Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues

Abstract

Abstract The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named large-volume fully automated cell reconstruction (LVACR), enabling the exploration of 3D cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (Populus simonii Carr. and Populus canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem of Pinus tabuliformis Carr. seedlings. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.

Plant phenotyping relevance

植物組織の3D細胞形態・接続性を画像から抽出するLVACRを開発し、ベンチマーク検証と実データ適用を行っており、植物フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe the LVACR framework, LSFM/micro-CT imaging, and analysis, but contain no authors' public deposit of the paper-specific phenotype datasets, image stacks, trained models, or analysis code. All URLs mentioned (ImageJ, GraphPad, XPIWIT, bioimage.io, Seurat, connected-components-3d, SR-Tesseler,

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