SOAX: A software for quantification of 3D biopolymer networks

Abstract

Filamentous biopolymer networks in cells and tissues are routinely imaged by confocal microscopy. Image analysis methods enable quantitative study of the properties of these curvilinear networks. However, software tools to quantify the geometry and topology of these often dense 3D networks and to localize network junctions are scarce. To fill this gap, we developed a new software tool called “SOAX”, which can accurately extract the centerlines of 3D biopolymer networks and identify network junctions using Stretching Open Active Contours (SOACs). It provides an open-source, user-friendly platform for network centerline extraction, 2D/3D visualization, manual editing and quantitative analysis. We propose a method to quantify the performance of SOAX, which helps determine the optimal extraction parameter values. We quantify several different types of biopolymer networks to demonstrate SOAX's potential to help answer key questions in cell biology and biophysics from a quantitative viewpoint.

Nanoscale movements of cellulose microfibrils in primary cell walls

“…To calculate axial or transverse strains from the AFM images, the axial or transverse distances between five to ten pairs of stable (nonsliding) microfibril vertices were measured in ImageJ for each set of experiments. To analyze microfibril orientation, microfibrils were automatically detected by SOAX software 36 as ‘snakes’ (segments), which are active contours represented by a series of points that align along the intensity ridges of the image. The SOAX parameters were manually adjusted, using a snake point separation of 0.7–1 pixel.…”

“…Since long snakes contribute to the orientation histogram in proportion to their length, the contribution of short snakes in noisy parts of the image was negligible. The snakes were cut at detected snakes junctions 36 prior to evaluating their orientation, to eliminate the very small contribution of sharp angle changes at intersections. The mean snake orientations were compared at each time point by paired t-test from three sets of experiments.…”

Strain-driven criticality underlies nonlinear mechanics of fibrous networks

“…We construct one such measure: the strain-derivative of average bending-angle in the network and using finite-size scaling demonstrate its divergence in the thermodynamic limit. Recently Xu et al have developed an image analysis software SOAX, which can accurately track fibers in 3D [54]. It is an interesting idea to use SOAX together with confocal shear cell rheology [55] to experimentally measure the average bending angle in reconstituted biopolymer networks.…”

Extracting microtubule networks from superresolution single-molecule localization microscopy data

“…, 2014, 2015), and, partly due to the limited spatial resolution, these have largely focused on regions near cell edges (Saban et al. , 2006; Gan et al.…”