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<p>Accurately distinguishing subtle phenotypic differences between <i>Caenorhabditis elegans</i> strains remains a major challenge in functional genetics and behavioural studies. Here, we evaluate how image resolution affects strain discrimination using an automated <i>Multiview</i> 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 <i>unc-1(vlt10)</i> strain, no traditional morphometric and kinematic descriptors reliably separated <i>vltIs66</i> from <i>N2</i>. We then trained a CNN–Transformer directly on image sequences. When trained on macro-camera data, the model failed to discriminate between <i>N2</i> and <i>vltIs66</i>, 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 <i>C.&#xa0;elegans</i>.</p>

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Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN–transformer models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月2日

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  • Jose-Julio Peñaranda-Jara,
  • Santiago Escobar-Benavides,
  • Joan-Carles Puchalt,
  • Antonio García-Garví,
  • Antonio-José Sánchez-Salmerón

摘要

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.