Spatial Mechanomics of Multi-Organ Fibrosis

PROJECT OVERVIEW

Fibrosis is one of the greatest unmet healthcare challenges of our time. It affects virtually every organ and is characterised by the progressive replacement of functional tissue with dense, stiff connective tissue, ultimately compromising organ function. Effective therapies remain limited, quality of life is poor, mortality is high, and the associated economic burden on healthcare systems is substantial. A major barrier to therapeutic progress is our limited understanding of the early events that initiate pathological extracellular matrix (ECM) deposition and the mechanisms by which tissue repair transitions into irreversible fibrosis.

Fibrosis is fundamentally a mechanical disease. As activated fibroblasts deposit ECM, tissue stiffness increases, and this altered mechanical environment feeds back to regulate fibroblast activation, epithelial regeneration, and immune cell behaviour. These interactions establish self-reinforcing mechano-chemical feedback loops that may ultimately lock tissues into a pathological fibrotic state. Testing this hypothesis in human disease has remained challenging because existing technologies either measure tissue mechanics without molecular resolution or provide spatial molecular profiling without information on mechanical forces.

This project will address this gap using spatial mechanomics, a technology recently developed in the Hallou Lab1. The platform combines AI-based cell segmentation, spatial transcriptomics and image-based force inference derived from physical models of tissue mechanics. This approach reconstructs cell-scale pressure, tension and mechanical stress while simultaneously capturing the transcriptomic and/or proteomic state of every cell within a tissue section.

Geoadditive structural equation modelling and AI/ML models are then used to identify mechano-associated genes and signalling pathways. Force measurements will be complemented by spatial mapping of tissue stiffness and viscoelasticity using atomic force microscopy on the same or adjacent sections, together with spatial proteomic profiling of ECM composition, generating integrated mechano-multi-omic tissue maps.

The platform will be applied to patient biopsies from three fibrotic diseases affecting distinct organs - lung2, gut3 and skin4 - obtained through the supervisory team's clinical cohorts, alongside matched healthy and, where available, early-stage disease samples. This comparative strategy is motivated by growing evidence that tissue repair is governed by organ-specific programmes. Distinguishing conserved mechanical mechanisms from organ-specific adaptations is therefore essential for identifying broadly relevant therapeutic targets while recognising tissue-specific biology.

Working closely with computational colleagues in the Hallou Lab, the student will develop AI and machine learning methods to integrate mechanical, transcriptomic, and matrix-composition data across tissues. Spatial statistical modelling will be used to determine which cell states generate and respond to mechanical stress, how mechanically active niches are organised relative to fibrotic lesions, and whether mechanical dysregulation precedes or follows ECM remodelling. Candidate mechano-associated genes and pathways will subsequently be validated in primary fibroblast and organoid cultures grown on substrates spanning physiological and pathological stiffnesses.

The project will generate the first spatial mechanomic atlas of human fibrosis, providing a quantitative framework for understanding how physical and molecular tissue microenvironments are jointly remodelled across organs and disease stages. Beyond advancing fundamental knowledge of fibrosis pathogenesis, this work has the potential to identify mechano-associated biomarkers for patient stratification and to uncover therapeutic strategies that target the mechano-chemical feedback mechanisms driving fibrosis, rather than its downstream molecular manifestations alone.

KEYWORDS

Fibrosis, computational biology, spatial genomics, mechanobiology.

TRAINING OPPORTUNITIES

The project is supported by a supervisory team with complementary computational and experimental expertise. The Hallou Lab, based at the Kennedy Institute in Oxford, combines wet and dry laboratory approaches to study the role of mechano-chemical interactions in cell fate decisions and tissue dynamics, and has expertise in the use of biophysical, machine learning and single cell/spatial omics methods applied to a variety of biological systems. Also at the Kennedy, the Midwood Lab is using a variety of molecular and genomic approaches to study fibrosis and immune mediated diseases and are expert in the field of matrix biology. Based at the NDM Translational Gastroenterology Unit the Friedrich Lab is expert with the used of digital pathology and spatial multi-omics for the study of fibrosis in the context of IBD, while Dr Kristina Clark brings unique clinical and translational research expertise in skin and gut fibrosis in the context of systemic fibrosis.

The ideal applicant will already have significant experience in the fields of fibrosis, tissue biology and/or mechanobiology as well as experience in single-cell and/or spatial transcriptomics experiments and data analysis, and coding experience in R and/or Python. Throughout this project, you will further develop your experimental research skills and become an expert in using ML /AI approaches to integrate and analyse spatial omics and mechanical data you will have generated in the lab.

You will be supervised on a day-to-day basis by Dr Adrien Hallou and Dr Raphael Blain, and there will be regular joint meetings with the entire supervisory team and other collaborators. You will be expected to present your work regularly at the weekly Hallou group meetings and will have the opportunity to attend regular seminars within the Institute and relevant seminars at the wider University. You will also have the opportunity to present your research at international meetings and conferences.

A core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects including tissue biology, inflammation, epigenetics, translational immunology, data analysis and single cell genomics. You will also have access to various courses run by the Medical Sciences Division Skills Training Team and other departments, and, like all students of the program, you will be required to attend a 2-day Statistical and Experimental Design course at NDORMS.

KEY PUBLICATIONS

1. A. Hallou, et al. A computational pipeline for spatial mechano-transcriptomics. Nature Methods 22(4), 737–750 (2025). https://doi.org/10.1038/s41592-025-02618-1
2. C.D. Buckley and K.S. Midwood. Tracing the origins of lung fibrosis. Nature Immunology 25(9), 1517-1519 (2024).
3. M. Friedrich, et al. IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies. Nature Medicine 27(11), 1970-1981 (2021). https://doi.org/10.1038/s41591-021-01520-5
4. K.E. Clark, et al. Characterization of a pathogenic nonmigratory fibroblast population in systemic sclerosis skin. JCI Insight 10(10), e185618 (2025). https://doi.org/10.1172/jci.insight.185618

THEMES

Fibrosis, tissue biology, spatial and single cell genomics, mechanobiology.

CONTACT INFORMATION OF ALL SUPERVISORS

Dr Adrien Hallou

Professor Kim Midwood

Dr Matthias Friedrich

Dr Kristina Clark

Dr Raphaël Blain