This work was supported by R01 EY032095 (ALK), VSTP pre-doctoral fellowship 5T32 EY7143-27 (JK), F31 EY-033225 (SCH), R01 EY035028 (FAD and ALK) and R01 EY-029772 (FAD).

All animal experiments performed at The Johns Hopkins University School of Medicine (JHUSOM) were approved by the Institutional Animal Care and Use Committee (IACUC) at the JHUSOM. All experiments performed at the University of California, San Francisco (UCSF) were performed in accordance with protocols approved by the UCSF Institutional Animal Care and Use Program.
1. Behavioral data collection
<ol>
	<li>Record OKR eye movements using the video-oculography method of choice to generate wave data (i.e., a time series of the eye&#39;s gaze angle in spherical coordinates). 
	NOTE: Representative data collected at JHUSOM were obtained using headpost implantation surgery and video-oculography, as previously described9,13 (Figure 1). Representative data collected from UCSF were obtained through the headpost implantation surgery and the video-oculography method, as described in previously10 (Figure 7).
	<ol>
		<li>Make note of stimulus and recording parameters: recording frame rate, stimulus speed and direction, and lengths of time between and after stimulus epochs. For sinusoidal stimuli, note the amplitude and frequency of the stimulus wave as well.</li>
	</ol>
	</li>
	<li>Export collected wave data as a .CSV file containing horizontal and vertical (azimuth and elevation) wave data.
	<ol>
		<li>Organize wave data as a tab-delimited .CSV file with two columns containing horizontal data (epxWave) and vertical data (epyWave).</li>
	</ol>
	</li>
</ol>
2. Installation of analysis software
<ol>
	<li>Download and install Python.
	<ol>
		<li>For graph supervision, install Spyder via Anaconda.</li>
		<li>To ensure graphs function correctly in Spyder, go to Tools &#62; Preferences &#62; Ipython Console &#62; Graphics &#62; Graphics Backend. Set Inline to Automatic.</li>
	</ol>
	</li>
	<li>Create a new Anaconda environment with Python.</li>
	<li>Install PyOKR via PyPi with pip install PyOKR to install the newest version along with package dependencies (Supplementary Coding File 1 and Supplementary Coding File 2)</li>
	<li>If a Windows computer is being used, run from PyOKR import OKR_win as o and then o.run().</li>
	<li>If a Mac computer is being used, run from PyOKR import OKR_osx as o and then o.run().</li>
</ol>
3. Analysis of wave data
<ol>
	<li>Initialization of analysis and file imports
	<ol>
		<li>Run o.run() in a .py script to open the user interface.</li>
		<li>Under File, use the function Open or the command Ctrl+O [iOS command] to open a browser that will allow the user to select the desired wave file.</li>
		<li>Under File, use the button Export Folder or the command Ctrl+E to open a folder browser that will allow the selection of an output folder to which final analyses will be exported.</li>
		<li>Input the final analysis file name under the Output file in a recommended format such as AnimalGenotype_AnimalNumber_Analysis.</li>
		<li>Set the program for an individual animal using the command Set Subject under File or the command Ctrl+S to initialize the dataset for an individual animal.</li>
	</ol>
	</li>
	<li>Definition of wave file parameters
	<ol>
		<li>To begin setting stimulus parameters, define directionality under Select stimulus direction by selecting one of the four cardinal directions. For sinusoidal stimuli, select one that contains (Horizontal) or (Vertical) accordingly, with the cardinal direction defining the initial direction of the sinusoidal wave.</li>
		<li>Set stimulus type under Select stimulus type as either Unidirectional, Oscillatory, or Oblique.</li>
		<li>After setting the directionality, import either one&#39;s own stimulus position dataset (Import own stimulus vector data) or automatically generate a vector based on parameters (Generate stimulus vector from parameters). If importing a stimulus vector, proceed with 3.2.3.1 and then skip to step 3.3. If generating a stimulus vector, proceed with the next steps.
		<ol>
			<li>If importing one&#39;s own vector data, import the distance values of the stimulus (i.e., a time series describing how far the stimulus moves between each adjacent acquisition frame) in the same format described in step 3.2.1. Additionally, analyze the entire dataset as one epoch rather than splitting it into individual epochs, as functionality for subsetting imported stimulus value has not been added as of PyOKR v1.1.2.</li>
		</ol>
		</li>
		<li>Under Stimulus parameters, set the parameters of the stimulus used for data collection.
		<ol>
			<li>Set the length of time of no stimulus at the beginning (head) and end (tail) of a given trial with Head and Tail.</li>
			<li>Set the amount of time a stimulus is shown, the amount of time of no-stimulus after, and the number of total epochs within a given trial with Length of epoch, Length of post-stimulus, and Number of Epochs, respectively.</li>
			<li>For unidirectional and oblique stimuli, set stimulus speed in degrees per second with Horizontal Speed and Vertical Speed.</li>
			<li>Set the capture rate of the collection camera with Capture frame rate.</li>
			<li>For sinusoidal stimuli, generate the sinusoidal wave for modeling oscillatory stimuli with Frequency and Amplitude.</li>
		</ol>
		</li>
		<li>After parameterization, make the appropriate model from the inputted stimulus information above with Generate stimulus vector from parameters.</li>
		<li>Select a given epoch for the inputted stimulus using Select epoch to scan through the total wave file.</li>
	</ol>
	</li>
	<li>Supervised selection of tracking phases
	<ol>
		<li>To identify regions of slow tracking, automatically select fast phase saccades with Preliminary adjustment by clicking either Unfiltered Data or Filtered Data, which will label potential saccades based on maximal velocity changes.</li>
		<li>Under Unfiltered Data, confirm that saccades are accurately selected with a blue dot. If automatic selection is not accurate, manually remove points with the Left Mouse Button (LMB) or add points with the Right Mouse Button (RMB). When fast phase saccades are adequately selected, save the points with the Middle Mouse Button (MMB) and close the graph.</li>
		<li>If automatic filtering is desired, set a Z-Score Threshold and click Filtered Data to automatically filter saccades. If necessary, use the same manual supervision as described in step 3.3.2 to remove any noise.</li>
		<li>After proper saccade selection, press Point Adjustment to select the region to remove. Alter top and bottom points through a similar control scheme as described previously in step 3.3.2. Edit top (green) points with the LMB or the RMB and edit bottom (red) points with the Shift+LMB or Shift+RMB. When points are properly placed, use the MMB to save the points. 
		NOTE: if using a Mac, bottom and top point adjustment are in two separate buttons and follow the same control scheme as described in step 3.3.2.</li>
	</ol>
	</li>
	<li>Analysis of slow-tracking phases
	<ol>
		<li>Set the order of the polynomial model using Set Polynomial Order to define the polynomial model that will be fitted to individual slow phases. 
		NOTE: For unidirectional or oblique stimuli, the default value is 1 since linearity is necessary to calculate tracking gain. For sinusoidal stimuli, a higher order is needed to model the curve of the wave, with a default of 15.</li>
		<li>To analyze the trace, select Final Analysis to generate the slow phase models (Figure 2) for the selected slow phases (see Figure 2A-D) and calculate the distances, velocities, and tracking gains averaged across the epoch (Figure 2E).</li>
		<li>To view the two-dimensional (2D) or three-dimensional (3D) graph of the selected regions, select View 2D graph or View 3D graph, respectively.</li>
		<li>Select Add epoch to save the collected values generated in step 3.4.2. To view all added values for a given animal as well as averages for collected trials, select View current dataset.</li>
		<li>After an epoch is added, cycle through the rest of the file with Select epoch, following steps 3.3.1 to 3.4.4.</li>
		<li>Once a wave file is fully analyzed, repeat this process for all other files for a given animal by opening new files, setting appropriate parameters, and analyzing them accordingly. By repeating steps 3.2.1-3.4.5 for each file, generate a final dataset containing all wave data for a given animal.</li>
	</ol>
	</li>
	<li>Final export of data
	<ol>
		<li>After data analysis is complete for a given animal, with all directions or stimuli analyzed, export the dataset via Export data. 
		NOTE: The raw dataset will be exported based on the Output file name and saved along the path set by Output Folder as a CSV containing individual epoch data with the total mean for each stimulus parameter.</li>
		<li>After exporting an individual animal, re-initialize the dataset with Ctrl+S and then repeat all previous steps to analyze a new animal.</li>
		<li>If needed, re-organize all the output data collected for multiple animals for easier analysis using the command Sort Data under the Analysis tab. 
		NOTE: This function will compile and sort all average values for all the analyzed animal files stored within the output folder to allow for easier generation of graphs and statistical comparisons. Sorting is reliant on the naming of the files as of v1.1.2. Use the recommended naming scheme as described in step 3.1.4 for each file (e.g., WT_123_Analysis).</li>
	</ol>
	</li>
</ol>

Python-Based Tool for Quantitative Optokinetic Reflex Assessment in Animals

<ol>
	<li>Stahl, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+eye+movements+to+assess+brain+function+in+mice.">Using eye movements to assess brain function in mice.</a> Vision Res. 44 (28), 3401-3410 (2004).</li><li>Kretschmer, F., Tariq, M., Chatila, W., Wu, B., Badea, T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+optomotor+and+optokinetic+reflexes+in+mice.">Comparison of optomotor and optokinetic reflexes in mice.</a> J Neurophysiol. 118, 300-316 (2017).</li><li>Bronstein, A. M., Patel, M., Arshad, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+brief+review+of+the+clinical+anatomy+of+the+vestibular-ocular+connections+-+How+much+do+we+know.">A brief review of the clinical anatomy of the vestibular-ocular connections - How much do we know.</a> Eye. 29 (2), 163-170 (2015).</li><li>Simpson, J. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+accessory+optic+system.">The accessory optic system.</a> Ann Rev Neurosci. 7, 13-41 (1984).</li><li>Hamilton, N. R., Scasny, A. J., Kolodkin, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+the+vertebrate+retinal+direction-selective+circuit.">Development of the vertebrate retinal direction-selective circuit.</a> Dev Biol. 477, 273-283 (2021).</li><li>Dobson, V., Teller, D. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+acuity+in+human+infants:+a+review+and+comparison+of+behavioral+and+electrophysiological+studies.">Visual acuity in human infants: a review and comparison of behavioral and electrophysiological studies.</a> Vision Res. 18 (11), 1469-1483 (1978).</li><li>Cahill, H., Nathans, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+optokinetic+reflex+as+a+tool+for+quantitative+analyses+of+nervous+system+function+in+mice:+Application+to+genetic+and+drug-induced+variation.">The optokinetic reflex as a tool for quantitative analyses of nervous system function in mice: Application to genetic and drug-induced variation.</a> PLoS One. 3 (4), e2055 (2008).</li><li>Cameron, D. 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=The+optokinetic+response+as+a+quantitative+measure+of+visual+acuity+in+zebrafish.">The optokinetic response as a quantitative measure of visual acuity in zebrafish.</a> J Vis Exp. (80), e50832 (2013).</li><li>Al-Khindi, 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=The+transcription+factor+Tbx5+regulates+direction-selective+retinal+ganglion+cell+development+and+image+stabilization.">The transcription factor Tbx5 regulates direction-selective retinal ganglion cell development and image stabilization.</a> Curr Biol. 32 (19), 4286-4298 (2022).</li><li>Harris, S. C., Dunn, F. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Asymmetric+retinal+direction+tuning+predicts+optokinetic+eye+movements+across+stimulus+conditions.">Asymmetric retinal direction tuning predicts optokinetic eye movements across stimulus conditions.</a> eLife. 12, 81780 (2015).</li><li>Sun, L. O., 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=Functional+assembly+of+accessory+optic+system+circuitry+critical+for+compensatory+eye+movements.">Functional assembly of accessory optic system circuitry critical for compensatory eye movements.</a> Neuron. 86 (4), 971-984 (2015).</li><li>Yonehara, K., 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=Congenital+Nystagmus+gene+FRMD7+is+necessary+for+establishing+a+neuronal+circuit+asymmetry+for+direction+selectivity.">Congenital Nystagmus gene FRMD7 is necessary for establishing a neuronal circuit asymmetry for direction selectivity.</a> Neuron. 89 (1), 177-193 (2016).</li><li>Kodama, T., Du Lac, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptive+acceleration+of+visually+evoked+smooth+eye+movements+in+mice.">Adaptive acceleration of visually evoked smooth eye movements in mice.</a> J Neurosci. 36 (25), 6836-6849 (2016).</li><li>Stahl, J. S., Van Alphen, A. M., De Zeeuw, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+video+and+magnetic+search+coil+recordings+of+mouse+eye+movements.">A comparison of video and magnetic search coil recordings of mouse eye movements.</a> J Neurosci Methods. 99 (1-2), 101-110 (2000).</li></ol>

The authors have no conflicts of interest.

PyOKR provides several advantages for studying visual responses reflected in eye movements. These include accuracy, accessibility, and data collection options, in addition to the ability to incorporate parameterization and variable stimulus speeds.
Direct eye tracking gain assessment provides an accurate characterization of eye movement that is a more direct quantitative metric than traditional manual counting of fast phase saccades (ETMs). Although useful, saccade counting provides an indirec...

Image stabilization relies on precise oculomotor responses to compensate for global optic flow that occurs during self-motion. This stabilization is driven primarily by two motor responses: the optokinetic reflex (OKR) and the vestibulo-ocular reflex (VOR)1,2,3. Slow global motion across the retina induces the OKR, which elicits reflexive eye rotation in the corresponding direction to stabilize the image1,2. This movement, known as the slow phase, is interrupted by compensatory saccades, known as the fast phase, in which the eye rapidly resets in the opposite direction to allow for a new slow phase. Here, we define these fast-phase saccades as eye-tracking movements (ETMs). Whereas the VOR relies on the vestibular system to elicit eye movements to compensate for head movements3, the OKR is initiated in the retina by the firing of ON and subsequent signaling to the Accessory Optic System (AOS) in the midbrain4,5. Due to its direct reliance on retinal circuits, the OKR has been frequently used to determine visual tracking ability in both research and clinical settings6,7.
The OKR has been studied extensively as a tool for assessing basic visual ability2,6,8, DSGC development9,10,11,12, oculomotor responses13, and physiological differences among genetic backgrounds7. The OKR is evaluated in head-fixed animals presented with a moving stimulus14. Oculomotor responses are typically captured using a variety of video tools, and eye-tracking motions are captured as OKR waveforms in the horizontal and vertical directions9. To quantify tracking ability, two primary metrics have been described: tracking gain (the velocity of the eye relative to the velocity of the stimulus) and ETM frequency (the number of fast phase saccades over a given time frame). Calculation of gain has been used historically to directly measure angular velocity of the eye to estimate tracking ability; however, these calculations are labor intensive and can be arbitrarily derived based on video-oculography collection methods and subsequent quantification. For more rapid OKR assessment, counting of ETM frequency has been used as an alternate method for measuring tracking acuity7. Although this provides a fairly accurate estimation of tracking ability, this method relies on an indirect metric to quantify the slow phase response and introduces a number of biases. These include an observer bias in saccade determination, a reliance on temporally consistent saccadic responses across a set epoch, and an inability to assess the magnitude of the slow phase response.
In order to address these concerns with current OKR assessment approaches and to enable a high throughput in-depth quantification of OKR parameters, we have developed a new analysis method to quantify OKR waveforms. Our approach uses an accessible Python-based software platform named &#34;PyOKR.&#34; Using this software, modeling and quantification of OKR slow phase responses can be studied in greater depth and with increased parameterization. The software provides accessible and reproducible quantitative assessments of responses to a myriad of visual stimuli and also two-dimensional visual tracking in response to horizontal and vertical motion.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>C57BL/6J&#160; mice</td>
			<td>Jackson Labs</td>
			<td>664</td>
			<td></td>
		</tr>
		<tr>
			<td>Igor Pro</td>
			<td>WaveMetrics</td>
			<td>RRID: SCR_000325</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>RRID: SCR_001622</td>
			<td></td>
		</tr>
		<tr>
			<td>Optokinetic reflex recording chamber - JHUSOM</td>
			<td>Custom-built</td>
			<td>N/A</td>
			<td>As described in Al-Khindi et al.(2022)9 and Kodama et al. (2016)13&#160;</td>
		</tr>
		<tr>
			<td>Optokinetic reflex recording chamber - UCSF</td>
			<td>Custom-built</td>
			<td>N/A</td>
			<td>As described in Harris and Dunn, 201510</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>Python Software Foundation</td>
			<td>RRID: SCR_008394</td>
			<td></td>
		</tr>
		<tr>
			<td>Tbx5 flox/+ mice</td>
			<td>Gift from B. Bruneau</td>
			<td>N/A</td>
			<td>As described in Al-Khindi et al.(2022)9&#160;</td>
		</tr>
		<tr>
			<td>Tg(Pcdh9-cre)NP276Gsat/Mmucd</td>
			<td>MMRRC</td>
			<td>MMRRC Stock # 036084-UCD; RRID: MMRRC_036084-UCD</td>
			<td></td>
		</tr>
	</tbody>
</table>

pyokr: a semi-automated method for quantifying optokinetic reflex tracking ability

The study of behavioral responses to visual stimuli is a key component of understanding visual system function. One notable response is the optokinetic reflex (OKR), a highly conserved innate behavior necessary for image stabilization on the retina. The OKR provides a robust readout of image tracking ability and has been extensively studied to understand visual system circuitry and function in animals from different genetic backgrounds. The OKR consists of two phases: a slow tracking phase as the eye follows a stimulus to the edge of the visual plane and a compensatory fast phase saccade that resets the position of the eye in the orbit. Previous methods of tracking gain quantification, although reliable, are labor intensive and can be subjective or arbitrarily derived. To obtain more rapid and reproducible quantification of eye tracking ability, we have developed a novel&#160;semi-automated analysis program, PyOKR, that allows for quantification of two-dimensional eye tracking motion in response to any directional stimulus, in addition to being adaptable to any type of video-oculography equipment. This method provides automated filtering, selection of slow tracking phases, modeling of vertical and horizontal eye vectors, quantification of eye movement gains relative to stimulus speed, and organization of resultant data into a usable spreadsheet for statistical and graphical comparisons. This quantitative and streamlined analysis pipeline, readily accessible via PyPI import, provides a fast and direct measurement of OKR responses, thereby facilitating the study of visual behavioral responses.

To validate the analysis method described above, we quantified OKR tracking gain on wave traces collected from wild-type mice and a conditional knockout mutant with a known tracking deficit. In addition, to test the broader applicability of our analysis method, we analyzed traces derived from a separate cohort of wild-type mice acquired using a different video-oculography collection method. The automatic filtering of saccades facilitates OKR data processing and analysis (Figure 3). Using rec...

Read this Scientific Article Protocol about Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses at JoVE.com

Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses

We describe here PyOKR, a semi-automated quantitative analysis method that directly measures eye movements resulting from visual responses to two-dimensional image motion. A Python-based user interface and analysis algorithm allows for higher throughput and more accurate quantitative measurements of eye-tracking parameters than previous methods.

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability

The study of behavioral responses to visual stimuli is a key component of understanding visual system function. One notable response is the optokinetic ...

pyokr-semi-automated-method-for-quantifying-optokinetic-reflex

Research

JoVE Journal

Neuroscience

558 Views.  The Johns Hopkins University School of Medicine. We describe here PyOKR, a semi-automated quantitative analysis method that directly measures eye movements resulting from visual responses to two-dimensional image motion. A Python-based user interface and analysis algorithm allows for higher throughput and more accurate quantitative measurements of eye-tracking parameters than previous methods. 

Video: Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses

Novel Apparatus and Method for Drug Reinforcement

Animal models of reinforcement have proven to be useful for understanding the neurobiological mechanisms underlying drug addiction. Operant drug self-administration and conditioned place preference (CPP) procedures are expansively used in animal research to model various components of drug reinforcement, consumption, and addiction in humans. For this study, we used a novel approach to studying drug reinforcement in rats by combining traditional CPP and self-administration methodologies. We assembled an apparatus using two Med Associate operant chambers, sensory stimuli, and a Plexiglas-constructed neutral zone. These modifications allowed our experiments to encompass motivational aspects of drug intake through self-administration and drug-free assessment of drug/cue conditioning strength with the CPP test. In our experiments, rats self-administered cocaine (0.75 mg/kg/inj, i.v.) during either four (e.g., the "short-term") or eight (e.g., the "long-term") alternating-day sessions in an operant environment containing distinctive sensory cues (e.g., olfactory and visual). On the alternate days, in the other (differently-cued) operant environment, saline was available for self-infusion (0.1 ml, i.v.). Twenty-four hours after the last self-administration/cue-pairing session, a CPP test was conducted. Consistent with typical CPP findings, there was a significant preference for the chamber associated with cocaine self-administration. In addition, in animals undergoing the long-term experiment, a significant positive correlation between CPP magnitude and the number of cocaine-reinforced lever responses. In conclusion, this apparatus and approach is time and cost effective, can be used to examine a wide array of topics pertaining to drug abuse, and provides more flexibility in experimental design than CPP or self-administration methods alone.

Operant drug self-administration and conditioned place preference (CPP) procedures are expansively used in research to model various components of drug reinforcement, consumption, and addiction in humans. In this report, we combined traditional CPP and self-administration methods as a novel approach to studying drug reinforcement and addiction in rats.

Animal models of reinforcement have proven to be useful for understanding the neurobiological mechanisms underlying drug addiction.  Operant drug ...

Quantifying the Activity of cis-Regulatory Elements in the Mouse Retina by Explant Electroporation

Transcription factors within cellular gene networks control the spatiotemporal pattern and levels of expression of their target genes by binding to cis-regulatory elements (CREs), short (&tilde;300-600 bp) stretches of genomic DNA which can lie upstream, downstream, or within the introns of the genes they control. CREs (i.e., enhancers/promoters) typically consist of multiple clustered binding sites for both transcriptional activators and repressors1-3. They serve as logical integrators of transcriptional input giving a unitary output in the form of spatiotemporally precise and quantitatively exact promoter activity. Most studies of mammalian cis-regulation to date have relied on mouse transgenesis as a means of assaying the enhancer function of CREs4-5. This technique is time-consuming, costly and, on account of insertion site effects, largely non-quantitative. On the other hand, quantitative assays for mammalian CRE function have been developed in tissue culture systems (e.g., dual luciferase assays), but the in vivo relevance of these results is often uncertain. 
Electroporation offers an excellent alternative to traditional mouse transgenesis in that it permits both spatiotemporal and quantitative assessment of cis-regulatory activity in living mammalian tissue. This technique has been particularly useful in the analysis of cis-regulation in the central nervous system, especially in the cerebral cortex and the retina6-8. While mouse retinal electroporation, both in vivo and ex vivo, has been developed and extensively described by Matsuda and Cepko6-7,9, we have recently developed a simple approach to quantify the activity of photoreceptor-specific CREs in electroporated mouse retinas10. Given that the amount of DNA that is introduced into the retina by electroporation can vary from experiment to experiment, it is necessary to include a co-electroporated 'loading control' in all experiments. In this respect, the technique is very similar to the dual luciferase assay used to quantify promoter activity in cultured cells. 
When assaying photoreceptor cis-regulatory activity, electroporation is usually performed in newborn mice (postnatal day 0, P0) which is the time of peak rod production11-12. Once retinal cell types become post-mitotic, electroporation is much less efficient. Given the high rate of rod birth in newborn mice and the fact that rods constitute more than 70% of the cells in the adult mouse retina, the majority of cells that are electroporated at P0 are rods. For this reason, rod photoreceptors are the easiest retinal cell type to study via electroporation. The technique we describe here is primarily useful for quantifying the activity of photoreceptor CREs.

Quantifying the Activity of cis-Regulatory Elements in the Mouse Retina by Explant Electroporation

This protocol describes a simple and inexpensive way to quantify the activity of cis-regulatory elements (i.e., enhancer/promoters) in living mouse retinas via explant electroporation. DNA preparation, retinal dissection, electroporation, retinal explant culture, and post-fixation analysis and quantification are described.

Transcription factors within cellular gene networks control the spatiotemporal pattern and levels of expression of their target genes by binding to ...

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the vestibular organ results in mild to severe equilibrium problems, such as vertigo, dizziness, oscillopsia, gait unsteadiness nausea and/or vomiting. A good and frequently used measure to quantify gaze stabilization is the gain, which is defined as the magnitude of compensatory eye movements with respect to imposed head movements. To test vestibular function more fully one has to realize that 3D VOR ideally generates compensatory ocular rotations not only with a magnitude (gain) equal and opposite to the head rotation but also about an axis that is co-linear with the head rotation axis (alignment). Abnormal vestibular function thus results in changes in gain and changes in alignment of the 3D VOR response.
Here we describe a method to measure 3D VOR using whole body rotation on a 6DF motion platform. Although the method also allows testing translation VOR responses 1, we limit ourselves to a discussion of the method to measure 3D angular VOR. In addition, we restrict ourselves here to description of data collected in healthy subjects in response to angular sinusoidal and impulse stimulation.
Subjects are sitting upright and receive whole-body small amplitude sinusoidal and constant acceleration impulses. Sinusoidal stimuli (f = 1 Hz, A = 4&deg;) were delivered about the vertical axis and about axes in the horizontal plane varying between roll and pitch at increments of 22.5&deg; in azimuth. Impulses were delivered in yaw, roll and pitch and in the vertical canal planes. Eye movements were measured using the scleral search coil technique 2. Search coil signals were sampled at a frequency of 1 kHz.
The input-output ratio (gain) and misalignment (co-linearity) of the 3D VOR were calculated from the eye coil signals 3.
Gain and co-linearity of 3D VOR depended on the orientation of the stimulus axis. Systematic deviations were found in particular during horizontal axis stimulation. In the light the eye rotation axis was properly aligned with the stimulus axis at orientations 0&deg; and 90&deg; azimuth, but gradually deviated more and more towards 45&deg; azimuth.
The systematic deviations in misalignment for intermediate axes can be explained by a low gain for torsion (X-axis or roll-axis rotation) and a high gain for vertical eye movements (Y-axis or pitch-axis rotation (see Figure 2). Because intermediate axis stimulation leads a compensatory response based on vector summation of the individual eye rotation components, the net response axis will deviate because the gain for X- and Y-axis are different.
In darkness the gain of all eye rotation components had lower values. The result was that the misalignment in darkness and for impulses had different peaks and troughs than in the light: its minimum value was reached for pitch axis stimulation and its maximum for roll axis stimulation.
Case Presentation
Nine subjects participated in the experiment. All subjects gave their informed consent. The experimental procedure was approved by the Medical Ethics Committee of Erasmus University Medical Center and adhered to the Declaration of Helsinki for research involving human subjects.
Six subjects served as controls. Three subjects had a unilateral vestibular impairment due to a vestibular schwannoma. The age of control subjects (six males and three females) ranged from 22 to 55 years. None of the controls had visual or vestibular complaints due to neurological, cardio vascular and ophthalmic disorders.
The age of the patients with schwannoma varied between 44 and 64 years (two males and one female). All schwannoma subjects were under medical surveillance and/or had received treatment by a multidisciplinary team consisting of an othorhinolaryngologist and a neurosurgeon of the Erasmus University Medical Center. Tested patients all had a right side vestibular schwannoma and underwent a wait and watch policy (Table 1; subjects N1-N3) after being diagnosed with vestibular schwannoma. Their tumors had been stabile for over 8-10 years on magnetic resonance imaging.

A method is described to measure three-dimensional vestibulo ocular reflexes (3D VOR) in humans using a six degrees of freedom (6DF) motion simulator. The gain and misalignment of the 3D angular VOR provide a direct measure of the quality of vestibular function. Representative data on healthy subjects are provided

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the ...

The Optokinetic Response as a Quantitative Measure of Visual Acuity in Zebrafish

Zebrafish are a proven model for vision research, however many of the earlier methods generally focused on larval fish or demonstrated a simple response. More recently adult visual behavior in zebrafish has become of interest, but methods to measure specific responses are new coming. To address this gap, we set out to develop a methodology to repeatedly and accurately utilize the optokinetic response (OKR) to measure visual acuity in adult zebrafish. Here we show that the adult zebrafish's visual acuity can be measured, including both binocular and monocular acuities. Because the fish is not harmed during the procedure, the visual acuity can be measured and compared over short or long periods of time. The visual acuity measurements described here can also be done quickly allowing for high throughput and for additional visual procedures if desired. This type of analysis is conducive to drug intervention studies or investigations of disease progression.

Zebrafish have been a valuable tool for many research areas. Here we demonstrate a method to elicit a visual response and calculate a functional visual acuity in the adult zebrafish.

Zebrafish are a proven model for vision research, however many of the  earlier methods generally focused on larval fish or demonstrated a simple  ...

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term behavioral tracking can capture and quantify natural animal behaviors, including those occurring infrequently. Behaviors such as exploration and social interactions can be best studied by observing unrestrained, freely behaving animals. Weakly electric fish (WEF) display readily observable exploratory and social behaviors by emitting electric organ discharge (EOD). Here, we describe three effective techniques to synchronously measure the EOD, body position, and posture of a free-swimming WEF for an extended period of time. First, we describe the construction of an experimental tank inside of an isolation chamber designed to block external sources of sensory stimuli such as light, sound, and vibration. The aquarium was partitioned to accommodate four test specimens, and automated gates remotely control the animals&#39; access to the central arena. Second, we describe a precise and reliable real-time EOD timing measurement method from freely swimming WEF. Signal distortions caused by the animal&#39;s body movements are corrected by spatial averaging and temporal processing stages. Third, we describe an underwater near-infrared imaging setup to observe unperturbed nocturnal animal behaviors. Infrared light pulses were used to synchronize the timing between the video and the physiological signal over a long recording duration. Our automated tracking software measures the animal&#39;s body position and posture reliably in an aquatic scene. In combination, these techniques enable long term observation of spontaneous behavior of freely swimming weakly electric fish in a reliable and precise manner. We believe our method can be similarly applied to the study of other aquatic animals by relating their physiological signals with exploratory or social behaviors.

We describe a set of techniques for studying spontaneous behavior of freely swimming weakly electric fish over an extended period of time, by synchronously measuring the animal&#39;s electric organ discharge timing, body position and posture both accurately and reliably in a specially designed aquarium tank inside a sensory isolation chamber.

Long-term behavioral tracking can capture and quantify natural animal behaviors, including those occurring infrequently. Behaviors such as exploration and ...

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer

A key challenge in neurobiology is to understand how neural circuits function to guide appropriate animal behaviors. Drosophila melanogaster is an excellent model system for such investigations due to its complex behaviors, powerful genetic techniques, and compact nervous system. Laboratory behavioral assays have long been used with Drosophila to simulate properties of the natural environment and study the neural mechanisms underlying the corresponding behaviors (e.g. phototaxis, chemotaxis, sensory learning and memory)1-3. With the recent availability of large collections of transgenic Drosophila lines that label specific neural subsets, behavioral assays have taken on a prominent role to link neurons with behaviors4-11. Versatile and reproducible paradigms, together with the underlying computational routines for data analysis, are indispensable for rapid tests of candidate fly lines with various genotypes. Particularly useful are setups that are flexible in the number of animals tested, duration of experiments and nature of presented stimuli. The assay of choice should also generate reproducible data that is easy to acquire and analyze. Here, we present a detailed description of a system and protocol for assaying behavioral responses of Drosophila flies in a large four-field arena. The setup is used here to assay responses of flies to a single olfactory stimulus; however, the same setup may be modified to test multiple olfactory, visual or optogenetic stimuli, or a combination of these. The olfactometer setup records the activity of fly populations responding to odors, and computational analytical methods are applied to quantify fly behaviors. The collected data are analyzed to get a quick read-out of an experimental run, which is essential for efficient data collection and the optimization of experimental conditions.

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer

We describe here a behavioral setup and data analysis method for assaying olfactory responses of up to 100 vinegar flies (Drosophila melanogaster). This system may be used with single or multiple olfactory stimuli, and adaptable for optogenetic activation or silencing of neuronal subsets.

A key challenge in neurobiology is to understand how neural circuits function to guide appropriate animal behaviors. Drosophila melanogaster is an ...

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor sources is an essential task for organism survival. Insects are well-studied organisms with regard to odor tracking, and their behavioral strategies have been applied to mobile robots for evaluation. This "bottom-up" approach is a fundamental way to develop biomimetic robots; however, the biological analyses and the modeling of behavioral mechanisms are still ongoing. Therefore, it is still unknown how such a biological system actually works as the controller of a robotic platform. To answer this question, we have developed an insect-controlled robot in which a male adult silkmoth (Bombyx mori) drives a robot car in response to odor stimuli; this can be regarded as a prototype of a future insect-mimetic robot. In the cockpit of the robot, a tethered silkmoth walked on an air-supported ball and an optical sensor measured the ball rotations. These rotations were translated into the movement of the two-wheeled robot. The advantage of this "hybrid" approach is that experimenters can manipulate any parameter of the robot, which enables the evaluation of the odor-tracking capability of insects and provides useful suggestions for robotic odor-tracking. Furthermore, these manipulations are non-invasive ways to alter the sensory-motor relationship of a pilot insect and will be a useful technique for understanding adaptive behaviors.

The capability to localize an odor source is necessary for insect survival and is expected to be applicable to artificial odor-tracking. The insect-controlled robot is driven by an actual silkmoth and enables us to evaluate the odor-tracking capability of insects through a robotic platform.

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor ...

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis ...

In Vivo Single-Molecule Tracking at the Drosophila Presynaptic Motor Nerve Terminal

An increasing number of super-resolution microscopy techniques are helping to uncover the mechanisms that govern the nanoscale cellular world. Single-molecule imaging is gaining momentum as it provides exceptional access to the visualization of individual molecules in living cells. Here, we describe a technique that we developed to perform single-particle tracking photo-activated localization microscopy (sptPALM) in Drosophila larvae. Synaptic communication relies on key presynaptic proteins that act by docking, priming, and promoting the fusion of neurotransmitter-containing vesicles with the plasma membrane. A range of protein-protein and protein-lipid interactions tightly regulates these processes and the presynaptic proteins therefore exhibit changes in mobility associated with each of these key events. Investigating how mobility of these proteins correlates with their physiological function in an intact live animal is essential to understanding their precise mechanism of action. Extracting protein mobility with high resolution in vivo requires overcoming limitations such as optical transparency, accessibility, and penetration depth. We describe how photoconvertible fluorescent proteins tagged to the presynaptic protein Syntaxin-1A can be visualized via slight oblique illumination and tracked at the motor nerve terminal or along the motor neuron axon of the third instar Drosophila larva.

In Vivo Single-Molecule Tracking at the Drosophila Presynaptic Motor Nerve Terminal

Here we illustrate how single molecule photo-activated localization microscopy can be carried out on the motor nerve terminal of a live Drosophila melanogaster third instar larva.

An increasing number of super-resolution microscopy techniques are helping to uncover the mechanisms that govern the nanoscale cellular world. ...

Tracking Drosophila Larval Behavior in Response to Optogenetic Stimulation of Olfactory Neurons

The ability of insects to navigate toward odor sources is based on the activities of their first-order olfactory receptor neurons (ORNs). While a considerable amount of information has been generated regarding ORN responses to odorants, the role of specific ORNs in driving behavioral responses remains poorly understood. Complications in behavior analyses arise due to different volatilities of odorants that activate individual ORNs, multiple ORNs activated by single odorants, and the difficulty in replicating naturally observed temporal variations in olfactory stimuli using conventional odor-delivery methods in the laboratory. Here, we describe a protocol that analyzes Drosophila larval behavior in response to simultaneous optogenetic stimulation of its ORNs. The optogenetic technology used here allows for specificity of ORN activation and precise control of temporal patterns of ORN activation. Corresponding larval movement is tracked, digitally recorded, and analyzed using custom written software. By replacing odor stimuli with light stimuli, this method allows for a more precise control of individual ORN activation in order to study its impact on larval behavior. Our method could be further extended to study the impact of second-order projection neurons (PNs) as well as local neurons (LNs) on larval behavior. This method will thus enable a comprehensive dissection of olfactory circuit function and complement studies on how olfactory neuron activities translate in to behavior responses.

Tracking Drosophila Larval Behavior in Response to Optogenetic Stimulation of Olfactory Neurons

This protocol analyzes navigational behavior of Drosophila larva in response to simultaneous optogenetic stimulation of its olfactory neurons. Light of 630 nm wavelength is used to activate individual olfactory neurons expressing a red-shifted channel rhodopsin. Larval movement is simultaneously tracked, digitally recorded, and analyzed using custom-written software.