Precise Analysis of Kymograph Data for Biological Atomic Force Microscopy using the Line Detection Algorithm

Abstract

Atomic Force Microscopy (AFM) has become a popular technique for studying the dynamics of single-molecule systems including membrane protein complexes in near native conditions. Though the past decades have seen significant enhancements in scanning speed, AFM remains a serial imaging process in which a single tip is physically rastered in x and y over a region of interest. A long-standing question is, “How can one improve the temporal resolution without sacrificing precision?” A kymograph is a graphical representation of spatial position over time, t, where the slow axis of the raster scan has been disabled, converting Z(x,y) → Z(x,t). Another overall challenge is a lack of objective analysis methods for biological AFM. A recent advance in this avenue is the Hessian blob algorithm [Marsh et al. Sci Rep 8, 978 (2018)], which employs scale space analysis along with local image curvature to define particles with high precision. While this method is independent of user parameters, it was developed for punctate “blobs” such as individual membrane protein protrusions, and cannot be applied to non-localized linear features such as kymographs. By utilizing the image skeleton rather than minimizing the determinant of the Hessian Matrix, we demonstrate a line detection algorithm to handle linear features while still avoiding the potential bias of a user defined threshold. The utility of the algorithm is shown by studying conformational dynamics of the general secretory (Sec) system of Escherichia coli. In particular, we analyzed the Sec translocase, which comprises SecA, the ATPase of the Sec system, bound to the translocon, SecYEG. This work demonstrates a statistical analysis of translocase kymographs in the presence and absence of ligands that modulate Sec system activity.