Biology subjects

Byrne, H. M.

Publications and source records attributed to Byrne, H. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Early Ontogenetic Development of Tessellated Calcified Cartilage in Chondrichthyans

Tessellated calcified cartilage (TCC) is a hallmark of the chondrichthyan skeleton, yet its development early in ontogeny across the four major groups (batoids, galeomorphs, squalomorphs, and holocephalans) remains poorly understood. Specialised traits of TCC, such as multi-layered TCC and internal mineralised trabeculae, typically develop in response to feeding mechanics. In this study, we evaluated TCC morphology in the jaws of 12 representative taxa to observe its structure at an early ontogenetic stage to determine whether these specialised features had yet developed. Batoids consistently exhibited well-developed, homogeneous, polygonal tesserae early in ontogeny regardless of jaw morphology or feeding habit. In contrast, galeomorphs displayed high morphological heterogeneity. Notably, we document the first report of an extensive internal trabecular network in a non-batoid elasmobranch, observed in Ginglymostoma cirratum, which may serve to resist the mechanical pressures of specialised suction feeding. Furthermore, we identified voussoir tesserae in galeomorphs for the first time, extending their documented presence across all elasmobranch groups, where they display an inverted aspect ratio (wider than tall) compared to mature forms. The durophagous Mustelus mustelus exhibited surprisingly poor TCC development despite being a durophagous feeder, pointing to a pronounced ontogenetic lag. In Squatina oculata, TCC was characterised by large and thick tesserae and extensive fused tesseral regions which may relate to its explosive ambush predation mode, whereas the holocephalan Chimaera exhibited a poorly mineralized, mesh-like structure without resolvable discrete tesserae or trabeculae-matching findings from previous studies. Across all specimens, multi-layered TCC was absent, confirming that multi-layering develops later in ontogeny. These results demonstrate that generalised models of TCC development based on one group or a few taxa fail to capture the broader diversity of TCC morphology. It also opens up many exciting avenues for further study, and forms the basis for comparisons with fossil chondrichthyans, to investigate the evolution of TCC.

zoology↗

bioRxiv2026

Inferring Cell-Cell Interaction Dynamics from Cell Trajectory Data Using Deep Attention Networks

Interactions between nearby cells are a key driver of cell movement in many biological systems, including collective cell migration and the immune response to cancer. However, inferring the interaction rules in a given system in a manner that is both accurate and biologically interpretable remains a challenge. A valuable experimental method for analysing cell-cell interaction dynamics is the tracking of individual cell locations over a series of time-lapse images, and in this work we present a model, based on the theory of deep attention networks, that learns how cell-cell interactions affect cell movement directly from cell trajectory data. Our approach requires no a priori assumptions about the mechanisms governing cell behaviour, enabling its application to cell trajectory data originating from a diverse range of biological systems. In addition to the model, we develop a suite of tools that exploit the models attention-based structure to present the learned interaction dynamics in an interpretable manner. Our model extends previous applications of deep attention networks to cell movement by providing deeper insights into cell-cell interaction dynamics, moving beyond inferring whether cells interact to inferring how these interactions affect cell movement, and providing the ability to infer type dependent cell-cell interaction dynamics in multi-type cell movement systems. By combining data-driven learning and structural interpretability, our approach represents a highly general methodology for linking cell trajectory data to mechanistic hypotheses, showing that deep attention networks constitute a powerful exploratory tool for characterising the effect of cell-cell interactions on cell movement in complex cellular systems.

cell biology↗

bioRxiv2026

netPCF: Geometry-Aware Pair Correlation Functions for Spatial Biology

Spatial organisation is a defining feature of biological systems, underpinning cellular interactions, tissue function, disease progression and therapeutic response. Identifying and quantifying spatial organisation may require methods that resolve relationships across spatial scales. The pair correlation function (PCF) quantifies spatial dependence between points across multiple length scales, but its standard Euclidean formulation is poorly suited to data defined on irregular, curved or otherwise structured domains, where tissue geometry may constrain biological organisation and distort Euclidean distances. Here, we introduce netPCF, a geometry-aware extension of the PCF for quantifying spatial organisation on complex biological domains. By representing tissue structures, anatomical surfaces and other constrained geometries as spatial networks, netPCF generalises the PCF beyond extrinsic Euclidean settings. The framework derives the expected behaviour of the statistic under complete spatial randomness using interpretable finite-support kernels, provides bootstrap-based uncertainty quantification, and includes practical criteria for assessing domain discretisation adequacy. We further extend netPCF to marked (labelled) biological data using feature kernels for categorical and continuous attributes, enabling unified analysis of cell identities, marker intensities, phenotypic states, gene expression and other quantitative features on structured domains in any spatial dimension. All methods are implemented in the open-source Python package spacenet. Synthetic studies show that netPCF recovers classical Euclidean behaviour on sufficiently resolved networks and is robust to common imaging noise. We demonstrate its utility in two biological applications. In three-dimensional imaging mass cytometry data from HER2+ breast carcinoma, netPCF separates tissue architecture-driven proximity from biologically meaningful endothelial and immune cell organisation. In reconstructed surfaces of developing murine embryos, netPCF identifies a transition in the Wnt1 -Wnt6 relationship from short-range co-localisation at E9.5 to spatial exclusion at E11.5, a pattern of ectodermal boundary refinement not captured by prior voxel-wise co-expression analysis. Overall, netPCF provides a statistically grounded and practical framework for quantifying spatial organisation on complex biological domains. Author summarySpatial organisation is central to many biological processes, but it is often measured using distances that ignore the shape of the tissue or structure being studied. We introduce netPCF, a method for quantifying multiscale spatial correlation in data that lie on complex biological domains, including irregular, curved, or branching structures. netPCF reconstructs the domain as a distance-preserving spatial network and estimates pair correlation along this intrinsic geometry, allowing spatial associations to be interpreted relative to the structure in which they occur. The framework includes uncertainty estimates and extensions for categorical and continuous markers, supporting analysis of cell types, marker intensities, phenotypic states, and gene expression patterns. In synthetic data, netPCF recovers expected spatial behaviour on well-resolved networks. In biological imaging data, it distinguishes apparent cell proximity caused by breast carcinoma tissue architecture from biologically meaningful cell organisation, and reveals a developmental transition in Wnt gene organisation over the surface of a murine embryo that direct co-expression analysis does not capture. netPCF is available in the open-source Python package spacenet, supplemented with online tutorials supporting practical use across spatial biology applications.

bioinformatics↗

bioRxiv2024

MuSpAn: A Toolbox for Multiscale Spatial Analysis

Advances in multiplex imaging and spatial omics have revolutionised spatial data generation in biology, revealing complex tissue organisation across multiple scales. However, methods for analysing these data have lagged behind, with fragmented, study-specific pipelines and limited guidance for tool selection. To address this, we introduce MuSpAn, a Multiscale Spatial Analysis package offering intuitive, flexible access to a wide range of mathematical tools - including spatial statistics, topological data analysis, geometry, and networks - within a unified framework. MuSpAn supports efficient data querying, is agnostic to imaging modality, and provides extensive documentation and community support. It enables users to create custom pipelines or conduct unbiased exploratory analyses. We demonstrate MuSpAns capacity to interrogate cross-compartmental cell interactions at multiple length scales in both normal and neoplastic tissue using mouse intestinal spatial transcriptomic datasets. Applied to a CMS4-like murine intestinal cancer model, MuSpAn identifies a continuum of fibroblastic functional phenotypes associated with discrete and coordinated fibroblast-immune interactions, highlighting its utility as a discovery tool across diverse biological contexts.

cell biology↗

bioRxiv2024

Multiscale modelling shows how cell-ECM interactions impact ECM fibre alignment and cell detachment

The extracellular matrix (ECM) is a dynamic network structure that surrounds, supports, and influences cell behaviour. It facilitates cell communication and plays an important role in cell functions such as growth and migration. One way that cells interact with the ECM is via focal adhesions, which enable them to sense and respond to matrix mechanical properties and exert traction forces that deform it. This mechanical interplay between cells and the ECM, many aspects of which remain incompletely understood, involves the coordination of processes acting at different spatial scales and is highly influenced by the mechanical properties of the cells, ECM and focal adhesion components. To gain a better understanding of these mechanical interactions, we have developed a multiscale agent-based model based on a mechanical description of forces that simultaneously integrates the mechanosensitive regulation of focal adhesions, cytoskeleton dynamics, and ECM deformation. We use our model to quantify cell-cell communication mediated by ECM deformation and to show how this process depends on the mechanical properties of cells, the ECM fibres and the topology of the ECM network. In particular, we analyse the influence of ECM stiffness and cell contraction activity in the transmission of mechanical cues between cells and how the distinct timescales associated with different processes influence cell-ECM interaction. Our model simulations predict increased ECM deformation for stronger cell contraction and a sweet spot of ECM stiffness for the transmission of mechanical cues along its fibres. We also show how the network topology affects the ability of stiffer ECMs to transmit deformation and how it can induce cell detachment from the ECM. Finally, we demonstrate that integrating processes across different spatial and temporal scales is crucial for understanding how mechanical communication influences cell behaviour. Author summaryThe cell surrounding is a dynamic fibrous network known as the extracellular matrix (ECM). It supports and influences cell behaviour, playing a key role in cell communication, growth, and migration. Cells sense the ECMs mechanical properties and exert traction forces on it, leading to the deformation of matrix fibres and the transmission of mechanical stress. These changes are transmitted along the ECM fibres, influencing the behaviour of neighbouring cells. Different subcellular structures and extracellular matrix components interact at various spatial and temporal scales, making mathematical modelling a valuable tool for analysing these interactions. We have developed a multiscale force-based model that quantifies mechanical stress transmission, captures cell detachment, and explores the impact of mechanical properties of both cells and the ECM. Our analysis shows that stronger cell contraction increases extracellular matrix deformation and suggests a range of extracellular matrix stiffness for effective mechanical cell-cell communication. We also use our model to investigate how ECM network topology can induce cell detachment by modifying the ability of stiff ECMs to transmit deformation when subject to cell-induced traction forces. Our results show the importance of coupling the processes occurring at different scales to capture the overall behaviour.

biophysics↗

bioRxiv2024

Integrating diverse statistical methods to analyse stage-discriminatory cell interactions in colorectal neoplasia

Spatial biology has the potential to unlock information about the disrupted cellular ecosystems that define human disease. Quantitative analysis of spatially-resolved cell interactions allows mapping of tissue self-organisation and assessment of why cells interact differently in physiological and pathological contexts. However, the complexity of mammalian tissues, that occur across a spectrum of length scales, presents significant challenges for spatial analysis, increasing the gap between our capacity to generate and biologically interpret these datasets. Here, we have adapted a range of mathematical tools to develop a suite of spatial descriptors, and deployed them to determine how cell interactions change as colorectal cancer progresses from benign precursors. We demonstrate that combining mathematical analyses permits insightful examination of tissue organisational structures and identifies variable cell-interaction pathways that underpin disease progression. Mathematical tool triangulation can cross-corroborate spatial biology findings, facilitating development of analysis pipelines that are robust to individual method limitations.

cancer biology↗

bioRxiv2024

Deciphering the diversity and sequence of extracellular matrix and cellular spatial patterns in lung adenocarcinoma using topological data analysis

Extracellular matrix (ECM) organization influences cancer development and progression. It modulates the invasion of cancer cells and can hinder the access of immune cells to cancer cells. Effective quantification of ECM architecture and its relationship to the position of different cell types is, therefore, important when investigating the role of ECM in cancer development. Using topological data analysis (TDA), particularly persistent homology and Dowker persistent homology, we develop a novel analysis pipeline for quantifying ECM architecture, spatial patterns of cell positions, and the spatial relationships between distinct constituents of the tumour microenvironment. We apply the pipeline to 44 surgical specimens of lung adenocarcinoma from the lung TRACERx study stained with picrosirius red and haematoxylin. We show that persistent homology effectively encodes the architectural features of the tumour microenvironment. Inference using pseudo-time analysis and spatial mapping to centimetre scale tissues suggests a gradual and progressive route of change in ECM architecture, with two different end states. Dowker persistent homology enables the analysis of spatial relationship between any pair of constituents of the tumour microenvironment, such as ECM, cancer cells, and leukocytes. We use Dowker persistent homology to quantify the spatial segregation of cancer and immune cells over different length scales. A combined analysis of both topological and non-topological features of the tumour microenvironment indicates that progressive changes in the ECM are linked to increased immune exclusion and reduced oxidative metabolism.

cancer biology↗

bioRxiv2023

Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics

Flaviviruses are arthropod-borne (arbo)viruses which can emerge rapidly and cause explosive epidemics of severe disease. Some of the most epidemiologically important flaviviruses, including dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), are transmitted by Aedes mosquitoes, most notably Aedes aegypti and Aedes albopictus. After a mosquito blood feeds on an infected host, virus enters the midgut and infects the midgut epithelium. The virus must then overcome a series of barriers before reaching the mosquito saliva and being transmitted to a new host. The virus must escape from the midgut (known as the midgut escape barrier; MEB), which is thought to be mediated by transient changes in the permeability of the midgut-surrounding basal lamina layer (BL) following blood feeding. Here, we present a mathematical model of the within-mosquito population dynamics of flaviviruses that includes the interaction of the midgut and BL which can account for the MEB. Our results indicate a dose-dependency of midgut establishment of infection as well as rate of escape from the midgut: collectively, these suggest that the extrinsic incubation period (EIP) - the time taken for DENV virus to be transmissible after infection - is shortened when mosquitoes imbibe more virus. Additionally, our experimental data indicates that multiple blood feeding events, which more closely mimic mosquito-feeding behavior in the wild, can hasten the course of infections, and our model predicts that this effect is sensitive to the amount of virus imbibed. Our model indicates that mutations to the virus which impact its replication rate in the midgut could lead to even shorter EIPs when double-feeding occurs. Mechanistic models of within-vector viral infection dynamics provide a quantitative understanding of infection dynamics and could be used to evaluate novel interventions that target the mosquito stages of the infection. Author summaryAedes mosquitoes are the main vectors of dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), all of which can cause severe disease in humans with dengue alone infecting an estimated 100-400 million people each year. Understanding the processes that affect whether, and at which rate, mosquitoes may transmit such viruses is, hence, paramount. Here, we present a mathematical model of virus dynamics within infected mosquitoes. By combining the model with novel experimental data, we show that the course of infection is sensitive to the initial dose of virus ingested by the mosquito. The data also indicates that mosquitoes which blood feed subsequent to becoming infected may be able to transmit infection earlier, which is reproduced in the model. This is important as many mosquito species feed multiple times during their lifespan and, any reduction in time to dissemination will increase the number of days that a mosquito is infectious and so enhance the risk of transmission. Our study highlights the key and complementary roles played by mathematical models and experimental data for understanding within-mosquito virus dynamics.

systems biology↗

bioRxiv2023

Extended correlation functions for spatial analysis of multiplex imaging data

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focussed on the quantification and analysis of the resulting point clouds which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labelling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: Topographical Correlation Maps (TCMs) can visualise local clustering and exclusion between cells; Neighbourhood Correlation Functions (NCFs) can identify colocalisation of two or more cell types; and weighted-PCFs (wPCFs) describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate. Impact statementThis paper introduces three methods for performing spatial analysis on multiplex digital pathology images. We apply the methods to synthetic datasets and regions of interest from a murine colorectal carcinoma, in order to illustrate their relative strengths and weaknesses. We note that these methods have wider application to marked point pattern data from other sources.

pathology↗

bioRxiv2023

Enhanced perfusion following exposure to radiotherapy: a theoretical investigation

Tumour angiogenesis leads to the formation of blood vessels that are structurally and spatially heterogeneous. Poor blood perfusion, in conjunction with increased hypoxia and oxygen heterogeneity, impairs a tumours response to radiotherapy. The optimal strategy for enhancing tumour perfusion remains unclear, preventing its regular deployment in combination therapies. In this work, we first identify vascular architectural features that correlate with enhanced perfusion following radiotherapy, using in vivo imaging data from vascular tumours. Then, we present a novel computational model to determine the relationship between these architectural features, blood perfusion, and tumour response to radiotherapy in silico. If perfusion is defined to be the proportion of vessels that support blood flow, we find that vascular networks with small mean diameters and large numbers of angiogenic sprouts show the largest increases in perfusion post-irradiation for both biological and synthetic tumours. We also identify cases where perfusion increases due to the pruning of hypoperfused vessels, rather than blood being rerouted. These results indicate the importance of considering network composition when determining the optimal irradiation strategy. In the future, we aim to use our findings to identify tumours that are good candidates for perfusion enhancement and to improve the efficacy of combination therapies. Author summaryDysregulated tumour vasculature often contains hypoperfused blood vessels which inhibit the delivery of blood-borne anticancer therapies. Radiotherapy, used to treat more than half of all cancer patients, causes DNA damage to vascular endothelial cells, preferentially impacting smaller vessels, leading to their death and vessel pruning. At the same time, experiments measuring changes in tumour perfusion post-irradiation produce varying outcomes and, therefore, the impact of irradiation-induced vessel pruning on network-scale perfusion remains unclear. In this study, we use recent (in vivo) imaging data to identify features of tumour vascular architectures that impact perfusion change post-irradiation. We then use a newly-developed computational framework, directly informed by the experimental observations, to elucidate the relationship between the vascular geometry and topology prior to radiotherapy and the irradiation-induced changes to network perfusion. We find that perfusion increases most significantly for networks of blood vessels with small mean diameters and large numbers of angiogenic sprouts. Our results also distinguish different mechanisms of perfusion increase and we identify cases where rerouting of blood flow causes previously hypoperfused vessels to become perfused. Our study sheds more light on the impact of radiotherapy on tumour blood flow; these insights could be useful for improving anti-cancer treatments.

systems biology↗

bioRxiv2022

Macrophage sensitivity to microenvironmental cues influences spatial heterogeneity of tumours

We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to an agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the three Es of cancer immunoediting: Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a human readable statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. In combination with the cross-PCF (describing interactions between vessels and tumour cells), we show further that each of the three Es of immunoediting is characterised by a distinct PCF signature. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples. Author summaryMultiplex images provide exquisitely detailed information about the spatial distribution and intensity of up to 40 biomarkers within two-dimensional tissue regions, creating challenges and opportunities for quantitative analysis. Although stain intensities are measured on a continuous scale, they are typically converted into discrete labels to simplify subsequent spatial analysis. In this paper we propose a new spatial statistic, the weighted pair correlation function (wPCF), which exploits, rather than neglects, the continuous variation in stain intensity contained in multiplex images, and can characterise both spatial and phenotypic heterogeneity. As proof-of-principle, we apply the wPCF to synthetic data that resemble multiplex images of solid tumours. We generate data from an agent-based model (ABM) that simulates macrophage-tumour interactions. The wPCF shows how the continuous label describing macrophage phenotype is spatially related to categorical labels associated with tumour cells and blood vessels. We demonstrate that correlation functions can categorise spatial relationships in a manner which is interpretable and quantitative. The methods we present can be used to analyse both ABM simulations and multiplex imaging data, with applications that go beyond macrophage phenotype to include other biological processes that exhibit continuous variation (e.g., cancer cell stemness, biomarkers for T-cell exhaustion, and levels of oxygenation).

systems biology↗

bioRxiv2021

A DNA-structured mathematical model of cell-cycle progression in cyclic hypoxia

New experimental data have shown how the periodic exposure of cells to low oxygen levels (i.e., cyclic hypoxia) impacts their progress through the cell-cycle. Cyclic hypoxia has been detected in tumours and linked to poor prognosis and treatment failure. While fluctuating oxygen environments can be reproduced in vitro, the range of oxygen cycles that can be tested is limited. By contrast, mathematical models can be used to predict the response to a wide range of cyclic dynamics. Accordingly, in this paper we develop a mechanistic model of the cell-cycle that can be combined with in vitro experiments, to better understand the link between cyclic hypoxia and cell-cycle dysregulation. A distinguishing feature of our model is the inclusion of impaired DNA synthesis and cell-cycle arrest due to periodic exposure to severely low oxygen levels. Our model decomposes the cell population into four compartments and a time-dependent delay accounts for the variability in the duration of the S phase which increases in severe hypoxia due to reduced rates of DNA synthesis. We calibrate our model against experimental data and show that it recapitulates the observed cell-cycle dynamics. We use the calibrated model to investigate the response of cells to oxygen cycles not yet tested experimentally. When the re-oxygenation phase is sufficiently long, our model predicts that cyclic hypoxia simply slows cell proliferation since cells spend more time in the S phase. On the contrary, cycles with short periods of re-oxygenation are predicted to lead to inhibition of proliferation, with cells arresting from the cell-cycle when they exit the S phase. While model predictions on short time scales (about a day) are fairly accurate (i.e, confidence intervals are small), the predictions become more uncertain over longer periods. Hence, we use our model to inform experimental design that can lead to improved model parameter estimates and validate model predictions.

cancer biology↗

bioRxiv2019

A mathematical dissection of the adaptation of cell populations to fluctuating oxygen levels

The disordered network of blood vessels that arises from tumour angiogenesis results in variations in the delivery of oxygen into the tumour tissue. This brings about regions of chronic hypoxia (i.e. sustained low oxygen levels) and regions with alternating phases of low and relatively higher oxygen levels within vascularised tumours, and makes it necessary for cancer cells to adapt to fluctuating environmental conditions. We use a phenotype-structured model to dissect the evolutionary dynamics of cell populations exposed to fluctuating oxygen levels. In this model, the phenotypic state of every cell is described by a continuous variable that provides a simple representation of its metabolic phenotype, ranging from fully oxidative to fully glycolytic, and cells are grouped into two competing populations that undergo heritable, spontaneous phenotypic variations at different rates. Model simulations indicate that, depending on the rate at which oxygen is consumed by the cells, nonlinear dynamic interactions between cells and oxygen can stimulate chronic hypoxia and cycling hypoxia. Moreover, the model supports the idea that under chronic-hypoxic conditions lower rates of phenotypic variation lead to a competitive advantage, whereas higher rates of phenotypic variation can confer a competitive advantage under cycling-hypoxic conditions. In the latter case, the numerical results obtained show that bet-hedging evolutionary strategies, whereby cells switch between oxidative and glycolytic phenotypes, can spontaneously emerge. We explain how these results can shed light on the evolutionary process that may underpin the emergence of phenotypic heterogeneity in vascularised tumours.

cancer biology↗

bioRxiv2019

Abnormal morphology biases haematocrit distribution in tumour vasculature and contributes to heterogeneity in tissue oxygenation

Oxygen heterogeneity in solid tumours is recognised as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal vascular structure of the tumour, but the precise mechanisms linking abnormal structure and compromised oxygen transport are only partially understood. In this paper, we investigate the role that RBC transport plays in establishing oxygen heterogeneity in tumour tissue. We focus on heterogeneity driven by network effects, which are challenging to observe experimentally due to the reduced fields of view typically considered. Motivated by our findings of abnormal vascular patterns linked to deviations from current RBC transport theory, we calculate average vessel lengths [Formula] and diameters [Formula] from tumour allografts of three cancer cell lines and observe a substantial reduction in the ratio [Formula] compared to physiological conditions. Mathematical modelling reveals that small values of the ratio{lambda} (i.e. {lambda} < 6) can bias haematocrit distribution in tumour vascular networks and drive heterogeneous oxygenation of tumour tissue. Finally, we show an increase in the value of{lambda} in tumour vascular networks following treatment with the anti-angiogenic cancer agent DC101. Based on our findings, we propose{lambda} as an effective way of monitoring the efficacy of antiangiogenic agents and as a proxy measure of perfusion and oxygenation in tumour tissue undergoing anti-angiogenic treatment. Significance statementOxygen heterogeneity in solid tumours is recognised as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal tumour vascular structure. We investigate the role that anomalies in RBC transport play in establishing oxygen heterogeneity in tumour tissue. We introduce a metric to characterise tumour vasculature (mean vessel length-to-diameter ratio,{lambda} ) and demonstrate how it predicts tissue oxygen heterogeneity. We also report an increase in{lambda} following treatment with the antiangiogenic agent DC101. Together, we propose{lambda} as an effective way of monitoring the action of anti-angiogenic agents and a proxy measure of oxygen heterogeneity in tumour tissue. Unravelling the causal relationship between tumour vascular structure and tissue oxygenation will pave the way for new personalised therapeutic approaches.

cancer biology↗

bioRxiv2019

Efferocytosis perpetuates substance accumulation inside macrophage populations

In both cells and animals, cannibalism can transfer harmful substances from the consumed to the consumer. Macrophages are immune cells that consume their own dead via a process called cannibalistic efferocytosis. Macrophages that contain harmful substances are found at sites of chronic inflammation, yet the role of cannibalism in this context remains unexplored. Here we take mathematical and experimental approaches to study the relationship between cannibalistic efferocytosis and substance accumulation in macrophages. Through mathematical modelling, we deduce that substances which transfer between individuals through cannibalism will concentrate inside the population via a coalescence process. This prediction was confirmed for macrophage populations inside a closed system. We used image analysis of whole slide photomicrographs to measure both latex microbead and neutral lipid accumulation inside murine bone marrow-derived macrophages (104 - 105 cells) following their stimulation into an inflammatory state ex vivo. While the total number of phagocytosed beads remained constant, cell death reduced cell numbers and efferocytosis concentrated the beads among the surviving macrophages. Since lipids are also conserved during efferocytosis, these cells accumulated lipid derived from the membranes of dead and consumed macrophages (becoming macrophage foam cells). Consequently, enhanced macrophage cell death increased the rate and extent foam cell formation. Our results demonstrate that cannibalistic efferocytosis perpetuates exogenous (e.g. beads) and endogenous (e.g. lipids) substance accumulation inside macrophage populations. As such, cannibalism has similar detrimental consequences in both cells and animals.

immunology↗

bioRxiv2019

A lipid-structured model for macrophage populations in atherosclerotic plaques

Atherosclerosis is a chronic inflammatory disease driven by the accumulation of pro-inflammatory, lipid-loaded macrophages at sites inside artery walls. These accumulations lead to the development of atherosclerotic plaques. The rupture of plaques that contain lipid-rich necrotic cores can trigger heart attacks and strokes via occlusion of blood vessels. We construct and analyse a system of partial integro-differential equations that model lipid accumulation by macrophages, including generating apoptotic cells and a necrotic core. The model includes the following cell behaviours: recruitment of macrophages into the plaque; macrophage ingestion of low density lipoproteins LDL and of apoptotic cells and necrotic material; lipid offloading to high density lipoproteins (HDL); macrophage emigration; and macrophage apoptosis and necrosis of apoptotic cells. With this model, we study how changes in parameters predict the characteristic features of plaque pathology. In particular, we find the qualitative form of lipid distribution across the macrophage population and show that high lipid loads can occur in the absence of LDL ingestion. We also demonstrate the importance of macrophage emigration in the model in mitigating and resolving inflammation and plaque lipid accumulation. ContributionsO_LIHZF: conceptualisation, formal analysis, investigation, methodology, visualisation, writing-- original draft preparation, writing--review and editing. C_LIO_LIHMB: conceptualisation, funding acquisition, methodology, project administration, resources, supervision, writing--review and editing. C_LIO_LIMRM: conceptualisation, funding acquisition, methodology, project administration, resources, supervision, writing--original draft, writing--review and editing. C_LI

immunology↗

bioRxiv2019

Age-structure as key to delayed logistic proliferation of scratch assays

Scratch assays are in-vitro methods for studying cell migration. In these experiments, a scratch is made on a cell monolayer and recolonisation of the scratched region is imaged to quantify cell migration rates. Typically, scratch assays are modelled by reaction diffusion equations depicting cell migration by Fickian diffusion and modelling proliferation by a logistic term. In a recent paper (Jin, W. et al. Bull Math Biol (2017)), the authors observed experimentally that during the early stage of the recolonisation process, there is a disturbance phase where proliferation is not logistic, and this is followed by a growth phase where proliferation appears to be logistic. The authors did not identify the precise mechanism that causes the disturbance phase but showed that ignoring it can lead to incorrect parameter estimates. The aim of this work is to show that a non-linear age-structured population model can account for the two phases of proliferation in scratch assays. The model consists of an age-structured cell cycle model of a cell population, coupled with an ordinary differential equation describing the resource concentration dynamics in the substrate. The model assumes a resource-dependent cell cycle threshold age, above which cells are able to proliferate. By studying the dynamics of the full system in terms of the subpopulations of cells that can proliferate and the ones that can not, we are able to find conditions under which the model captures the two-phase behaviour. Through numerical simulations we are able to show that the resource concentration in the substrate regulates the biphasic dynamics.

cell biology↗

bioRxiv2017

The importance of geometry in the corneal micropocket assay

The corneal micropocket angiogenesis assay is an experimental protocol for studying vessel network formation, or neovascularization, in vivo. The assay is attractive due to the ease with which the developing vessel network can be observed in the same animal over time. Measurements from the assay have been used in combination with mathematical modeling to gain insights into the mechanisms of angiogenesis. While previous modeling studies have adopted planar domains to represent the assay, the hemispherical shape of the cornea and asymmetric positioning of the angiogenic source can be seen to affect vascular patterning in experimental images. As such, we aim to better understand: i) how the geometry of the assay influences vessel network formation and ii) how to relate observations from planar domains to those in the hemispherical cornea. To do so, we develop a three-dimensional, off-lattice mathematical model of neovascularization in the cornea, using a spatially resolved representation of the assay for the first time. Relative to the detailed model, we predict that the adoption of planar geometries has a noticeable impact on vascular patterning, leading to increased vessel merging, or anastomosis, in particular when circular geometries are adopted. Significant differences in the dynamics of diffusible aniogenesis simulators are also predicted between different domains. In terms of comparing predictions across domains, the distance of the vascular front to the limbus metric is found to have low sensitivity to domain choice, while metrics such as densities of tip cells and vessels and vascularized fraction are sensitive to domain choice. Given the widespread adoption and attractive simplicity of planar tissue domains, both in silico and in vitro, the differences identified in the present study should prove useful in relating the results of previous and future theoretical studies of neovascularization to in vivo observations in the cornea.\n\nAuthor summaryNeovascularization, or the formation of new blood vessels, is an important process in development, wound healing and cancer. The corneal micropocket assay is used to better understand the process and, in the case of cancer, how it can be controlled with drug therapies for improved patient outcomes. In the assay, the hemispherical shape of the cornea can influence the way the vessel network forms. This makes it difficult to directly compare results from experiments with the predictions of mathematical models or cell culture experiments, which are typically performed on flat substrates or planar matrices. In this study, we use mathematical modeling to investigate how the hemispherical shape of the cornea affects vessel formation and to identify how sensitive different measurements of neovascularization are to geometry.

physiology↗