Biology subjects

Shephard, N.

Publications and source records attributed to Shephard, N..

3 recordsLinked to original sources

bioRxiv2026

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

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.

biophysics↗

bioRxiv2025

Altered striatal dopamine dynamics and behavior in Grin2a mutant mice, a genetic mouse model of schizophrenia

Schizophrenia (SCZ), a complex psychiatric disorder with a strong genetic basis, is thought to involve, at least in part, dopamine dysregulation in the striatum. Recent large-scale exome sequencing has identified multiple SCZ risk genes, including GRIN2A (encoding an NMDA (N-methyl-D-aspartate) receptor subunit) and AKAP11 (encoding a protein kinase A binding protein), which is also a risk gene for bipolar disorder (BD). However, the mechanisms by which these genetic risk factors perturb dopamine circuits and cause psychotic symptoms remain poorly understood. We asked how behavior, dorsomedial striatal dopamine (DA) dynamics and the activity of D1-/D2-spiny projection neurons (SPNs) were altered in Grin2a and Akap11 mutants. In the open field, Grin2a knockout mice display hyperlocomotion and an abnormal organization of naturalistic behaviors. They also displayed heightened behavioral responses to objects and auditory stimuli. Grin2a heterozygous animals mostly showed intermediate phenotypes, suggesting a dose-dependent effect of Grin2a loss. Further, Grin2a knockout mutants exhibited aberrant dopamine events, altered coupling of dopamine with locomotor features, and exaggerated stimuli-evoked dopamine responses. Notably, heterozygous animals also showed altered striatal SPN events in the open field reflecting dysregulated neural activity in both dSPNs and iSPNs. Compared to Grin2a mutants, Akap11 mutants displayed opposite phenotypes, showing reduced locomotion with a shift from high- to low-velocity movement and distinct alterations in striatal neural activity. Together, GRIN2A (SCZ risk) and AKAP11 (BD/SCZ risk) mutations induce different, often opposite, effects on striatal dopamine signaling and behavior, suggesting that these two risk genes act through distinct mechanisms.

neuroscience↗

bioRxiv2024

Under or Over? Tracing Complex DNA Topologies with High-Resolution Atomic Force Microscopy

The topology of DNA plays a crucial role in the regulation of cellular processes and genome stability. Despite its significance, DNA topology remains challenging to determine due to the length and conformational complexity of individual topologically constrained DNA molecules. We demonstrate unparalleled resolution of complex DNA topologies using Atomic Force Microscopy (AFM) in aqueous conditions. We present a new high-throughput automated pipeline to determine DNA topology from raw AFM images, using deep-learning methods to trace the backbone of individual DNA molecules and identify crossing points. Our pipeline efficiently determines which segment passes over which, including the handling of challenging crossings, where the path of each molecule may be harder to resolve. We demonstrate the wider applicability of our tracing method by determining the structure of stalled replication intermediates from Xenopus egg extracts, including theta structures and late replication products. By developing new methodologies to accurately trace the DNA path through every crossing, we determine the topology of plasmids, knots and catenanes from the E. coli Xer recombination system. In doing so we uncover a recurrent depositional effect and reveal its origins using coarse-grained simulations. Our approach is broadly applicable to a range of nucleic acid structures, including those which interact with proteins, and opens avenues for understanding fundamental biological processes which are regulated by or affect DNA topology.

biophysics↗