Biology subjects

Bull, J. A.

Publications and source records attributed to Bull, J. A..

10 recordsLinked to original sources

bioRxiv2026

Discovery of Covalent Wild-Type Isocitrate Dehydrogenase 1 (IDH1) Inhibitors Targeting Cys269

Isocitrate dehydrogenase 1 (IDH1) catalyses the interconversion of isocitrate and -ketoglutarate and is overexpressed in several cancers, supporting tumour survival and treatment resistance. Inhibitors targeting the oncogenic mutant forms of IDH1 also inhibit the wild-type (WT) protein, but lose substantial potency due to greater competition with native substrates (isocitrate and Mg2+). We hypothesised that a covalent inhibitor may be more effective at overcoming the higher substrate affinity of wild-type IDH1. Here, a covalent fragment-based approach was used to develop IDH1-C269 selective inhibitors which prevent the key regulatory segment from forming an -helix required for catalytic competency. Following the identification of C269-selective fragments, we used X-ray crystallography to characterise IDH1-fragment bound complexes and guide structure-based design efforts. Inspired by the unexpected detection of bound isocitrate molecule in an X-ray crystal structure, fragment expansion yielded a series of compounds exploiting both an adjacent site and a unique water-mediated hydrogen bonding network. This series achieved strong potency (reaching IC50 = 47.5 nM, a 155-fold improvement in potency relative to the original fragment), retained activity in the presence of competing Mg2+, showed selectivity against IDH2 and reduced NADPH concentrations in a relevant PDAC cell model. Together, these findings present a mechanistic rationale for the covalent targeting of wild-type IDH1 and provide structurally validated inhibitors for further development.

cancer 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

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↗

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↗

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↗

bioRxiv2020

Chemical and structural investigation of the paroxetine-human serotonin transporter complex

Antidepressants target the serotonin transporter (SERT) by inhibiting serotonin reuptake. Structural and biochemical studies aiming to understand the binding of small-molecules to conformationally dynamic transporters like SERT often require thermostabilizing mutations and antibodies to stabilize a specific conformation. Such modifications to SERT have led to questions about the relationships of these structures to the bona fide conformation and inhibitor binding poses of the wild-type transporter. To address these concerns, we characterized wild-type SERT with truncated N- and C-termini and thermostabilized variants of SERT bound with paroxetine using x-ray crystallography, single particle cryo-EM and biochemical techniques. Moreover, using a C-H functionalization approach to synthesize enantiopure analogues, we replaced the halide of the fluorophenyl group in paroxetine with either bromine or iodine. We then exploited the anomalous scattering of Br and I to define the pose of the respective paroxetine analog. These structures provide mutually consistent insights into how paroxetine and its analogs bind to the central substrate-binding site of SERT, stabilize the outward-open conformation, and inhibit serotonin transport.

biochemistry↗

bioRxiv2019

Semaphorin 3A induces cytoskeletal paralysis in tumor-specific CD8+ T cells

Semaphorin-3A (Sema3A) regulates tumor angiogenesis, but its role in modulating anti-tumor immunity is unclear. We demonstrate that Sema3A secreted within the tumor microenvironment (TME) suppresses tumor-specific CD8+ T cell function via Neuropilin-1 (NRP1), a receptor that is upregulated upon activation with T cells cognate antigen. Sema3A inhibits T cell migration, assembly of the immunological synapse, and tumor killing. It achieves these functional effects through hyper-activating the acto-myosin system in T cells leading to cellular paralysis. Finally, using a clear cell renal cell carcinoma patient cohort, we demonstrate that human tumor-specific CD8+ T cells express NRP1 and are trapped in Sema3A rich regions of tumors. Our study establishes Sema3A as a potent inhibitor of anti-tumor immunity.

immunology↗

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↗

bioRxiv2018

The impact of tumor stromal architecture on therapy response and clinical progression

-- Advances in molecular oncology research culminated in the development of targeted therapies that act on defined molecular targets either on tumor cells directly (such as inhibitors of oncogenic kinases), or indirectly by targeting the tumor microenvironment (such as anti-angiogenesis drugs). These therapies can induce strong clinical responses, when properly matched to patients. Unfortunately, most targeted therapies ultimately fail as tumors evolve resistance. Tumors consist not only of neoplastic cells, but also of stroma, whereby \"stroma\" is the umbrella term for non-tumor cells and extracellular matrix (ECM) within the tumor microenvironment, possibly excluding immune cells1. We know that tumor stroma is an important player in the development of resistance. We also know that stromal architecture is spatially complex, differs from patient to patient and changes with therapy. However, to this date we do not understand the link between spatial and temporal changes in stromal architecture and response of tumors to therapy, in space and time. In this project we sought to address this gap of knowledge using a combination of mathematical and statistical modeling, experimental in vivo studies, and analysis of clinical samples in therapies that target tumor cells directly (in lung and breast cancers) and indirectly (in kidney cancer). This knowledge will inform therapy choices and offer new angles for therapeutic interventions. Our main question is: how does spatial architecture of stroma impact the emergence or evolution of resistance to targeted therapies, and how can we use this knowledge clinically?

cancer biology↗