Data-driven image analysis to determine antibody-induced dissociation of cell-cell adhesion and antibody pathogenicity in pemphigus

Pemphigus vulgaris (PV) is a blistering autoimmune disease that affects the skin and mucous membranes. The mechanisms by which PV antibodies induce loss of cohesion in keratinocytes are not fully understood. It is accepted that the process starts with antibody binding to desmosomal targets, which leads to its disassembly and subsequent structural changes to cell-cell adhesions. In vitro imaging of desmosome molecules has been used to characterize this initial phase. However, there remains an untapped potential of image analysis in providing us with more in-depth knowledge regarding biophysical changes after antibody binding. Currently, there is no quantitative framework from immunofluorescence images in PV pathology. Here, we seek to establish a correlation of biophysical changes with antibody pathogenicity by examining the effects of PV antibodies on adhesion molecules and the cytoskeletal network. Specifically, we introduced a data-driven approach to quantitatively evaluate perturbations in adhesion molecules following antibody treatment. We identify distinct imaging signatures that mark the impact of antibody binding on the remodeling of adhesion molecules and introduce a pathogenicity score to compare the relative effects of different antibodies. From this analysis, we showed that the biophysical response of keratinocytes to distinct PV antibodies is highly specific, allowing for accurate prediction of their pathogenicity. For instance, the high pathogenicity scores of the PVIgG and AK23 antibodies show strong agreement with their reported PV pathology. Our data-driven approach offers a detailed framework for the action of antibodies in pemphigus and paves the way for the development of effective diagnostic and therapeutic strategies.