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

[EN] 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...

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Autores: Peñaranda-Jara, José Julio, Escobar-Benavides, Santiago Nahuel, Puchalt-Rodríguez, Joan Carles|||0000-0002-9432-8319, García-Garví, Antonio|||0000-0002-3676-8287, Sánchez Salmerón, Antonio José|||0000-0003-1896-5356
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::c9a8452f9372b552aa357057acab02ab
Acceso en línea:https://riunet.upv.es/handle/10251/235740
Access Level:acceso abierto
Palabra clave:Convolutional neural networks
Phenotypic differentiation
Deep learning
Statistical analysis
C. elegans
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spelling Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer modelsPeñaranda-Jara, José JulioEscobar-Benavides, Santiago NahuelPuchalt-Rodríguez, Joan Carles|||0000-0002-9432-8319García-Garví, Antonio|||0000-0002-3676-8287Sánchez Salmerón, Antonio José|||0000-0003-1896-5356Convolutional neural networksPhenotypic differentiationDeep learningStatistical analysisC. elegans[EN] 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.This study was supported by Comunitat Valenciana (Spain) under grant INVEST/2023/541. The authors thank the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFEDER/2018/025.Nature Publishing GroupDepartamento de Ingeniería de Sistemas y AutomáticaEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería IndustrialEscuela Técnica Superior de Ingeniería InformáticaGeneralitat ValencianaUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20262026-03-02journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/235740reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengGeneralitat Valenciana https://doi.org/10.13039/501100003359 INVEST%2F2023%2F541Generalitat Valenciana https://doi.org/10.13039/501100003359 IDIFEDER%2F2018%2F025 Sistemas de Fabricación Inteligentes para la Industria 4.0open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::c9a8452f9372b552aa357057acab02ab2026-06-13T07:49:27Z
dc.title.none.fl_str_mv Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
title Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
spellingShingle Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
Peñaranda-Jara, José Julio
Convolutional neural networks
Phenotypic differentiation
Deep learning
Statistical analysis
C. elegans
title_short Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
title_full Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
title_fullStr Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
title_full_unstemmed Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
title_sort Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
dc.creator.none.fl_str_mv Peñaranda-Jara, José Julio
Escobar-Benavides, Santiago Nahuel
Puchalt-Rodríguez, Joan Carles|||0000-0002-9432-8319
García-Garví, Antonio|||0000-0002-3676-8287
Sánchez Salmerón, Antonio José|||0000-0003-1896-5356
author Peñaranda-Jara, José Julio
author_facet Peñaranda-Jara, José Julio
Escobar-Benavides, Santiago Nahuel
Puchalt-Rodríguez, Joan Carles|||0000-0002-9432-8319
García-Garví, Antonio|||0000-0002-3676-8287
Sánchez Salmerón, Antonio José|||0000-0003-1896-5356
author_role author
author2 Escobar-Benavides, Santiago Nahuel
Puchalt-Rodríguez, Joan Carles|||0000-0002-9432-8319
García-Garví, Antonio|||0000-0002-3676-8287
Sánchez Salmerón, Antonio José|||0000-0003-1896-5356
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería de Sistemas y Automática
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Industrial
Escuela Técnica Superior de Ingeniería Informática
Generalitat Valenciana
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Convolutional neural networks
Phenotypic differentiation
Deep learning
Statistical analysis
C. elegans
topic Convolutional neural networks
Phenotypic differentiation
Deep learning
Statistical analysis
C. elegans
description [EN] 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.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-03-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/235740
url https://riunet.upv.es/handle/10251/235740
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Generalitat Valenciana https://doi.org/10.13039/501100003359 INVEST%2F2023%2F541
Generalitat Valenciana https://doi.org/10.13039/501100003359 IDIFEDER%2F2018%2F025 Sistemas de Fabricación Inteligentes para la Industria 4.0
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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