Name: Video Processing and Manual Counting for C. elegans Lawn Avoidance Assay
Uploaded: 2023-05-19T16:00:00
Description: Watch this Scientific Journal Video about Video Processing and Manual Counting for C. elegans Lawn Avoidance Assay at JoVE.com

The video demonstrates how to process images of PA14 plates containing L4 stage worms using Python and ImageJ. It covers measuring plate dimensions, marking coordinates for cropping videos, saving results, and analyzing worm counts for occupancy rates, highlighting the accuracy of counts from images vs. direct observations.

video processing and manual counting for c. elegans lawn avoidance assay

Smartphone-Based Imaging for C. elegans Lawn Avoidance Assay

Among the many advantages of studying C. elegans, its simple nervous system offers the opportunity to study how changes at the genetic and cellular level impact network function and behavioral output. Despite having a limited number of neurons, C. elegans display a wide range of complex behaviors. One of these is lawn avoidance, in which the bacterivorous nematode responds to a harmful food source by leaving the bacterial lawn. C. elegans avoid lawns of pathogenic bacteria1,2,3, lawns of bacteria that produce toxins or are spiked with toxins1,4, and even RNAi-expressing bacteria whose target gene knockdown is detrimental to the health of the worms4,5. Studies have shown that worms respond to external cues such as metabolites produced by the pathogenic bacteria1,6, or internal cues that indicate that the food is making them sick4,7. These cues are processed through conserved signaling pathways, such as the mitogen-activated protein kinase (MAPK) pathway and the transforming growth factor beta (TGF&#946;) pathway, and require communication between the gut and the nervous system4,6,7,8.
Although the assay is simple, the learned behavior develops over many hours, often overnight. While there are mutants that are incapable of leaving, in which case scoring avoidance at just one time point is sufficient to demonstrate the defect, many mutants do leave eventually but are slower to come out. For these, the movement of the worms needs to be tracked every few hours, which can be difficult to do overnight. Counting itself also takes time, creating a lag time between the plates, and thus limits the number of plates that can be tested at the same time. Using an imaging setup to record many plates simultaneously for the whole duration of the assay would be very useful, but the cost of setup can be prohibitive, depending on the funding situation of the research lab.
To address this, we devised a very simple method that uses smartphones to record avoidance assays. Each phone can record time-lapse videos of up to six assay plates. To provide transmitted light, we use a light emitting diode (LED) light box that can be easily purchased online. Assay plates are placed on an elevated platform, supported by hollow rectangular tunnels, that focus the incoming light, creating contrast. We also provide a Python script that converts the videos into audio video interleave (AVI) files showing 10 s clips of each hourly time point. The videos are then cropped to individual plates and saved in separate files to use for manual counting.
The method provides a low-cost procedure that is also extremely easy to use, using items that are readily available to most people. Here, we describe the method using the well-established lawn avoidance assay against the human pathogen Pseudomonas aeruginosa (PA14), whose protocol has been previously described2,9. Finally, we also review the considerations and limitations of the imaging method for those that want to apply it to other C. elegans behavior experiments.

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay  

A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

Among the many behaviors displayed by C-elegans, pathogenic lawn avoidance is the worm's defense strategy to minimize its exposure to harmful pathogens. In our lab, we use this behavior as a tool to identify genes involved in the proper functioning of the neurons that mediate this behavior. The lawn avoidance behavior is a simple assay, however it develops over many hours.Hence, it is convenient if the assay can be recorded to observe behavior over long time periods. Also, if multiple assays can be recorded at the same time, that's even better. Although there are many imaging solutions described in the literature, even a few modestly priced cameras may be out of reach for labs with limited resources.Also, some may want to try the assay, but do not plan on doing it long term. For such cases, short-term solutions such as this protocol might help. Our amazing protocol offers an easy, low cost way to record worm behavior.We use smartphones to record the assay, then process the video through a Python script that makes it suitable for counting.

Watch this Scientific Journal Video about Video Processing and Manual Counting for C. elegans Lawn Avoidance Assay at JoVE.com

Video Processing and Manual Counting for C. elegans Lawn Avoidance Assay  

Video Processing and Manual Counting for C. elegans Lawn Avoidance Assay

Identify the dimensions of a PA14 plate containing the L4 stage worms by running the Python script extract_frame.py. Then open the JPEG file in ImageJ. From the menu, choose Analyze, then Set Measurements.Ensure that the display label box is checked and close the window. Using the straight line tool, measure the diameter of a plate by drawing a line across it. Then choose Analyze, then Measure from the menu.If the video is in 1080 pixels, each plate will be about 480 pixels wide. Enter this information in the notepad to be used later and close the results window. Using the multi-point tool, mark points on the upper left side of each plate which will become the upper left corner of the cropped videos.Mark it in order of when the plates were started. After creating a point for all the plates, choose Analyze, then Measure from the menu. Measurements including the X and Y coordinates of the points will appear in the results window.To process multiple videos, repeat the process in ImageJ with other JPEG files. All X and Y coordinates will be listed in the same results window. Save the results window into a CSV file.The file should be saved to the same directory as the movie files. To find the starting time for each plate, play the movie either on the computer or phone and take note of the starting times of each set of plates placed under the camera. Open the results.csv file with the coordinates and add a start column. For each row corresponding to individual plates, enter appropriate start time in seconds under the start column. Save the file.To crop and trim the videos, run the crop_n_trim. py script. Choose the results.csv file. Enter the plate dimensions and the pixel value noted earlier. The script will now read each row of the results.csv file to find the correct movie file after the script finishes running. A folder will appear with the same name as the movie, followed by the start time in which videos corresponding to the assay will be saved. For manual counting, open each AVI file in ImageJ.Count the worms that are visible outside the lawn and then calculate the occupancy rate for each time point. The wild type N2 worms progressively left the bacterial lawn and stayed outside as noted in the occupancy rate over time. The counts made directly from the plates were compared against counts from imaged worms.The counts made from imaged worms turned out to be highly accurate. When three trials for each strain were averaged together, the N2 and NPR1 strains yielded 99.5%and 96.2%accuracy respectively.

Research

JoVE Journal

Behavior

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually ...