FAST: Filamentous Actin Segmentation Tool for quantifying cytoskeletal organization

Abstract

Studying how actin filaments are assembled into different subcellular structures can provide insights into both physiological processes and the mechanisms of disease. However, quantifying the size, abundance, and organization of different classes of actin structure from optical microscopy data remains a challenge. To address this, we developed a deep learning based Filamentous Actin Segmentation Tool (FAST) to accurately and efficiently segment and quantify different classes of actin structure from phalloidin stained confocal microscopy images. We evaluated the performance of this tool to segment and quantify the abundance of different classes of actin structure in different cell lines and with dynamic changes in actin organization using lifeact-GFP during drug treatments. FAST enables quantification of different classes of actin structure from actin images alone, without the need for specific antibodies against proteins in different actin structures and hence can be a useful tool for researchers studying for studying actin related pathways involved in cell motility, cancer metastasis, and drug development. Summary StatementWe developed Filamentous Actin Segmentation Tool (FAST), that leverages deep learning and antibody assisted labeling to segment and quantify actin structures from optical microscopy images.