The PECAn image and statistical analysis pipeline identifies Minute cell competition genes and features

Michael E. Baumgartner (), Paul F. Langton, Remi Logeay, Alex Mastrogiannopoulos, Anna Nilsson-Takeuchi, Iwo Kucinski, Jules Lavalou and Eugenia Piddini ()
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Michael E. Baumgartner: University Walk
Paul F. Langton: University Walk
Remi Logeay: University Walk
Alex Mastrogiannopoulos: University Walk
Anna Nilsson-Takeuchi: University Walk
Iwo Kucinski: University of Cambridge
Jules Lavalou: University Walk
Eugenia Piddini: University Walk

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Investigating organ biology often requires methodologies to induce genetically distinct clones within a living tissue. However, the 3D nature of clones makes sample image analysis challenging and slow, limiting the amount of information that can be extracted manually. Here we develop PECAn, a pipeline for image processing and statistical data analysis of complex multi-genotype 3D images. PECAn includes data handling, machine-learning-enabled segmentation, multivariant statistical analysis, and graph generation. This enables researchers to perform rigorous analyses rapidly and at scale, 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 cell growth in competing wing discs and identify, by statistical regression analysis, tissue parameters that model and correlate with competitive death. Furthermore, using PECAn, we identify several genes with a role in cell competition by conducting an RNAi-based screen.

Date: 2023
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DOI: 10.1038/s41467-023-38287-x

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