Spatial Proteomics: Conquering Highplex Analysis of Spatial Proteomics Images

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.