Development, validation, and implementation of the antibody-secreting cell maturity index: Universal prediction of human plasma cell maturity.

Defining the maturity of long-lived antibody-secreting cells (ASCs) is important for vaccine optimization and research into autoimmune diseases, but current assessment of plasma cell maturity is limited. We developed a universal, robust method to define plasma cell maturity using a meta-analysis of public, human cytometry data, with harmonized expression of ASC maturity markers across samples. Vaccination or infection samples were selected to train a random forest-based machine learning prediction model (performance of r = 0.866). After evaluation (performance of r = 0.616), the final model, named the antibody-secreting cell maturity index (ASC-ME), was applied to compartments with long-term ASC survival niches, namely bone marrow and gut. Both compartments showed increased ASC maturity, biologically validating the ASC-ME model. Example analyses highlight the broad spectrum for model application, e.g., in vaccine research, clinical trials or in ASC-related autoimmune diseases. Overall, our ASC-ME model, accessible via an online platform, offers a robust approach in evaluating ASC maturity.