Automatic extraction of actin networks in plants

The actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana, including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.Author summary: Most biological cells contain an internal network of protein filaments called the cytoskeleton. This helps provide shape and structure to the cell, and is used for transporting cargo within the cell. One example is actin, the thinnest part of the cytoskeleton, which is present as both individual filaments and thicker bundles. Actin forms complex branched networks that continually change as required. Studying the actin network is difficult since it has hard to accurately find the shape of the network and follow how this changes over time. Here, by focusing on plants, we develop a novel computational algorithm called DRAGoN that can automatically extract the shape of the actin network, giving seventeen precise numerical measurements that describe the network. We then use our algorithm to examine actin in the model plant Arabidopsis thaliana, finding significant differences in the actin network between tissue types, key actin mutants and when the cells are attacked by powdery mildew. This work has important potential applications to global food security and is likely to be easily adaptable to organisms other than plants.