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

Summary

This article describes a simple, low-cost method of recording the lawn avoidance behavior of Caenorhabditis elegans, using readily available items such as a smartphone and a light emitting diode (LED) light box. We also provide a Python script to process the video file into a format more amenable for counting.

Abstract

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually leave their food source and prefer to remain outside the bacterial lawn. The assay is an easy way to test the worms' ability to sense external or internal cues to properly respond to harmful conditions. Though a simple assay, counting is time consuming, particularly with multiple samples, and assay durations that span overnight are inconvenient for researchers. An imaging system that can image many plates over a long period is useful but costly. Here, we describe a smartphone-based imaging method to record lawn avoidance in C. elegans. The method requires only a smartphone and a light emitting diode (LED) light box, to serve as a transmitted light source. Using free time-lapse camera applications, each phone can image up to six plates, with sufficient sharpness and contrast to manually count worms outside the lawn. The resulting movies are processed into 10 s audio video interleave (AVI) files for every hourly time point, then cropped to show each single plate to make them more amenable for counting. This method is a cost-effective way for those looking to examine avoidance defects and can potentially be extended to other C. elegans assays.

Introduction

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 bacteria^1,2,3, lawns of bacteria that produce toxins or are spiked with toxins^1,4, and even RNAi-expressing bacteria whose target gene knockdown is detrimental to the health of the worms^4,5. Studies have shown that worms respond to external cues such as metabolites produced by the pathogenic bacteria^1,6, or internal cues that indicate that the food is making them sick^4,7. These cues are processed through conserved signaling pathways, such as the mitogen-activated protein kinase (MAPK) pathway and the transforming growth factor beta (TGFβ) pathway, and require communication between the gut and the nervous system^4,6,7,8.

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 described^2,9. 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.

Protocol

1. Setting up the imaging apparatus (Figure 1A-E)

1. Ensure that a smartphone camera with the following minimum requirements is available:
   12 megapixel (MP) camera
   1080p resolution video
   5 GB of storage space (20 min video is 3-4 Gb)
   Time-lapse video app from the application store (free applications available)
2. Place the LED light box on the bottom rack of the 25 °C incubator where the assay will take place.
3. To hide the dotted pattern on the LED light surface, spread two sheets of tissues to cover the entire surface of the LED box.
4. Make an elevated stage for the specimen (Figure 1A,D). The elevated stage is a clear plastic sheet supported by hollow rectangular tunnels. Tunnels function like a condenser to focus light, providing better contrast to the specimen (Figure 1C). Ensure that the tunnel's walls are somewhat dark to minimize light scatter. This study used brown paper boxes. The dimension of the tunnel is 5.5 cm x 17 cm x 4.5 cm (W x L x H). The LED light box can fit up to five tunnels.
5. Place another rack above the stage to place the phones for recording (Figure 1B,E). Each phone will record three to six plates (one to two rows of three plates), so adjust the rack height accordingly. This will be approximately 15 cm above the specimen (Figure 1B).
6. Put a power strip inside the incubator to plug in the phones during overnight recording.

2. Preparation of buffers and media

1. Prepare M9 buffer by adding 3 g of KH₂PO₄, 6 g of Na₂HPO₄, and 5 g of NaCl to 1 L of distilled H₂O. Sterilize by autoclaving at 121 °C for 20 min. Cool the buffer and then add 1 mL of 1 M MgSO₄.
2. Prepare 1 M KPO₄ buffer by adding 108.3 g of KH₂PO₄ and 35.6 g of K₂HPO₄ to 1 L of H₂O. Adjust the pH to 6.0 by adding KOH. Sterilize by autoclaving.
3. Prepare worm bleaching solution by mixing 1 mL of bleach, 0.4 mL of 1 M NaOH, and 2.6 mL of H₂O.
4. Prepare nematode growth media (NGM) agar plates.
   1. Add 3 g of NaCl, 2.5 g of bacto peptone, and 17 g of bacto agar in a 3 L flask. Add 975 mL of distilled water and insert a stir bar.
   2. Sterilize by autoclaving, then cool to 55 °C, and add 1 mL of cholesterol (5 mg/mL in ethanol), 1 mL of 1 M CaCl₂, 1 mL of 1 M MgSO₄, and 25 mL of 1 M KPO₄ buffer (pH 6.0). Stir to mix well. Pour into 6 cm plates. Let the plates dry for at least 2 days.
5. Seed NGM agar plates with OP50 E. coli by pipetting approximately 1 mL of an overnight culture of OP50 to form a lawn of bacteria. Leave at room temperature (RT) until ready to use.

3. Preparation of high-peptone NGM plates (for PA14)

NOTE: These plates should be made at least 5 days before the assay.

1. Make NGM containing 0.35% peptone. Mix 0.3 g of NaCl, 0.35 g of bacto peptone, and 1.7 g of bacto agar in a 250 mL Erlenmeyer flask. Add 97.5 mL of distilled water and insert a stir bar.
2. Cover the mouth of the flask with aluminum foil and autoclave at 121 °C for 20 min.
3. Cool down to 55 °C and add 0.1 mL of cholesterol (5 mg/mL in ethanol), 0.1 mL of 1 M CaCl₂, 0.1 mL of 1 M MgSO₄, and 2.5 mL of 1 M KPO₄ buffer (pH 6.0). Stir to mix well.
4. Pour high-peptone NGM into 35 mm Petri dishes.
5. Dry the plates for at least 2 days.

4. Synchronizing worms by bleaching

NOTE: Start this step 3 days before the assay.

1. Take plates with gravid adult worms and collect them into a 1.7 mL microtube by washing the plates with M9 buffer.
2. Remove as much liquid as possible, then add 400 µL of bleaching solution. Wait about 4-5 min with intermittent vortexing, until the adult worm bodies break, releasing the eggs.
3. Add M9 buffer to fill the rest of the microtube to dilute the bleaching solution. Spin at maximum speed (12,000 to 13,000 x g) for 1-2 s. Remove the supernatant and wash three more times with M9 buffer.
4. Transfer the eggs into an empty 35 mm Petri dish containing M9 buffer. Let the eggs hatch overnight at 20 °C. In the absence of food, hatched worms will arrest at the L1 larval stage, synchronizing the developmental stage of all worms.
   NOTE: Coating the 35 mm Petri dish with gelatin solution (0.05% gelatin in autoclaved water) can prevent the eggs from sticking to the bottom and minimize egg loss.
5. The next day, transfer L1 stage worms to OP50 seeded NGM plates.
6. Incubate the worms at 20 °C for 53-54 h until the worms reach the L4 larval stage.

5. Preparation of bacteria ( Pseudomonas aeruginosa, PA14)

NOTE: Start this step 4 days before the assay.

1. Streak thawed bacteria from -80 °C on a Luria Bertani (LB) agar plate without any antibiotic and incubate overnight at 37 °C.
   NOTE: Always use fresh bacteria. Streaked plates should be stored at 4 °C for no more than 1 week.
2. Inoculate a single colony into 3 mL of King's broth and grow overnight in a 37 °C shaking incubator.
3. The next day, seed 7 µL of the overnight culture onto the high-peptone NGM plates and incubate at 37 °C for 24 h.
4. Move the seeded plates to RT and incubate for another 24 h before use. Once ready, use the plate within the next 24 h.

6. Preparing to record

NOTE: Do this right before the assay.

1. Plug in the smartphone to the power strip connected to a power outlet. Make sure to disable the auto-lock setting to prevent the phone returning to the lock screen while recording.
2. Open the time-lapse camera app and set the time-lapse interval to 2 s. Set the video quality to 1080p at 30 fps.
3. Place the smartphone with the screen facing up to record with the back-facing camera. Check the screen to make sure that the paper box tunnels fit within the field of view.

7. Lawn avoidance assay

1. Using a platinum wire pick, transfer 30 synchronized L4 stage worms (53-54 h from L1) to the PA14 plate. Place the worms in the middle of the bacteria lawn. For each condition in this study, two plates were tested (i.e., 60 worms per condition).
2. Place the two plates on the elevated stage of the recording apparatus with the lid facing down. The side with the agar will be facing up toward the camera.
3. On the smartphone screen, tap where the plate is, so the camera can focus on the assay plates. It helps to have a label or writing on the plate as the camera can use that to focus correctly.
   NOTE: Writing on the bottom of the plates does not interfere with the imaging of worms as long as it is toward the edge. Fortunately, worms stay near the lawn even after they leave, so an unobstructed view is only needed of the immediate area surrounding the lawn.
4. Start the recording.
5. Once the recording has started, add more plates to the stage. There may be a significant lag time between the plates due to the time it takes to transfer worms by picking. Note the lag time afterward so that each condition can be counted at the time it began.
6. Record for 20 h from the last set of plates placed on the stage. In the final time-lapse video, 20 h of recording will result in a 20 min long video.
   NOTE: It may be worthwhile to count the worms directly from the plates after the assay, at least at the beginning for the first few occasions. This can be compared against values obtained through video imaging to ensure they yield similar numbers.

8. Processing of video using Python script

1. Transfer the movie file to a computer for processing. The extension will be a MOV (iPhone) or MP4 file (Android).
2. Use a Python code to process the videos. The code can be found at github.com/khyoon201/wormavoid.
3. To run the Python scripts, ensure that the following are preinstalled on the computer: ffmpeg, a tool for converting video files (directions for installation can be found on its website, ffmpeg.org/download), and the Python packages os, pandas, tkinter, and ffmpeg-python.
4. Find the dimensions and coordinates of each plate using the extract_frame.py script.
   1. Run the extract_frame.py script. A window will appear to select the video file stored on the computer. After running is completed, a jpeg file with the same name will appear in the same directory.
   2. Open the jpeg file in ImageJ (imagej.org).
   3. From the menu, choose Analyze > Set Measurements. Ensure that the Display Label box is checked (Figure 2A). Close the window.
   4. Using the Straight Line tool, measure the diameter of a plate by drawing a line across it, then choosing Analyze > Measure from the menu. If the video is in 1080p, each plate will be about 480 pixels wide. Write this information down and close the Results window.
   5. Using the Multi-point tool, mark points on the upper left side of each plate. These points will become the upper left corner of the cropped videos (Figure 2B). The order matters; mark in order of when the plates were started. After creating a point for all the plates, choose Analyze > Measure from the menu. Measurements, including the X and Y coordinates of the points, will appear in the Results window.
   6. 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.
   7. Save the Results window into a csv file. The file should be saved to the same directory as the movie files.
5. Find the starting time for each plate.
   1. 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.
   2. Open the Results.csv file with the coordinates and add a "start" column. For each row corresponding to individual plates, enter the appropriate start time, in seconds, under the "start" column (e.g., if the start time is 0:00:08, enter 8). Save.
      NOTE: The column name must be "start" (in lower case, without quotation marks) to be recognized by the next script for cropping and trimming.
6. Crop and trim the videos.
   1. Run the crop_n_trim.py script.
   2. When prompted, choose the Results.csv file.
      NOTE: Make sure the Results.csv file and all the movie files are in the same directory.
   3. Enter the plate dimensions. Enter the pixel value noted earlier.
      ​NOTE: The script will now read each row of the Results.csv file to find the correct movie file by reading the file name in the "label" column, and crop according to the coordinates indicated in columns "X" and "Y". The start time of each plate will be determined by the time indicated in the "start" column. After the script finishes running, a folder will appear with the same name as the movie, followed by the start time (e.g., "Movie1_8"), in which 10 s videos corresponding to each hourly time point of the assay will be saved.

9. Manual counting using ImageJ

1. Open each AVI file in ImageJ.
2. Count the worms that are visible outside the lawn. Worms that are overlapped in one frame usually move apart in another frame so that they can be counted correctly.
3. Calculate the occupancy rate for each time point:
   Occupancy rate = (total worms - number of worms outside the lawn)/total worms
   NOTE: The worms will move in and out of the lawn during the video, but this will not significantly alter the results. Try to go with the number that seems to be the average, or the number of worms at the exact hourly time point (5 s into the video).

Results

The first video produced by the script is 1 h from the start of the assay. The video for 0 h is not saved, as worms start the assay inside the lawn, so the occupancy rate is always 100%.

Wild-type N2 worms are compared against npr-1 mutants, whose lawn avoidance defect is well-established in the literature^6,10 (Figure 3A-E). As can be seen in the wild type, worms progressively leave the bacterial lawn and stay outside (Figure 3A,B). Results are plotted in a graph to show the change in occupancy rate over time (Figure 3B). Worms outside are clearly seen in the video, but worms inside the thick bacterial lawn are harder to distinguish (Figure 3D,E). However, because there are exactly 30 worms in each plate, the number of worms still inside the lawn can be calculated by subtracting the counted worms from the total of 30.

Although this assumption could potentially introduce counting errors, especially if some worms end up near the walls of the plate where it may be difficult to see, this was not a significant concern. When counts made directly from the plates were compared against counts from imaged worms, counts from imaged worms turned out to be highly accurate. When three trials for each strain were averaged together, the N2 and npr-1 strains yielded 99.5% and 96.2% accuracy, respectively (Figure 3B,C). Of note, there was a slightly higher tendency to miss a few npr-1 worms due to its high motility¹¹, whereas wild-type worms tended to stay near the lawn.

Figure 1: Imaging apparatus. (A) A schematic view of the imaging setup. (B) Imaging apparatus set up inside an incubator set to 25 °C for PA14 lawn avoidance assays. (C) Comparison of worms imaged with or without the tunnel. (D) A close-up view of how the plates are mounted on top of the tunnels. (E) The height of the phone is adjusted so that up to six 35 mm plates can fit into the screen. Please click here to view a larger version of this figure.

Figure 2: Determining plate coordinates using ImageJ. (A) In Analyze > Set Measurements, the Display label box must be checked (red dotted box). (B) A single frame extracted from the video is used to plot coordinates that are used for cropping. Points made using the Multi-point tool are in yellow. These serve as the upper left corners of the final cropped videos (marked as a dotted white box). Please click here to view a larger version of this figure.

Figure 3: Representative image and assay results. (A) After several hours of PA14 exposure, most worms leave the lawn and stay outside. (B) Representative results of the lawn avoidance assay. The movement of worms is followed every hour to determine the occupancy rate. The open squares at the 20 h time point indicate the average value determined from directly counting from plates from C. (C) To assess the accuracy of the counts made through the video, the worms were also counted directly at the end of the assay and compared against the values obtained through video imaging. Values indicate the worm numbers inside/outside the lawn. (D,E) L4 worms are clearly seen outside the bacterial lawn (black arrowhead), whereas worms inside are harder to see (white arrowhead). Worms that are overlapped in one frame can usually be distinguished apart in another frame from the same movie (black outline arrowhead). The number on the bottom right indicates the frame number out of the total frames of the 10 s video clip (30 frame/s). Please click here to view a larger version of this figure.

Discussion

Imaging animal behavior, rather than relying on direct observation, is not only convenient but also has the advantage of leaving visual documentation. This allows for blind analysis by an objective third person, or could even be used for automated analysis using image recognition techniques. Despite the advantages, the standard equipment usually offered is high in cost, so one is committed to the setup once purchased.

Using smartphones to collect video recordings of simple C. elegans behavior offers several advantages. It requires minimal familiarity with tech knowledge and is extremely easy to set up, using items that can be procured easily and cheaply. Another advantage is the portability of a smartphone-it can fit in small spaces, and since it has its own storage, it does not need to be connected back to a computer. This allows the setup to be placed anywhere, even when space is extremely limited. Moving the recorded video files to the computer is convenient-the files are not that big since they are encoded in a compressed MPEG-4 format. Moving files is especially convenient when wireless options of file transfer are available.

Because the worms are imaged without any magnification, the worms captured in the videos consist of only a few pixels. L4 worms are just big enough to be captured without magnification, but the small pixel size limits its use for high-quality image recognition and movement tracking. Using the zoom lens offered by more recent models or attaching a zoom lens adaptor may help obtain more detailed images, although we have not tried this ourselves. However, this would also reduce the field of view and the number of plates that can be imaged simultaneously.

To make counting easier, the videos are cropped to show individual plates, and trimmed to 10 s videos corresponding to every hour of the assay. This is also important as converting the videos into AVI format significantly increases the file size, and cropping and trimming the videos ensure that the file sizes are more manageable. The cropped AVI files could also potentially be used to count the worms automatically with an image recognition algorithm. For the wild-type strain, we found that a crude form of automated counting is possible in ImageJ, using simple thresholding. However, when mutants with a smaller body size are used, automated counts produce more errors.

There have been many efforts to image worms and automate analyses. Traditionally, worms were recorded through a camera attached to a dissecting microscope, which usually only allows the imaging of a few worms at once due to its limited field of view. The need to image more worms simultaneously for higher throughput analyses pushed researchers to develop creative imaging approaches. One way was to use modified flatbed scanners to image lifespan assays, such as WormScan or the Lifespan Machine^12,13. A high-resolution scanner can image worms so that moving live worms can be distinguished from unmoving dead worms.

For tracking worm movements at a higher fps rate, a camera is attached to a lens, and worms are imaged without a microscope^14,15. Churgin et al., who developed WorMotel¹⁴, a method for long term imaging of individual worms grown in a polydimethylsiloxane (PDMS) multi-well plate, provide detailed explanations on factors to consider when choosing the right camera and lens¹⁶. This method also has the added advantage of being relatively modest in cost.

Capturing worms without a microscope inevitably results in images that lack the resolution for detailed analysis on the locomotion or gait of worms. To remedy this, Barlow et al. employed a strategy of using six cameras arranged in a three by two array to capture a single 96-well plate¹⁷. Each camera is set up to image only four x four wells of the 96-well plate, resulting in a much higher size and resolution of the imaged worms.

Because C. elegans has a clear body, lighting also has to be adjusted to provide contrast from the background. Our method used illumination from a flat LED light box, passed through a narrow tunnel to focus the light. The dimensions were determined by the size of the plate imaged; the 5.5 cm width fit the 35 mm plate used for the avoidance assay. To image a larger area, the tunnel will have to be wider, but we found that the height also needs to be increased as well to obtain the same focusing effect. The downside is that, with higher tunnels, more of the walls can be seen through the plate, obstructing the view at the edge of the plate. Another strategy that could be employed is to use LED string lights arranged in a circular ring (LED ring). The light, coming from many directions, scatters on the surface of the worm's body, creating light worms against a dark background^14,16,18. This could work not only for bigger plates, but for imaging in smaller spaces that cannot fit a LED light box.

With many available imaging strategies developed by the worm community, researchers may want to try out a few options to find the right one that fits their need. The imaging method described here is cheap and approachable enough that it can easily be used in undergraduate classrooms, or as a temporary solution before investing in a long-term setup.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
35 mm Petri dish	SPL	#10035
Bacto agar	BD	#214010
Bacto Peptone	BD	#211677
CaCl2	DAEJUNG	2507-1400
Cholesterol	BioBasic	CD0122
Dipotassium hydrogen phosphate (K2HPO4)	JUNSEI	84120-0350
Glycerol	BioBasic	GB0232
King B Broth	MB cell	MB-K0827
LED light box multi-pad	Artmate	N/A	This is a USB powered, LED light pad for tracing and drawing purposes. Artmate is a Korean brand, but searching for "LED light box for tracing" in any search engine should yield numerous options from other brands. Overall dimension is around 9" x 12" (A4 size). For example, from amazon US: https://www.amazon.com/LITENERGY-Ultra-Thin-Adjustable-Streaming-Stenciling/dp/B07H7FLJX1/ref=sr_1_5?crid=YMYU0VYY226R&keywords= LED%2Blight%2Bbox&qid=1674183224&sprefix =led%2Blight%2Bbo%2Caps%2C270&sr=8-5&th=1
MgSO4	DAEJUNG	5514-4400
Plastic paper sleeve (clear)	Smead	#85753	Any clear plastic sheet with a bit of stiffness can be used as stage. For example, from Amazon US: https://www.amazon.com/Smead-Organized-Translucent-Project-85753/dp/B07HJTRCT7/ref=psdc_1069554_t3_B09J48GXQ 8
Potassium dihydrogen phosphate (KH2PO4)	JUNSEI	84185-0350
Power strip			To accommodate 3 phones and one LED box, you need at least 4 outlets.
Smartphone	N/A	N/A	Minimum requirement: 12MP wide camera, 1080p HD video recording at 30fps
Sodium chloride(NaCl)	DAEJUNG	#7548-4100
Sodium phosphate dibasic anhydrous (Na2HPO4)	YAKURI	#31727