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-baseret billeddannelse til C. elegans græsplæneundgåelsesassay

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 smartphone-baseret billeddannelsesmetode til C. elegans græsplæneundgåelsesanalyse  

En smartphone-baseret billeddannelsesmetode til 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  

Videobehandling og manuel tælling for C. elegans Lawn Avoidance Assay

Identificer dimensionerne på en PA14-plade, der indeholder L4-trinormene, ved at køre Python-scriptet extract_frame.py. Åbn derefter filen JPEG i ImageJ. Vælg Analysér i menuen og derefter Indstil målinger.Sørg for, at displayetiketboksen er markeret, og luk vinduet. Brug værktøjet til lige linje til at måle diameteren på en plade ved at tegne en linje på tværs af den. Vælg derefter Analysér og derefter Mål i menuen.Hvis videoen er i 1080 pixels, vil hver plade være ca. 480 pixels bred. Indtast disse oplysninger i notesblokken, der skal bruges senere, og luk resultatvinduet. Brug flerpunktsværktøjet til at markere punkter øverst til venstre på hver plade, som bliver øverste venstre hjørne af de beskårne videoer.Marker det i rækkefølge, da pladerne blev startet. Når du har oprettet et punkt for alle pladerne, skal du vælge Analyser og derefter Mål i menuen. Målinger, herunder punkternes X- og Y-koordinater, vises i resultatvinduet.For at behandle flere videoer skal du gentage processen i ImageJ med andre JPEG-filer. Alle X- og Y-koordinater vises i det samme resultatvindue. Gem resultatvinduet i en CSV-fil.Filen skal gemmes i samme mappe som filmfilerne. For at finde starttidspunktet for hver plade skal du afspille filmen enten på computeren eller telefonen og notere starttiderne for hvert sæt plader, der er placeret under kameraet. Åbn resultaterne.CSV-fil med koordinaterne og tilføj en startkolonne. For hver række, der svarer til individuelle plader, skal du angive det passende starttidspunkt i sekunder under startkolonnen. Gem filen.For at beskære og trimme videoerne skal du køre crop_n_trim. py script. Vælg resultaterne.CSV-fil. Indtast pladedimensionerne og pixelværdien, der blev nævnt tidligere. Scriptet læser nu hver række af resultaterne.CSV-fil for at finde den korrekte filmfil, når scriptet er færdig med at køre. En mappe vises med samme navn som filmen efterfulgt af starttidspunktet, hvor videoer, der svarer til analysen, gemmes. For manuel optælling skal du åbne hver AVI-fil i ImageJ.Tæl de orme, der er synlige uden for plænen, og beregn derefter belægningsgraden for hvert tidspunkt. De vilde type N2-orme forlod gradvist bakterieplænen og blev udenfor som bemærket i belægningsgraden over tid. Optællingerne foretaget direkte fra pladerne blev sammenlignet med tællinger fra afbildede orme.Optællingerne foretaget af afbildede orme viste sig at være meget nøjagtige. Når tre forsøg for hver stamme blev beregnet som gennemsnit sammen, gav N2- og NPR1-stammerne henholdsvis 99,5 % og 96,2 %nøjagtighed.

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 ...