Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively

Abstract

Mutants with abnormal patterns of locomotion, also known as uncoordinated (Unc) mutants, have facilitated the genetic dissection of many important aspects of nervous system function and development in the nematode Caenorhabditis elegans. Although a large number of distinct classes of Unc mutants can be distinguished by an experienced observer, precise quantitative definitions of these classes have not been available. Here we describe a new approach for using automatically-acquired image data to quantify the locomotion patterns of wild-type and mutant worms. We designed an automated tracking and imaging system capable of following an individual animal for long time periods and saving a time-coded series of digital images representing its motion and body posture over the course of the recording. We have also devised methods for measuring specific features from these image data that can be used by the classification and regression tree classification algorithm to reliably identify the behavioral patterns of specific mutant types. Ultimately, these tools should make it possible to evaluate with quantitative precision the behavioral phenotypes of novel mutants, gene knockout lines, or pharmacological treatments.