Helen M. Byrne

Epithelial monolayers are the foundation of many mammalian organs and have a significant impact on numerous biological processes, including the secretion of cytokines, absorption of waste products and barrier function. During organ development epithelia are subject to many external forces due to the growth of the surrounding tissues. They exhibit complex responses to such forces, with deformation occurring on short timescales (seconds), and relaxation and remodelling occurring on longer timescales (minutes or hours). Existing mathematical and computational models do not typically account for subcellular remodelling, assuming instead that the response to an applied force or strain acts on a single timescale. In this paper we extend an off-lattice, cell-centre modelling framework to account for subcellular and tissue remodelling by introducing a Dynamic Reference Frame (DRF). The DRF provides a discrete, cell-based analogue of morpho-elasticity: it decouples the evolution of each cell's mechanical reference configuration from its instantaneous deformation, providing a phenomenological description of subcellular remodelling processes - cytoskeletal reorganisation, myosin turnover and adhesion bond remodelling - at the cellular scale. Using this extended multicellular model, we reproduce multiscale responses to external mechanical forces, including creep and stress relaxation experiments, and demonstrate that the history of applied deformation influences tissue recovery upon release. Additionally we identify and quantify where and how these multiscale responses occur. The resulting framework allows for more detailed descriptions and analyses of the development and function of biological tissues.

Cells are plastic, highly heterogeneous and change over time. High-content timelapse imaging promises to reveal dynamic cell behaviors, enabling more accurate identification of cell state and cell fate prediction for biological hypothesis generation and perturbation screens. To empower live-cell imaging based screening, we report the development of (1) a Shape, Appearance, Motion (SAM) “phenome”; a universal set of 2185 image-derived features that act as a image-“transcriptome” to comprehensively quantify an object’s instantaneous phenotype; (2) the SAM-Phenotype-Observation-Tool (SPOT), for image-“sequencing” analysis of phenomes. We validate the effectiveness of unbiased SAM-SPOT workflow on publicly available computer vision and 2D single cell imaging datasets. Importantly, we demonstrate that SAM-phenome outperforms features generated by deep learning AI models trained on >1 million fixed single cell and >5000 single cell video frames, respectively. SAM-phenome and SPOT deliver high-throughput, object-treatment-agnostic, comprehensive screening readouts of dynamics, promising to advance novel molecular target discovery and new medicine development.

Live cells in tissue are plastic, phenotypically dynamic, and modify their function in response to genetic and environmental perturbations. To unleash the power of live-cell imaging to identify phenotype-genotype-function coupling over time, we report the development of a standardized Shape-Appearance-Motion (SAM) “phenome” and SAM-Phenotype-Observation-Tool (SPOT), that act as an image-“transcriptome” and image-“transcriptome analyzer” respectively, and provide an unbiased and comprehensive description of morpho-dynamic phenotypes without prior knowledge. We apply SAM-SPOT to our simulated organoids database with known ground-truth and >1.6 million mouse and human organoid instances with defined genetic and chemical perturbations. SAM-SPOT can effectively and robustly characterize 3D morpho-dynamics from 2D projection videos. Combined with single-cell RNA sequencing, SAM-SPOT reveals that altered WNT signaling, but not mutant RAS or p53, predisposes intestinal organoids to irregular morphogenesis. SAM-SPOT advances biomedical discovery by empowering live-cell imaging to identify phenotype-genotype-function relationships through large-scale and cost-effective label-free live-cell imaging.

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 MuSpAn’s 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.

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 it’s 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 model’s 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.

The cellular basis for site-specific inflammation remains unclear. In human fingers, proximal interphalangeal (PIP) joints are preferentially affected by inflammatory arthritis, whereas distal interphalangeal joints are spared, providing a model to investigate the predilection of inflammation to distinct sites. Here we combine single-cell RNA sequencing, imaging and X-ray tomography to examine cellular composition, spatial organization and structure of finger joints during fetal development. PIP joints had a larger synovial volume and were enriched for PI16 ⁺ ‘universal’ fibroblasts. These cells were located in perivascular regions and at developing tendon–ligament interfaces. PI16 ⁺ fibroblasts exhibited both a shared inflammatory and cell-type-specific response to cytokine stimulation, suggesting that the combination of their spatial location and transcriptional responses promote inflammation. We suggest that differences in the stoichiometry of mesenchymal cells established in utero, including the key role of PI16 ⁺ fibroblasts, is a general principle that drives inflammation susceptibility across tissues.

We develop a multiscale agent-based model (ABM) to investigate the effect that mechanical interactions between proliferating tumour cells and the surrounding vasculature have on the oxygen supply to the tumour microenvironment (TME), the tumour’s growth dynamics, and its response to radiotherapy. Our model extends existing models of tumour spheroid growth by incorporating vessel deformation due to mechanical forces between vessel walls and neighbouring tumour cells. These forces generate an effective pressure which compresses vessels, driving occlusion and pruning. This, in turn, leads to a hypoxic oxygen landscape which stimulates angiogenesis. A key feature of our model is the treatment of mechanical cell interactions with the tumour microenvironment, which we represent with two forces. The first is Stokes’ drag which is widely used in ABMs to represent resistance to cell movement. The second is a friction force which accounts for resistance due to the continual breaking and reforming of cell-extracellular matrix (ECM) adhesions. The importance of this friction force is demonstrated by numerical simulation. When Stokes’ drag dominates, pressure gradients dissipate across the tissue and vessel compression is negligible. By contrast, as the strength of the friction force increases, larger pressure gradients form, leading to significant vessel compression. We perform extensive numerical simulations to investigate how model parameters that control vascular remodelling and friction influence tumour vascularisation, which we spatially quantify using the cross-pair correlation function. This, in turn, alters the oxygen landscape and drives changes in tumour morphology. Finally, we highlight the importance of accounting for both mechanisms when simulating tumour responses to treatment with radiotherapy. We observe that vascular remodelling critically alters the tumour’s susceptibility to treatment and post-radiotherapy regrowth. Tumour regrowth is especially impacted by vessel remodelling, with certain vascular landscapes able to rebound quickly post-radiotherapy, resulting in fast tumour regrowth.

We present a mathematical model that describes the synergistic mechanism of combined Vesicular Stomatitis Virus (VSV) and Vaccinia Virus (VV). The model captures the dynamic interplay between tumor cells, viral replication, and the interferon-mediated immune response, revealing a `ping-pong' synergy where VV-infected cells produce B18R protein that neutralizes interferon-$\alpha$, thereby enhancing VSV replication within the tumor. Numerical simulations demonstrate that this combination achieves complete tumor clearance in approximately 50 days, representing an 11% acceleration compared to VV monotherapy (56 days), while VSV alone fails to eradicate tumors. Through bifurcation analysis, we identify critical thresholds for viral burst size and B18R inhibition, while sensitivity analysis highlights infection rates and burst sizes as the most influential parameters for treatment efficacy. Temporal optimization reveals that therapeutic outcomes are maximized through immediate VSV administration followed by delayed VV injection within a 1-19 day window, offering a strategic approach to overcome the timing and dosing challenges inherent in OVT.