Abstract
BackgroundCaenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis.ResultsWe examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation.ConclusionsWorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a “quick and easy,” convenient, high-throughput, and automated solution for nematode research.
Highlights
Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone
Caenorhabditis elegans nematodes are powerful genetic model organisms which are instrumental for research on a wide range of biological questions
In addition to using wild-type worms (N2), we examined the RNA interference (RNAi) response in rrf3(pk1426) mutants, which are hypersensitive to RNAi [16]
Summary
Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis. WorMachine enables automated calculation of many morphological and Fluorescent features and accessible machine learning techniques for higher level features-based analysis (described in detail in the Implementation and Methods sections), such as classification and phenotype scoring. WorMachine is entirely MATLAB-based and combines the capabilities of different programs into one software package; the user-friendly interface was designed to suit investigators with no background in MATLAB, image processing, or machine learning, and it requires no additional plugins or installations. WorMachine is not limited to any specific image format, resolution, acquisition software, or microscope
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