Multidimensional phenotyping predicts lifespan and quantifies health in Caenorhabditis elegans

摘要

High dimensional biomedical data are used to quantify health and diagnose diseases. Combining the most informative features collected in the best conditions is crucial for predictive power. Using high-resolution videos and extraction of hundreds of morphological, postural and behavioral features, we characterized the phenotypic evolution of worms as they age. Out of the 1019 features extracted, 896 correlate with relative age. We used machine-learning to predict age and lifespan of individual C. elegans (age R2 = 0.79; remaining life R2 = 0.77; lifespan R2 = 0.72). The quality of these predictions increased with the number of features added sequentially to the model, supporting the use of multiple features to quantify ageing. We evaluated the relationship between ageing and the phenotypic progression. In our conditions, for isogenic wild-type worms, the rate of phenotypic alterations scales with the lifespan of the individuals.