The video outlines instructions for setting up an imaging apparatus using a smartphone and LED light box for conducting time-lapse experiments. It details requirements for equipment, placement of components, and settings for optimal video recording to capture assays within an incubator environment.

setting up the imaging apparatus for c. elegans lawn avoidance assay

Imaging basato su smartphone per il saggio di evitamento del prato di C. elegans

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.

Autore in primo piano: un metodo di imaging basato su smartphone per il saggio di evitamento del prato di C. elegans  

Un metodo di imaging basato su smartphone per il saggio di evitamento del prato di C. elegans

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 Setting up the Imaging Apparatus for C. elegans Lawn Avoidance Assay at JoVE.com

Setting up the Imaging Apparatus for C. elegans Lawn Avoidance Assay  

Messa a punto dell'apparato di imaging per il saggio di prevenzione del prato di C. elegans

Per configurare l'apparato di imaging, utilizzare uno smartphone con una specifica minima di 12 megapixel fotocamera 1080 pixel di risoluzione, cinque gigabyte di spazio di archiviazione, in un'app video timeLapse dall'app store. Posizionare la scatola luminosa a LED sul rack inferiore di un incubatore impostato a 25 gradi Celsius dove si svolgerà il test. Per nascondere il motivo tratteggiato sulla superficie luminosa del LED, stendere due fogli di tessuto per coprire l'intera superficie della scatola LED.Realizzare un palcoscenico elevato per il campione costituito da un foglio di plastica trasparente sostenuto da tunnel rettangolari cavi. Assicurati che le pareti del tunnel siano un po 'scure per ridurre al minimo la dispersione della luce. Posiziona un altro rack sopra il palco per posizionare i telefoni per la registrazione.Ogni telefono registrerà da tre a sei piastre, quindi regola l'altezza del rack di conseguenza. Inserire una ciabatta all'interno dell'incubatore per collegare i telefoni durante la registrazione notturna. Per preparare la registrazione, collegare lo smartphone alla ciabatta collegata a una presa di corrente.Assicurarsi di disabilitare l'impostazione di blocco automatico per evitare che il telefono ritorni alla schermata di blocco durante la registrazione. Apri l'app fotocamera timelapse e imposta la qualità video su 1080 pixel a 30 fotogrammi al secondo. Impostare l'intervallo di timelapse su due secondi.Posizionare lo smartphone con lo schermo rivolto verso l'alto per registrare con la fotocamera posteriore e assicurarsi che i tunnel della scatola di carta si adattino al campo visivo.

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Behavior

Setting up the Imaging Apparatus for C. elegans Lawn Avoidance Assay

1.7K Views.  Yonsei University Wonju College of Medicine. Per configurare l'apparato di imaging, utilizzare uno smartphone con una specifica minima di 12 megapixel fotocamera 1080 pixel di risoluzione, cinque gigabyte di spazio di archiviazione, in un'app video timeLapse dall'app store. Posizionare la scatola luminosa a LED sul rack inferiore di un incubatore impostato a 25 gradi Celsius dove si svolgerà il test. Per nascondere il motivo tratteggiato sulla superficie luminosa del LED, stendere due fogli di tessuto per coprire l'intera superficie de...

Video: Setting up the Imaging Apparatus for C. elegans Lawn Avoidance Assay  

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

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.

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.

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

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Comportamento

To set up the imaging apparatus, use a smartphone with a minimum specification of 12 megapixel camera 1080 pixel resolution, five gigabyte storage space, in a timeLapse video app from the application store. Place the LED light box on the bottom rack of an incubator set at 25 degrees Celsius where the assay will take place. To hide the dotted pattern on the LED light surface, spread two sheets of tissues to cover the entire surface of the LED box.Make an elevated stage for the specimen comprising of a clear plastic sheet supported by hollow rectangular tunnels. Ensure that the tunnel's walls are somewhat dark to minimize light scatter. Place another rack above the stage to place the phones for recording.Each phone will record three to six plates, so adjust the rack height accordingly. Put a power strip inside the incubator to plug in the phones during overnight recording. To prepare for the recording, plug in the smartphone to the power strip connected to a power outlet.Ensure to disable the auto lock setting to prevent the phone from returning to the lock screen while recording. Open the timelapse camera app, and set the video quality to 1080 pixels at 30 frames per second. Set the timelapse interval to two seconds.Place the smartphone with the screen facing up to record with the back camera and ensure that the paper box tunnels fit within the field of view.

C. elegans Lawn Avoidance Assay

C. elegans Lawn Avoidance Assay  

Using a platinum wire pick, transfer 30 synchronized L4 stage worms to the PA14 plate. Place the worms in the middle of the PA14 bacteria lawn. In this study, for each condition, two plates were tested.Place the two plates on the elevated stage of the recording apparatus with the lid facing down such that the agar side is facing up toward the camera. On the smartphone screen, tap where the plate is, so the camera can focus on the assay plates. Start the recording, then add more plates to the stage.Record for 20 hours from the last set of plates placed on the stage. In the final time-lapse video, 20 hours of recording will result in a 20-minute-long video. Wild-type N2 worms were compared against npr-1 mutants.The wild-type worms progressively left the bacterial lawn and stayed outside. Worms outside the thick bacterial lawn were clearly seen in the video, but worms inside the bacterial lawn were harder to distinguish.

C. elegans Saggio per evitare il prato

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.