The codes for the nuclei detection and cell tracking are available on the GitHub ( https://github.com/JerrySongCST/Arabidopsis_root_cortex_cell_tracking ).
Open resource ↗JerrySongCST/Arabidopsis_root_cortex_cell_tracking · lines:152-227Unverified paper record
In-Depth Quantification of Cell Division and Elongation Dynamics at the Tip of Growing Arabidopsis Roots Using 4D Microscopy, AI-Assisted Image Processing and Data Sonification.
Plant & cell physiology · 1 Dec 2023 · 10.1093/pcp/pcad105
Abstract
One of the fundamental questions in plant developmental biology is how cell proliferation and cell expansion coordinately determine organ growth and morphology. An amenable system to address this question is the Arabidopsis root tip, where cell proliferation and elongation occur in spatially separated domains, and cell morphologies can easily be observed using a confocal microscope. While past studies revealed numerous elements of root growth regulation including gene regulatory networks, hormone transport and signaling, cell mechanics and environmental perception, how cells divide and elongate under possible constraints from cell lineages and neighboring cell files has not been analyzed quantitatively. This is mainly due to the technical difficulties in capturing cell division and elongation dynamics at the tip of growing roots, as well as an extremely labor-intensive task of tracing the lineages of frequently dividing cells. Here, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots. We also implemented a data sonification tool that facilitates human recognition of cell division synchrony. Using these tools, we revealed previously unnoted lineage-constrained dynamics of cell division and elongation, and their contribution to the root zonation boundaries.
Plant phenotyping relevance
生長中のシロイヌナズナ根端における細胞分裂・伸長という植物形態動態を、4D顕微鏡、AI画像処理、追跡、データソニフィケーションで半自動定量する手法を開発しており、フェノタイピング手法が中心である。
abstractHere, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots.
abstractWe also implemented a data sonification tool that facilitates human recognition of cell division synchrony.
Code and data availability
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