Deep Spatial Proteomics: connecting cellular neighbourhoods to functional states

Project Information

Spatial Proteomics

Grant agreement ID: 101115681

DOI

10.3030/101115681

EC signature date 18 September 2023

Start date 1 January 2024

End date 31 December 2028

Funded under

• European Research Council (ERC)

Total cost

€ 1 470 851,00

EU contribution

€ 1 470 851,00

Coordinated by

MAX DELBRUECK CENTRUM FUER MOLEKULARE MEDIZIN IN DER HELMHOLTZ-GEMEINSCHAFT (MDC)
Germany

Periodic Reporting for period 1 - Spatial Proteomics (Deep Spatial Proteomics: connecting cellular neighbourhoods to functional states)

Reporting period: 2024-01-01 to 2026-06-30

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the reporting period, the project focused on establishing the experimental, analytical, and computational foundations required to enable deep spatial proteomics at single-cell resolution in tissue. Activities were structured around the first two work packages and aimed at creating a robust and scalable basis for subsequent biological and clinical applications.
From a technical perspective, ultrasensitive tissue proteomics workflows were developed and optimised for FFPE samples, enabling reliable protein identification and quantification from single cells and small cellular communities. In parallel, scalable and automated sample processing workflows, including robotic handling, were implemented to ensure reproducibility and throughput. These workflows were systematically benchmarked and demonstrated high quantitative accuracy and precision.
In parallel, an integrated analytical framework combining multiplex immunofluorescence imaging with spatially resolved proteomics was established. High-plex imaging protocols were validated on laser microdissection-compatible membrane slides, enabling direct linkage between spatial imaging data and proteomic measurements. On the computational side, a comprehensive and open-source pipeline was developed termed “openDVP” to support cell segmentation, cell type classification, cellular neighbourhood analysis, multimodal data integration, and interactive visualisation. Together, these developments ensure scalability, reproducibility, and applicability to large tissue cohorts.
Overall, these activities resulted in the successful establishment of the core experimental and computational infrastructure required by the project. A key scientific achievement is the demonstration that deep, quantitative proteome analysis can be robustly combined with high-resolution spatial information at the level of a few and even individual cells in FFPE tissue, substantially de-risking the project and enabling the forthcoming application phase.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The results generated so far establish a new experimental and analytical framework for deep spatial proteomics, enabling quantitative analysis of protein expression and functional states at single-cell resolution directly in tissue. By integrating ultrasensitive proteomics with multiplex imaging and scalable computational analysis, the project overcomes key limitations of existing spatial biology approaches.
The primary scientific impact lies in enabling a functional, proteome-level view of cellular neighbourhoods in intact tissue, representing a significant advance beyond current spatial transcriptomic and imaging-based methods. Application to cancer immunotherapy is expected to generate new insights into how spatial immune–tumour interactions influence therapeutic response, with potential relevance for biomarker discovery and patient stratification. More broadly, the methodologies developed are applicable across disease areas where tissue context is critical.
Further uptake and long-term success will depend on continued research and demonstration in biologically and clinically relevant settings, including application to larger tissue cohorts and validation across studies. Open availability of computational tools, access to advanced proteomics and imaging infrastructure, and compatibility with established FFPE-based pathology workflows will support scalability, reproducibility, and future translational applications. Overall, the project lays the groundwork for a new class of spatially resolved, proteome-centric analyses of tissue biology with long-term significance for understanding disease mechanisms and therapeutic response.