bioRxiv preprint doi: https://doi.org/10.1101/2021.03.05.434067; this version posted July 3, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Deep learning-based quantification of arbuscular mycorrhizal 2 fungi in plant roots 3 Edouard Evangelisti1*, Carl Turner2, Alice McDowell3, Liron Shenhav1, Temur Yunusov1, Aleksandr 4 Gavrin1, Emily K. Servante4, Clément Quan1, Sebastian Schornack1*. 5 1 Sainsbury Laboratory, University of Cambridge. Cambridge, UK. 6 2 Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge. 7 Cambridge, UK. 8 3 Present address: Department of Biochemistry, University of Cambridge. Cambridge, UK. 9 4 Department of Plant Sciences, University of Cambridge. Cambridge, UK. 10 * Correspondence:
[email protected];
[email protected] 11 Summary 12 Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular 13 mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current 14 approaches to quantifying the extent of root colonisation and the abundance of hyphal structures in 15 mutant roots rely on staining and human scoring involving simple, yet repetitive tasks prone to 16 variations between experimenters. 17 We developed AMFinder which allows for automatic computer vision-based identification and 18 quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root 19 images using convolutional neural networks. 20 AMFinder delivered high-confidence predictions on image datasets of roots of multiple plant hosts 21 (Nicotiana benthamiana, Medicago truncatula, Lotus japonicus, Oryza sativa) and captured the 22 altered colonisation in ram1-1, str and smax1 mutants.