Accurately distinguishing subtle phenotypic differences between Caenorhabditis elegans strains remains a major challenge in functional genetics and behavioural studies. Here, we evaluate how image resolution affects strain discrimination using an automated Multiview system combining macroscopic plate-level imaging with high-resolution microscopic single-worm recordings. Three strains were analysed: wild-type N2, a transgenic line with mild dysfunction of GABAergic neurons leading to subtle locomotor alterations (vltIs66), and a strongly uncoordinated mutant strain (unc-1(vlt10)). The three strains were analysed using traditional locomotion and shape descriptors. While both imaging modalities detected clear differences for the strongly uncoordinated unc-1(vlt10) strain, no traditional morphometric and kinematic descriptors reliably separated vltIs66 from N2. We then trained a CNN–Transformer directly on image sequences. When trained on macro-camera data, the model failed to discriminate between N2 and vltIs66, whereas the same architecture trained on micro-camera sequences achieved robust separation, revealing phenotype-specific patterns not captured by conventional descriptors. To quantify the impact of spatial detail, micro-camera recordings were progressively downscaled by factors of 2, 4, 8 and 16 and the model was retrained at each effective resolution. This resolution sweep showed that performance remains stable under moderate downsampling but degrades markedly at coarse resolutions, indicating a minimum effective pixel density required for subtle phenotype classification. These findings highlight the importance of high-resolution, sequence-based deep learning for detecting fine locomotor differences in C. elegans.
Peñaranda-Jara et al. (Mon,) studied this question.