MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy
Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-02-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/80918 |
| _version_ | 1797904721636229120 |
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| author | Valentyna Zinchenko Johannes Hugger Virginie Uhlmann Detlev Arendt Anna Kreshuk |
| author_facet | Valentyna Zinchenko Johannes Hugger Virginie Uhlmann Detlev Arendt Anna Kreshuk |
| author_sort | Valentyna Zinchenko |
| collection | DOAJ |
| description | Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organisation of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources. |
| first_indexed | 2024-04-10T09:53:28Z |
| format | Article |
| id | doaj.art-adb0e85e3fec468eb694ee3b8bcd5cab |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-10T09:53:28Z |
| publishDate | 2023-02-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-adb0e85e3fec468eb694ee3b8bcd5cab2023-02-16T15:26:44ZengeLife Sciences Publications LtdeLife2050-084X2023-02-011210.7554/eLife.80918MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopyValentyna Zinchenko0https://orcid.org/0000-0001-6900-0656Johannes Hugger1Virginie Uhlmann2Detlev Arendt3https://orcid.org/0000-0001-7833-050XAnna Kreshuk4https://orcid.org/0000-0003-1334-6388Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, GermanyEuropean Bioinformatics Institute, European Molecular Biology Laboratory (EMBL), Cambridge, United KingdomEuropean Bioinformatics Institute, European Molecular Biology Laboratory (EMBL), Cambridge, United KingdomDevelopmental Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, GermanyCell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, GermanyElectron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organisation of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources.https://elifesciences.org/articles/80918machine learningmorphologyrepresentation learning |
| spellingShingle | Valentyna Zinchenko Johannes Hugger Virginie Uhlmann Detlev Arendt Anna Kreshuk MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy eLife machine learning morphology representation learning |
| title | MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy |
| title_full | MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy |
| title_fullStr | MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy |
| title_full_unstemmed | MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy |
| title_short | MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy |
| title_sort | morphofeatures for unsupervised exploration of cell types tissues and organs in volume electron microscopy |
| topic | machine learning morphology representation learning |
| url | https://elifesciences.org/articles/80918 |
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