Alice Leonora Bedford Pyne

We use DNA origami to make tadpole-shaped polymers and study their viscoelasticity when prepared in dense solutions using microrheology.

Summary: Shelterin binds telomeres, regulates telomere length, and protects chromosome ends from mis-repair, which is essential for genome stability. Telomere end-protection uniquely depends on the TRF2 subunit, which assembles telomeric DNA into a protective T-loop structure in vivo , yet the biochemical mechanism underlying this process remains unresolved. Using single-molecule optical tweezers and atomic force microscopy, we show that the human shelterin complex tethers distal telomeric DNA repeats, looping the intervening DNA. These bridges withstand tens of piconewtons of applied load, rupture at a force that scales with telomeric repeat number and re-form on relaxation. Structure-function analysis demonstrates that this force-resistant tethering is TRF2-dependent under our conditions and depends on multiple interfaces, with the TRFH domain making a major but non-exclusive contribution: introducing the TRF2 TRFH domain into TRF1 is sufficient to confer tethering, whereas its deletion from TRF2 reduces but does not abolish activity. We propose telomeric dsDNA tethering by TRF2 as a candidate first step in T-loop formation, preceding invasion of the telomeric 3’ ssDNA overhang.

In the cell, DNA must be tightly packed to facilitate its organization into the nucleus, where recognition of homologous sequences underpins key processes such as recombination. Yet the structural basis of DNA–DNA pairing remains unknown. Here, we combine high-resolution atomic force microscopy and atomistic MD simulations to provide the first direct visualization of DNA pairing in the presence of divalent ions. We show that strongly paired DNAs often achieve groove-to-groove alignment, driven by ionic bridges connecting the minor grooves of the two duplexes. These contacts are further stabilized by sequence-specific interactions, whose strength and specificity vary with the ion type. This mechanism of ion-mediated groove alignment provides a molecular framework for the long-hypothesized ‘helical alignment’ model, in which homologous recognition would be facilitated by preserving structural register between the two helices. Together, our findings reveal a fundamental principle by which divalent ions promote DNA–DNA pairing, with broad implications for chromosomal condensation and genome organization.

The structural dynamics of DNA underpin essential biological processes, yet conventional structural biology methods often obscure conformational heterogeneity through ensemble averaging. Atomic force microscopy (AFM) provides single‐molecule topographical maps capable of capturing both local and global variation, but extracting quantitative insight from these images remains challenging. Here, we introduce an automated framework that reduces AFM data to spline representations of the DNA backbone and applies cyclic Procrustes analysis to quantify shape similarity across ensembles. Using purified topoisomers of 339 bp DNA minicircles ranging from relaxed to highly negatively supercoiled, we resolved and measured the relative abundance of conformational states across the different topoisomers, capturing gradual transitions among open circles, compact conformations, and self‐crossing structures that are invisible to techniques such as gel electrophoresis or cryoelectron microscopy (cryo‐EM). We show that beyond quantification, Procrustes distances provide supervisory signals for neural network training, enabling feature extraction tuned to conformational geometry and supporting robust conformation classification of AFM images. Extending the same spline representation to molecular dynamics simulations allows experimental and computational ensembles to be directly compared, establishing a common shape‐based framework for probing conformational variability. Together, these advances transform AFM from a descriptive imaging tool into a quantitative platform for mapping conformational continua, with broad applicability to DNA and other dynamic biomolecular systems.

Atomic force microscopy (AFM) enables nanometre-scale, label-free imaging of biomolecules and surfaces under near-native conditions, yet quantitative analysis of AFM data remains limited compared to other bioimaging modalities. This limitation largely arises from the absence of open, automated tools capable of addressing AFM-specific artefacts, data formats, and topographical outputs. Here, we present the latest version of TopoStats, an open-source Python package for automated and quantitative AFM image analysis, developed as a deep-learning enabled advancement of our original TopoStats software to support more complex samples and richer molecular characterisation. The pipeline integrates all key processing stages, including image flattening and noise correction, object detection and segmentation, morphometric feature extraction, and strand tracing with topological classification. Designed for accessibility and reproducibility, TopoStats adheres to the FAIR for Research Software (FAIR4RS) principles and provides configurable workflows adaptable to diverse biological samples. Combining high-resolution AFM and our analysis pipeline allows the quantification of subtle structural changes within a heterogeneous sample set, revealing properties not accessible with other structural biology techniques. We demonstrate the effectiveness of our pipeline to differentiate between plasmids with both different topology and sequence, by extracting meaningful quantitative descriptors that distinguish the samples with statistical significance. Collectively, these developments establish TopoStats as a versatile framework for high-throughput, quantitative AFM analysis, advancing AFM from a fundamentally qualitative visualisation technique toward a quantitative analytical tool.

Science is losing knowledge it cannot afford to lose. Negative results go unpublished, hard-won expertise walks out the door with departing researchers, and preservation efforts remain fragmented. The consequences are wasted resources, duplicated effort, and missed discoveries. In this perspective, we argue that the research community can act now by embracing alternative dissemination channels, improving documentation best practices, and building sustainable digital infrastructure. We envision moderated platforms for sharing null results and practical know-how, community-driven standards, and AI-powered tools that lower barriers to implementation. With coordinated effort, science can become more open, efficient, and resilient for future generations.

CRISPR–Cas9 is a powerful genome-editing tool ¹ , but genome-wide off-target activity can hinder therapeutic applications. Negative supercoiling ((−)SC) has been implicated in off-target activity, but a molecular-level understanding is lacking. Here, using (−)SC DNA minicircles, we observe supercoiling-driven structural defects in the DNA that are resolved by Cas9 binding. Cryo-electron microscopy structures of Cas9 bound in both the on-target and off-target configurations highlight that the Cas9 HNH domain is poised in a more catalytically competent conformation. New DNA–RNA mismatch geometries are accommodated across the protospacer and structural plasticity in the protospacer adjacent motif distal region of the protospacer is topology dependent. Together, our study reveals the molecular basis for (−)SC-induced Cas9 targeting and provides a framework for the design of next-generation high-fidelity CRISPR effectors with topological context.

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

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.