Conquering High-plex Analysis of Spatial Proteomics Images: Article in Trillium Pathology 2025

In this year’s Trillium Pathology, Volker Bruns (Fraunhofer IIS), Sonja Fritzsche (Max Delbrück Center for Molecular Medicine), and Fabian Coscia (Max Delbrück Center for Molecular Medicine) have contributed an article on the high-plex analysis of spatial proteomics images (learn more about the analysis of high-plex panels using MIKAIA^®). The article offers a comprehensive overview of various primary and secondary analysis steps, including cell segmentation, cell typing, cellular neighborhood analysis, cell-cell connections, and quantification of spatial heterogeneity.

Abstract

Spatially resolved proteomics has been named the Nature method of the year 2024. To understand and reverse-engineer processes within the tumor microenvironment, in developmental biology, in autoimmune diseases or other areas, researchers need to gain a full understanding of the situation. A deep characterization of cells is desired. Spatially resolved proteomics subsumes a group of methods that promise to facilitate exactly this – they capture the proteome while preserving its spatial origin, allowing to uncover causal relationships and pathways based on observations which cell types attract or reject each other. While fluorescence microscopy has long been used to investigate the co-expression of 2, 3 or 4 markers, in spatial proteomics 10, 20, or even up 100 markers are used in parallel, allowing unprecedented insights. Such a large cocktail of antibodies helps decipher the status and function of individual cells or find rare cell types. Establishing such panels in a lab and for a specific tissue, however, is laborious and expensive, but nonetheless spatial proteomics has gained a lot of traction and has become available in many labs and core facilities. While instruments and kits are now more widely available, the bioinformatic analysis of such datasets remains a challenge. This article focuses on methods and gives an overview of primary and secondary image analysis.

Keywords: immunofluorescence, slide alignment, IHC, AI, computational pathology

Don’t forget to read the full issue of Trillium Pathology 2025 …

This year’s edition delves into the latest advancements in diagnostic techniques, the impact of artificial intelligence on pathology, and the current trends in research that may soon find their way into the diagnostic routine.

… or go back and have a look at Trillium Pathology 2024.

Image copyright (cover image): Fraunhofer IIS