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Quantifying complexity in DNA structures with high resolution Atomic Force Microscopy
Elizabeth P Holmes1, Max C Gamill1, James I Provan2,3
1School of Chemical, Materials and Biological Engineering, University of Sheffield, Sheffield, UK.
Nature Communications
|July 2, 2025
Summary
This study introduces a new automated pipeline combining Atomic Force Microscopy (AFM) and deep learning to analyze DNA topology. This method quantifies DNA length, conformation, and complex topological structures with high resolution.
Area of Science:
- Molecular Biology
- Biophysics
- Genomics
Background:
- DNA topology is crucial for cellular functions and genome stability.
- Quantifying DNA topology in complex molecules is technically challenging.
- Existing methods lack the throughput and resolution for detailed analysis.
Purpose of the Study:
- To develop a high-throughput, high-resolution method for quantifying DNA topology.
- To analyze the structure and topology of complex DNA molecules, including replication intermediates and recombination products.
- To investigate the impact of surface immobilization on DNA topology.
Main Methods:
- High-resolution Atomic Force Microscopy (AFM) combined with a novel automated deep-learning pipeline.
- Deep learning for DNA backbone tracing and identification of crossing points.
- Coarse-grained simulations to model surface immobilization effects.
Main Results:
- The pipeline accurately quantifies length, conformation, and topology of individual DNA molecules.
- Analysis of stalled replication intermediates (theta structures, late products) and E. coli Xer recombination products (plasmids, knots, catenanes).
- Quantification of surface immobilization effects on twist-writhe partitioning.
Conclusions:
- The developed pipeline offers unprecedented resolution and throughput for DNA topology analysis.
- This method enables detailed structural and topological studies of biologically relevant DNA molecules.
- The findings provide insights into how DNA topology regulates fundamental biological processes.
