Using adjusted local assortativity with Molecular Pixelation unveils colocalization of membrane proteins with immunological significance - Rhomberg-Kauert, Jan; Karlsson, Max; Thiagarajan, Divya; Kallas, Tomasz; Karlsson, Filip; Fredriksson, Simon; Dahlberg, Johan; Martinez Barrio, Alvaro (Frontiers in Immunology, 2024)
Advances in spatial proteomics and protein colocalization are a driving force in the understanding of cellular mechanisms and their influence on biological processes. New methods in the field of spatial proteomics call for the development of algorithms and open up new avenues of research. The newly introduced Molecular Pixelation (MPX) provides spatial information of surface proteins and their relationship with each other on single cells. This allows for in-silico representation of neighborhoods of membrane proteins as graphs. In order to analyze this new data modality, we adapt local assortativity in networks of MPX single cell graphs and create a method which is able to capture detailed information of the spatial relationships of proteins. The introduced method can in turn evaluate the pairwise colocalization of proteins and access higher order similarity, which can assess the colocalization of multiple proteins at the same time. We evaluated the method with publicly available MPX datasets where T cells were treated with a chemokine to study uropod formation. We demonstrate that adjusted local assortativity detects the effects of the stimuli both at a single and multiple marker level, which enhances the understanding of the uropod formation. We also applied our method on treating cancerous B cell-lines with a therapeutic antibody. With the adjusted local assortativity, we can recapitulate the effect of Rituximab on the polarity of CD20. Our computational method together with MPX improves our understanding not only of the formation of cell polarity and protein colocalization under stimuli, but also advancing the overall insight into immune reaction and reorganization of cell surface proteins that in turn allows the design of novel therapies. We foresee its applicability to other types of biological spatial data when represented as undirected graphs.