This work was supported, in part, by Marine Biological Laboratory Whitman Early Career Fellowship (JYS). We acknowledge Evan Burns, Mit Patel, Melanie Melo, and Skylar Widman for helping with some of the preliminary analysis and code testing. We thank Mark Slabodnick for discussion and suggestions. WFM acknowledges support from NIH grant R35 GM130327.

NOTE: A population of approximately one hundred S. coeruleus cells were cultured in accordance with a previously published JoVE protocol8.
1. Sample preparation
<ol>
	<li>Cut a piece of 250 &#181;m-thick silicone spacer sheet (Table of Materials) slightly smaller in both height and width than a microscope slide. Using a 5/16&#34; hole punch, create circular wells. Be mindful of leaving sufficient space between neighboring wells to ensure a good seal. 
	NOTE: A space of 3 mm between neighboring wells was found to be sufficient. With practice, ten wells can be placed on a single sample slide.</li>
	<li>Initiate regeneration of OA by incubating the cells in 10% sucrose or 2% urea for 2 min (Figure 1A). Then, wash three times with fresh medium8. Gently pipette approximately ten Stentor into each well. Be careful of matching sample volume to the well volume as closely as possible. 
	NOTE: For the well dimensions used here, 12.5 &#181;L of sample was pipetted into each well.</li>
	<li>Close the wells by gently lowering a piece of coverglass (see Table of Materials) over the wells starting from one edge. Use a narrow and slightly flexible object, such as a 10 &#181;L pipette tip, to press down on the coverglass where it contacts the silicone sheet, to ensure a good seal.</li>
</ol>
2. Visible light microscopy time-lapse
<ol>
	<li>Place the sample on the microscope stage, and set magnification to the lowest available such that one well fits in frame in its entirety. 
	NOTE: A 1.6x camera adaptor and 1x magnification on the objective were used on the microscope for this protocol.</li>
	<li>Begin time-lapse. Acquire a 10 s video at 30 frames per second of each well at each timepoint. If using a microscope setup with a motorized X-Y stage, automate the entire time-lapse. Otherwise, ensure that a user is present at each timepoint to manually translate the sample to record each well. 
	NOTE: Avoid leaving the sample illuminated when not imaging to avoid heating and evaporation. The sample volumes are small, and evaporation will lead to air bubbles.</li>
	<li>Save movies as TIFF, MOV, or AVI. 
	NOTE: These are the most common non-proprietary video file types. Depending on the specific microscope software, the videos may save by default to a proprietary file type, but then can be exported to one of the aforementioned file types.</li>
	<li>Use a pixel to millimeter conversion factor for physical scale, and perform a calibration or use known pixel size from the camera used. To calibrate, acquire a clear image of a calibration slide or a clear ruler at the same magnification settings as the videos. Using any image viewing program, count the number of pixels between marks of a known physical distance. 
	NOTE: For example, if the image of a ruler shows the 1 mm mark and 2 mm mark to be separated by 100 pixels, the conversion factor is 1 mm per 100 pixels. Alternatively, to derive this factor from camera pixel size, simply multiply the camera pixel size by the magnification. For example, if the camera used has 3.45 &#181;m pixels, and the magnification used was 1.6x, then the conversion is 3.45 &#181;m * 1.6 = 5.52 &#181;m per 1 pixel.</li>
</ol>
3. Cell tracking
<ol>
	<li>Download the two scripts, TrackCells.m and CleanTraces.m, to an easy-to-remember location on the computer. If the data videos are not already on this computer, transfer them onto this computer. 
	NOTE: The data videos and the scripts do not need to be in the same folder.</li>
	<li>Organize data videos into folders, one for each timepoint. Use the script TrackCells.m first to perform automated cell tracking in the data videos. Open TrackCells.m and run the script.</li>
	<li>Choose Add to Path if prompted by a pop-up window., which will typically only happen when the script is run for the first time from a given folder. When prompted, Select One or More Data Videos to Initiate Tracking, navigate to the data videos (section 2). Select multiple video files by using shift-click, control-click, or by holding down the left mouse button while moving over the files to highlight them.</li>
	<li>Once satisfied with the list of files in the File name: box at the bottom of the prompt window, click on Open. Perform a test run on one video first to confirm all parameters are set correctly (see discussion below). 
	&#8203;NOTE: This script will now create a folder for each video file chosen. It will then write into this folder each frame of the video as a .jpg file and a file named position_estimates.mat, which contains all traces found in the video. Depending on the size of the videos, the number of videos, and the speed of the computer, this script can take hours to run.</li>
</ol>
4. Trace verification
<ol>
	<li>Verify that steps described in section 3 were followed correctly by checking that there are no error messages before proceeding. Use the CleanTraces.m script to manually reject anomalous or false traces. Open CleanTraces.m in the MATLAB editor window by double-clicking on the file.</li>
	<li>At the prompt Select data folder output by TrackCells.m. It will have the same name as the video file, navigate to one of the folders created as described in section 3. Choose only one folder. 
	NOTE: This script will now display the traces one-by-one in a pop-up window. They are overlaid on the frame of the video where the trace starts, in green, and the frame of the video where the trace ends, in magenta. Therefore, a valid trace should link a green cell and a magenta cell.</li>
	<li>When prompted, enter 1 to keep the trace and 0 to reject the trace. Press Enter to move on to the next trace. 
	NOTE: New traces will continue to display until either there are no more traces, or the first sixty have been shown. When this process is complete, the script will automatically create a folder named CLEAN TRACES for saving the outputs and display all remaining traces on top of the first frame of the video (Figure 1B). This image is automatically saved as LabeledTraces.png for future reference. All traces the user had chosen to keep will be saved in the file clean_traces.mat.</li>
	<li>Complete this step for all videos in one time point before continuing. 
	&#8203;NOTE: For the data in this manuscript, one video was acquired for each well at each timepoint, for a total of ten videos per timepoint.</li>
</ol>
5. Data visualization
NOTE: To visually compare the motility of the entire cell population across different timepoints, all traces from section 4 were translated to begin at the origin and create one radial displacement versus time plot for each timepoint (Figure 1C-F, see Supplemental Figure S1 for all time points).
<ol>
	<li>Begin by downloading the script titled SpaghettiPlots.m. Open and run the script. When a window file browser window pops up with the prompt Select time folder containing well data (clean_traces.mat) to graph, navigate to the folder of one of the time points. Note that this folder should contain within it a folder for each of the analyzed videos.</li>
	<li>When prompted Calibration: What is the number of pixels per millimeter?, type in the calibration value found in step 2.4, and press Enter. 
	NOTE: The script will now combine the traces from all the analyzed videos at this time point into a single plot (Figure 1C-F). Faint dotted circles in the plot indicate radial displacements of 1, 2, 3, and 4 mm.</li>
	<li>Adjust axes of the plot as necessary by changing line 55 of the script, which by default, is set to rr = 4 for including a radius of up to 4 mm. Save the plot.</li>
</ol>

<ol>
	<li>Lillie, F. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+smallest+parts+of+stentor+capable+of+regeneration;+a+contribution+on+the+limits+of+divisibility+of+living+matter.">On the smallest parts of stentor capable of regeneration; a contribution on the limits of divisibility of living matter.</a> Journal of Morphology. 12 (1), 239-249 (1896).</li><li>Morgan, T. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regeneration+of+proportionate+structures+in+Stentor.">Regeneration of proportionate structures in Stentor.</a> The Biological Bulletin. 2 (6), 311-328 (1901).</li><li>Tartar, V., Kerkut, G. 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> The Biology of Stentor. , (1961).</li><li>Tartar, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reactions+of+Stentor+coeruleus+to+certain+substances+added+to+the+medium.">Reactions of Stentor coeruleus to certain substances added to the medium.</a> Experimental Cell Research. 13 (2), 317-332 (1957).</li><li>Kelleher, J. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+kinetic+model+for+microtubule+polymerization+during+oral+regeneration+in+Stentor+coeruleus.">A kinetic model for microtubule polymerization during oral regeneration in Stentor coeruleus.</a> Biosystems. 9 (4), 269-279 (1977).</li><li>Slabodnick, M. 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=The+kinase+regulator+Mob1+acts+as+a+patterning+protein+for+Stentor+morphogenesis.">The kinase regulator Mob1 acts as a patterning protein for Stentor morphogenesis.</a> PLOS Biology. 12 (5), 1001861 (2014).</li><li>Wan, K. Y., 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=Reorganization+of+complex+ciliary+flows+around+regenerating+Stentor+coeruleus.">Reorganization of complex ciliary flows around regenerating Stentor coeruleus.</a> Philosophical Transactions of the Royal Society B: Biological Sciences. 375 (1792), 20190167 (2020).</li><li>Lin, A., Makushok, T., Diaz, U., Marshall, W. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+the+study+of+regeneration+in+Stentor.">Methods for the study of regeneration in Stentor.</a> Journal of Visualized Experiments JoVE. (136), e57759 (2018).</li><li>Sood, P., McGillivary, R., Marshall, W. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+transcriptional+program+of+regeneration+in+the+giant+single+cell,+Stentor+coeruleus.">The transcriptional program of regeneration in the giant single cell, Stentor coeruleus.</a> bioRxiv. , 240788 (2017).</li><li>Onsbring, H., Jamy, M., Ettema, T. J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+sequencing+of+Stentor+cell+fragments+reveals+transcriptional+changes+during+cellular+regeneration.">RNA sequencing of Stentor cell fragments reveals transcriptional changes during cellular regeneration.</a> Current Biology. 28 (8), 1281-1288 (2018).</li></ol>

The authors have nothing to disclose.

Many particle and cell tracking algorithms currently exist, some entirely free. Cost and user-friendliness are often trade-offs requiring compromise. Additionally, many of the existing cell-tracking programs are designed to track slow crawling motion of tissue culture cells, rather than the fast swimming motion of Stentor, which rotates while swimming and can undergo sudden changes of direction. After testing many of these options, the protocol presented here is intended to be a one-stop solution to go all the way from data acquisition to data visualization using only low-cost equipment and a single scientific software package (Table of Materials). This is of particular interest to research labs at teaching-focused institutions or biophysics teaching labs where most of the needed equipment is already available, as it lowers the barrier for students to ask their own questions and arrive at quantitative results within a short timeframe.
The method presented in this manuscript can be adapted to quantitatively characterize the motility of a population of organisms in the hundred microns to millimeter size scale. Application to smaller or larger organisms will require changes to how the video data must be acquired. As applied to a population of regenerating S. coeruleus here, this protocol yielded a timeline to the functional recovery of motility across the cell population, which complements what is currently known about the transcriptional timeline. For example, it is known from RNA sequencing that synthesis of genes encoding ciliary motility proteins does not take place until several hours into the regeneration process, long after the formation of the cilia themselves9, 10. The statistically significant drop in motility from hours 0-3 (Phase 1) to hour 4 (Phase 2) demonstrated above is consistent with this lag in expression of ciliary motility factors compared to the expression of genes involved in ciliary assembly. No transcriptomic study of Stentor regeneration to date had been carried out beyond hour nine, so little is known about the genomic activity. The lack of statistically significant difference between hours 8-10 (Phase 3) and hours 11+ (Phase 4) suggests that the cells have fully regenerated by this time, and few genes relevant to cell motility should be differentially regulated beyond hour 9.
Two major limitations to this method are its failure to track clustered and/or touching cells, and its inability to quantify more complex aspects of motion than swim speeds or trajectories. All particle/cell tracking algorithms have difficulty following cells that are temporarily visually obstructed by another cell, and when multiple cells spend multiple frames in close proximity; the script included here is not an exception. Fortunately, the frequency of the former can be effectively reduced simply by limiting the number of cells placed inside one well as discussed in the next paragraph. Specifically for the S. coeruleus population investigated here, their preference to settle in colonies (Figure 3) caused some of these cells not to be identified by the script. However, as all these cells exhibit little to no motion, statistics on the motile cell population is unaffected. Additionally, videos where multiple cells elude tracking are easy to identify and manually exclude from further analysis. Regarding the quantification of complex aspects of motion, the transition from directionless to directed swimming seen in Supplemental Figure S1 suggests quantitative changes in the time correlation of direction, average acceleration, and potentially other measurables. Speed and range, the only quantities analyzed in this protocol, represent only a small aspect of motion.
Lastly, a few parts of the protocol are of particular importance or consequence in practical use. Assembly of the multiwell sample slides with thin silicone sheets, as described in protocol section 1, requires practice. It is easy to introduce leaks or air bubbles into at least one well while sealing the sample with the coverglass. The wells can be divided over multiple sample slides to allow the use of smaller coverglass, which will make the process easier (and mistakes less costly). Though the assay presented here can be used to characterize the motility patterns of a cell population at a single timepoint, it is demonstrated here as a tool to compare motility patterns of a single population over a long time-lapse (over 10 h). As with any long time-lapse, evaporation of a wet sample is unavoidable. Evaporation can be minimized by a secure seal of the silicone spacer in the imaging chamber to both the glass slide and the coverslip. Failure to do this will result in wells leaking into each other or air bubbles forming inside the wells. For researchers unfamiliar with the use of silicone spacers, pasteurized spring water or other clean medium should be pipetted into each well to test for leakage before use. The dimensions of the sample wells were all chosen to work well for S. coeruleus, which are approximately 1 mm in size. This includes silicone spacer thickness (chosen to constrain the S. coeruleus cells under investigation to a two-dimensional plane), 5/16&#34; diameter, and 10 cells per well. These numbers should be adjusted when adapting this protocol for other cell types.
Several parameters in the TrackCells.m script can be tweaked to optimize automatic cell identification and trace validation. The parameters MinCellArea and MaxCellArea (Lines 15 and 16 of script) allow the user to set the acceptable range of sizes for a cell in square pixels. What values are best depends on many details of the experiment including cell size, camera pixel size, magnification, and whether there are objects which could be misidentified as cells, for example, air bubbles. The default values of 300 and 1500 were optimal for the exact equipment and settings provided in the protocol. After MinCellArea and MaxCellArea have been optimized, tune the parameter Threshold (Line 17) to change image contrast. When incorrectly set, the cell outlines will be poorly identified or missed altogether, thereby hindering correct tracking. If no value of Threshold works well for correct tracking, try with a video with higher contrast or that is more in focus. Line 218 of the script, bad_trks = find(strk_trks &#62; 20); is responsible for discarding traces that deviate from the predicted motion (via Kalman filter) too many times. As is, the script has this threshold for too many set at 20. Decrease this integer value to as far as 1 to discard traces more aggressively. Increase the value to discard less aggressively. Much of TrackCells.m was adopted from the multi-object tracking tutorial freely accessible at http://studentdavestutorials.weebly.com/multi-bugobject-tracking.html, which the reader could refer to for more detail.&#160;Supplemental Videos 1-3 are example data videos for a test-run of the scripts.
The ability of S. coeruleus to fully regenerate a lost OA has been first observed over a century ago, yet many open questions remain regarding the recovery of motility and behavioral states. The protocol presented here is intended to demystify the combination of brightfield imaging, automated cell identification and tracking, and data visualization the authors employed to quantitatively characterize the motility of a population of regenerating Stentor. This workflow is broadly applicable to the investigation of motility in general, and the scripts and protocol included can be readily adopted to work with other biological systems of similar size and speed, for example, other ciliates such as Paramecium and Blepharisma. Furthermore, changing the imaging chamber (e.g., using dry wells or different size wells coupled with different imaging magnification) greatly expands the applicability of this workflow. The current tool kit for Stentor includes surgical dissection, osmotic shock, RNAi, DNA/RNA analysis, and small molecule inhibitors6. The high-throughput method of quantifying motility presented here adds a complementary degree of characterization and will be used in future work to investigate motility phenotypes.

Stentor coeruleus (Stentor) is a well-known model organism that has been used to study unicellular regeneration owing to its large size, ability to withstand several microsurgical techniques, and ease of culturing in a laboratory setting1,2,3. Early regeneration studies focused on the largest and most morphologically distinct feature in Stentor&#8212;the OA&#8212;which is shed completely upon chemical shock4,5,6. De novo replacement of a lost OA begins with the emergence of a new membranellar band&#8212;an array of cilia that gradually shift towards the anterior of the cell before forming a functional OA over eight morphological stages3. These stages have been observed sequentially, regardless of temperature, and provide a universal reference point for nearly all studies5.
Mechanistic analysis of Stentor regeneration requires tools for measuring the timing of regeneration that are robust and simple enough to be applied to multiple samples as part of a chemical or molecular screen. The standard method for performing a cell-based assay is imaging, in this case, imaging the formation of new OA during regeneration. However, such imaging-based assays are most effective when the regenerating structure contains distinct molecular components that can be used as markers, so that they would be easily detected in a fluorescence image. In the case of the Stentor OA, the known components (cilia, basal bodies) are also present on the rest of the cell surface; therefore, recognizing the restoration of the OA cannot be achieved simply by looking for the presence or absence of a component.
Rather, some form of shape recognition would be required to detect an OA, and this is potentially very challenging given the fact that Stentor cells often change shape via a rapid contractile process. This paper presents an alternative assay for regeneration that relies on the motile activity of the body and OA cilia. As the OA regenerates, the newly formed cilia undergo reproducible changes in position and activity, which in turn, affects the swimming motility of the cell. By analyzing motility, it is possible to perform an assay for &#34;functional regeneration&#34; that quantifies regeneration by quantifying the function of the regenerated structures. Previous analysis of Stentor ciliary function during regeneration used particle image velocimetry, combined with tracer beads added to the external media, to observe changes in flow pattern at different stages of regeneration7; however, this approach requires laborious imaging of individual cells and their associated flow fields, one at a time.
By using the motion of the cell itself as a proxy for cilia-generated flow, it would be possible to analyze larger numbers of cells in parallel, using low-resolution imaging systems compatible with high-throughput screening platforms. This assay can, in principle, be used to study development and functional regeneration in other swimming organisms in the hundreds of microns to millimeters size scale. Section 1 of the protocol describes the construction of a multiwell sample slide, which allows for high-throughput imaging of a population of cells over up to an entire day. Details are provided for how to adjust for use with other cell types. Section 2 of the protocol covers the acquisition of video data for this assay, which can be accomplished on a dissection microscope with a digital single-lens reflex camera. Section 3 of the protocol provides a walk-through of cell tracking and cell speed calculation using MATLAB code (Supplemental Information). Section 4 of the protocol explains how to turn the numerical results into plots as shown in Figure 1C-F and Figure 2C for easy interpretation of results.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.25 mm-thick silicone sheet</td>
			<td>Grace Bio-Labs</td>
			<td>CWS-S-0.25</td>
			<td></td>
		</tr>
		<tr>
			<td>24 x 50 mm, #1.5 coverglass</td>
			<td>Fisher Scientific</td>
			<td>NC1034527</td>
			<td>As noted in Discussion, smaller coverglass can be used if fewer sample wells are placed on one slide.</td>
		</tr>
		<tr>
			<td>CCD camera</td>
			<td></td>
			<td></td>
			<td>We used Nikon D750</td>
		</tr>
		<tr>
			<td>Chlamydomonas 137c WT strain</td>
			<td>Chlamydomonas Resource Center</td>
			<td>CC-125</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MATHWORKS</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB Image Processing Toolbox</td>
			<td>MATHWORKS</td>
			<td></td>
			<td>needed for TrackCells.m and CleanTraces.m</td>
		</tr>
		<tr>
			<td>MATLAB Statistics and Machine Learning Toolbox</td>
			<td>MATHWORKS</td>
			<td></td>
			<td>needed for TrackCells.m</td>
		</tr>
		<tr>
			<td>Microscope with camera port</td>
			<td></td>
			<td></td>
			<td>We used Zeiss AxioZoom v1.6 and Leica S9E</td>
		</tr>
		<tr>
			<td>Pasteurized Spring Water</td>
			<td>Carolina</td>
			<td>132458</td>
			<td></td>
		</tr>
		<tr>
			<td>TAP Growth Media</td>
			<td>ThermoFisher Scientific</td>
			<td>A1379801</td>
			<td>Can also be made for much cheaper following recipe from Chlamy Resource Center</td>
		</tr>
	</tbody>
</table>

analysis of motility patterns of stentor during and after oral apparatus regeneration using cell tracking

Stentor coeruleus is a well-known model organism for the study of unicellular regeneration. Transcriptomic analysis of individual cells revealed hundreds of genes&#8212;many not associated with the oral apparatus (OA)&#8212;that are differentially regulated in phases throughout the regeneration process. It was hypothesized that this systemic reorganization and mobilization of cellular resources towards growth of a new OA will lead to observable changes in movement and behavior corresponding in time to the phases of differential gene expression. However, the morphological complexity of S. coeruleus necessitated the development of an assay to capture the statistics and timescale. A custom script was used to track cells in short videos, and statistics were compiled over a large population (N ~100). Upon loss of the OA, S. coeruleus initially loses the ability for directed motion; then starting at ~4 h, it exhibits a significant drop in speed until ~8 h. This assay provides a useful tool for the screening of motility phenotypes and can be adapted for the investigation of other organisms.

The goal of this assay is to quantify the gradual change of movement patterns and phased increase in movement speed from cells within a large (N ~100) regenerating Stentor population. To aid interpretation of results, the custom code included in this protocol generates two types of plots: an overlay of all cell movement traces in a set of video data (Figure 1C-F and Figure S1), and a plot of swim speed by hour since the start of regeneration (Figure 2C). A population of Stentor, which are regenerating normally, should demonstrate a steady transition from directionless swimming to directed swimming (Figure 1G). While each cell undergoes this transition in full during the OA regeneration process spanning ~8 h, variation between individuals necessitates looking at the movement of a large population in aggregate for trends to become clear.
Figure 1C-F show the collective motility of the same population of Stentor cells at 2, 3, 7, and 8 h into the OA regeneration process. These plots show both the expected increase in number of motile individuals (number of visible traces) and the four-fold increase in range of the most motile cells within the population (radial displacement). It is important to note that axes are different between the different panels of Figure 1C-F, with 1 mm (Hour 2), 2 mm (Hours 3 and 7), and 4 mm (Hour 8) chosen as most appropriate to showcase the motion. At 2 h&#8212;the earliest time point&#8212;no cells move more than 1 mm during the 10 s video, whereas at 9 h, many cells traversed more than 5 mm during the same length of time. Supplemental Figure S1 provides these plots for the full time-lapse, with identical axes used across all plots to give a clearer view of the expected increase of motility in a successful experiment.
There is one caveat to the interpretation of this example series of plots. The fastest swimming cells were able to swim across the well within the video, especially if (by chance) they started close to the edge; thus they are restricted from swimming entirely in a straight line, and their movement range, as visualized in this way, will appear smaller. While this does complicate any attempts to quantify the distance traveled by the cells, it does not alter the qualitative trend that the range of movement increases over at least 8 h. This is consistent with the known morphological regeneration timeline (Figure 2A)3.
To compile population statistics, the tracked cells were classified into three distinct categories: non-motile with no visible holdfast, non-motile with visible holdfast, and motile. These categories of behavior were previously defined by Tartar, but never quantified for their prevalence over time3. Figure 2B shows a stacked histogram summarizing relative cell counts across these categories as a function of hours into regeneration. At all time points, over half of the cell population was not observably motile. Additionally, at later times, a significant number of the cells were found in colonies with visible holdfasts, strongly suggesting they had explored the environment and preferentially attached near others of their kind. An example of this can be seen in the final inset of Figure 2C.
This assay allows for statistical comparison of swim speeds during each phase of motility recovery. The mean and standard deviation for data presented in Figure 2C are summarized below (Table 1). Once these statistical quantities have been extracted by cell tracking, the motion exhibited by the population of cells in the different phases can be rigorously compared using the unpaired t-test. As a concrete example, for the data shown, the t-statistic comparing the swim speeds in Phase 1 and Phase 2 is calculated by:
<img alt="Equation 1" src="/files/ftp_upload/62352/62352eq02.jpg" />
where x1 and x2 denote the mean speed during Phases 1 and 2, respectively; s1 and s2 denote the standard deviations of the speed during Phases 1 and 2, respectively; n1 and n2 and denote the number of traces extracted during Phases 1 and 2, respectively. The resulting t-statistic t12 can then be translated into a p-value for hypothesis testing. For the data shown in Figure 2C, the transitions from Phase 1 to Phase 2 (p &#60; 0.1%) and from Phase 2 to Phase 3 (p &#60; 0.1%) are statistically significant, whereas the transition from Phase 3 to Phase 4 (p &#62; 20%) is not. It is important to note that the tendency for the cells to settle into small colonies during Phase 4 (Figure 2B, inset), as is apparent in the raw videos, is one example of behavioral difference not well captured by measurement of swim speeds alone. This explains the large standard deviation measured during Phase 4 (0.26 mm/s), which in turn, contributed to a lower t-statistic when comparing it against Phase 3.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62352/62352fig01.jpg" /> 
Figure 1: Cell tracking reveals gradual recovery of directed swimming. (A) Sucrose shock induces Stentor coeruleus to shed membranellar band. (B) Example tracking result of regenerating cells in a 10 s video with striped circles indicating air bubbles in the well. (C-F) Overlay of all trajectories at 2 h, 3 h, 7 h, and 8 h. Times were rounded to the closest hour during compilation of data. Dotted circles indicate radial displacements of one millimeter. (G) Cell motility evolves from directionless swimming characterized by short circular trajectories to directed swimming characterized by long sinusoidal trajectories. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62352/62352fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62352/62352fig02.jpg" /> 
Figure 2: Motile subpopulation demonstrates four distinct phases of behavior through OA regeneration. (A)&#160;Morphology of Stentor regeneration. (B) Normalized counts of cells at each time that were (blue) not motile, with no visible holdfast; (orange) not motile, with visible holdfast; (green) motile. Multiple datasets that span different subsets of hours were combined for this figure, so total cell count per hour ranged from 57 to over 376. Legend shows representative cells under each category as visualized by false color, in magenta and green. (C) Boxplot of swim speed; boxes extend from Q1 to Q3 quartile values at each time with median value indicated in green. The scatter overlays are the average speed of each motile cell. Inset shows representative population behavior during each of the four phases. Abbreviations: OA = oral apparatus; Q1 = first quartile; Q3 = third quartile. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62352/62352fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62352/62352fig03.jpg" /> 
Figure 3: Motility analysis as a phenotype screening tool requires large numbers of cells. (A, B) Tracking results from two wells at 12 h. (C, D) Tracking results from the same two wells at 18 h. Stentor cells display a preference to attach in clusters, with some wells settling into colonies hours sooner than others. Striped areas indicate air bubbles in the well. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62352/62352fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Mean (mm/s)</td>
			<td>Standard Deviation (mm/s)</td>
		</tr>
		<tr>
			<td>Phase 1</td>
			<td>0.14</td>
			<td>0.24</td>
		</tr>
		<tr>
			<td>Phase 2</td>
			<td>0.06</td>
			<td>0.13</td>
		</tr>
		<tr>
			<td>Phase 3</td>
			<td>0.16</td>
			<td>0.16</td>
		</tr>
		<tr>
			<td>Phase 4</td>
			<td>0.15</td>
			<td>0.26</td>
		</tr>
	</tbody>
</table>
Table 1: Comparison of swim speeds during each phase of motility recovery. 
Supplemental Figure S1: Population swimming patterns through regeneration and beyond. (A-M)&#160;Motion of Stentor coeruleus cells 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, and 18 h, respectively, after the initiation of oral apparatus regeneration. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62352/S1.PNG" target="_blank">Please click here to download this File.</a>
Supplemental Videos 1-3: Example microscopy videos of S. coeruleus for script debugging. Section 5 of protocol can be performed on this set of three data videos for a quick confirmation that the scripts are running correctly. In addition, these videos provide a concrete example of contrast and resolution requirements for the data videos. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62352/Video.zip" target="_blank">Please click here to download this File.</a>
Supplemental Files: <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62352/Files.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Analysis of Motility Patterns of Stentor During and After Oral Apparatus Regeneration Using Cell Tracking at JoVE.com

Analysis of Motility Patterns of Stentor During and After Oral Apparatus Regeneration Using Cell Tracking

We present a protocol for the characterization of motility and behavior of a population of hundred micron- to millimeter-sized cells using brightfield microscopy and cell tracking. This assay reveals that Stentor coeruleus transitions through four behaviorally distinct phases when regenerating a lost oral apparatus.

Analysis of Motility Patterns of Stentor During and After Oral Apparatus Regeneration Using Cell Tracking

Stentor coeruleus is a well-known model organism for the study of unicellular regeneration. Transcriptomic analysis of individual cells revealed hundreds ...

analysis-motility-patterns-stentor-during-after-oral-apparatus

Nanyang Technological University

Research

JoVE Journal

Biology

1.7K Views.  Scripps, Pitzer, and Claremont McKenna of The Claremont Colleges. We present a protocol for the characterization of motility and behavior of a population of hundred micron- to millimeter-sized cells using brightfield microscopy and cell tracking. This assay reveals that Stentor coeruleus transitions through four behaviorally distinct phases when regenerating a lost oral apparatus.

Video: Analysis of Motility Patterns of Stentor During and After Oral Apparatus Regeneration Using Cell Tracking

Assessing Neural Stem Cell Motility Using an Agarose Gel-based Microfluidic Device

While microfluidic technology is reaching a new level of maturity for macromolecular assays, cell-based assays are still at an infant stage1. This is largely due to the difficulty with which one can create a cell-compatible and steady microenvironment using conventional microfabrication techniques and materials. We address this problem via the introduction of a novel microfabrication material, agarose gel, as the base material for the microfluidic device. Agarose gel is highly malleable, and permeable to gas and nutrients necessary for cell survival, and thus an ideal material for cell-based assays. We have shown previously that agarose gel based devices have been successful in studying bacterial and neutrophil cell migration2. In this report, three parallel microfluidic channels are patterned in an agarose gel membrane of about 1mm thickness. Constant flows with media/buffer are maintained in the two side channels using a peristaltic pump. Cells are maintained in the center channel for observation. Since the nutrients and chemicals in the side channels are constantly diffusing from the side to center channel, the chemical environment of the center channel is easily controlled via the flow along the side channels. Using this device, we demonstrate that the movement of neural stem cells can be monitored optically with ease under various chemical conditions, and the experimental results show that the over expression of epidermal growth factor receptors (EGFR) enhances the motility of neural stem cells. Motility of neural stem cells is an important biomarker for assessing cells aggressiveness, thus tumorigenic factor3. Deciphering the mechanism underlying NSC motility will yield insight into both disorders of neural development and into brain cancer stem cell invasion.

We demonstrate that the over expression of epidermal growth factor receptors (EGFR) enhances the motility of neural stem cells(NSCs) using a novel agarose gel based microfluidic device. This technology can be readily adaptable to other mammalian cell systems where cell sources are scarce, such as human neural stem cells, and the turn around time is critical.

While microfluidic technology is reaching a new level of maturity for macromolecular assays, cell-based assays are still at an infant stage1. This is ...

Visually Mediated Odor Tracking During Flight in Drosophila

Flying insects use visual cues to stabilize their heading in a wind stream. Many animals additionally track odors carried in the wind. As such, visual stabilization of upwind tracking directly aids in odor tracking. But do olfactory signals directly influence visual tracking behavior independently from wind cues? Additionally, recent advances in olfactory molecular genetics and neurophysiology have motivated novel quantitative behavioral analyses to assess the behavioral influence of (e.g.) genetically inactivating specific olfactory activation circuits. We modified a magnetic tether system originally devised for vision experiments by equipping the arena with narrow laminar flow odor plumes. Here we focus on experiments that can be performed after a fly is tethered and is able to navigate in the magnetic arena. We show how to acquire video images optimized for measuring body angle, how to judge stable odor tracking, and we illustrate two experiments to examine the influence of visual cues on odor tracking.

Here we describe how to optimize the acquired video image for an olfactory magnetic-tether (OMT) apparatus. We also describe two sample experimental protocols for studying visuo-olfactory fusion.

Flying insects use visual cues to stabilize their heading in a wind stream. Many animals additionally track odors carried in the wind. As such, visual ...

Immunocytochemical Analysis of Human Pluripotent Stem Cells using a Self-Made Cytospin Apparatus

Human pluripotent stem cells (hPSCs) that include human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) are exciting cell sources due to their limitless self-renewal capabilities and their potential to differentiate into multiple cell types. The pluripotent state of hPSCs is typically assessed by techniques such as qPCR, immunocytochemistry, and by other in vitro and in vivo differentiation strategies into multiple cell types. Among these, immunocytochemical techniques have been developed for routine characterization of the undifferentiated state of hPSCs based on analysis of candidate intracellular and cell-surface biomarkers. Given the fact that hPSCs grow as colonies, problems arise in quantifying the expression of these markers at the individual cell level on a routine basis. Flow cytometry analyses serve to address this issue but require cell numbers and use of reagents that are not normally conducive for routine quality control assessment of hPSC cultures. Thus, the development of practical and reproducible means of creating monolayer cell samples with preserved integrity for marker evaluation has many advantages in stem cell research. This greatly benefits immunocytochemical analysis because individual cells from the monolayer can be easily observed and quantified for the expression of specific markers. Towards this goal, a self-made cytospin apparatus was constructed and optimized for use with immunocytochemical staining. Two cell-surface markers (SSEA3/SSEA4) expression were analyzed in a variant BG01 stem cell line for the purpose of this protocol.

Suspension immunocytochemical staining of human pluripotent stem cells (hPSCs) for cell-surface markers (SSEA-3/SSEA-4) was achieved based on use of a self-made cytospin apparatus to create a monolayer of cells for observation and quantification.

Human pluripotent stem cells (hPSCs) that include human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) are exciting cell ...

Fate Mapping of Human Embryonic Stem Cells by Teratoma Formation 

Human embryonic stem cells (hESCs) have an unlimited capacity for self-renewal, and the ability to differentiate into cells derived from all three embryonic germ layers (1). Directed differentiation of hESCs into specific cell types has generated much interest in the field of regenerative medicine (e.g., (2-5)), and methods for determining the in vivo fate of selected or manipulated hESCs are essential to this endeavor. We have adapted a highly efficient teratoma formation assay for this purpose. A small number of specifically selected hESCs is mixed with undifferentiated wild type hESCs and Phaseolus vulgaris lectin to form a cell pellet. This is grafted beneath the kidney capsule in an immunodeficient mouse. As few as 2.5 x 105 hESCs are needed to form a 16 cm3 teratoma within 8-12 weeks. The fate of the originally selected hESCs can then be determined by immunohistochemistry. This method provides a valuable tool for characterizing tissue-specific reagents for cell-based therapy.

Fate Mapping of Human Embryonic Stem Cells by Teratoma Formation

Directed differentiation of hESCs into specific cells has generated much interest in regenerative medicine. We provide a concise, step-by-step protocol for determining the in vivo fate of selected hESCs that provides a valuable tool for characterizing tissue-specific reagents for cell-based therapy.

Human embryonic stem cells (hESCs) have an unlimited capacity for self-renewal, and the ability to differentiate into cells derived from all three ...

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Cell migration is a dynamic process, which is important for embryonic development, tissue repair, immune system function, and tumor invasion 1, 2. During directional migration, cells move rapidly in response to an extracellular chemotactic signal, or in response to intrinsic cues 3 provided by the basic motility machinery. Random migration occurs when a cell possesses low intrinsic directionality, allowing the cells to explore their local environment.
Cell migration is a complex process, in the initial response cell undergoes polarization and extends protrusions in the direction of migration 2. Traditional methods to measure migration such as the Boyden chamber migration assay is an easy method to measure chemotaxis in vitro, which allows measuring migration as an end point result. However, this approach neither allows measurement of individual migration parameters, nor does it allow to visualization of morphological changes that cell undergoes during migration.
Here, we present a method that allows us to monitor migrating cells in real time using video - time lapse microscopy. Since cell migration and invasion are hallmarks of cancer, this method will be applicable in studying cancer cell migration and invasion in vitro. Random migration of platelets has been considered as one of the parameters of platelet function 4, hence this method could also be helpful in studying platelet functions. This assay has the advantage of being rapid, reliable, reproducible, and does not require optimization of cell numbers. In order to maintain physiologically suitable conditions for cells, the microscope is equipped with CO2 supply and temperature thermostat. Cell movement is monitored by taking pictures using a camera fitted to the microscope at regular intervals. Cell migration can be calculated by measuring average speed and average displacement, which is calculated by Slidebook software.

This method allows monitoring of cells in real time and quantitative measurements of different cell migration parameters such as speed, displacement, and velocity. Unlike the traditional methods, this real time approach is not based on endpoint quantitative migration measurements; instead it allows monitoring and calculating different parameters continuously.

Cell migration is a dynamic process, which is important for embryonic development, tissue repair, immune system function, and tumor invasion 1, 2. During ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

Rapid Genetic Analysis of Epithelial-Mesenchymal Signaling During Hair Regeneration

Hair follicle morphogenesis, a complex process requiring interaction between epithelia-derived keratinocytes and the underlying mesenchyme, is an attractive model system to study organ development and tissue-specific signaling. Although hair follicle development is genetically tractable, fast and reproducible analysis of factors essential for this process remains a challenge. Here we describe a procedure to generate targeted overexpression or shRNA-mediated knockdown of factors using lentivirus in a tissue-specific manner. Using a modified version of a hair regeneration model 5, 6, 11, we can achieve robust gain- or loss-of-function analysis in primary mouse keratinocytes or dermal cells to facilitate study of epithelial-mesenchymal signaling pathways that lead to hair follicle morphogenesis. We describe how to isolate fresh primary mouse keratinocytes and dermal cells, which contain dermal papilla cells and their precursors, deliver lentivirus containing either shRNA or cDNA to one of the cell populations, and combine the cells to generate fully formed hair follicles on the backs of nude mice. This approach allows analysis of tissue-specific factors required to generate hair follicles within three weeks and provides a fast and convenient companion to existing genetic models.

Tissue-specific analysis of a hair follicle regeneration model using lentivirus to mediate gain- or loss-of-function.

Hair follicle morphogenesis, a complex process requiring interaction between epithelia-derived keratinocytes and the underlying mesenchyme, is an ...

Cell Tracking Using Photoconvertible Proteins During Zebrafish Development

Embryogenesis is a dynamic process that is best studied by using techniques that allow the documentation of developmental changes in vivo. The use of genetically-encoded fluorescent proteins has proven a valuable strategy for elucidating dynamic morphogenetic processes as they occur in the intact organism. During the past decade, the development of photoactivatable and photoconvertible fluorescent proteins has opened the possibility to investigate the fate of discrete subpopulations of tagged proteins1. Unlike photoactivatable proteins, photoconvertible fluorescent proteins (PCFPs) are readily tracked and imaged in their native emission state prior to photoconversion, making it easier to identify and select regions by optical inspection. PCFPs, such as Kaede2, KikGR3, Dendra4 and EosFP5, can be shifted from green to red upon exposure to UV or blue light due to a His-Tyr-Gly tripeptide sequence which forms a green chromophore that can be photoconverted to a red one by a light-catalyzed &beta;-elimination and subsequent extension of a &pi;-conjugated system3. PCFPs and their monomeric variants are useful tools for tracking cells6-10 and studying protein dynamics11-14, respectively. During recent years, PCFPs have been expressed in different animal model, such as zebrafish6, chicken7,8 and mouse9,10 for cell fate tracking. Here we report a protocol for cell-specific photoconversion of PCFPs in the living zebrafish embryo and further tracking of photoconverted proteins at later developmental stages. This methodology allows studying, in a tissue-specific manner, cell biological events underlying morphogenesis in the zebrafish animal model.

Here, we present a method for the photoactivated switch of photoconvertible fluorescent proteins (PCFPs) in the living zebrafish embryo and further tracking of photoconverted protein at specific time points during development. This methodology allows monitoring of cell biological events underlying different developmental processes in a live vertebrate organism.

Embryogenesis is a dynamic process that is best studied by using techniques that allow the documentation of developmental changes in vivo. The use of ...

Visualizing Cytoplasmic Flow During Single-cell Wound Healing in Stentor coeruleus

Although wound-healing is often addressed at the level of whole tissues, in many cases individual cells are able to heal wounds within themselves, repairing broken cell membrane before the cellular contents leak out. The giant unicellular organism Stentor coeruleus, in which cells can be more than one millimeter in size, have been a classical model organism for studying wound healing in single cells. Stentor cells can be cut in half without loss of viability, and can even be cut and grafted together. But this high tolerance to cutting raises the question of why the cytoplasm does not simply flow out from the size of the cut. Here we present a method for cutting Stentor cells while simultaneously imaging the movement of cytoplasm in the vicinity of the cut at high spatial and temporal resolution. The key to our method is to use a &#34;double decker&#34; microscope configuration in which the surgery is performed under a dissecting microscope focused on a chamber that is simultaneously viewed from below at high resolution using an inverted microscope with a high NA lens. This setup allows a high level of control over the surgical procedure while still permitting high resolution tracking of cytoplasm.

Visualizing Cytoplasmic Flow During Single-cell Wound Healing in Stentor coeruleus

The giant ciliate Stentor coeruleus is a classical system for studying regeneration and wound healing in single cells.&#160;By imaging Stentor cells simultaneously at low and high magnification it is possible to measure cytoplasmic flows before, during, and after wounding.

Although wound-healing is often addressed at the level of whole tissues, in many cases individual cells are able to heal wounds within themselves, ...

FRET Imaging in Three-dimensional Hydrogels

Imaging of F&#246;rster resonance energy transfer (FRET)&#160;is a powerful tool for examining cell biology in real-time. Studies utilizing FRET commonly employ two-dimensional (2D) culture, which does not mimic the three-dimensional (3D) cellular microenvironment. A method to perform quenched emission FRET imaging using conventional widefield epifluorescence microscopy of cells within a 3D hydrogel environment is presented. Here an analysis method for ratiometric FRET probes that yields linear ratios over the probe activation range is described. Measurement of intracellular cyclic adenosine monophosphate (cAMP) levels is demonstrated in chondrocytes under forskolin stimulation using a probe for EPAC1 activation (ICUE1) and the ability to detect differences in cAMP signaling dependent on hydrogel material type, herein a photocrosslinking hydrogel (PC-gel, polyethylene glycol dimethacrylate) and a thermoresponsive hydrogel (TR-gel). Compared with 2D FRET methods, this method requires little additional work. Laboratories already utilizing FRET imaging in 2D can easily adopt this method to perform cellular studies in a 3D microenvironment. It can further be applied to high throughput drug screening in engineered 3D microtissues. Additionally, it is compatible with other forms of FRET imaging, such as anisotropy measurement and fluorescence lifetime imaging (FLIM), and with advanced microscopy platforms using confocal, pulsed, or modulated illumination.

F&#246;rster resonance energy transfer (FRET) imaging is a powerful tool for real-time cell biology studies. Here a method for FRET imaging cells in physiologic three-dimensional (3D) hydrogel microenvironments using conventional epifluorescence microscopy is presented. An analysis for ratiometric FRET probes that yields linear ratios over the activation range is described.

Imaging of F&#246;rster resonance energy transfer (FRET)&#160;is a powerful tool for examining cell biology in real-time. Studies utilizing FRET commonly ...

Analysis of Motility Patterns of Stentor During and After Oral Apparatus Regeneration Using Cell Tracking

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Chapters in this video

Assessing Neural Stem Cell Motility Using an Agarose Gel-based Microfluidic Device

Visually Mediated Odor Tracking During Flight in Drosophila

Immunocytochemical Analysis of Human Pluripotent Stem Cells using a Self-Made Cytospin Apparatus

Fate Mapping of Human Embryonic Stem Cells by Teratoma Formation

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Rapid Genetic Analysis of Epithelial-Mesenchymal Signaling During Hair Regeneration

Cell Tracking Using Photoconvertible Proteins During Zebrafish Development

Visualizing Cytoplasmic Flow During Single-cell Wound Healing in Stentor coeruleus

FRET Imaging in Three-dimensional Hydrogels