Stepper and DC Motor Controller</td><td>Standa (Vilnius, Lithuania)</td><td>8SMC4-USB-B9-B9</td><td>Dedicated controllers for the STANDA motorized stage capable of communicating with MATLAB</td></tr><tr><td>Finntip-61</td><td>Finnpipette Ky, Helsinki</td><td>FINNTIP-61, 200-1000&amp;mu;L</td><td>PIPETTE TIPS FOR FINNPIPETTES, 400/BOX. It is used to restrain the fly</td></tr><tr><td>Carving Pen Shaping/Thread Burning Tool</td><td>Max Wax</td><td></td><td>The tip of the carving pen is designed to transfer wax to the head of fly</td></tr><tr><td>MATLAB</td><td>Mathworks, Natick, MA, USA</td><td>main program plus Image Acquisition, Image Analysis, and Instrument Control toolboxes.</td><td>Programming language used to implement the algorithms</td></tr></tbody></table>

automated charting of the visual space of housefly compound eyes

This paper describes the automatic measurement of the spatial organization of the visual axes of insect compound eyes, which consist of several thousands of visual units called ommatidia. Each ommatidium samples the optical information from a small solid angle, with an approximate Gaussian-distributed sensitivity (half-width on the order of 1&#730;) centered around a visual axis. Together, the ommatidia gather the visual information from a nearly panoramic field of view. The spatial distribution of the visual axes thus determines the eye's spatial resolution. Knowledge of the optical organization of a compound eye and its visual acuity is crucial for quantitative studies of neural processing of the visual information. Here we present an automated procedure for mapping a compound eye's visual axes, using an intrinsic, in vivo optical phenomenon, the pseudopupil, and the pupil mechanism of the photoreceptor cells. We outline the optomechanical setup for scanning insect eyes and use experimental results obtained from a housefly, Musca domestica, to illustrate the steps in the measurement procedure.

Animals and optical stimulation 
Experiments are performed on houseflies (Musca domestica) obtained from a culture maintained by the Department of Evolutionary Genetics at the University of Groningen. Before the measurements, a fly is immobilized by gluing it with a low-melting-point wax in a well-fitting tube. The fly is subsequently mounted on the stage of a motorized goniometer. The center of the two rotary stages coincides with the focal point of a microscopic setup24<...

Watch this Scientific Journal Video about Automated Charting of the Visual Space of Housefly Compound Eyes at JoVE.com

Automated Charting of the Visual Space of Housefly Compound Eyes

The protocol here describes the measurement of the spatial organization of the visual axes of housefly eyes, mapped by an automatic device, using the pseudopupil phenomenon and the pupil mechanism of the photoreceptor cells.

This paper describes the automatic measurement of the spatial organization of the visual axes of insect compound eyes, which consist of several thousands ...

automated-charting-of-the-visual-space-of-housefly-compound-eyes

Research

JoVE Journal

Biology

1.8K Views.  University of Groningen. The protocol here describes the measurement of the spatial organization of the visual axes of housefly eyes, mapped by an automatic device, using the pseudopupil phenomenon and the pupil mechanism of the photoreceptor cells.

Video: Automated Charting of the Visual Space of Housefly Compound Eyes

Live-imaging of the Drosophila Pupal Eye

Inherent processes of Drosophila pupal development can shift and distort the eye epithelium in ways that make individual cell behavior difficult to track during live cell imaging. These processes include: retinal rotation, cell growth and organismal movement. Additionally, irregularities in the topology of the epithelium, including subtle bumps and folds often introduced as the pupa is prepared for imaging, make it challenging to acquire in-focus images of more than a few ommatidia in a single focal plane. The workflow outlined here remedies these issues, allowing easy analysis of cellular processes during Drosophila pupal eye development. Appropriately-staged pupae are arranged in an imaging rig that can be easily assembled in most laboratories. Ubiquitin-DE-Cadherin:GFP and GMR-GAL4&#8211;driven UAS-&#945;-catenin:GFP are used to visualize cell boundaries in the eye epithelium 1-3. After deconvolution is applied to fluorescent images captured at multiple focal planes, maximum projection images are generated for each time point and enhanced using image editing software. Alignment algorithms are used to quickly stabilize superfluous motion, making individual cell behavior easier to track.

Live-imaging of the Drosophila Pupal Eye

This protocol presents an efficient method for imaging the live Drosophila pupal eye neuroepithelium. This method compensates for tissue movement and uneven topology, enhances visualization of cell boundaries through the use of multiple GFP-tagged junction proteins, and uses an easily-assembled imaging rig.

Inherent processes of Drosophila pupal development can shift and distort the eye epithelium in ways that make individual cell behavior difficult to track ...

Developmental Biology

Vision Training Methods for Sports Concussion Mitigation and Management

There is emerging evidence supporting the use vision training, including light board training tools, as a concussion baseline and neuro-diagnostic tool and potentially as a supportive component to concussion prevention strategies. This paper is focused on providing detailed methods for select vision training tools and reporting normative data for comparison when vision training is a part of a sports management program. The overall program includes standard vision training methods including tachistoscope, Brock&#8217;s string, and strobe glasses, as well as specialized light board training algorithms. Stereopsis is measured as a means to monitor vision training affects. In addition, quantitative results for vision training methods as well as baseline and post-testing *A and Reaction Test measures with progressive scores are reported. Collegiate athletes consistently improve after six weeks of training in their stereopsis, *A and Reaction Test scores. When vision training is initiated as a team wide exercise, the incidence of concussion decreases in players who participate in training compared to players who do not receive the vision training. Vision training produces functional and performance changes that, when monitored, can be used to assess the success of the vision training and can be initiated as part of a sports medical intervention for concussion prevention.

This paper describes a protocol to conduct, quantitatively monitor, and assess the success of vision training initiated as part of a sports medical management program including intervention for concussion prevention and performance enhancement.

There is emerging evidence supporting the use vision training, including light board training tools, as a concussion baseline and neuro-diagnostic tool ...

Behavior

VisualEyes: A Modular Software System for Oculomotor Experimentation

Eye movement studies have provided a strong foundation forming an understanding of how the brain acquires visual information in both the normal and dysfunctional brain.1 However, development of a platform to stimulate and store eye movements can require substantial programming, time and costs. Many systems do not offer the flexibility to program numerous stimuli for a variety of experimental needs. However, the VisualEyes System has a flexible architecture, allowing the operator to choose any background and foreground stimulus, program one or two screens for tandem or opposing eye movements and stimulate the left and right eye independently. This system can significantly reduce the programming development time needed to conduct an oculomotor study. The VisualEyes System will be discussed in three parts: 1) the oculomotor recording device to acquire eye movement responses, 2) the VisualEyes software written in LabView, to generate an array of stimuli and store responses as text files and 3) offline data analysis. Eye movements can be recorded by several types of instrumentation such as: a limbus tracking system, a sclera search coil, or a video image system. Typical eye movement stimuli such as saccadic steps, vergent ramps and vergent steps with the corresponding responses will be shown. In this video report, we demonstrate the flexibility of a system to create numerous visual stimuli and record eye movements that can be utilized by basic scientists and clinicians to study healthy as well as clinical populations.

Neural control and cognitive processes can be studied through eye movements. The VisualEyes software allows an operator to program stimuli on two computer screens independently using a simple, custom scripting language. The system can stimulate tandem eye movements (saccades and smooth pursuit) or opposing eye movements (vergence) or any combination.

Eye movement studies have provided a strong foundation forming an understanding of how the brain acquires visual information in both the normal and ...

Neuroscience

Long-term Live Imaging of Drosophila Eye Disc

Live imaging provides the ability to continuously track dynamic cellular and developmental processes in real time. Drosophila larval imaginal discs have been used to study many biological processes, such as cell proliferation, differentiation, growth, migration, apoptosis, competition, cell-cell signaling, and compartmental boundary formation. However, methods for the long-term ex vivo culture and live imaging of the imaginal discs have not been satisfactory, despite many efforts. Recently, we developed a method for the long-term ex vivo culture and live imaging of imaginal discs for up to 18 h. In addition to using a high insulin concentration in the culture medium, a low-melting agarose was also used to embed the disc to prevent it from drifting during the imaging period. This report uses the eye-antennal discs as an example. Photoreceptor R3/4-specific m&#948;0.5-Ga4 expression was followed to demonstrate that photoreceptor differentiation and ommatidial rotation can be observed during a 10 h live imaging period. This is a detailed protocol describing this simple method.

Long-term Live Imaging of Drosophila Eye Disc

This protocol describes a detailed method for the long-term ex vivo culture and live imaging of a Drosophila imaginal disc. It demonstrates photoreceptor differentiation and ommatidial rotation within the 10 h period of live imaging of the eye disc. The protocol is simple and does not require expensive setup.

Live imaging provides the ability to continuously track dynamic cellular and developmental processes in real time. Drosophila larval imaginal discs have ...

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie neurodegeneration. While fly techniques for the manipulation and study of neuronal subsets have grown increasingly sophisticated, the richness of the resultant behavioral phenotypes has not been captured at a similar detail. To be able to study subtle fly leg movements for comparison amongst mutants requires the ability to automatically measure and quantify high-speed and rapid leg movements. Hence, we developed a machine-learning algorithm for automated leg claw tracking in freely walking flies, Feature Learning-based Limb segmentation and Tracking (FLLIT). Unlike most deep learning methods, FLLIT is fully automated and generates its own training sets without a need for user annotation, using morphological parameters built into the learning algorithm. This article describes an in depth protocol for carrying out gait analysis using FLLIT. It details the procedures for camera setup, arena construction, video recording, leg segmentation and leg claw tracking. It also gives an overview of the data produced by FLLIT, which includes raw tracked body and leg positions in every video frame, 20 gait parameters, 5 plots and a tracked video. To demonstrate the use of FLLIT, we quantify relevant diseased gait parameters in a fly model of Spinocerebellar ataxia 3.

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

We describe detailed protocols for using FLLIT, a fully automated machine learning method for leg claw movement tracking in freely moving Drosophila melanogaster and other insects. These protocols can be used to quantitatively measure subtle walking gait movements in wild type flies, mutant flies and fly models of neurodegeneration.

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie ...

Visualizing Ocular Morphogenesis by Lightsheet Microscopy Using rx3:GFP Transgenic Zebrafish

Vertebrate eye development is a complex process that begins near the end of embryo gastrulation and requires the precise coordination of cell migration, proliferation, and differentiation. Time-lapse imagining offers unique insight to the behavior of cells during eye development because it allows us to visualize oculogenesis in vivo. Zebrafish are an excellent model to visualize this process due to their highly conserved vertebrate eye and their ability to develop rapidly and externally while remaining optically transparent. Time-lapse imaging studies of zebrafish eye development are greatly facilitated by use of the transgenic zebrafish line Tg(rx3:GFP). In the developing forebrain, rx3:GFP expression marks the cells of the single eye field, and GFP continues to be expressed as the eye field evaginates to form an optic vesicle, which then invaginates to form an optic cup. High resolution time lapse imaging of rx3:GFP expression, therefore, allows us to track the eye primordium through time as it develops into the retina. Lightsheet microscopy is an ideal method to image ocular morphogenesis over time due to its ability to penetrate thicker samples for fluorescent imaging, minimize photobleaching and phototoxicity, and image at a high speed. Here, a&#160;protocol is&#160;provided&#160;for time-lapse imaging of ocular morphogenesis using a commercially available lightsheet microscope and an image processing workstation to analyze the resulting data. This protocol details the procedures for embryo anesthesia, embedding in low melting temperature agarose, suspension in the imaging chamber, setting up the imaging parameters, and finally analyzing the imaging data using image analysis software. The resulting dataset can provide valuable insights into the process of ocular morphogenesis, as well as perturbations to this process as a result of genetic mutation, exposure to pharmacological agents, or other experimental manipulations.

Here, a&#160;protocol is&#160;provided&#160;for time-lapse imaging of ocular morphogenesis using a commercially available lightsheet microscope and an image processing workstation to analyze the resulting data. This protocol details the procedures for embryo anesthesia, embedding in low melting temperature agarose, suspension in the imaging chamber, setting up the imaging parameters, and finally analyzing the imaging data using image analysis software.&#160;

Vertebrate eye development is a complex process that begins near the end of embryo gastrulation and requires the precise coordination of cell migration, ...

Unbiased Quantification of Ommatidia in Drosophila Eye Models

Quantifying Drosophila melanogaster Eye Phenotypes: A Computational Approach Integrating ilastik and Flynotyper

The Drosophila melanogaster compound eye is a well-structured and comprehensive array of around 800 ommatidia, exhibiting a symmetrical and hexagonal pattern. This regularity and ease of observation make the Drosophila eye system a powerful tool to model various human neurodegenerative diseases. However, ways of quantifying abnormal phenotypes, such as manual ranking of eye severity scores, have limitations, especially when ranking weak alterations in eye morphology. To overcome these limitations, computational approaches have been developed such as Flynotyper. The use of a ring light allows for better qualitative images accessing the intactness of individual ommatidia. However, these images cannot be analyzed by Flynotyper directly due to shadows on ommatidia introduced by the ring light. Here, we describe an unbiased way to quantify rough eye phenotypes observed in Drosophila disease models by combining two software, ilastik and Flynotyper. By preprocessing the images with ilastik, successful quantification of the rough eye phenotype can be achieved with Flynotyper.

Author Spotlight: Quantifying Rough Eye Phenotypes in Drosophila Models of Amyotrophic Lateral Sclerosis with Frontotemporal Dementia

The Drosophila eye system is a useful tool for studying various biological processes, particularly human neurodegenerative diseases. However, manual quantification of rough eye phenotypes can be biased and unreliable. Here we describe a method by which ilastik and Flynotyper are used to quantify eye phenotype in an unbiased way.

The Drosophila melanogaster compound eye is a well-structured and comprehensive array of around 800 ommatidia, exhibiting a symmetrical and hexagonal ...

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens.
This manuscript describes the &#34;Drosophila Island Assay&#34; and &#34;Island Assay Analysis&#34; algorithms for&#160;high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The &#34;Drosophila Island Assay&#34; algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The &#34;Island Assay Analysis&#34; script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

The island assay is a relatively new, cost-effective assay that can be used to evaluate the basic locomotor behavior of Drosophila melanogaster. This manuscript describes algorithms for automatic data processing and objective quantification of island assay data, making this assay a sensitive, high-throughput readout for large genetic or pharmacological screens.

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other ...

Studying Membrane Protein Trafficking in Drosophila Photoreceptor Cells Using eGFP-Tagged Proteins

Membrane protein trafficking regulates the incorporation and removal of receptors and ion channels into the plasma membrane. This process is fundamentally important for cell function and cell integrity of neurons. Drosophila photoreceptor cells have become a model for studying membrane protein trafficking. Besides rhodopsin, which upon illumination becomes internalized from the photoreceptor membrane and is degraded, the transient receptor potential-like (TRPL) ion channel in Drosophila exhibits a light-dependent translocation between the rhabdomeral photoreceptor membrane (where it is located in the dark) and the photoreceptor cell body (to which it is transported upon illumination). This intracellular transport of TRPL can be studied in a simple and non-invasive way by expressing eGFP-tagged TRPL in photoreceptor cells. The eGFP fluorescence can then be observed either in the deep pseudopupil or by water immersion microscopy. These methods allow detection of fluorescence in the intact eye and are therefore useful for high-throughput assays and genetic screens for Drosophila mutants defective in TRPL translocation. Here, the preparation of flies, the microscopic techniques, as well as quantification methods used to study this light-triggered translocation of TRPL are explained in detail. These methods can be applied also for trafficking studies on other Drosophila photoreceptor proteins, for example, rhodopsin. In addition, by using eGFP-tagged rhabdomeral proteins, these methods can be used to assess the degeneration of photoreceptor cells.

Studying Membrane Protein Trafficking in Drosophila Photoreceptor Cells Using eGFP-Tagged Proteins

Here, non-invasive methods are described for localization of photoreceptor membrane proteins and assessment of retinal degeneration in the Drosophila compound eye using eGFP fluorescence.

Membrane protein trafficking regulates the incorporation and removal of receptors and ion channels into the plasma membrane. This process is fundamentally ...

High-Resolution Imaging and Analysis of Drosophila Eye Defects

A Rapid, Simple Workflow for Quantification of External Adult Drosophila Structures

The Drosophila compound eye is a precisely patterned tissue that has revealed molecular mechanisms and biological processes that drive morphogenesis. It is a simple structure of repeating unit eyes, termed ommatidia, that is used to characterize genetic interactions and gene functions. Mutations that affect eye architecture can be easily detected and analyzed; hence, this system is frequently used in under-resourced institutions. Further phenotypic analysis often includes a Scanning Electron Microscope (SEM) to generate high-magnification images suitable for quantitative analysis. However, SEMs are expensive and require costly reagents; sample preparation spans days; and, often, they need full-time staff for sample preparation and instrument maintenance. This limits their utility at under-resourced institutions or during budgetary austerity. In entomology, the use of high-resolution digital imaging technology is a common practice for the identification and characterization of species. This paper describes a method that combines strategies and allows for high-resolution digital imaging of adult Drosophila structures and quantitative analysis using the open software ImageJ. The workflow is a rapid and student-friendly alternative that remedies the limitations of underfunded and under-resourced research facilities with a cost-effective and rapid approach to quantitative phenotypic analysis.

Here, we present a rapid, low-cost, workflow for high-resolution imaging of adult Drosophila eyes to quantify patterning and growth defects. We describe our protocol for sample preparation by point-mounting, high-resolution image acquisition, and image analysis.

The Drosophila compound eye is a precisely patterned tissue that has revealed molecular mechanisms and biological processes that drive morphogenesis. It ...

Automated Charting of the Visual Space of Housefly Compound Eyes

Summary

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Chapters in this video

Live-imaging of the Drosophila Pupal Eye

Vision Training Methods for Sports Concussion Mitigation and Management

VisualEyes: A Modular Software System for Oculomotor Experimentation

Long-term Live Imaging of Drosophila Eye Disc

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Visualizing Ocular Morphogenesis by Lightsheet Microscopy Using rx3:GFP Transgenic Zebrafish

Quantifying Drosophila melanogaster Eye Phenotypes: A Computational Approach Integrating ilastik and Flynotyper

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Studying Membrane Protein Trafficking in Drosophila Photoreceptor Cells Using eGFP-Tagged Proteins

A Rapid, Simple Workflow for Quantification of External Adult Drosophila Structures