Biology subjects

Walker-Samuel, S.

Publications and source records attributed to Walker-Samuel, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Quantitative Susceptibility Mapping for Differentiating Hydroxyapatite and Calcium Oxalate Breast Calcifications at 3T: A Phantom Study

PurposeBreast microcalcifications trigger 70-80% of unnecessary biopsies because current imaging cannot distinguish malignancy-associated hydroxyapatite (HA) from benign-associated calcium oxalate (CaOx). Quantitative susceptibility mapping (QSM) could exploit the susceptibility contrast between these minerals (HA: {Delta}{chi} {approx} -7 ppm; CaOx: {Delta}{chi} {approx} -1 ppm relative to water), but no study has demonstrated compositional differentiation at clinical field strength. This work assessed susceptibility and R2* relaxation rate maps for microcalcification differentiation at 3 T using tissue-mimicking phantoms. MethodsA phantom comprising 12 tubes, each containing co-embedded HA and CaOx particles in BaCl2-crosslinked alginate gels (pure alginate, adipose-mimicking, and fibroglandular tissue-mimicking relaxation properties; n = 4 per type), were scanned at 0.70 mm and 0.86 mm isotropic resolution using a multi-echo gradient echo sequence. A consensus-aligned QSM pipeline and mono-exponential R2* fitting was developed. A digital twin phantom simulation quantified the contributions of partial volume effects and Total Variation (TV) regularisation to susceptibility underestimation. ResultsQSM detected HA in 18/24 measurements ({Delta}{chi}peak = -0.37 {+/-} 0.07 ppm in alginate at 0.70 mm) and CaOx in 0/24. R2* mapping detected HA in 23/24 and CaOx in 22/24. The digital twin identified TV regularisation as the dominant signal loss mechanism (57.5% loss), exceeding partial volume effects (24.3% loss). Combined parameters yielded three classification categories: QSM-positive with elevated R2* (HA), QSM-negative with moderate R2* (CaOx), and neither elevated (no calcification). ConclusionQSM at 3 T enables categorical HA detection while R2* provides complementary CaOx sensitivity, together enabling two-parameter microcalcification classification from a single multi-echo acquisition.

biophysics↗

bioRxiv2026

Mechanisms of macular oedema development and therapeutic response: An in-silico modelling study

PurposeTo investigate the interplay between biomechanics, fluid dynamics, and solute transport in Diabetic Macular Oedema (DMO) using a mechanics-based computational model, aiming to elucidate mechanisms behind variable treatment outcomes. MethodsWe developed a multiphysics model of the retina within a porous media framework. The model integrates OCT-derived geometry, vascular leakage, retinal biomechanics (including Muller cell fibre architecture), retinal pigment epithelium (RPE) function, and anti-VEGF transport. We simulated oedema development and therapeutic response by varying these parameters systematically. ResultsModel results showed that active RPE pumping is essential for maintaining retinal dehydration. Our simulations revealed a critical trade-off related to Muller cell architecture: the physiological z-shaped orientation protects against oedema but impedes anti-VEGF drug delivery to leaky vessels. In contrast, a pathological, vertical Muller cell alignment increases oedema susceptibility but allows for a faster therapeutic response due to improved drug diffusion. ConclusionsMuller cell orientation presents a trade-off between biomechanical protection and therapeutic efficacy, offering a novel mechanistic explanation for the variable patient responses to anti-VEGF therapy observed clinically. This in-silico framework is a powerful tool for dissecting DMO pathophysiology and has the potential to guide the development of personalised treatment strategies.

biophysics↗

bioRxiv2025

Real time evaluation of the liver microcirculation by whole organ machine perfusion within an MRI system

ObjectivesMachine perfusion of organs outside of the body is a growing area of research with significant applications in the fields of organ preservation and transplantation, but more widely it offers a new approach to study disease processes and to evaluate new therapeutics and devices. Magnetic Resonance Imaging (MRI) allows for non-invasive assessment of organ structure and function, enabling quantitative measurement of tissue perfusion and microstructure. In this study, we demonstrate that MR imaging sequences can be obtained from machine-perfused porcine livers using a modified perfusion rig for MR compatibility and highlight the quantitative measures that can be obtained through this methodology. Materials and Methods7 porcine livers were retrieved fresh from the abattoir using a previously published protocol and following transport in cold preservative underwent perfusion with oxygenated autologous blood inside a 3T clinical MRI scanner using a custom modified perfusion rig. Multiple MR imaging sequences were acquired: T2-weighted imaging, Diffusion Weighted Imaging and Dynamic Contrast Enhanced imaging following injection of Gadolinium dye into the portal vein and hepatic artery. Histological analysis was performed to assess preservation injury to the liver. Control samples for histology were obtained from livers with similar preservation periods but preserved in standard cold storage on ice (Static Cold Storage). ResultsConcurrent MR imaging and machine perfusion were successfully performed, allowing dynamic measurement of tissue perfusion to be obtained in ex vivo livers, including calculation of gadolinium contrast enhancement curves and Apparent Diffusion Coefficient maps. Segmentation of vessels down to a radius of 0.45mm allowed detailed morphological analysis of the vascular network, including extraction of clinically relevant parameters such as vessel tortuosity. Histological evaluation showed better preservation of the hepatic acinar structure in perfused than non-perfused livers. ConclusionsOur results demonstrate that MR imaging of machine-perfused organs enables high-resolution quantitative evaluation of whole-organ vascular morphology and flow dynamics. This platform provides opportunities to study vascular pathology in diseased human organs and evaluate novel therapeutic interventions, with particular relevance for drug-delivery strategies.

physiology↗

bioRxiv2024

Tube2FEM: a general-purpose highly-automated pipeline for flow related processes in (embedded) tubular objects

This paper presents a comprehensive and highly-automated open-source pipeline for simulating flow and flow-related processes in (embedded) tubular structures. Addressing a critical gap in computational fluid dynamics (CFD) and simulation sciences, it facilitates the transition from raw three-dimensional imaging, graph networks, or CAD models of tubular objects to refined, simulation-ready meshes. This transition, traditionally labor-intensive and challenging, is streamlined and highly-automated through a series of innovative steps that include surface mesh processing, centre-line construction, anisotropic mesh generation, and volumetric meshing, leading to Finite Element Method (FEM) simulations. The pipeline leverages a range of open-source software and libraries, notably GIBBON, FEniCS, and Paraview, to provide flexibility and broad applicability across different simulation scenarios, ranging from biomedical to industrial applications. We demonstrate the versatility of our approach through five distinct applications, including the mesh generation for soil-root systems, lung airways, microcirculation networks, and portal vein networks, each originating from a different data source. Moreover, for several of these cases, we incorporate Computational Fluid Dynamics (CFD) simulations and strategies for 3D-1D coupling between the embedding domain and the embedded structures. Finally, we outline some future perspectives aimed at enhancing accuracy, reducing computational time, and incorporating advanced modeling and boundary condition strategies to further refine the frameworks capabilities.

bioengineering↗

bioRxiv2024

Examination of the role of mutualism in immune evasion

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumor requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumor implemented as a 2D-hexagonal lattice model suggests tumor survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumors with equivalent growth rates and no mutualism are more likely to be eliminated than tumors with mutualism. Tumors with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumors, mutualism is critical for early tumorigenesis.

bioinformatics↗

bioRxiv2024

A unique subset of pericystic endothelium associates with aberrant microvascular remodelling and impaired blood perfusion early in polycystic kidney disease

Hallmarks of autosomal dominant polycystic kidney disease (ADPKD), the most common hereditary kidney anomaly, include expanding fluid-filled epithelial cysts, inflammation, and fibrosis. Despite previous work showing the potential of vascular-based therapies, renal microvascular alterations in ADPKD, and their timing, are poorly understood. Using single-cell transcriptomics of human kidney microvasculature, we identify a population of endothelial cells adjacent to cysts in ADPKD. This pericystic endothelium, distinguishable by its expression of osteopontin (SPP1), has a distinct molecular profile compared to the common endothelial cell injury signature in other kidney diseases. SPP1+ pericystic endothelium was also present in an orthologous mouse model of ADPKD before overt kidney functional decline. By interrogating geometric, topological and fractal properties from three-dimensional imaging of early ADPKD mouse kidneys, we show that pericystic endothelium associates with disorganisation and non-uniformity of the renal cortical microvasculature. Concurrently, we detected region-specific reductions in cortical blood flow within ADPKD murine kidneys using arterial spin labelling. We conclude that ADPKD kidneys contain a unique subset of endothelium manifesting with aberrant remodelling and impaired blood perfusion. Its detection, prior to renal functional decline, advocates the vasculature as a therapeutic target to modulate or preserve renal function in early ADPKD.

pathology↗

bioRxiv2024

Reconstructing microvascular network skeletons from 3D images: what is the ground truth?

Structural changes to microvascular networks are increasingly highlighted as markers of pathogenesis in a wide range of disease, e.g. Alzheimers disease, vascular dementia and tumour growth. This has motivated the development of dedicated 3D imaging techniques, alongside the creation of computational modelling frameworks capable of using 3D reconstructed networks to simulate functional behaviours such as blood flow or transport processes. Extraction of 3D networks from imaging data broadly consists of two image processing steps: segmentation followed by skeletonisation. Much research effort has been devoted to segmentation field, and there are standard and widely-applied methodologies for creating and assessing gold standards or ground truths produced by manual annotation or automated algorithms. The Skeletonisation field, however, lacks widely applied, simple to compute metrics for the validation or optimisation of the numerous algorithms that exist to extract skeletons from binary images. This is particularly problematic as 3D imaging datasets increase in size and visual inspection becomes an insufficient validation approach. In this work, we first demonstrate the extent of the problem by applying 4 widely-used skeletonisation algorithms to 3 different imaging datasets. In doing so we show significant variability between reconstructed skeletons of the same segmented imaging dataset. Moreover, we show that such a structural variability propagates to simulated metrics such as blood flow. To mitigate this variability we introduce a new, fast and easy to compute super-metric that compares the volume, connectivity, medialness, correct bifurcation point identification and homology of the reconstructed skeletons to the original segmented data. We then show that such a metric can be used to select the best performing skeletonisation algorithm for a given dataset, as well as to optimize its parameters. Finally, we demonstrate that the super-metric can also be used to quickly identify how a particular skeletonisation algorithm could be improved, becoming a powerful tool in understanding the complex implication of small structural changes in a network.

bioengineering↗

bioRxiv2024

The temporal evolution of cancer hallmarks

Cancer hallmarks describe key physiological characteristics that distinguish cancers from normal tissues. The temporal order in which these hallmarks appear during cancer pathogenesis is of interest from both evolutionary and clinical perspectives but has not been investigated before. Here, we order hallmarks based on the allele frequency and selective advantage of mutations in cancer hallmark genes across >10k untreated primary tumors and >8K healthy tissues. Using this novel approach, we identified a common evolutionary trajectory for 27 of 32 cancer types with genomic instability as the first and immune evasion as the last hallmark. We demonstrated widespread positive selection in cancer and strong negative selection in normal tissues for all hallmarks. Notable exceptions to the hallmark ordering in tumours were melanomas (uveal and skin) suggesting that strong environmental factors could disrupt common evolutionary paths. Clustering of hallmark trajectories across patients revealed 2 clusters defined by early or late genomic instability, with differential prognosis. Our study is the first to identify the temporal order of cancer hallmarks during tumorigenesis and demonstrate a prognostic value that could be exploited for early detection and risk stratification across multiple cancer types.

cancer biology↗

bioRxiv2023

tUbe net: a generalisable deep learning tool for 3D vessel segmentation

Deep learning has become an invaluable tool for bioimage analysis but, while open-source cell annotation software such as cellpose are widely used, an equivalent tool for three-dimensional (3D) vascular annotation does not exist. With the vascular system being directly impacted by a broad range of diseases, there is significant medical interest in quantitative analysis for vascular imaging. We present a new deep learning model, coupled with a human-in-the-loop training approach, for segmentation of vasculature that is generalisable across tissues, modalities, scales and pathologies. To create a generalisable model, a 3D convolutional neural network was trained using curated data from modalities including optical imaging, computational tomography and photoacoustic imaging. Through this varied training set, the model was forced to learn common features of vessels cross-modality and scale. Following this, the pre-trained foundation model was fine-tuned to different applications with a minimal amount of manually labelled ground truth data. It was found that the foundation model could be specialised to a new datasets using as little as 0.3% of the volume of said dataset for fine-tuning. The fine-tuned model was able to segment 3D vasculature with a high level of accuracy (DICE coefficient between 0.89 and 0.98) across a range of applications. These results show a general model trained on a highly varied data catalogue can be specialised to new applications with minimal human input. This model and training approach enables users to produce accurate segmentations of 3D vascular networks without the need to label large amounts of training data.

microbiology↗

bioRxiv2023

Physics-informed deep generative learning for quantitative assessment of the retina

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks that requires no human input and out-performs human labelling. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.

biophysics↗

bioRxiv2022

A three-dimensional, discrete-continuum model of blood flow in microvascular networks

We present a 3D discrete-continuum model to simulate blood pressure in large microvascular tissues in the absence of known capillary network architecture. Our hybrid approach combines a 1D Poiseuille flow description for large, discrete arteriolar and venular networks coupled to a continuum-based Darcy model, point sources of flux, for transport in the capillary bed. We evaluate our hybrid approach using a vascular network imaged from the mouse brain medulla/pons using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). We use the fully-resolved vascular network to predict the hydraulic conductivity of the capillary network and generate a fully-discrete pressure solution to benchmark against. Our results demonstrate that the discrete-continuum methodology is a computationally feasible and effective tool for predicting blood pressure in real-world microvascular tissues when capillary microvessels are poorly defined.

biophysics↗

bioRxiv2022

Three-dimensional imaging and single-cell transcriptomics of the human kidney implicate perturbation of lymphatics in alloimmunity

Studies of the structural and molecular features of the lymphatic vasculature, which clears fluid, macromolecules and leukocytes from the tissue microenvironment, have largely relied on animal models, with limited information in human organs beyond traditional immunohistochemical assessment. Here, we use three-dimensional imaging and single-cell RNA-sequencing to study lymphatics in the human kidney. We found a hierarchical arrangement of lymphatic vessels within human kidneys, initiating along specialised nephron epithelium in the renal cortex and displaying a distinct, kidney-specific transcriptional profile. In chronic transplant rejection we found kidney allograft lymphatic expansion alongside a loss of structural hierarchy, with human leukocyte antigen-expressing lymphatic vessels infiltrating the medulla, presenting a putative target for alloreactive antibodies. This occurred concurrently with lymphatic vessels invading and interconnecting tertiary lymphoid structures at early stages of lymphocyte colonisation. Analysis of intercellular signalling revealed upregulation of co-inhibitory molecule-mediated CD4+ T cell-lymphatic crosstalk in rejecting kidneys, potentially acting to limit local alloimmune responses. Overall, we delineate novel structural and molecular features of human kidney lymphatics and reveal perturbations to their phenotype and transcriptome in the context of alloimmunity. SUMMARYLymphatics regulate fluid balance and immune cell accumulation but are under-studied in human organs such as the kidney. Jafree and colleagues profiled human kidney lymphatics using three-dimensional imaging and single-cell RNA-sequencing, revealing structural and transcriptional perturbations in rejecting kidney transplants.

immunology↗

bioRxiv2021

Multiscale three-dimensional imaging of intact human organs down to the cellular scale using hierarchical phase-contrast tomography

Human organs are complex, three-dimensional and multiscale systems. Spatially mapping the human body down through its hierarchy, from entire organs to their individual functional units and specialised cells, is a major obstacle to fully understanding health and disease. To meet this challenge, we developed hierarchical phase-contrast tomography (HiP-CT), an X-ray phase propagation technique utilising the European Synchrotron Radiation Facilitys Extremely Brilliant Source: the worlds first high-energy 4th generation X-ray source. HiP-CT enabled three-dimensional and non-destructive imaging at near-micron resolution in soft tissues at one hundred thousand times the voxel size whilst maintaining the organs structure. We applied HiP-CT to image five intact human parenchymal organs: brain, lung, heart, kidney and spleen. These were hierarchically assessed with HiP-CT, providing a structural overview of the whole organ alongside detail of the organs individual functional units and cells. The potential applications of HiP-CT were demonstrated through quantification and morphometry of glomeruli in an intact human kidney, and identification of regional changes to the architecture of the air-tissue interface and alveolar morphology in the lung of a deceased COVID-19 patient. Overall, we show that HiP-CT is a powerful tool which can provide a comprehensive picture of structural information for whole intact human organs, encompassing precise details on functional units and their constituent cells to better understand human health and disease.

physiology↗

bioRxiv2020

Multi-fluorescence high-resolution episcopic microscopy (MF-HREM) for three dimensional imaging of adult murine organs

Three-dimensional microscopy of large biological samples (>0.5 cm3) is transforming biological research. Many existing techniques require trade-offs between image resolution, sample size and method complexity. A simple robust instrument with the potential to perform large volume 3D imaging currently exists in the form of the Optical HREM, however the development of the instrument to date is limited to single fluorescent wavelength imaging with non-specific eosin staining. This work presents developments to realize the potential of the HREM to become Multi-fluorescent High Resolution Episcopic Microscopy (MF-HREM). MF-HREM is a serial-sectioning and block-facing wide-field fluorescence imaging technique, which does not require tissue clearing or optical sectioning. Multiple developments are detailed in sample preparation and image post-processing to enable multiple specific stains in large samples, and show how these enable segmentation and quantification of the data. The application of MF-HREM is demonstrated in a variety of biological contexts: 3D imaging of whole tumor vascular networks and tumor cell invasion in xenograft tumors up to 7.5 mm3 at resolutions of 2.75 m, quantification of glomeruli volume in the adult mouse kidney, and quantification of vascular networks and white matter track orientation in adult mouse brain.

biophysics↗

bioRxiv2019

Modelling the transport of fluid through heterogeneous, whole tumours in silico

It is critically important to understand and predict fluid transport within both physiological and pathological tissues in order to develop effective treatment strategies. Recent advances in high-resolution optical imaging allow the acquisition of whole tumour vascular networks which can be used to parameterise computational models to predict the fluid dynamics at all length scales across the tissue. This enables hypothesis testing around the role of the tumour microenvironment in determining transport characteristics, which would otherwise be unavailable using traditional experiments.\n\nIn this study, we present a novel computational framework for the efficient simulation of vascular blood flow and interstitial fluid transport based on complete three-dimensional, whole tumour vasculature obtained using high-resolution optical imaging. This framework comprises a Poiseuille flow model which simulates vascular blood flow within the vessel network, coupled via point sources of flux to a porous medium model describing interstitial fluid transport. We develop a computational algorithm for prescription of network boundary conditions and validation of tissue-scale fluid transport against measured in vivo perfusion data acquired using biomedical imaging tools. We present simulations of the model on orthoptic murine glioma and human colorectal carcinoma xenograft data (GL261 and LS147T, respectively), and perform sensitivity analysis on key unknown parameters relating to the tissue microenvironment, to understand their impact in predicting vascular and interstitial flow. Finally, we simulate radially varying vascular normalisation in a LS147T tumour and hypothesise that uniform normalisation is required to lower tumour interstitial fluid pressure.\n\nOur computational framework permits predictions of whole tumour fluid dynamics which incorporate the inherent architectural heterogeneities appearing at the micron-scale, and outputs three-dimensional spatial maps detailing these flow properties from micro to macro length scales. This provides vital information on the tumour microenvironment which could enable the design and delivery of future anti-cancer therapies.\n\nAuthor summaryThe structure of tumours varies widely, with dense and chaotically-formed networks of blood vessels that differ between each individual tumour and even between different regions of the same tumour. This atypical environment can inhibit the delivery of anti-cancer therapies. Computational tools are urgently required which incorporate micron-scale tumour biomechanics to predict tissue-scale fluid dynamics, and consequently the efficacy of cancer therapies.\n\nWe have developed a computational framework which integrates the complex tumour vascular architecture to predict fluid transport across all lengths scales in whole tumours. This enables computationally efficient hypothesis testing of cancer therapies which manipulate the tumour microenvironment in order to improve drug delivery to tumours.

cancer biology↗

bioRxiv2017

Combining optical imaging of cleared tissue with mathematical modelling to predict drug delivery and therapeutic response

Understanding how drugs are delivered to diseased tissue, and their subsequent spatial and temporal distribution, is a key factor in the development of effective, targeted therapies. However, the interaction between the pathophysiology of diseased tissue and individual therapeutic agents can be complex, and can vary significantly between individuals. In cancer, suboptimal dosing resulting from poor delivery can cause reduced treatment efficacy, upregulation of resistance mechanisms and can even stimulate growth. Preclinical tools to better understand drug delivery are therefore urgently required, which incorporate the inherent variability and heterogeneity of human disease. To meet this need, we have combined multiscale mathematical modelling, high-resolution optical imaging of intact, optically-cleared tumour tissue from animal models, and in vivo magnetic resonance imaging (MRI). Our framework, named REANIMATE (REAlistic Numerical Image-based Modelling of biologicAl Tissue substratEs) allows large tissue samples to be investigated as if it were a living sample, in detailed, highly controlled, computational experiments. Specifically, we show that REANIMATE can be used to predict the heterogeneous delivery of specific therapeutic agents, in disparate two murine xenograft models of human colorectal carcinoma. Given the wide adoption of optical clearing equipment in biomedical research laboratories, REANIMATE enables a new paradigm in cancer drug development, which could also be applied to other disease areas.

biophysics↗

bioRxiv2017

Investigating low-velocity fluid flow in tumours using convection-MRI

Several distinct fluid flow phenemena occur in solid tumours, including intravascular blood flow and interstitial convection. To probe low-velocity flow in tumors resulting from raised interstitial fluid pressure, we have developed a novel magnetic resonance imaging (MRI) technique named convection-MRI. It uses a phase-contrast acquisition with a dual-inversion vascular nulling preparation to separate intra- and extra-vascular flow. Here, we report the results of experiments in flow phantoms, numerical simulations and tumor xenograft models to investigate the technical feasibility of convection-MRI. We report a good correlation between estimates of effective fluid pressure from convection-MRI with gold-standard, invasive measurements of interstitial fluid pressure in mouse models of human colorectal carcinoma and show that convection-MRI can provide insights into the growth and response to vascular-targeting therapy in colorectal cancers.

biophysics↗

bioRxiv2017

Insights into cerebral haemodynamics and oxygenation utilising in vivo mural cell imaging and mathematical modelling

The neurovascular mechanisms underpinning the local regulation of cerebral blood flow (CBF) and oxygen transport remain elusive. In this study we have combined novel in vivo imaging of cortical microvascular and mural cell architecture with mathematical modelling of blood flow and oxygen transport, to provide new insights into CBF regulation that would be inaccessible in a conventional experimental context. Our study implicates vasomotion of smooth muscle actin-covered vessels, rather than pericyte-covered capillaries, as the main mechanism for modulating tissue oxygenation. We also resolve seemingly paradoxical observations in the literature around reduced blood velocity in response to arteriolar constrictions and deduce the cause to be propagation of constrictions to upstream penetrating arterioles. We provide support for pericytes acting as signalling conduits for upstream smooth muscle activation, and erythrocyte deformation as a complementary regulatory mechanism. Finally, we caution against the use of blood velocity as a proxy measurement for flow. Our combined imaging-modelling platform complements conventional experimentation allowing cerebrovascular physiology to be probed in unprecedented detail.

biophysics↗