A Robust Network-based Spatiotemporal Analysis of Filamentous Structures

Abstract

Abstract The actin cytoskeleton forms a dynamic network composed of filaments that remain flexible when bundled up, leading to complex filamentous structures in plant cells. Understanding the properties of these filamentous structures under different conditions and in different cell types can provide insight into their function. Yet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties. To address this problem, we devised a network-based approach termed Gra ph of F ilaments over T ime (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from imaging data. Our comparative analyses using both synthetic and real-world actin cytoskeleton networks of Arabidopsis thaliana hypocotyls exposed to different treatments demonstrated that GraFT accurately traces and tracks actin filamentous structures. Moreover, GraFT facilitates automated quantification of properties for filamentous structures, providing fine-grained insights of effects of different treatments on the level of individual structures. Therefore, GraFT offers a substantial step towards an automated framework facilitating robust spatiotemporal studies of the plant actin cytoskeleton.

Plant phenotyping relevance

植物細胞の画像からアクチン繊維構造を追跡・定量するGraFT手法を開発し、合成データと実画像で精度検証しており、表現型取得・抽出が研究の中心です。

Code and data availability

The preprint provides a public GitHub repository with the GraFT tool and data-processing code (MIT licensed), and states that all data files (the Arabidopsis actin cytoskeleton imaging datasets used for the phenotyping analyses) are deposited on Zenodo. The GitHub URL is an allowed URL; the Zenodo DOI is not among the,