2025/04/24
paper · doi:10.17912/micropub.biology.001562
<p>The nematode <i><a href="https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=6239" id="4751ef63-65ef-4d15-8864-7a0cc6fb60ca">Caenorhabditis elegans</a></i> is a powerful model organism for fluorescent imaging studies due to its simplicity, transparency, well-characterized anatomy, and ease of genetic manipulation. However, the scale and statistical power of <i><a href="https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=6239" id="2e72f439-e35e-4dd3-a32f-b6b4102a86b6">C. elegans</a> </i>imaging experiments can be limited by the time and effort required to manually quantify fluorescence intensity in individual worms. Recent advances in automated image analysis have used artificial intelligence models and user-supplied training data sets to automate biological image quantification. While these tools have the potential to significantly expedite a variety of research applications in <i><a href="https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=6239" id="c4c0ed65-5662-4a22-92ce-16128fb56f61">C. elegans</a></i> and other model organisms, they can be difficult to implement and troubleshoot, particularly for researchers with little or no computational training. Here, we introduce a simple method to automate <i><a href="https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=6239" id="8f047bb5-0165-4057-81f7-1114f7f0cc5c">C. elegans</a> </i>fluorescence quantification that is accessible to users able to install the free program R and edit a single line of code described here.</p>