bioRxiv Science⌕ Search

Biology subjects

Waters, S. L.

Publications and source records attributed to Waters, S. L..

5 recordsLinked to original sources

Organ-specific fibroblast dynamics revealed via an integrated experimental-computational framework

Fibrosis is a progressive pathological process driven by dysregulated fibroblast activity and excessive extracellular matrix deposition, leading to scarring, functional decline and eventually organ failure. Although fibroblasts are key mediators of fibrotic remodelling, it remains unclear whether their behaviours are conserved across tissues or organ specific. Here, we combine experimental and computational approaches to dissect fibroblast dynamics underlying cardiac and pulmonary fibrosis. Using in vitro assays, we demonstrate that cardiac and lung fibroblasts differ in morphology, proliferation, and collagen matrix organisation, likely reflecting tissue-specific mechanical and biochemical demands. Lung fibroblasts display higher proliferative capacity and increased myofibroblast activation, forming condition-dependent collagen architectures, with fibres that are more aligned under proinflammatory and fibrotic conditions, but less so under anti-inflammatory conditions. In contrast, cardiac fibroblasts consistently generate diffuse, isotropic collagen matrices across all cytokine conditions. Integrating these data into a mechanistic computational model of fibroblast-collagen interactions, we show that fibroblast motility, rather than cell-cell interactions, dominates tissue dynamics, and that coupling between motility and local collagen density regulates matrix architecture. Simulations reveal that collagen-dependent motility and density-dependent collagen secretion prevent maladaptive matrix alignment, identifying potential mechanisms restraining fibrotic progression. Our findings uncover organ-specific fibroblast behaviours and highlight how integrating experimental and computational frameworks can illuminate the dynamic rules governing fibrosis and inform targeted antifibrotic strategies.

cell biology↗

Optimal control of immune checkpoint inhibitor therapy in a heart-tumour model

Autoimmune myocarditis, or cardiac muscle inflammation, is a rare but frequently fatal side-effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite the dangers that side-effects such as these pose to patients, they are rarely, if ever, included explicitly when mechanistic mathematical modelling of cancer therapy is used for optimization of treatment. In this paper, we develop a two-compartment mathematical model which incorporates the impact of ICIs on both the heart and the tumour. Such a model can be used to inform the conditions under which autoimmune myocarditis may develop as a consequence of treatment. We use this model in an optimal control framework to design optimized dosing schedules for three types of ICI therapy that balance the positive and negative effects of treatment. We show that including the negative side-effects of ICI treatment explicitly within the mathematical framework significantly impacts the predictions for the optimized dosing schedule, thus stressing the importance of a holistic approach to optimizing cancer therapy regimens.

immunology↗

Utilising an in silico model to predict outcomes in senescence-driven acute liver injury

Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet demand and transplant recipients require lifelong immunosuppression. The identification of alternative treatments, e.g. cell therapies, able to tip resolution of injury from inflammation to regeneration requires an understanding of the host response to the degree of injury. We adopt a combined in vivo-in silico approach and develop a mathematical model of acute liver disease able to predict the host response to injury. We utilise the Mdm2fl/fl mouse model together with a single Cre induction through intravenous injection of the hepatotropic Adeno-associated Virus Serotype 8 Cre (AAV8.Cre) to model acute liver injury. We derive a complementary ordinary differential equation model to capture the dynamics of the key cell players in the injury response together with the extracellular matrix. We show that the mathematical model is able to predict the host response to moderate injury via qualitative comparison of the model predictions with the experimental data. We then use the model to predict the host response to mild and severe injury, and test these predictions in vivo, obtaining good qualitative agreement.

cell biology↗

Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors

Autoimmune myocarditis is a rare, but frequently fatal, side effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite extensive experimental work on the causes, development and progression of this disease, much still remains unknown about the importance of the different immunological pathways involved. We present a mathematical model of autoimmune myocarditis and the effects of ICIs on its development and progression to either resolution or chronic inflammation. From this, we gain a better understanding of the role of immune cells, cytokines and other components of the immune system in driving the cardiotoxicity of ICIs. We parameterise the model using existing data from the literature, and show that qualitative model behaviour is consistent with disease characteristics seen in patients in an ICI-free context. The bifurcation structures of the model show how the presence of ICIs increases the risk of developing autoimmune myocarditis. This predictive modelling approach is a first step towards determining treatment regimens that balance the benefits of treating cancer with the risk of developing autoimmune myocarditis.

immunology↗

Experimental and mathematical modelling of magnetically labelled mesenchymal stromal cell delivery

A key challenge for stem cell therapies is the delivery of therapeutic cells to the repair site. Magnetic targeting has been proposed as a platform for defining clinical sites of delivery more effectively. In this paper we use a combined in vitro experimental and mathematical modelling approach to explore the magnetic targeting of mesenchymal stromal cells (MSCs) labelled with magnetic nanoparticles using an external magnet. This study aims to (i) demonstrate the potential of magnetic tagging for MSC delivery, (ii) examine the effect of red blood cells (RBCs) on MSC capture efficacy and (iii) highlight how mathematical models can provide both insight into mechanics of therapy and predictions about cell targeting in vivo. In vitro MSCs are cultured with magnetic nanoparticles and circulated with RBCs over an external magnet. Cell capture efficacy is measured for varying magnetic field strengths and RBC percentages. We use a 2D continuum mathematical model to represent the flow of magnetically tagged MSCs with RBCs. Numerical simulations demonstrate qualitative agreement with experimental results showing better capture with stronger magnetic fields and lower levels of RBCs. We additionally exploit the mathematical model to make hypotheses about the role of extravasation and identify future in vitro experiments to quantify this effect.

bioengineering↗