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

Summary

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.

Abstract

Stentor coeruleus is a well-known model organism for the study of unicellular regeneration. Transcriptomic analysis of individual cells revealed hundreds of genes—many not associated with the oral apparatus (OA)—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.

Introduction

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 setting^1,2,3. Early regeneration studies focused on the largest and most morphologically distinct feature in Stentor—the OA—which is shed completely upon chemical shock^4,5,6. De novo replacement of a lost OA begins with the emergence of a new membranellar band—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 "functional regeneration" 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.

Protocol

NOTE: A population of approximately one hundred S. coeruleus cells were cultured in accordance with a previously published JoVE protocol⁸.

1. Sample preparation

1. Cut a piece of 250 µm-thick silicone spacer sheet (Table of Materials) slightly smaller in both height and width than a microscope slide. Using a 5/16" 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.
2. 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 µL of sample was pipetted into each well.
3. 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 µL pipette tip, to press down on the coverglass where it contacts the silicone sheet, to ensure a good seal.

2. Visible light microscopy time-lapse

1. 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.
2. 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.
3. 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.
4. 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 µm pixels, and the magnification used was 1.6x, then the conversion is 3.45 µm * 1.6 = 5.52 µm per 1 pixel.

3. Cell tracking

1. 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.
2. 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.
3. 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.
4. 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).
   ​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.

4. Trace verification

1. 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.
2. 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.
3. 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.
4. Complete this step for all videos in one time point before continuing.
   ​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.

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).

1. 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.
2. 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.
3. 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.

Results

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 reg...

Discussion

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 w...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
0.25 mm-thick silicone sheet	Grace Bio-Labs	CWS-S-0.25
24 x 50 mm, #1.5 coverglass	Fisher Scientific	NC1034527	As noted in Discussion, smaller coverglass can be used if fewer sample wells are placed on one slide.
CCD camera			We used Nikon D750
Chlamydomonas 137c WT strain	Chlamydomonas Resource Center	CC-125
MATLAB	MATHWORKS
MATLAB Image Processing Toolbox	MATHWORKS		needed for TrackCells.m and CleanTraces.m