Biology subjects

Wiggins, L.

Publications and source records attributed to Wiggins, L..

7 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↗

bioRxiv2026

Adapting the OpenFlexure Microscope for Affordable Live-Cell Imaging

Live-cell imaging (LCI) captures dynamic cellular behaviours missed in fixed samples. Researchers in resource-limited settings are underrepresented in the adoption of LCI due to the prohibitive cost of dedicated systems. We adapted the 3D-printed OpenFlexure Microscope (OFM) for LCI in humid tissue culture incubators. We relocated electronics to prevent corrosion, and printed in acrylonitrile styrene acrylate (ASA) to improve image stability in prolonged experiments. Current-limiting resistors reduced heat buildup inside sealed incubators, while our simple graphical user interface (GUI)-based application enabled rapid setup of timelapse experiments. Using a 48-hour docetaxel treatment of breast cancer cells, we validated how the LCI-OFM seamlessly fits into established bioimaging pipelines to generate biologically meaningful data. We further demonstrated broad applicability of the LCI-OFM by imaging Leishmania-infected macrophages in a containment level 3 laboratory and gut fungi in an anaerobic environment. This accessible platform expands opportunities for resource-limited researchers to study locally-relevant health challenges.

cell biology↗

bioRxiv2025

Characterising cancer-stroma interactions through high-content phenotyping from microscopy time-lapses

Understanding how cancer-stromal interactions shape cancer progression requires tools that can capture dynamic phenotypic changes in physiologically relevant conditions. Traditional approaches for studying co-culture interactions, such as transcriptomics and flow cytometry, provide valuable insights but are limited by their static nature and reliance on fixed or dissociated cells. In contrast, label-free time-lapse microscopy preserves temporal and spatial context, enabling observation of live-cell behaviours over time. A major challenge, however, lies in the analysis of the resulting high-dimensional datasets. Using co-cultures of breast cancer cells and cancer-associated fibroblasts (CAFs) as a model system, we show that the CellPhe toolkit enables label-free identification and phenotypic characterisation of different cell types within complex live-cell imaging datasets. Our analysis shows that exposure to CAFs drives marked phenotypic shifts in breast cancer cells, including elongation, loss of cell-cell adhesion, and redistribution of intracellular components - hallmarks of epithelial-mesenchymal transition (EMT). To probe the underlying mechanisms, we performed a Luminex immunoassay on CAF-conditioned media and identified secreted analytes strongly associated with EMT induction. Together, these results highlight how automated phenotyping can be integrated with molecular profiling to identify and characterise cellular processes shaped by stromal interactions and reveal the signalling mediators that drive them.

cancer biology↗

bioRxiv2025

CellPhePy: a Python implementation of the CellPhe toolkit for automated cell phenotyping from microscopy time-lapse videos

We previously developed the CellPhe toolkit1, an open-source R package for automated cell phenotyping from ptychography time-lapse videos. To align with the growing adoption of python-based image analysis tools and to enhance interoperability with widely used software for cell segmentation and tracking, we developed a python implementation of CellPhe, named CellPhePy. CellPhePy preserves all of the core functionality of the original toolkit, including single-cell phenotypic feature extraction, time-series analysis, feature selection and cell type classification. In addition, CellPhePy introduces significant enhancements, such as an improved method for identifying features that differentiate cell populations and extended support for multiclass classification, broadening its analytical capabilities. Notably, the CellPhePy package supports CellPose segmentation and TrackMate tracking, meaning that a set of microscopy images are the only required input with segmentation, tracking and feature extraction fully automated for downstream analysis, without reliance on external applications. The workflows increased flexibility and modularity make it adaptable to different imaging modalities and fully customisable to address specific research questions. CellPhePy can be installed via PyPi or GitHub, and we also provide a CellPhePy GUI to aid user accessibility.

cell biology↗

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↗

bioRxiv2023

Exploring the impact of variability in cell segmentation and tracking approaches

Segmentation and tracking are essential preliminary steps in the analysis of almost all live cell imaging applications. Although the number of open-source software systems that facilitate automated segmentation and tracking continue to evolve, many researchers continue to opt for manual alternatives for samples that are not easily auto-segmented, tracing cell boundaries by hand and re-identifying cells on consecutive frames by eye. Such methods are subject to inter-user variability, introducing idiosyncrasies into the results of downstream analysis that are a result of subjectivity and individual expertise. Such methods are also susceptible to intra-user variability, meaning findings are challenging to reproduce. Here we demonstrate and quantify the degree of intra- and inter-user variability in manual cell segmentation and tracking by comparing the phenotypic metrics extracted from cells segmented and tracked by different members of our research team. Furthermore, we compare the segmentation results for a ptychographic cell image obtained using different automated software and demonstrate the high dependence of performance on their imaging modality optimisation. Our results show that choice of segmentation and tracking methods should be considered carefully in order to enhance the quality and reproducibility of results.

cell biology↗

bioRxiv2021

Sodium accumulation in breast cancer predicts malignancy and treatment response

Breast cancer is the leading cause of cancer-related death in women worldwide. Development of novel noninvasive diagnostic and predictive pathophysiological biomarkers would represent a significant clinical improvement. Here, we explored the utility of non-invasive 23Na MRI to profile tumour physiology using preclinical mouse models of breast cancer. We establish that tissue Na+ concentration ([Na+]) is elevated vs non-tumour regions across multiple different tumour models. Ex vivo SBFI fluorescence imaging corroborated that this elevation in tumour [Na+] is due to increased intracellular [Na+]. Effective treatment with cytotoxic chemotherapy reduced tumour tissue [Na+], but was not detected by 1H diffusion-weighted imaging (DWI). Moreover, combining 23Na MRI and DWI measurements enabled superior classification accuracy of tumour vs non-tumour regions compared to either parameter alone. Quantification of breast tumour tissue [Na+] using 23Na MRI thus represents a novel, accurate, non-invasive diagnostic and predictive imaging biomarker.

cancer biology↗