Single cell resolution 3D imaging and segmentation within intact live tissues
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arXiv
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| Format: | Preprint |
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2025
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| author | Paci, G. Vicente-Munuera, P. Fernandez-Mosquera, I. Miranda, A. Lau, K. Zhang, Q. Barrientos, R. Mao, Y. |
| author_facet | Paci, G. Vicente-Munuera, P. Fernandez-Mosquera, I. Miranda, A. Lau, K. Zhang, Q. Barrientos, R. Mao, Y. |
| contents | Epithelial cells form diverse structures from squamous spherical organoids to densely packed pseudostratified tissues. Quantification of cellular properties in these contexts requires high-resolution deep imaging and computational techniques to achieve truthful three-dimensional (3D) structural features. Here, we describe a detailed step-by-step protocol for sample preparation, imaging and deep-learning-assisted cell segmentation to achieve accurate quantification of fluorescently labelled individual cells in 3D within live tissues. We share the lessons learned through troubleshooting 3D imaging of Drosophila wing discs, including considerations on the choice of microscopy modality and settings (objective, sample mounting) and available segmentation methods. In addition, we include a computational pipeline alongside custom code to assist replication of the protocol. While we focus on the segmentation of cell outlines from membrane labelling, this protocol applies to a wide variety of samples, and we believe it be valuable for studying other tissues that demand complex analysis in 3D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Single cell resolution 3D imaging and segmentation within intact live tissues Paci, G. Vicente-Munuera, P. Fernandez-Mosquera, I. Miranda, A. Lau, K. Zhang, Q. Barrientos, R. Mao, Y. Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Cell Behavior Tissues and Organs Epithelial cells form diverse structures from squamous spherical organoids to densely packed pseudostratified tissues. Quantification of cellular properties in these contexts requires high-resolution deep imaging and computational techniques to achieve truthful three-dimensional (3D) structural features. Here, we describe a detailed step-by-step protocol for sample preparation, imaging and deep-learning-assisted cell segmentation to achieve accurate quantification of fluorescently labelled individual cells in 3D within live tissues. We share the lessons learned through troubleshooting 3D imaging of Drosophila wing discs, including considerations on the choice of microscopy modality and settings (objective, sample mounting) and available segmentation methods. In addition, we include a computational pipeline alongside custom code to assist replication of the protocol. While we focus on the segmentation of cell outlines from membrane labelling, this protocol applies to a wide variety of samples, and we believe it be valuable for studying other tissues that demand complex analysis in 3D. |
| title | Single cell resolution 3D imaging and segmentation within intact live tissues |
| topic | Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Cell Behavior Tissues and Organs |
| url | https://arxiv.org/abs/2501.19203 |