Implicit Laplacian of Enhanced Edge: An Unguided Algorithm for Accurate and Automated Quantitative Analysis of Cytoskeletal Images

The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, which is broadly associated with immunity and diseases of human and plants. To date, most analyses aiming at defining the temporal and spatial dynamics of the cytoskeleton have relied on qualitative and quantitative analysis of fluorescence images to describe cytoskeletal function. While state-of-the-art, these approaches have limitations: the diverse shape and brightness of the cytoskeleton cause considerable inaccuracy in both human-driven and automated approaches, and the widely adopted image projection process (3D to 2D) leads to bias and information loss. Here, we describe the development and application of Implicit Laplacian of Enhanced Edge (ILEE), an unguided approach that uses a 2D/3D-compatible local thresholding algorithm for the quantitative evaluation of cytoskeletal status and organization at high performance. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing numerous biologically-interpretable indices measuring the density, bundling, severing, branching, and directionality of the cytoskeleton. The data presented herein demonstrate that ILEE resolves the defects of classic cytoskeleton analysis approaches, enables the measurement of novel cytoskeletal features, and yields quantitatively descriptive data with superior accuracy, stability, and robustness. We released the ILEE algorithm as an open-source library and further developed a Google Colab interface as a community resource.