Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids
3D cell culture models are important tools for the development and testing of new therapeutics. In combination with immunoassays and 3D confocal microscopy, crucial information like morphological or metabolic changes can be examined during drug testing. However, a common limitation of immunostaining...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-09-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2022-1078 |
| _version_ | 1797947895112007680 |
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| author | Bruch Roman Vitacolonna Mario Rudolf Rüdiger Reischl Markus |
| author_facet | Bruch Roman Vitacolonna Mario Rudolf Rüdiger Reischl Markus |
| author_sort | Bruch Roman |
| collection | DOAJ |
| description | 3D cell culture models are important tools for the development and testing of new therapeutics. In combination with immunoassays and 3D confocal microscopy, crucial information like morphological or metabolic changes can be examined during drug testing. However, a common limitation of immunostainings is the number of dyes that can be imaged simultaneously, as overlaps in the spectral profiles of the different dyes may result in cross talk. We therefore present a 3D deep learning method, able to predict fluorescent stainings of specific antigens on the basis of a nuclei staining. Using the proliferation marker Ki67, we showed that the presented model was able to predict the Ki67 staining with a strong correlation to the real signal. Additional analysis showed, that the model was not relying on signal cross talk. This approach, based on staining of the cell nuclei and subsequent prediction of the target antigen, could reduce the number of parallel antigen stains to a minimum and incompatible staining panels could be circumvented in the future. |
| first_indexed | 2024-04-10T21:33:45Z |
| format | Article |
| id | doaj.art-7be0b8c3e2734a808e9858ae563c2a6a |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-04-10T21:33:45Z |
| publishDate | 2022-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-7be0b8c3e2734a808e9858ae563c2a6a2023-01-19T12:47:02ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042022-09-018230530810.1515/cdbme-2022-1078Prediction of Fluorescent Ki67 Staining in 3D Tumor SpheroidsBruch Roman0Vitacolonna Mario1Rudolf Rüdiger2Reischl Markus3Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology,Eggenstein-Leopoldshafen, GermanyCenter for Mass Spectrometry and Optical Spectroscopy (CeMOS) and Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences,Mannheim, GermanyCenter for Mass Spectrometry and Optical Spectroscopy (CeMOS) and Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences,Mannheim, GermanyInstitute for Automation and Applied Informatics, Karlsruhe Institute of Technology,Eggenstein-Leopoldshafen, Germany3D cell culture models are important tools for the development and testing of new therapeutics. In combination with immunoassays and 3D confocal microscopy, crucial information like morphological or metabolic changes can be examined during drug testing. However, a common limitation of immunostainings is the number of dyes that can be imaged simultaneously, as overlaps in the spectral profiles of the different dyes may result in cross talk. We therefore present a 3D deep learning method, able to predict fluorescent stainings of specific antigens on the basis of a nuclei staining. Using the proliferation marker Ki67, we showed that the presented model was able to predict the Ki67 staining with a strong correlation to the real signal. Additional analysis showed, that the model was not relying on signal cross talk. This approach, based on staining of the cell nuclei and subsequent prediction of the target antigen, could reduce the number of parallel antigen stains to a minimum and incompatible staining panels could be circumvented in the future.https://doi.org/10.1515/cdbme-2022-1078label synthesis3dfluorescence microscopydeep learningki67 prediction |
| spellingShingle | Bruch Roman Vitacolonna Mario Rudolf Rüdiger Reischl Markus Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids Current Directions in Biomedical Engineering label synthesis 3d fluorescence microscopy deep learning ki67 prediction |
| title | Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids |
| title_full | Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids |
| title_fullStr | Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids |
| title_full_unstemmed | Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids |
| title_short | Prediction of Fluorescent Ki67 Staining in 3D Tumor Spheroids |
| title_sort | prediction of fluorescent ki67 staining in 3d tumor spheroids |
| topic | label synthesis 3d fluorescence microscopy deep learning ki67 prediction |
| url | https://doi.org/10.1515/cdbme-2022-1078 |
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