Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI.
Department of Plant Biology, Michigan State University, East Lansing, MI.
J Cell Biol. 2023 Feb 6;222(2). doi: 10.1083/jcb.202203024. Epub 2022 Dec 19.
The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, broadly associated with immunity and diseases in humans and plants. To date, most studies describing cytoskeleton dynamics and function rely on qualitative/quantitative analyses of cytoskeletal images. While state-of-the-art, these approaches face general challenges: the diversity among filaments causes considerable inaccuracy, and the widely adopted image projection leads to bias and information loss. To solve these issues, we developed the Implicit Laplacian of Enhanced Edge (ILEE), an unguided, high-performance approach for 2D/3D-based quantification of cytoskeletal status and organization. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing biologically interpretable indices measuring the density, bundling, segmentation, branching, and directionality of the cytoskeleton. Our data demonstrated that ILEE resolves the defects of traditional approaches, enables the detection of novel cytoskeletal features, and yields data with superior accuracy, stability, and robustness. The ILEE toolbox is available for public use through PyPI and Google Colab.
真核细胞骨架在细胞信号转导和运输中发挥着重要作用，与人类和植物的免疫和疾病广泛相关。迄今为止，大多数描述细胞骨架动力学和功能的研究都依赖于对细胞骨架图像的定性/定量分析。尽管这些方法是最先进的，但它们仍面临着一些普遍的挑战：由于纤维的多样性导致了相当大的不准确性，以及广泛采用的图像投影会导致偏差和信息丢失。为了解决这些问题，我们开发了 Implicit Laplacian of enhanced Edge (ILEE)，这是一种用于基于 2D/3D 的细胞骨架状态和组织定量的无引导、高性能方法。我们使用 ILEE 构建了一个 Python 库，以实现自动化的细胞骨架图像分析，提供可生物解释的指标，用于测量细胞骨架的密度、束状、分割、分支和方向。我们的数据表明，ILEE 解决了传统方法的缺陷，能够检测到新的细胞骨架特征，并提供具有更高准确性、稳定性和鲁棒性的数据。ILEE 工具包可通过 PyPI 和 Google Colab 供公众使用。
