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PECAn, a pipeline for image processing and statistical analysis of complex mosaic 3D tissues

View ORCID ProfileMichael E. Baumgartner, Paul F. Langton, Alex Mastrogiannopoulos, Remi Logeay, Eugenia Piddini
doi: https://doi.org/10.1101/2021.07.06.451317
Michael E. Baumgartner
1School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
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  • For correspondence: eugenia.piddini@bristol.ac.uk meb3226@gmail.com
Paul F. Langton
1School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
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Alex Mastrogiannopoulos
1School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
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Remi Logeay
1School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
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Eugenia Piddini
1School of Cellular and Molecular Medicine, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, UK
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  • For correspondence: eugenia.piddini@bristol.ac.uk meb3226@gmail.com
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Abstract

Investigating organ biology requires sophisticated methodologies to induce genetically distinct clones within a tissue. Microscopic analysis of such samples produces information-rich 3D images. However, the 3D nature and spatial anisotropy of clones makes sample analysis challenging and slow and limits the amount of information that can be extracted manually. Here we have developed a pipeline for image processing and statistical data analysis which automatically extracts sophisticated parameters from complex multi-genotype 3D images. The pipeline includes data handling, machine-learning-enabled segmentation, multivariant statistical analysis, and graph generation. This enables researchers to run rigorous analyses on images and videos at scale and in a fraction of the time, without requiring programming skills. We demonstrate the power of this pipeline by applying it to the study of Minute cell competition. We find an unappreciated sexual dimorphism in Minute competition and identify, by statistical regression analysis, tissue parameters that model and predict competitive death.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 06, 2021.
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PECAn, a pipeline for image processing and statistical analysis of complex mosaic 3D tissues
Michael E. Baumgartner, Paul F. Langton, Alex Mastrogiannopoulos, Remi Logeay, Eugenia Piddini
bioRxiv 2021.07.06.451317; doi: https://doi.org/10.1101/2021.07.06.451317
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PECAn, a pipeline for image processing and statistical analysis of complex mosaic 3D tissues
Michael E. Baumgartner, Paul F. Langton, Alex Mastrogiannopoulos, Remi Logeay, Eugenia Piddini
bioRxiv 2021.07.06.451317; doi: https://doi.org/10.1101/2021.07.06.451317

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