Deep-Learning-Enhanced TopoStats for the Automated Quantification of DNA and Complex Biomolecular Structures

Atomic force microscopy (AFM) enables nanometer-scale, label-free imaging of biomolecules and surfaces under near-native conditions, yet quantitative analysis of AFM data remains limited. This limitation largely arises from the absence of open, automated tools capable of addressing AFM-specific artifacts, data formats, and topographical outputs. Here, we present a substantial advancement to TopoStats, an open-source Python package for AFM image analysis, which introduces advanced analysis algorithms and deep learning capabilities to support more complex samples and richer molecular characterization. The new pipeline facilitates a shift from simple object segmentation and morphometric feature extraction toward automated tracing of molecules through complex crossings, enabling topological classification and curvature analysis. We demonstrate this by differentiating between plasmids with both different topology and sequence using quantitative descriptors, such as the number of crossings and median curvature, that distinguish the samples with statistical significance. Designed for accessibility and reproducibility, TopoStats adheres to the Findable, Accessible, Interoperable, and Reusable (FAIR) for Research Software principles and provides configurable workflows to allow the quantification of subtle structural changes within heterogeneous biological samples. This advances AFM from a fundamentally qualitative visualization technique toward a quantitative analytical tool, revealing properties not accessible with other structural biology techniques.