Biology subjects

Gilmore, A.

Publications and source records attributed to Gilmore, A..

12 recordsLinked to original sources

bioRxiv2026

NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics

Spatial transcriptomics captures molecular states within cells and their organisation in tissue. However, integrating fine-grained gene information with spatial context at scale remains challenging for existing foundation models. Here we present NexuST, a hierarchical foundation model that repeatedly interleaves gene-level molecular modelling with cell-level spatial modelling, allowing the two levels to refine one another during end-to-end pretraining. For pretraining, we curated HumanST-46M, comprising 45.7 million human cells from 72 datasets across 11 organs and three imaging-based platforms. Across four held-out datasets totalling approximately 2.6 million cells, NexuST achieved state-of-the-art or competitive performance in cell-type annotation, region prediction, gene recovery and neighbourhood-composition prediction. We find that cell-intrinsic expression remains informative even for spatial tasks, as shown by an expression-only PCA baseline, while NexuST shows particularly strong gains where spatial context is essential. Overall, NexuST establishes a hierarchical framework that can serve as a general backbone for future spatial transcriptomics foundation models.

bioinformatics↗

bioRxiv2026

FluoroFate: A generalisable platform for time-resolved single-cell analysis of cell fate enables quantification of cell death dynamics

Fundamental cellular decisions of life and death are governed by intricate and tightly regulated intracellular signalling pathways that determine whether cells proliferate, enter quiescence, or undergo programmed cell death (apoptosis). Live-cell fluorescence imaging enables these processes to be observed in real time at single-cell resolution, but two problems limit their study. First, existing biosensors do not allow apoptotic status and cell cycle progression to be resolved in tandem within the same cell. Second, interpreting live-cell imaging data is challenging even where multiplex reporters exist, as the biological meaning of fluorescent signals depends on their temporal ordering, and large-scale imaging experiments generate complex, multidimensional data that are difficult to analyse systematically and at scale. Here we address both problems. We present FluoroFate, a generalisable and user-friendly graphical interface-driven tool for time-resolved single-cell analysis of multiplex live-cell imaging datasets, which integrates existing, robust deep learning-based segmentation, cell tracking, and temporal classification methods to quantify fluorescent reporter dynamics in individual cells across time without the need for specialist computational expertise. Alongside FluoroFate, we develop tricistronic Fluorescent Ubiquitination-based Cell Cycle Indicator (Fucci) and apoptosis biosensors, enabling simultaneous monitoring of cell cycle progression and caspase activation within the same cell. Applying FluoroFate, we resolve apoptotic and non-apoptotic cell death at the single-cell level based on the temporal ordering of Annexin V and propidium iodide signals, identifying distinct kinetic and phenotypic cell death profiles in response to pharmacological perturbation. We highlight divergent temporal dynamics and modes of cell death between birinapant and cycloheximide treatment, reflecting differences in how TNF/TNFR1 signalling is disrupted by these agents. At the single-cell level, we uncover parallel, independently regulated death programmes, demonstrating that loss of RIPK1 selectively impairs apoptotic cell death whilst leaving non-apoptotic death largely unaffected. We then use FluoroFate to analyse timelapse images of our combined Fucci-apoptosis reporters, resolving cell cycle progression and caspase activation within the same cell over time. Together, FluoroFate and our new cell cycle and apoptosis biosensors represent a broadly applicable platform for extracting mechanistic insight from live-cell imaging data.

cell biology↗

bioRxiv2026

A Comparative Evaluation of Structural MRI Foundation Models for Brain Age Regression and Sex Classification

Foundation models pre-trained on large neuroimaging datasets offer a promising approach to overcome the limited sample sizes typical of clinical imaging studies, yet their generalization across diverse populations remains unclear. We present the first systematic benchmark of four publicly available structural MRI foundation models: AnatCL, BrainIAC, 3D-Neuro-SimCLR, and SwinBrain. Using T1-weighted MRIs from the Parkinson's Progression Markers Initiative (PPMI), Healthy Brain Network (HBN), and Nathan Kline Institute (NKI) datasets, we evaluate these models on sex classification, brain age prediction, and body mass index prediction, comparing against models trained from FreeSurfer-derived cortical thickness and cortical surface area features. Submitted models are evaluated using a standardized frozen feature probing framework. The evaluation methods are available in BrainFMBench, a living benchmark for structural brain MRI foundation models hosted on GitHub, where new models can be added through pull requests. Although some foundation models outperformed FreeSurfer on particular tasks and datasets, 3D-Neuro-SimCLR and AnatCL outperformed the baselines overall, with 3D-Neuro-SimCLR demonstrating the most consistent performance (with the notable exception of HBN sex classification). The remaining models did not consistently outperform the baselines, indicating that the advantage of learned representations over morphometric features was not consistent across datasets and tasks for these models. In addition, cross-model feature correlation analysis reveals that foundation model representations correlate differently with traditional cortical measurements. These findings position structural MRI foundation models, particularly 3D-Neuro-SimCLR and AnatCL, as promising avenues to boost the performance of predictive models in neuroimaging.

neuroscience↗

bioRxiv2025

Vessels hiding in plain sight: quantifying brain vascular morphology in anatomical MR images using deep learning

Non-invasive assessment of brain blood vessels with magnetic resonance (MR) imaging provides important information about brain health and aging. Time-of-flight MR angiography (TOF-MRA) in particular is commonly used to assess the morphology of blood vessels, but acquisition of MRA is time-consuming and is not as commonly employed in research studies or in the clinic as the more standard T1- or T2-weighted MR contrasts (T1w/T2w). To enable quantification of brain blood vessel morphology in T1w/T2w images, we trained a neural network model, anat2vessels, on a dataset with paired MR/MRA. The model provides accurate segmentations as assessed in cross-validation on ground truth images, particularly in cases where T2w images are used. In addition, correlation between features that are extracted from model-based vessel segmentations and from ground truth account for as much as 78% of the variance in these features. We further evaluated the model in another dataset that does not include MRA and found that anat2vessels-based vessel morphology features contain information about aging that is not captured by cortical thickness features that are routinely extracted from T1w/T2w images. Moreover, we found that vessel morphology features are associated with individual variability in blood pressure and cognitive abilities. Taken together these results suggest that anat2vessels could be fruitfully applied to a range of existing and new datasets to assess the role of brain blood vessels in aging and brain health. The methods are provided as open-source software in https://github.com/nrdg/anat2vessels/.

neuroscience↗

bioRxiv2023

A New Role for RNA G-quadruplexes in Aging and Alzheimer's Disease

INTRODUCTIONAs the world population ages, new molecular targets in aging and Alzheimers Disease (AD) are needed to combat the expected influx of new AD cases. Until now, the role of RNA structure in aging and neurodegeneration has largely remained unexplored. METHODSIn this study, we examined human hippocampal postmortem tissue for the formation of RNA G-quadruplexes (rG4s) in aging and AD. RESULTSWe found that rG4 immunostaining strongly increased in the hippocampus with both age and with AD severity. We further found that neurons with accumulation of phospho-tau immunostaining contained rG4s, that rG4 structure can drive tau aggregation, and that rG4 staining density depended on APOE genotype in the human tissue examined. DISCUSSIONCombined with previous studies showing the dependence of rG4 structure on stress and the extreme power of rG4s at oligomerizing proteins, we propose a model of neurodegeneration in which chronic rG4 formation is linked to proteostasis collapse. These morphological findings suggest that further investigation of RNA structure in neurodegeneration is a critical avenue for future treatments and diagnoses.

neuroscience↗

bioRxiv2023

Novel in-silico predicted matrikines are differential mediators of in vitro and in vivo cellular metabolism

The exogenous application of small peptides can beneficially affect clinical skin appearance (wrinkles) and architecture (collagen and elastic fibre deposition and epidermal thickness). However, the discovery of new bioactive peptides has not been underpinned by any guiding hypothesis. As endogenous extracellular matrix (ECM)-derived peptides produced during tissue remodelling can act as molecular signals influencing cell metabolism, we hypothesised that protease cleavage site prediction could identify putative novel matrikines with beneficial activities. Here, we present an in silico to in vivo discovery pipeline, which enables the prediction and characterisation of peptide matrikines which differentially influence cellular metabolism in vitro. We use this pipeline to further characterise a combination of two novel ECM peptide mimics (GPKG and LSVD) which act in vitro to enhance the transcription of ECM organisation and cell proliferation genes and in vivo to promote epithelial and dermal remodelling. This pipeline approach can both identify new matrikines and provide insights into the mechanisms underpinning tissue homeostasis and repair.

cell biology↗

bioRxiv2022

A role for the anterior hippocampus in autobiographical memory construction regardless of temporal distance

Mounting evidence suggests distinct functional contributions of the anterior and posterior hippocampus to autobiographical memory retrieval, but how these subregions function under different retrieval demands as memories age is not yet understood. Specifically, autobiographical memory retrieval is not a homogenous process, rather, it is thought to consist of multiple stages: an early stage of memory construction and a later stage of detailed elaboration, which may differently engage the hippocampus over time. In the present study, we analyzed data from 40 participants who constructed and overtly elaborated upon recent and remote memories in response to picture cues in the fMRI scanner. We previously reported a temporal gradient in the posterior hippocampus during the elaboration period of autobiographical retrieval, with posterior hippocampal activation observed for recent but not remote timepoints. Here, we consider the previously unanalyzed construction stage of retrieval, where participants searched for and selected a memory. We found no evidence of a temporal gradient during memory construction, instead observing strong anterior hippocampus activity regardless of memory remoteness. Our findings suggest a unique contribution of the anterior hippocampus to the construction process of autobiographical retrieval over time. These findings highlight that retrieval processes, which have yet to be considered in current models of systems consolidation, offer novel insights to hippocampal subregion function over time.

neuroscience↗

bioRxiv2020

Parallel Hippocampal-Parietal Circuits for Self- and Goal-oriented Processing

The hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. The human hippocampus has been thought of as being solely functionally connected to a set of neocortical regions known as the default mode network (DMN), which supports self-referential cognition. Using individual-specific precision functional mapping of resting state fMRI data, we found the anterior hippocampus (head and body) to be preferentially connected to the DMN as expected. The hippocampal tail, however, was strongly preferentially connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This resting state functional connectivity (RSFC) anterior-posterior dichotomy was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the head and body of the hippocampus (DMN), relatively sparing the tail (PMN). Anterior and posterior hippocampal connectivity was network-specific even though the DMN and PMN are interdigitated in medial parietal cortex. The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel, but distinct circuits between the hippocampus and medial parietal cortex for self vs. goal-oriented processing.

neuroscience↗

bioRxiv2019

Force-independent interactions of talin and vinculin govern integrin-mediated mechanotransduction

Talin, vinculin and paxillin are core components of the dynamic link between integrins and actomyosin. Here we study the mechanisms that mediate their activation and association using a mitochondrial-targeting assay, structure-based mutants, and advanced microscopy. As expected, full-length vinculin and talin are auto-inhibited and do not interact with each other in this state. Contrary to previous models that propose a critical role for forces driving talin-vinculin association, our data show that force-independent relief of auto-inhibition is sufficient to mediate their tight interaction. Interestingly, paxillin can bind to both talin and vinculin when either is inactive. Further experiments demonstrate that adhesions containing paxillin and vinculin can form without talin following integrin activation. However, these are largely deficient in exerting traction forces to the matrix. Our observations lead to a model whereby paxillin contributes to talin and vinculin recruitment into nascent adhesions. Activation of the talin-vinculin axis subsequently leads to the engagement with the traction force-machinery and focal adhesion maturation.

cell biology↗

bioRxiv2019

Variations in Structural MRI Quality Impact Measures of Brain Anatomy: Relations with Age

Subject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy or indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to more directly measure image quality from T1-weighted images to understand if these measures of image quality: 1) relate to rigorous quality-control checks visually completed by human raters; 2) are associated with sociodemographic variables of interest; 3) influence regional estimates of cortical surface area, cortical thickness, and subcortical volumes from the commonly-used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range (N=388; ages 5-21). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical surface area, cortical thickness, and subcortical volumes from multiple regions across the brain ([~]23.4% of all areas investigated). We believe these results are important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

neuroscience↗

bioRxiv2018

ACE: A Workbench using Evolutionary Genetic Algorithms for analysing association in TCGA Data

Modern methods in generating molecular data have dramatically scaled in recent years, allowing researchers to efficiently acquire large volumes of information. However, this has increased the challenge of recognising interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in the cancer genome atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be selected for searching, and highlights significant correlations within the chosen data. It is based on an evolutionary algorithm which is capable of producing results for very large searches in a short time.

bioinformatics↗

bioRxiv2017

Bcl-xL as a poor prognostic biomarker and predictor of response to adjuvant chemotherapy specifically in BRAF-mutant stage II and III colon cancer

BackgroundBRAF mutation occurs in 8-15% of colon cancers (CC), and is associated with poor prognosis in metastatic disease. Compared to wild-type BRAF (BRAFWT) disease, stage II/III CC patients with BRAF mutant (BRAFMT) tumors have shorter overall survival after relapse; however, time-to-relapse is not significantly different. The aim of this investigation was to identify, and validate, novel predictors of relapse of stage II/III BRAFMT CC.\n\nPatients and methodsWe used gene expression data from a cohort of 460 patients (GSE39582) to perform a supervised classification analysis based on risk-of-relapse within BRAFMT stage II/III CC, to identify transcriptomic biomarkers associated with prognosis within this genotype. These findings were validated using immunohistochemistry in an independent population-based cohort of Stage II/III CC (n=691), applying Cox proportional hazards analysis to determine associations with survival.\n\nResultsHigh gene expression levels of Bcl-xL, a key regulator of apoptosis, were associated with increased risk of relapse, specifically in BRAFMT tumors (HR=8.3, 95% CI 1.7-41.7), but not KRASMT/BRAFWT or KRASWT/BRAFWT tumors. High Bcl-xL protein expression in BRAFMT, untreated, stage II/III CC was confirmed to be associated with an increased risk of death in an independent cohort (HR=12.13, 95% CI 2.49-59.13). Additionally, BRAFMT tumors with high levels of Bcl-xL protein expression appeared to benefit from adjuvant chemotherapy (P for interaction =0.006), indicating the potential predictive value of Bcl-xL expression in this setting.\n\nConclusionsThese findings provide evidence that Bcl-xL gene and/or protein expression identifies a poor prognostic subgroup of BRAFMT stage II/III CC patients, who may benefit from adjuvant chemotherapy.\n\nKey MessageO_LIUsing a combination of computational biology discovery and immunohistochemistry validation in independent patient cohorts, we show that high expression of the apoptosis regulator Bcl-xL is associated with disease relapse specifically within BRAF mutant stage II/III colon cancer.\nC_LIO_LIThis data could enable tailored disease management to reduce relapse rates in the most aggressive subtype.\nC_LI

cancer biology↗