Deep machine learning for cell segmentation and quantitative analysis of radial plant growth

• Verfasst von: Zakieva, Alexandra [VerfasserIn]
• Verfasst von: Cerrone, Lorenzo [VerfasserIn]
• Verfasst von: Greb, Thomas [VerfasserIn]
• Titel: Deep machine learning for cell segmentation and quantitative analysis of radial plant growth
• Verf.angabe: Alexandra Zakieva, Lorenzo Cerrone, Thomas Greb
• E-Jahr: 2023
• Jahr: 28 April 2023
• Umfang: 10 S.
• Fussnoten: Online verfügbar 18. April 2023, Artikelversion 28. April 2023 ; Gesehen am 16.06.2023
• Titel Quelle: Enthalten in: Cells & development
• Ort Quelle: Amsterdam : Elsevier, 2021
• Jahr Quelle: 2023
• Band/Heft Quelle: 174(2023) vom: Apr., Artikel-ID 203842, Seite 1-10
• ISSN Quelle: 2667-2901
• DOI: doi:10.1016/j.cdev.2023.203842
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Arabidopsis hypocotyl
• Sach-SW: Automated image analysis
• Sach-SW: Cambium
• Sach-SW: PlantSeg
• Sach-SW: Quantitative histology
• Sach-SW: Radial plant growth
• Sach-SW: Wood formation
• K10plus-PPN: 1850444412

Abstract

Plants produce the major part of terrestrial biomass and are long-term deposits of atmospheric carbon. This capacity is to a large extent due to radial growth of woody species - a process driven by cambium stem cells located in distinct niches of shoot and root axes. In the model species Arabidopsis thaliana, thousands of cells are produced by the cambium in radial orientation generating a complex organ anatomy enabling long-distance transport, mechanical support and protection against biotic and abiotic stressors. These complex organ dynamics make a comprehensive and unbiased analysis of radial growth challenging and asks for tools for automated quantification. Here, we combined the recently developed PlantSeg and MorphographX image analysis tools, to characterize tissue morphogenesis of the Arabidopsis hypocotyl. After sequential training of segmentation models on ovules, shoot apical meristems and adult hypocotyls using deep machine learning, followed by the training of cell type classification models, our pipeline segments complex images of transverse hypocotyl sections with high accuracy and classifies central hypocotyl cell types. By applying our pipeline on both wild type and phloem intercalated with xylem (pxy) mutants, we also show that this strategy faithfully detects major anatomical aberrations. Collectively, we conclude that our established pipeline is a powerful phenotyping tool comprehensively extracting cellular parameters and providing access to tissue topology during radial plant growth.