Understanding how cells function within their native tissue environment is a central challenge in modern biology and medicine. While single-cell technologies have transformed our ability to characterise cellular states, many approaches rely on dissociated cells and therefore lose information on spatial organisation, cellular neighbourhoods, and tissue context. Yet cellular function is strongly shaped by interactions with neighbouring cells and the local microenvironment, particularly in complex tissues such as tumours, where spatial organisation influences immune responses, disease progression, and therapeutic outcome.
Recent advances in spatial transcriptomics and multiplex imaging have begun to address this gap, but comparable approaches for deep, quantitative proteome analysis in intact tissue remain limited. Proteins are the primary functional molecules in cells and the direct targets of most therapies, yet existing spatial proteomics methods often lack sufficient sensitivity, scalability, or compatibility with clinically relevant formalin-fixed paraffin-embedded (FFPE) tissue. As a result, there is currently no robust framework to systematically link cellular neighbourhoods with functional protein states at single-cell resolution in patient samples.
The overall objective of this project is to establish deep spatial proteomics as a scalable and quantitative approach to connect cellular neighbourhoods with functional states directly in tissue. To achieve this, the project aims to (i) develop ultrasensitive and automated tissue proteomics workflows compatible with FFPE samples and single-cell resolution, (ii) integrate these workflows with high-plex multiplex immunofluorescence imaging and open computational frameworks for spatial analysis, and (iii) apply the resulting platform to clinically relevant questions in cancer immunotherapy. By combining experimental innovation with advanced computational analysis, the project seeks to move beyond descriptive spatial mapping toward a mechanistic and predictive understanding of tissue organisation.
The expected impact of the project is threefold. First, it will deliver a new methodological framework enabling quantitative, spatially resolved proteome analysis in complex tissues at unprecedented depth and scale. Second, it will provide open and reusable computational tools for integrating imaging, spatial statistics, and proteomics data, facilitating adoption by the wider research community. Third, through application to head and neck squamous cell carcinoma, the project aims to generate new insights into how spatial immune–tumour interactions influence response to checkpoint immunotherapy, with potential relevance for biomarker development and patient stratification. More broadly, the project is expected to contribute to a conceptual shift in how cellular function is studied in tissues by explicitly incorporating spatial context and cellular neighbourhoods into proteome-level analyses.
