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Fluorescently labeled nuclear morphology is highly informative of neurotoxicity
Neurotoxicity can be detected in live microscopy by morphological changes such as retraction of neurites, fragmentation, blebbing of the neuronal soma and ultimately the disappearance of fluorescently labeled neurons. However, quantification of these features is often difficult, low-throughput, and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449453/ https://www.ncbi.nlm.nih.gov/pubmed/36093369 http://dx.doi.org/10.3389/ftox.2022.935438 |
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author | Wang, Shijie Linsley, Jeremy W. Linsley, Drew A. Lamstein, Josh Finkbeiner, Steven |
author_facet | Wang, Shijie Linsley, Jeremy W. Linsley, Drew A. Lamstein, Josh Finkbeiner, Steven |
author_sort | Wang, Shijie |
collection | PubMed |
description | Neurotoxicity can be detected in live microscopy by morphological changes such as retraction of neurites, fragmentation, blebbing of the neuronal soma and ultimately the disappearance of fluorescently labeled neurons. However, quantification of these features is often difficult, low-throughput, and imprecise due to the overreliance on human curation. Recently, we showed that convolutional neural network (CNN) models can outperform human curators in the assessment of neuronal death from images of fluorescently labeled neurons, suggesting that there is information within the images that indicates toxicity but that is not apparent to the human eye. In particular, the CNN’s decision strategy indicated that information within the nuclear region was essential for its superhuman performance. Here, we systematically tested this prediction by comparing images of fluorescent neuronal morphology from nuclear-localized fluorescent protein to those from freely diffused fluorescent protein for classifying neuronal death. We found that biomarker-optimized (BO-) CNNs could learn to classify neuronal death from fluorescent protein-localized nuclear morphology (mApple-NLS-CNN) alone, with super-human accuracy. Furthermore, leveraging methods from explainable artificial intelligence, we identified novel features within the nuclear-localized fluorescent protein signal that were indicative of neuronal death. Our findings suggest that the use of a nuclear morphology marker in live imaging combined with computational models such mApple-NLS-CNN can provide an optimal readout of neuronal death, a common result of neurotoxicity. |
format | Online Article Text |
id | pubmed-9449453 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94494532022-09-08 Fluorescently labeled nuclear morphology is highly informative of neurotoxicity Wang, Shijie Linsley, Jeremy W. Linsley, Drew A. Lamstein, Josh Finkbeiner, Steven Front Toxicol Toxicology Neurotoxicity can be detected in live microscopy by morphological changes such as retraction of neurites, fragmentation, blebbing of the neuronal soma and ultimately the disappearance of fluorescently labeled neurons. However, quantification of these features is often difficult, low-throughput, and imprecise due to the overreliance on human curation. Recently, we showed that convolutional neural network (CNN) models can outperform human curators in the assessment of neuronal death from images of fluorescently labeled neurons, suggesting that there is information within the images that indicates toxicity but that is not apparent to the human eye. In particular, the CNN’s decision strategy indicated that information within the nuclear region was essential for its superhuman performance. Here, we systematically tested this prediction by comparing images of fluorescent neuronal morphology from nuclear-localized fluorescent protein to those from freely diffused fluorescent protein for classifying neuronal death. We found that biomarker-optimized (BO-) CNNs could learn to classify neuronal death from fluorescent protein-localized nuclear morphology (mApple-NLS-CNN) alone, with super-human accuracy. Furthermore, leveraging methods from explainable artificial intelligence, we identified novel features within the nuclear-localized fluorescent protein signal that were indicative of neuronal death. Our findings suggest that the use of a nuclear morphology marker in live imaging combined with computational models such mApple-NLS-CNN can provide an optimal readout of neuronal death, a common result of neurotoxicity. Frontiers Media S.A. 2022-08-24 /pmc/articles/PMC9449453/ /pubmed/36093369 http://dx.doi.org/10.3389/ftox.2022.935438 Text en Copyright © 2022 Wang, Linsley, Linsley, Lamstein and Finkbeiner. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Toxicology Wang, Shijie Linsley, Jeremy W. Linsley, Drew A. Lamstein, Josh Finkbeiner, Steven Fluorescently labeled nuclear morphology is highly informative of neurotoxicity |
title | Fluorescently labeled nuclear morphology is highly informative of neurotoxicity |
title_full | Fluorescently labeled nuclear morphology is highly informative of neurotoxicity |
title_fullStr | Fluorescently labeled nuclear morphology is highly informative of neurotoxicity |
title_full_unstemmed | Fluorescently labeled nuclear morphology is highly informative of neurotoxicity |
title_short | Fluorescently labeled nuclear morphology is highly informative of neurotoxicity |
title_sort | fluorescently labeled nuclear morphology is highly informative of neurotoxicity |
topic | Toxicology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449453/ https://www.ncbi.nlm.nih.gov/pubmed/36093369 http://dx.doi.org/10.3389/ftox.2022.935438 |
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