We thank Pedro Fernandez-Funez for the use of the microscope and camera used in this protocol. We would also like to thank Ava Schapman for providing feedback on the clarity of the protocol. Financial support was provided by The Wallin Neuroscience Discovery Fund to Nam Chul Kim.

1. Preparing for quantification
<ol>
	<li>Download and install ilastik11, ImageJ, and the ImageJ plugin Flynotyper1. See Table of Materials for website links to installation. Download the appropriate packages according to the computer operating system (Mac, Windows, Linux, etc). Follow the installation instructions exactly as stated. 
	NOTE: Following the directions exactly as stated in the provided links is imperative. The computer used needs a 64-bit operating system; otherwise, there are no other required specifications. See the Table of Materials for the computer specifications of the system used for this protocol.</li>
	<li>Use a standard image to train the machine learning model. Take an image of a healthy fly eye using a light microscope with a ring light (such as w1118 x GMR-GAL4), using the center of the eye as the focal point. Analyze males and females separately; therefore, acquire a standard image for males and for females. 
	NOTE: Settings will vary widely depending on the microscope and camera. See the second paragraph in the discussion for more general information on fly preparation and image acquisition.</li>
	<li>Take images of the experimental fly eyes using the same settings used for the standard image acquisition. 
	NOTE:Good orientation is paramount for accurate quantification. See Figure 4 as an example for proper orientation.</li>
</ol>
2. Using ilastik to detect ommatidia from fly eye images
<ol>
	<li>Before starting analysis, make sure all experimental groups, regardless of experimental condition, are in the same file folder to make the following processes easier. Make sure files are named appropriately to identify experimental groups and distinguish between males and females. 
	NOTE:&#160;We recommend printing out the protocol and using the computer to zoom in on figures, as this protocol is most easily followed visually.</li>
	<li>Open the software. 
	NOTE:&#160;It is advised not to have other applications open while running the software; otherwise, the computer might run very slowly, particularly in older or less robust systems.</li>
	<li>Click on Create New Project | Other Workflows, choose Cell Density Counting, and select a location to save the project file (Figure 1A).</li>
	<li>Click on 1. Input Data | Add New | Add Separate Image(s). Choose the standard image (Figure 1B).</li>
	<li>Click on 2. Feature Selection and select features to use. Select the features shown in Figure 1C; choose more sigma values to increase sensitivity (i.e., 10, 15, 20, etc). 
	NOTE: If too many boxes are checked, the program might run for a long time.</li>
	<li>Click on 3. Counting and set the Foreground sigma value to 5.00. Mark 50 or more individual ommatidium using the Foreground tool on the standard image. 
	NOTE:&#160;Fifty markings are typically adequate for analysis; however, the more ommatidia marked, the more accurate the results will be (Figure 1D).</li>
	<li>Click on 3. Counting and use the Background tool to mark the outside of the ommatidia (Figure 1E). Draw green lines in a lattice shape around the ommatidia. 
	NOTE: Similar to more markings on the ommatidia, the more green lines drawn, the more accurate the results will be.</li>
	<li>Click on 4. Density Export | Choose Export Image Settings to set image settings (Figure 1F). Click on Output File Info | select Format and tif for further analysis in Flynotyper.</li>
	<li>Click on 5. Batch Processing | Select Raw Data Files and select all experimental fly photos to be analyzed (Figure 1G). The list of imported images to be analyzed is seen under Select Raw Data Files (Figure 1H). Click Process all Files at the bottom of the 5. Batch Processing section. 
	NOTE: As a reminder, male and female analyses should be done separately. Depending on the computer used, this step can take 10 min or longer, especially if there are many imported images.</li>
	<li>After the software has finished, check the same file folder containing the experimental fly eye images; there will be black images named &#34;YourSampleName_Probabilities&#34; (Figure 1I).</li>
</ol>
3. Using ImageJ to prepare photos for Flynotyper
<ol>
	<li>Open the ilastik-generated .tif file (the black boxes) in ImageJ. 
	NOTE:&#160;Each ilastik generated .tif file needs to be handled separately.</li>
	<li>Use the Rectangle Select tool to make a rectangle around just the eye. After the rectangle has been drawn, crop the area by either choosing Ctrl + X or Ctrl + C (Figure 2A). Wait for a black box in the shape of the rectangle outline to appear. 
	NOTE: The computer operating system will determine whether to use &#39;Ctrl + X&#39; or &#39;Ctrl + C&#39;. Usually, &#39;Ctrl + X&#39; is for Windows and &#39;Ctrl + C&#39; is used for Mac.</li>
	<li>Open the now cut fly eye using ImageJ; click on File | New | Internal Clipboard (Figure 2B).</li>
	<li>Save the cut fly eye as a JPEG by clicking on File | Save As | Jpeg. The cut images are now in the same file folder as the original experimental images. 
	NOTE: To make the next step easier, it is recommended to create a new file folder called &#39;cut&#39; and put all of the cut images into that folder.</li>
</ol>
4. Using Flynotyper to calculate phenotypic scores
<ol>
	<li>Open Flynotyper as an ImageJ plugin by clicking on Plugins | Flynotyper.</li>
	<li>Select Add Genotypes and open the folder containing the cut fly eye images (cut file folder). The folder name will appear under Genotypes (Figure 2C).</li>
	<li>Check the Light microscope and Vertically boxes but adjust accordingly if needed. Check Stability and Distance to the Center boxes under Rank Ommatidia by: (Figure 2C).</li>
	<li>For Number of ranked ommatidia considered, input 200. 
	NOTE: In general,&#160;200 is a good number but adjust accordingly to preferences.</li>
	<li>Click Run and wait for the results of the analysis to appear (Figure 2D). 
	NOTE: This step may take 5 min or longer (or shorter) depending on the processing power of the computer used.</li>
	<li>Copy and paste the Sample File and P-Score into statistical software for further analysis.</li>
</ol>

Unbiased Quantification of Ommatidia in Drosophila Eye Models

<ol>
	<li>Iyer, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+assessment+of+eye+phenotypes+for+functional+genetic+studies+using+Drosophila+melanogaster.">Quantitative assessment of eye phenotypes for functional genetic studies using Drosophila melanogaster.</a> G3. 6 (5), 1427-1437 (2016).</li><li>Thomas, B. J., Wassarman, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+fly's+eye+view+of+biology.">A fly's eye view of biology.</a> Trends Genet. 15 (5), 184-190 (1999).</li><li>Roignant, J. -. Y., Treisman, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pattern+formation+in+the+Drosophila+eye+disc.">Pattern formation in the Drosophila eye disc.</a> Int J Dev Biol. 53 (5-6), 795-804 (2009).</li><li>Diez-Hermano, S., Ganfornina, M. D., Vegas, E., Sanchez, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Machine+learning+representation+of+loss+of+eye+regularity+in+a+Drosophila+neurodegenerative+model.">Machine learning representation of loss of eye regularity in a Drosophila neurodegenerative model.</a> Front Neurosci. 14 (1), 1-14 (2020).</li><li>Appocher, C., Klima, R., Feiguin, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+screening+in+Drosophila+reveals+the+conserved+role+of+REEP1+in+promoting+stress+resistance+and+preventing+the+formation+of+Tau+aggregates.">Functional screening in Drosophila reveals the conserved role of REEP1 in promoting stress resistance and preventing the formation of Tau aggregates.</a> Hum Mol Genet. 23 (25), 6762-6772 (2014).</li><li>Pandey, U. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HDAC6+rescues+neurodegeneration+and+provides+an+essential+link+between+autophagy+and+the+UPS.">HDAC6 rescues neurodegeneration and provides an essential link between autophagy and the UPS.</a> Nature. 447 (7146), 860-864 (2007).</li><li>Outa, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validation+of+a+Drosophila+model+of+wild-type+and+T315I+mutated+BCR-ABL1+in+chronic+myeloid+leukemia:+an+effective+platform+for+treatment+screening.">Validation of a Drosophila model of wild-type and T315I mutated BCR-ABL1 in chronic myeloid leukemia: an effective platform for treatment screening.</a> Haematologica. 105 (2), 387-397 (2019).</li><li>Shirinian, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+transgenic+Drosophila+melanogaster+model+to+study+human+T-lymphotropic+virus+oncoprotein+Tax-1-driven+transformation+in+vivo.">A transgenic Drosophila melanogaster model to study human T-lymphotropic virus oncoprotein Tax-1-driven transformation in vivo.</a> J Virol. 89 (15), 8092-8095 (2015).</li><li>Diez-Hermano, S., Valero, J., Rueda, C., Ganfornina, M. D., Sanchez, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+image+analysis+method+to+measure+regularity+in+biological+patterns:+a+case+study+in+a+Drosophila+neurodegenerative+model.">An automated image analysis method to measure regularity in biological patterns: a case study in a Drosophila neurodegenerative model.</a> Mol Neurodegener. 10 (1), 1-10 (2015).</li><li>Yusuff, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drosophila+models+of+pathogenic+copy-number+variant+genes+show+global+and+non-neuronal+defects+during+development.">Drosophila models of pathogenic copy-number variant genes show global and non-neuronal defects during development.</a> PLoS Genet. 16 (6), e1008792-e1008792 (2020).</li><li>Berg, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ilastik:+interactive+machine+learning+for+(bio)image+analysis.">ilastik: interactive machine learning for (bio)image analysis.</a> Nat Methods. 16, 1226-1232 (2019).</li><li>Chalmers, M. R., Kim, J., Kim, N. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eip74EF+is+a+dominant+modifier+for+ALS-FTD-linked+VCPR152H+phenotypes+in+the+Drosophila+eye+model.">Eip74EF is a dominant modifier for ALS-FTD-linked VCPR152H phenotypes in the Drosophila eye model.</a> BMC Res Notes. 16 (1), 1-5 (2023).</li><li>Baek, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TDP-43+and+PINK1+mediate+CHCHD10S59L+mutation-induced+defects+in+Drosophila+and+in+vitro.">TDP-43 and PINK1 mediate CHCHD10S59L mutation-induced defects in Drosophila and in vitro.</a> Nat Commun. 12 (1), 1-20 (2021).</li></ol>

The authors have no conflicts of interest to disclose.

The ommatidia of Drosophila comprise a useful system for studying various biological functions and genetic diseases. The regularity of ommatidia is a good measurement to examine the effect of genetic mutations4. Even though several methods for calculating ommatidial regularity exist, such as manual ranking, these methods can be heavily biased. To overcome this biased approach, semi-automatic tools have been developed1,11.
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The Drosophila melanogaster genome contains ~75% of human disease-related gene orthologs. Additionally, during Drosophila eye development, approximately two-thirds of the genes in the genome are expressed, making the Drosophila eye an outstanding genetic system to investigate various molecular and cellular functions, development, and disease models1,2. Thus, the Drosophila eye system is a useful experimental tool to study various biological processes.
The Drosophila compound eye is a well-structured and comprehensive array of ~800 ommatidia that exhibit a symmetrical and hexagonal pattern3. The regularity of this hexagonal pattern can be used to estimate the effect of introducing mutations and gene expression changes in eye morphology4. Previous studies that require evaluation of eye morphology have heavily relied on manual ranking of the severity of eye phenotypes detected by the naked eye. To rank the eye phenotypes, external eye morphology images are taken by a stereomicroscope5,6. The eye phenotype of each group is assessed by splitting the external eye into four areas and calculating the proportion of degeneration in each area5,6. Then, the values are used to calculate averages that are compared with the values obtained from control flies7. The scoring is based on the extent of fusion, loss of ommatidia, and bristle organization7,8. Fly eye photos taken with a stereomicroscope are acquired by one researcher, and the eye phenotype analysis is performed by another researcher with triple validation sets7,8.
When it comes to ranking weak alterations in eye morphology with the naked eye, there are limitations4. To overcome these limitations, computational approaches such as FLEYE and Flynotyper have been developed1,9. Flynotyper is a novel computational method for quantitatively estimating morphological changes in the Drosophila eye system1. It automatically detects the Drosophila eye and individual ommatidium, calculating Phenotypic Scores (P-Scores) based on the irregularity of theeye1. A higher P-Score indicates the fly eye is more degenerated. This software was successfully used in quantifying the abnormality of Drosophila eyes10. Although Flynotyper ensures an automated process, it still cannot be successfully applied to some eye images taken by various light microscopy methods.
Qualitatively, we prefer a ring light source compared to a single-point light source, as it offers a more accurate representation of each ommatidium. However, when the ring light is used, it generates a ring-shaped shadow at the top of each ommatidium due to the hemispherical shape of the ommatidium. This ring-shaped shadow inhibits accurate ommatidial detection by Flynotyper, leading to incorrect calculation of P-Scores.
To overcome these issues, we implemented ilastik, a machine learning-based tool for various analyses, to classify ommatidia in fly eye images11. We then fed the ilastik-generated images into Flynotyper to calculate P-Scores. This allows us to quantify the Drosophila eye morphological defects unbiasedly1.

<table><tbody><tr><td>Computer specifications</td><td></td><td></td><td>Ryzen 5, 16 GB RAM, Nvidia RTX 3070 Super, Windows 10</td></tr><tr><td>Flynotyper</td><td>Iyer, J. et al. (2016)</td><td>Download software here: https://flynotyper.sourceforge.net/imageJ.html</td><td>Open source software. Do not use Flynotyper 2.0. At the time of publication, 2.0 was fairly new and this protocol is optimized for the original version of Flynotyper.</td></tr><tr><td>ilastik</td><td>Berg, S. et al. (2019)</td><td>Download software here: https://www.ilastik.org/download.html</td><td>Open source software. Download Version 1.4.0.post1 under Regular Builds corresponding to your computer operating system.</td></tr><tr><td>ImageJ</td><td></td><td>Download software here: https://imagej.net/ij/download.html</td><td>Open source software. Versions 1.53 and 1.54 were used. 1.54 is the updated version and is the default download.</td></tr><tr><td>Leica Application Suite (LAS X)</td><td>Leica Microsystems</td><td>LASX Office 1.4.6 28433</td><td>System and software used for z-stack acquisition.</td></tr><tr><td>Leica Z16 APO microscope with a DMC2900 camera</td><td>Leica Microsystems</td><td>10 447 173, 12 730 466</td><td>Referred to as Z-stack microscope and camera in the text. This product is now archived.</td></tr></tbody></table>

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.

In a previous study, we used this protocol to determine genetic modifiers of mutant VCP protein linked to amyotrophic lateral sclerosis with frontotemporal dementia (ALS-FTD)12. Additionally, this method was also used in another paper to assess the toxicity of CHCHD10S59L-mediated ALS-FTD, even when using an older stereomicroscope13. To further validate these results, we used ilastik and Flynotyper in the same ALS-FTD disease mutation as Baek et al.<sup class="xr...

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.

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 ...

quantifying-drosophila-melanogaster-eye-phenotypes-computational

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Research

JoVE Journal

Developmental Biology

751 Views.  University of Minnesota. We zijn geïnteresseerd in het ontwikkelen van ziektemodellen voor ziekten zoals ALS, FTD en multisysteemproteïteïnopathie om hun moleculaire pathologieën te begrijpen. Een model dat we gebruiken is het Drosophila oogsysteem. De regelmaat en het symmetrische patroon van het oog kunnen worden gebruikt om het effect van het introduceren van mutaties en veranderingen in genexpressie te evalueren.Ons protocol pakt een lacune aan in de manieren om fenoty...

Video: Auteur in de schijnwerpers: kwantificering van fenotypes van ruwe ogen in Drosophila-modellen  van amyotrofische laterale sclerose met frontotemporale demen

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 ...

Quantifying Abdominal Pigmentation in Drosophila melanogaster

Pigmentation is a morphologically simple but highly variable trait that often has adaptive significance. It has served extensively as a model for understanding the development and evolution of morphological phenotypes. Abdominal pigmentation in Drosophila melanogaster has been particularly useful, allowing researchers to identify the loci that underlie inter- and intraspecific variations in morphology. Hitherto, however, D. melanogaster abdominal pigmentation has been largely assayed qualitatively, through scoring, rather than quantitatively, which limits the forms of statistical analysis that can be applied to pigmentation data. This work describes a new methodology that allows for the quantification of various aspects of the abdominal pigmentation pattern of adult D. melanogaster. The protocol includes specimen mounting, image capture, data extraction, and analysis. All the software used for image capture and analysis feature macros written for open-source image analysis. The advantage of this approach is the ability to precisely measure pigmentation traits using a methodology that is highly reproducible across different imaging systems. While the technique has been used to measure variation in the tergal pigmentation patterns of adult D. melanogaster, the methodology is flexible and broadly applicable to pigmentation patterns in myriad different organisms.

Quantifying Abdominal Pigmentation in Drosophila melanogaster

This work presents a method to quickly and precisely quantify the abdominal pigmentation of Drosophila melanogaster using digital image analysis. This method streamlines the procedures between phenotype acquisition and data analysis and includes specimen mounting, image acquisition, pixel value extraction, and trait measurement.

Pigmentation is a morphologically simple but highly variable trait that often has adaptive significance. It has served extensively as a model for ...

Genetics

Imaging and Analysis of Tissue Orientation and Growth Dynamics in the Developing Drosophila Epithelia During Pupal Stages

Within multicellular organisms, mature tissues and organs display high degrees of order in the spatial arrangements of their constituent cells. A remarkable example is given by sensory epithelia, where cells of the same or distinct identities are brought together via cell-cell adhesion showing highly organized planar patterns. Cells align to one another in the same direction and display equivalent polarity over large distances. This organization of the mature epithelia is established over the course of morphogenesis. To understand how the planar arrangement of the mature epithelia is achieved, it is crucial to track cell orientation and growth dynamics with high spatiotemporal fidelity during development in vivo. Robust analytical tools are also essential to identify and characterize local-to-global transitions. The Drosophila pupa is an ideal system to evaluate oriented cell shape changes underlying epithelial morphogenesis. The pupal developing epithelium constitutes the external surface of the immobile body, allowing long-term imaging of intact animals. The protocol described here is designed to image and analyze cell behaviors at both global and local levels in the pupal abdominal epidermis as it grows. The methodology described can be easily adapted to the imaging of cell behaviors at other developmental stages, tissues, subcellular structures, or model organisms.

Imaging and Analysis of Tissue Orientation and Growth Dynamics in the Developing Drosophila Epithelia During Pupal Stages

This protocol is designed for the imaging and analysis of the dynamics of cell orientation and tissue growth in the Drosophila abdominal epithelia as the fruit fly undergoes metamorphosis. The methodology described here can be applied to the study of different developmental stages, tissues, and subcellular structures in Drosophila or other model organisms.

Within multicellular organisms, mature tissues and organs display high degrees of order in the spatial arrangements of their constituent cells. A ...

Preparation of Adult Drosophila Eyes for Thin Sectioning and Microscopic Analysis

Drosophila has long been used as model system to study development, mainly due to the ease with which it is genetically tractable. Over the years, a plethora of mutant strains and technical tricks have been developed to allow sophisticated questions to be asked and answered in a reasonable amount of time. Fundamental insight into the interplay of components of all known major signaling pathways has been obtained in forward and reverse genetic Drosophila studies. The fly eye has proven to be exceptionally well suited for mutational analysis, since, under laboratory conditions, flies can survive without functional eyes. Furthermore, the surface of the insect eye is composed of some 800 individual unit eyes (facets or ommatidia) that form a regular, smooth surface when looked at under a dissecting microscope. Thus, it is easy to see whether a mutation might affect eye development or growth by externally looking for the loss of the smooth surface ('rough eye' phenotype; Fig. 1) or overall eye size, respectively (for examples of screens based on external eye morphology see e.g.1). Subsequent detailed analyses of eye phenotypes require fixation, plastic embedding and thin-sectioning of adult eyes.
The Drosophila eye develops from the so-called eye imaginal disc, a bag of epithelial cells that proliferate and differentiate during larval and pupal stages (for review see e.g. 2). Each ommatidium consists of 20 cells, including eight photoreceptors (PR or R-cells; Fig. 2), four lens-secreting cone cells, pigment cells ('hexagon' around R-cell cluster) and a bristle. The photoreceptors of each ommatidium, most easily identified by their light sensitive organelles, the rhabdomeres, are organized in a trapezoid made up of the six "outer" (R1-6) and two "inner" photoreceptors (R7/8; R8 [Fig. 2] is underneath R7 and thus only seen in sections from deeper areas of the eye). The trapezoid of each facet is precisely aligned with those of its neighbors and the overall anteroposterior and dorsoventral axes of the eye (Fig. 3A). In particular, the ommatidia of the dorsal and ventral (black and red arrows, respectively) halves of the eye are mirror images of each other and correspond to two chiral forms established during planar cell polarity signaling (for review see e.g. 3).
The method to generate semi-thin eye sections (such as those presented in Fig. 3) described here is slightly modified from the one originally described by Tomlinson and Ready4. It allows the morphological analysis of all cells except for the transparent cone cells. In addition, the pigment of R-cells (blue arrowheads in Fig. 2 and 3) can be used as a cell-autonomous marker for the genotype of a R-cell, thus genetic requirements of genes in a subset of R-cells can readily be determined5,6.

Preparation of Adult Drosophila Eyes for Thin Sectioning and Microscopic Analysis

A standard approach to prepare adult Drosophila eyes for semi-thin sectioning and light microscopic analysis is presented here. The protocol can be used for gross morphological analysis of eye defects, or with the indicated adjustments can be used to determine genetic requirements of genes in specific cell types of the eye (e.g. clonal analysis of photoreceptors) or for electron microscopic analysis.

Drosophila has long been used as model system to study development, mainly due to the ease with which it is genetically tractable. Over the years, a ...

Biology

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 ...

Neuroscience

Assessing the Cellular Immune Response of the Fruit Fly, Drosophila melanogaster, Using an In Vivo Phagocytosis Assay

In all animals, innate immunity provides an immediate and robust defense against a broad spectrum of pathogens. Humoral and cellular immune responses are the main branches of innate immunity, and many of the factors regulating these responses are evolutionarily conserved between invertebrates and mammals. Phagocytosis, the central component of cellular innate immunity, is carried out by specialized blood cells of the immune system. The fruit fly, Drosophila melanogaster, has emerged as a powerful genetic model to investigate the molecular mechanisms and physiological impacts of phagocytosis in whole animals. Here we demonstrate an injection-based in vivo phagocytosis assay to quantify the particle uptake and destruction by Drosophila blood cells, hemocytes. The procedure allows researchers to precisely control the particle concentration and dose, making it possible to obtain highly reproducible results in a short amount of time. The experiment is quantitative, easy to perform, and can be applied to screen for host factors that influence pathogen recognition, uptake, and clearance.

Assessing the Cellular Immune Response of the Fruit Fly, Drosophila melanogaster, Using an In Vivo Phagocytosis Assay

This protocol describes an in vivo phagocytosis assay in adult Drosophila melanogaster to quantify phagocyte recognition and clearance of microbial infections.

In all animals, innate immunity provides an immediate and robust defense against a broad spectrum of pathogens. Humoral and cellular immune responses are ...

Immunology and Infection

Methods for Staging Pupal Periods and Measurement of Wing Pigmentation of Drosophila guttifera

Diversified species of Drosophila (fruit fly) provide opportunities to study mechanisms of development and genetic changes responsible for evolutionary changes. In particular, the adult stage is a rich source of morphological traits for interspecific comparison, including wing pigmentation comparison. To study developmental differences among species, detailed observation and appropriate staging are required for precise comparison. Here we describe protocols for staging of pupal periods and quantification of wing pigmentation in a polka-dotted fruit fly, Drosophila guttifera. First, we describe the method for detailed morphological observation and definition of pupal stages based on morphologies. This method includes a technique for removing the puparium, which is the outer chitinous case of the pupa, to enable detailed observation of pupal morphologies. Second, we describe the method for measuring the duration of defined pupal stages. Finally, we describe the method for quantification of wing pigmentation based on image analysis using digital images and ImageJ software. With these methods, we can establish a solid basis for comparing developmental processes of adult traits during pupal stages.

Methods for Staging Pupal Periods and Measurement of Wing Pigmentation of Drosophila guttifera

Protocols for staging pupal periods and measurement of wing pigmentation of Drosophila guttifera are described. Staging and quantification of pigmentation provide a solid basis for studying developmental mechanisms of adult traits and enable interspecific comparison of trait development.

Diversified species of Drosophila (fruit fly) provide opportunities to study mechanisms of development and genetic changes responsible for evolutionary ...

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 ...

Studying Membrane Protein Trafficking in Drosophila Photoreceptor Cells

<a href='https://app.jove.com/v/63375/studying-membrane-protein-trafficking-drosophila-photoreceptor-cells'>Source: Wagner, K. et al., Studying Membrane Protein Trafficking in Drosophila Photoreceptor Cells Using eGFP-Tagged Proteins.&#160;J. Vis. Exp. (2022).</a>This video demonstrates the visualization of membrane protein trafficking in Drosophila photoreceptor cells using eGFP-tagged TRPL proteins. It details the preparation of transgenic flies for imaging, the capture of fluorescence images of photoreceptor cells, and the quantification of TRPL trafficking by measuring relative eGFP fluorescence in the rhabdomeres.

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

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