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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F., Eckert, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Maps+of+the+acute+zones+of+fly+eyes.">Maps of the acute zones of fly eyes.</a> Journal of Comparative Physiology. A. 156, 525-538 (1985).</li><li>Warrant, E. J., Barth, F. G., Schmid, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+design+of+compound+eyes+and+the+illumination+of+natural+habitats.">The design of compound eyes and the illumination of natural habitats.</a> Ecology of Sensing. , 187-213 (2001).</li><li>Warrant, E. J., Kelber, A., Kristensen, N. P., Kristensen, N. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eyes+and+vision.+In.">Eyes and vision. 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C. 4, 108-112 (2016).</li><li>Floreano, D., 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=Miniature+curved+artificial+compound+eyes.">Miniature curved artificial compound eyes.</a> Proceedings of the National Academy of Sciences of the United States of America. 110, 9267-9272 (2013).</li><li>Song, Y. 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=Digital+cameras+with+designs+inspired+by+the+arthropod+eye.">Digital cameras with designs inspired by the arthropod eye.</a> Nature. 497 (7447), 95-99 (2013).</li><li>Douglass, J. K., Wehling, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+mapping+of+compound+eye+visual+sampling+parameters+with+FACETS,+a+highly+automated+wide-field+goniometer.">Rapid mapping of compound eye visual sampling parameters with FACETS, a highly automated wide-field goniometer.</a> Journal of Comparative Physiology A. 202 (12), 839-851 (2016).</li><li>Franceschini, N., Snyder, A. 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G., Autrum, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pseudopupils+of+compound+eyes.">Pseudopupils of compound eyes.</a> Handbook of Sensory Physiology, Vol VII/6A. , 357-439 (1979).</li><li>Stavenga, D. G., Kruizinga, R., Leertouwer, H. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dioptrics+of+the+facet+lenses+of+male+blowflies+Calliphora+and+Chrysomia.">Dioptrics of the facet lenses of male blowflies Calliphora and Chrysomia.</a> Journal of Comparative Physiology A. 166, 365-371 (1990).</li><li>Straw, A. D., Warrant, E. J., O'Carroll, D. 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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 beginning of measurements, the viewed positions of both the eye and the pseudopupil can change considerably even with small rotations of the goniometric device. These changes are minimized by positioning the eye center at the goniometric rotation center, where the deep pseudopupil is observed. By correlating the subsequently measured images, the facet pattern can be tracked. Importantly, the rotation angle has to be adjusted to a rather small value because in areas where the facet pattern is very regular, the correlation procedure is prone to erroneous results.
Here, we have presented a partial eye map (Figure 8), which demonstrates the feasibility of highly automated mapping of the visual acuity in insect eyes. The interommatidial angles of around 2.0&#176;-2.5&#176;, evident from comparing the facet spacings to the 5&#176; shifts between successive image centers, correspond well to data that were previously derived, in a much more laborious manner, from the same species (Musca domestica)25. Complete eye maps of the visual space of houseflies and other insects will be published elsewhere.
The method presented here allows mapping the visual axes of a complete eye, in vivo, within only a few hours, which will be extremely difficult to achieve with other approaches. The automatized charting method is illustrated here for the case of the housefly, but it can be simply extended to the compound eyes of other insects, such as butterflies. However, instead of the pupillary reflection, the butterfly eyeshine then serves as the visual axis marker21,24. An alternative method is the recently developed X-ray microtomography26. This valuable approach yields detailed anatomical maps, but is vulnerable to optical errors, specifically where the visual axes of the ommatidia are skewed to the eye surface21, or if tissue processing distorts the eye geometry enough to compromise measurements. The visualization of the pseudopupil is more or less straightforward in fly eyes that have a shiny pupil mechanism. This is less easy in compound eyes with a poorly reflecting pupil mechanism, as, for example, in bees21. Yet, for many other insect species, such as soldier flies or bees, the fluorescence of the visual pigments in the ommatidial rhabdoms can be used. The application of fluorophores that create a strong rhabdom fluorescence offers another opportunity to estimate the spatial organization of the eye's visual space27.

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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&#956;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.

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

Nanyang Technological University

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

Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of the visual system provide more empirical evidence for functional applications. Investigation into the sensory system provides information about the sensory capacity, learning and memory ability, and establishes known baseline behaviour in which to gauge deviations (Burghardt, 1977). However, unlike mammalian or avian systems, testing for learning and memory in a reptile species is difficult. Furthermore, using an operant paradigm as a psychophysical measure of sensory ability is likewise as difficult. Historically, reptilian species have responded poorly to conditioning trials because of issues related to motivation, physiology, metabolism, and basic biological characteristics. Here, I demonstrate an operant paradigm used a novel model lizard species, the Jacky dragon (Amphibolurus muricatus) and describe how to test peripheral sensitivity to salient speed and motion characteristics. This method uses an innovative approach to assessing learning and sensory capacity in lizards. I employ the use of random-dot kinematograms (RDKs) to measure sensitivity to speed, and manipulate the level of signal strength by changing the proportion of dots moving in a coherent direction. RDKs do not represent a biologically meaningful stimulus, engages the visual system, and is a classic psychophysical tool used to measure sensitivity in humans and other animals. Here, RDKs are displayed to lizards using three video playback systems. Lizards are to select the direction (left or right) in which they perceive dots to be moving. Selection of the appropriate direction is reinforced by biologically important prey stimuli, simulated by computer-animated invertebrates.

Testing visual sensitivity in lizards using an operant conditioning paradigm that employs video playback of random-dot kinematograms and computer-generated invertebrates.

Badania wizualne Wrażliwość na prędkość i kierunek ruchu w Jaszczurki

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of ...

Dissecting the Non-human Primate Brain in Stereotaxic Space

The use of non-human primates provides an excellent translational model for our understanding of developmental and aging processes in humans1-6. In addition, the use of non-human primates has recently afforded the opportunity to naturally model complex psychiatric disorders such as alcohol abuse7. Here we describe a technique for blocking the brain in the coronal plane of the vervet monkey (Chlorocebus aethiops sabeus) in the intact skull in stereotaxic space. The method described here provides a standard plane of section between blocks and subjects and minimizes partial sections between blocks. Sectioning a block of tissue in the coronal plane also facilitates the delineation of an area of interest. This method provides manageable sized blocks since a single hemisphere of the vervet monkey yields more than 1200 sections when slicing at 50&mu;m. Furthermore by blocking the brain into 1cm blocks, it facilitates penetration of sucrose for cyroprotection and allows the block to be sliced on a standard cryostat.

The non-human primate is an important translational species for our understanding of the normal processing of the brain. The anatomical organization of the primate brain can provide important insights into normal and pathological conditions in humans.

The use of non-human primates provides an excellent translational model for our understanding of developmental and aging processes in humans1-6. In ...

Semi-automated Optical Heartbeat Analysis of Small Hearts

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our Semi-automatic Optical Heartbeat Analysis (SOHA) uses a novel movement detection algorithm that is able to detect cardiac movements associated with individual contractile and relaxation events. The program provides a host of physiologically relevant readouts including systolic and diastolic intervals, heart rate, as well as qualitative and quantitative measures of heartbeat arrhythmicity. The program also calculates heart diameter measurements during both diastole and systole from which fractional shortening and fractional area changes are calculated. Output is provided as a digital file compatible with most spreadsheet programs. Measurements are made for every heartbeat in a record increasing the statistical power of the output. We demonstrate each of the steps where user input is required and show the application of our methodology to the analysis of heart function in all three genetically tractable heart models.

We have developed a Semi-automated Optical Heartbeat Analysis method (SOHA) for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts. We demonstrate the application of our methodology to the analysis of heart function in fruit fly and embryonic mouse hearts.

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our ...

Using the optokinetic response to study visual function of zebrafish

Optokinetic response (OKR) is a behavior that an animal vibrates its eyes to follow a rotating grating around it. It has been widely used to assess the visual functions of larval zebrafish1-5. Nevertheless, the standard protocol for larval fish is not yet readily applicable in adult zabrafish. Here, we introduce how to measure the OKR of adult zebrafish with our simple custom-built apparatus using a new protocol which is established in our lab. Both our apparatus and step-by-step procedure of OKR in adult zebrafish are illustrated in this video. In addition, the measurements of the larval OKR, as well as the optomotor response (OMR) test of adult zebrafish, are also demonstrated in this video. This OKR assay of adult zebrafish in our experiment may last for up to 4 hours. Such OKR test applied in adult fish will benefit to visual function investigation more efficiently when the adult fish vision system is manipulated.
Su-Qi Zou and Wu Yin contributed equally to this paper.

Optokinetic response has been widely used to assess the visual functions of larval zebrafish. Nevertheless, the standard protocol for larval fish is not yet readily applicable in adults1-5. Here, we introduce how to measure the OKR of adult zebrafish using a new protocol which is established in our lab.

Optokinetic response (OKR) is a behavior that an animal vibrates its eyes to follow a rotating grating around it. It has been widely used to assess the ...

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 ...

Trajectory Data Analyses for Pedestrian Space-time Activity Study

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission1-3. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data4.
Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.
The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation5 involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping6 and density volume rendering7. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.

A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.

It is well recognized that human movement in the  spatial and temporal dimensions has direct influence on disease transmission1-3.  An infectious disease ...

Design and Analysis of Temperature Preference Behavior and its Circadian Rhythm in Drosophila

The circadian clock regulates many aspects of life, including sleep, locomotor activity, and body temperature (BTR) rhythms1,2. We recently identified a novel Drosophila circadian output, called the temperature preference rhythm (TPR), in which the preferred temperature in flies rises during the day and falls during the night 3. Surprisingly, the TPR and locomotor activity are controlled through distinct circadian neurons3. Drosophila locomotor activity is a well&#160;known circadian behavioral output and has provided strong contributions to the discovery of many conserved mammalian circadian clock genes and mechanisms4. Therefore, understanding TPR will lead to the identification of hitherto unknown molecular and cellular circadian mechanisms. Here, we describe how to perform and analyze the TPR assay. This technique not only allows for dissecting the molecular and neural mechanisms of TPR, but also provides new insights into the fundamental mechanisms of the brain functions that integrate different environmental signals and regulate animal behaviors. Furthermore, our recently published data suggest that the fly TPR shares features with the mammalian BTR3. Drosophila are ectotherms, in which the body temperature is typically behaviorally regulated. Therefore, TPR is a strategy used to generate a rhythmic body temperature in these flies5-8. We believe that further exploration of Drosophila TPR will facilitate the characterization of the mechanisms underlying body temperature control in animals.

Design and Analysis of Temperature Preference Behavior and its Circadian Rhythm in Drosophila

We recently identified a novel Drosophila circadian output, temperature preference rhythm (TPR), in which the preferred temperature in flies rises during the day and falls during the night. TPR is regulated independently from another circadian output, locomotor activity. Here&#160;we describe the design and analysis of TPR in Drosophila.

The circadian clock regulates many aspects of life, including sleep, locomotor activity, and body temperature (BTR) rhythms1,2. We recently identified a ...

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis (diguanylate cyclases) and degradation (phosphodiesterases) of the second messenger c-di-GMP. In contrast, little information is available regarding the molecular mechanisms and cellular components through which this signaling molecule regulates a diverse range of cellular processes. Most of the known effector proteins belong to the PilZ family or are degenerated diguanylate cyclases or phosphodiesterases that have given up on catalysis and have adopted effector function. Thus, to better define the cellular c-di-GMP network in a wide range of bacteria experimental methods are required to identify and validate novel effectors for which reliable in silico predictions fail.
We have recently developed a novel Capture Compound Mass Spectrometry (CCMS) based technology as a powerful tool to biochemically identify and characterize c-di-GMP binding proteins. This technique has previously been reported to be applicable to a wide range of organisms1. Here we give a detailed description of the protocol that we utilize to probe such signaling components. As an example, we use Pseudomonas aeruginosa, an opportunistic pathogen in which c-di-GMP plays a critical role in virulence and biofilm control. CCMS identified 74% (38/51) of the known or predicted components of the c-di-GMP network. This study explains the CCMS procedure in detail, and establishes it as a powerful and versatile tool to identify novel components involved in small molecule signaling.

The ubiquitous second messenger c-di-GMP controls growth and behavior of many bacteria. We have developed a novel Capture Compound Mass Spectrometry based technology to biochemically identify and characterize c-di-GMP binding proteins in virtually any bacterial species.

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis ...

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

Laser capture microdissection (LCM) has allowed gene expression analysis of single cells and enriched cell populations in tissue sections. LCM is a great tool for the study of the molecular mechanisms underlying cell differentiation and the development and progression of various diseases, including glaucoma. Glaucoma, which comprises a family of progressive optic neuropathies, is the most common cause of irreversible blindness worldwide. Structural changes and damage within the trabecular meshwork (TM) can result in increased intraocular pressure (IOP), which is a major risk factor for developing glaucoma. However, the precise molecular mechanisms involved are still poorly understood. The ability to perform gene expression analysis will be crucial in obtaining further insights into the function of these cells and its role in the regulation of IOP and glaucoma development. To achieve this, a reproducible method for isolating highly enriched TM from frozen sections of mouse eyes and a method for downstream gene expression analysis, such as RT-qPCR and RNA-Seq is needed. The method described herein is developed to isolate highly pure TM from mouse eyes for downstream digital PCR and microarray analysis. In addition, this technique can be easily adapted for the isolation of other highly enriched ocular cells and cell compartments that have been difficult to isolate from mouse eyes. The combination of LCM and RNA analysis can contribute to a more comprehensive understanding of the cellular events underlying glaucoma.

Here, we describe a protocol for a reproducible laser capture microdissection (LCM) for isolating trabecular meshwork (TM) for downstream RNA analysis. The ability to analyze changes in gene expression in the TM will help in understanding the underlying molecular mechanisms of TM-related ocular diseases.

Laser capture microdissection (LCM) has allowed gene expression analysis of single cells and enriched cell populations in tissue sections. LCM is a great ...

Measuring the Confluence of iPSCs Using an Automated Imaging System

This study focuses on understanding how growing iPSCs on different ECM coating substrates can affect cell confluence. A protocol to assess iPSC confluence in real time has been established without the need to count cells in single cell suspension to avoid any growth perturbation. A high-content image analysis system was used to assess iPCS confluence on 4 different ECMs over time in an automated manner. Different analysis settings were used to assess cell confluence of adherent iPSCs and only a slight difference (at 24 and 48 hours with laminin) has been observed whether a 60, 80 or 100% mask was applied. We also show that laminin lead to the best confluence compared to Matrigel, vitronectin and fibronectin.

The goal of the protocol is to compare different extracellular matrix (ECM) coating conditions to assess how differential coating affects the growth rate of induced pluripotent stem cells (iPSCs). In particular, we aim to set up conditions to obtain optimal growth of iPSC cultures.

This study focuses on understanding how growing iPSCs on different ECM coating substrates can affect cell confluence. A protocol to assess iPSC confluence ...

Automated Charting of the Visual Space of Housefly Compound Eyes

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Badania wizualne Wrażliwość na prędkość i kierunek ruchu w Jaszczurki

Dissecting the Non-human Primate Brain in Stereotaxic Space

Semi-automated Optical Heartbeat Analysis of Small Hearts

Using the optokinetic response to study visual function of zebrafish

Preparation of Adult Drosophila Eyes for Thin Sectioning and Microscopic Analysis

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Design and Analysis of Temperature Preference Behavior and its Circadian Rhythm in Drosophila

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

Measuring the Confluence of iPSCs Using an Automated Imaging System