Implicit Laplacian of Enhanced Edge (ILEE): An unguided quantitative cytoskeletal imaging algorithm

Abstract

The eukaryotic cytoskeleton plays essential roles in cell signaling, trafficking, and motion. Recent work towards defining the temporal and spatial dynamics of cytoskeletal organization, including as a function of cell status, has utilized quantitative analysis of cytoskeletal fluorescence images as a standard approach to define cytoskeletal function. However, due to the uneven spatial distribution of the cytoskeleton, including varied shape and unstable binding efficiency to staining markers, these approaches may not segment cytoskeletal fractions accurately. Additionally, quantitative approaches currently suffer from human bias as well as information loss caused by z-axis projection of raw images. To overcome these obstacles, we developed Implicit Laplacian of Enhanced Edge (ILEE), a cytoskeletal component segmentation algorithm, which uses an 2D/3D-compatible, unguided local thresholding approach, therefore providing less biased and stable results. Empowered by ILEE, we constructed a Python based library for automated quantitative analysis of cytoskeleton images, which computes cytoskeletal indices that covers density, bundling, severing, branching, and directionality. Comparing to various classic approaches, ILEE library generates descriptive data with higher accuracy, robustness, and efficiency. In addition to the analysis described herein, we have developed an open-access ILEE library for community use.