Biology subjects

O'Toole, P. J.

Publications and source records attributed to O'Toole, P. J..

7 recordsLinked to original sources

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

High resolution live cell imaging to define ultrastructural and dynamic features of the halotolerant yeast Debaryomyces hansenii

Although some budding yeasts have proved tractable and intensely studied models, others are more recalcitrant. Debaryomyces hansenii, an important yeast species in food and biotechnological industries with curious physiological characteristics, has proved difficult to manipulate genetically and remains poorly defined. To remedy this, we have combined live cell fluorescent dyes with high resolution imaging techniques to define the sub-cellular features of D. hansenii, such as the mitochondria, nuclei, vacuoles and the cell wall. Using these tools, we define biological processes like the cell cycle, organelle inheritance and different membrane trafficking pathways of D. hansenii for the first time. Beyond this, reagents designed to study Saccharomyces cerevisiae proteins were used to access proteomic information about D. hansenii. Finally, we optimised the use of label free holotomography to image yeast, defining the physical parameters and visualising sub-cellular features like membranes and vacuoles. Not only does this work shed light on D. hansenii but this combinatorial approach serves as a template for how other cell biological systems, which are not amenable to standard genetic procedures, can be studied.

cell biology↗

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↗

bioRxiv2019

Metastatic breast cancer cells induce altered microglial morphology and electrical excitability in vivo

BackgroundAn emerging problem in the treatment of breast cancer is the increasing incidence of metastases to the brain. Metastatic brain tumours are incurable and can cause epileptic seizures and cognitive impairment, so better understanding of this niche, and the cellular mechanisms, is urgently required. Microglia are the resident brain macrophage population, becoming \"activated\" by neuronal injury, eliciting an inflammatory response. Microglia promote proliferation, angiogenesis and invasion in brain tumours and metastases. However, the mechanisms underlying microglial involvement appear complex and better models are required to improve understanding of function.\n\nMethodsHere, we sought to address this need by developing a model to study metastatic breast cancer cell-microglial interactions using intravital imaging combined with ex vivo electrophysiology. We implanted an optical window on the parietal bone to facilitate observation of cellular behaviour in situ in the outer cortex of heterozygous Cx3cr1GFP/+ mice. Results: We detected GFP-expressing microglia in Cx3cr1GFP/+ mice up to 350 {micro}m below the window without significant loss of resolution. When DsRed-expressing metastatic MDA-MB-231 breast cancer cells were implanted in Matrigel under the optical window, significant accumulation of activated microglia around invading tumour cells could be observed. This inflammatory response resulted in significant cortical disorganisation and aberrant spontaneously-occurring local field potential spike events around the metastatic site.\n\nConclusionsThese data suggest that peritumoral microglial activation and accumulation may play a critical role in local tissue changes underpinning aberrant cortical activity, which offers a possible mechanism for the disrupted cognitive performance and seizures seen in patients with metastatic breast cancer.

cancer biology↗

bioRxiv2017

Ultra-fast super-resolution imaging of biomolecular mobility in tissues

Super-resolution techniques have addressed many biological questions, yet molecular quantification at rapid timescales in live tissues remains challenging. We developed a light microscopy system capable of sub-millisecond sampling to characterize molecular diffusion in heterogeneous aqueous environments comparable to interstitial regions between cells in tissues. We demonstrate our technique with super-resolution tracking of fluorescently labelled chemokine molecules in a collagen matrix and ex vivo lymph node tissue sections, outperforming competing methods.

biophysics↗