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

Smarttelefonbasert bildebehandling for C. elegans plenunngåelsesanalyse

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.

Forfatter Spotlight: En smarttelefonbasert bildebehandlingsmetode for C. elegans plenunngåelsesanalyse  

En smarttelefonbasert bildebehandlingsmetode for C. elegans plenunngåelsesanalyse

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  

Videobehandling og manuell telling for C. elegans Lawn Avoidance Assay

Identifiser dimensjonene til en PA14-plate som inneholder L4-trinnormene, ved å kjøre Python-skriptet extract_frame.py. Åpne deretter JPEG-filen i ImageJ. Fra menyen velger du Analyser og deretter Angi målinger.Kontroller at det er merket av for skjermetiketten, og lukk vinduet. Bruk det rettlinjede verktøyet til å måle diameteren på en plate ved å tegne en linje over den. Velg deretter Analyser og deretter Mål fra menyen.Hvis videoen er i 1080 piksler, vil hver plate være omtrent 480 piksler bred. Skriv inn denne informasjonen i notisblokken som skal brukes senere, og lukk resultatvinduet. Ved hjelp av flerpunktsverktøyet merker du punkter øverst til venstre på hver plate som blir øvre venstre hjørne av de beskårne videoene.Merk den i rekkefølge etter når platene ble startet. Når du har opprettet et punkt for alle platene, velger du Analyser og deretter Mål på menyen. Målinger inkludert X- og Y-koordinatene til punktene vises i resultatvinduet.For å behandle flere videoer, gjenta prosessen i ImageJ med andre JPEG-filer. Alle X- og Y-koordinater vil bli oppført i samme resultatvindu. Lagre resultatvinduet i en CSV-fil.Filen skal lagres i samme katalog som filmfilene. Hvis du vil finne starttidspunktet for hver plate, spiller du av filmen på datamaskinen eller telefonen og noterer starttidene for hvert platesett som er plassert under kameraet. Åpne resultatene.CSV-fil med koordinatene og legg til en startkolonne. For hver rad som tilsvarer individuelle plater, skriv inn passende starttid i sekunder under startkolonnen. Lagre filen.For å beskjære og trimme videoene, kjør crop_n_trim. PY-skript. Velg resultatene.CSV-fil. Angi platedimensjonene og bildepunktverdien som ble angitt tidligere. Skriptet vil nå lese hver rad av resultatene.CSV-fil for å finne riktig filmfil etter at skriptet er ferdig. En mappe vises med samme navn som filmen, etterfulgt av starttidspunktet der videoer som tilsvarer analysen vil bli lagret. For manuell telling, åpne hver AVI-fil i ImageJ.Tell ormene som er synlige utenfor plenen, og beregn deretter beleggsprosenten for hvert tidspunkt. De ville type N2-ormene forlot gradvis bakterieplenen og holdt seg ute som nevnt i beleggsprosenten over tid. Tellingene som ble gjort direkte fra platene ble sammenlignet med tellinger fra avbildede ormer.Tellingene fra avbildede ormer viste seg å være svært nøyaktige. Når tre forsøk for hver stamme ble gjennomsnittet sammen, ga N2- og NPR1-stammene henholdsvis 99,5% og 96,2% nøyaktighet.

Research

JoVE Journal

Behavior

Video Processing and Manual Counting 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 ...