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Tackling Topology with TopoStats

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Nearly all processes that act on DNA alter its topology, producing knotted, catenated, and supercoiled forms. Determining how these variations in DNA topology affect fundamental DNA interactions is challenging because of the length scale at which they occur, 100x less than the wavelength of light. High-resolution atomic force microscopy (AFM) is unique in its ability to visualise DNA structure and interactions in liquid with sub-molecular resolution without the need for labelling or averaging. Technological developments in high resolution AFM now allow it to visualise single DNA molecules in liquid with sub-molecular resolution measuring the twist, writhe, and topology of individual molecules in liquid as they ‘explore’ their complex conformational space.

However, a rate-limiting step for the widespread adoption of AFM to solve problems inaccessible to the traditional tools of structural biology is a lack of open software pipelines to analyse the increasing volumes of data produced. Automated analysis tools/software pipelines for AFM would reduce reliance on an experienced researcher, minimise selection bias and facilitate the growth of AFM as a quantitative imaging technique. We have developed TopoStats (, an open-source Python utility that loads raw AFM data and handles data cleaning and processing through to identification and characterisation of individual DNA molecules [1].

We use TopoStats to quantify the effect of supercoiling on DNA structure, demonstrating that DNA under superhelical stress is far richer in structure, e.g., containing kinks and defects, than can be observed in short linear sequences [2]. We have built on this foundation to develop tools that can accurately identify, isolate and trace the structure of individual DNA molecules, automatically pinpointing DNA crossings even in complex DNA structures such as knots and catenanes. Our new image analysis routines can almost unambiguously automatically identify under- and over-passing segments of DNA at each crossing, thus allowing full identification of the knot/catenane type and chirality. The information obtained within the AFM is much richer than purely a measure of topology, showing the heterogeneity in structures within each topological population. We propose that our combination of high-resolution microscopy and automated analysis could enable the field to probe how variations in the local structure and conformation of topologically complex DNA affect its interactions with essential cellular proteins [3].

References: [1] Beton, JG et al. Methods 193, 68-79 (2021) [2] Pyne, ALB , Noy A et al. Nature Communications. 12, 1053 (2021) [3] Dos Santos, A et al. Nature Communications (accepted)

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