Biology subjects

Shirgill, S.

Publications and source records attributed to Shirgill, S..

4 recordsLinked to original sources

bioRxiv2026

SynthMLM: A framework for interpretable analysis and synthetic localisation data generation for SMLM

Quantitative analysis of single-molecule localisation microscopy (SMLM) data remains challenging because biologically diverse, well-annotated datasets are limited, whilst nanoscale protein organisation is heterogeneous and difficult to describe with hand-tuned metrics. We present SynthMLM, a framework that infers interpretable structural descriptors from experimental SMLM data and uses these descriptors to generate synthetic localisation datasets. We demonstrate SynthMLM by generating descriptor-matched synthetic datasets corresponding to diverse experimental SMLM datasets and evaluating their agreement with real data using descriptor-level and embedding-based measures. By enabling controlled generation of synthetic localisation data, SynthMLM provides a practical resource for benchmarking SMLM analysis methods, testing algorithm failure modes, and developing machine-learning workflows where large, labelled datasets are required.

biophysics↗

bioRxiv2026

Mathematical modelling of a novel bioactive glass treatment for bacterial biofilms

Chronic wounds (principally pressure sores, venous leg ulcers and diabetic foot ulcers) are a drain on global health services and remain a major area of unmet clinical need. Chronic wounds are characterised by a bacterial biofilm (densely aggregated colonies of bacteria encased by a matrix of extracellular polymeric substances), which hinders innate immune response and can prevent wound healing. Bioactive glass (BG) fibres doped with antimicrobial metal ions, such as silver, can offer a promising treatment for chronic wound infections, where silver is well known for its antimicrobial activity against a range of pathogens and is commonly used in wound dressings. We first present a system of non-linear partial differential equations to model the treatment of a chronic wound biofilm infection with BG fibres. The BG fibres are assumed to have two mechanisms of action against the biofilm: physical disruption of the top layers of the biofilm by the BG fibres; and release of antimicrobial silver ions from the BG fibres, which then diffuse into the biofilm and can kill the bacteria. Treatment-associated parameters are estimated from in vitro experimental data using a combination of least-squares minimisation and Approximate Bayesian Computation (ABC). Sensitivity investigations are performed on other parameters that cannot currently be calculated experimentally to investigate their influence on treatment efficacy. We thus predict key parameter regimes that should lead to biofilm eradication, crucially informing the future design of metal-doped BG fibres to maximise treatment efficacy.

microbiology↗

bioRxiv2025

Nanoscale spatial-omics via contrastive embedding of single-molecule localisation data

Omics approaches have revolutionised biology, and cells can now be routinely characterised on the genomic, transcriptomic and proteomic levels. However, there is an additional pillar; the (nanoscale) spatial organisation of molecules in the cell - information now accessible through super-resolution microscopy. We present a contrastive learning framework for nanoscale spatial-omics that embeds single-molecule localisation microscopy data into a latent space representing protein architecture directly to enabling comparative analysis. Using simulated and experimental data, we demonstrate its ability to enable new bioanalysis capabilities including assessing changes to cellular nanoscale architecture arising from pharmacological treatments, cell type, fluorophore selection or data-processing workflows. The approach supports downstream tasks such as clustering proteins by nanoscale organisation, mapping dose-response trajectories and identifying batch effects in replicate datasets, establishing contrastive learning as a scalable foundation for nanoscale spatial-omics and providing a platform for comparative phenotyping, quality control, and hypothesis generation.

bioinformatics↗

bioRxiv2024

Nano-org, a functional resource for single-molecule localisation microscopy data

We present a publicly accessible, curated, and functional resource, termed "nano-org", containing single-molecule localisation microscopy (SMLM) data representing the nanoscale distributions of proteins in cells. Nano-org is searchable by comparing the statistical similarity of the datasets it contains. This unique functionality allows the resource to be used to understand the relationships of nanoscale architectures between proteins, cell types or conditions, enabling a new field of spatial nano-omics.

biophysics↗