MTM-6, a phosphoinositide phosphatase, is required to promote synapse formation in Caenorhabditis elegans

Deep phenotyping unveils hidden traits and genetic relations in subtle mutants

Discovering mechanistic insights from phenotypic information is critical for the understanding of biological processes. For model organisms, unlike in cell culture, this is currently bottlenecked by the non-quantitative nature and perceptive biases of human observations, and the limited number of reporters that can be simultaneously incorporated in live animals. An additional challenge is that isogenic populations exhibit significant phenotypic heterogeneity. These difficulties limit genetic approaches to many biological questions. To overcome these bottlenecks, we developed tools to extract complex phenotypic traits from images of fluorescently labelled subcellular landmarks, using C. elegans synapses as a test case. By population-wide comparisons, we identified subtle but relevant differences inaccessible to subjective conceptualization. Furthermore, the models generated testable hypotheses of how individual alleles relate to known mechanisms or belong to new pathways. We show that our model not only recapitulates current knowledge in synaptic patterning but also identifies novel alleles overlooked by traditional methods.

Experimenter scoring of cellular imaging data can be biased. This study describes an automated and unbiased multidimensional phenotyping method that relies on machine learning and complex feature computation of imaging data, and identifies weak alleles affecting synapse morphology in live C. elegans.