Comparative Analysis of Antibodies and Heavily Glycosylated Macromolecular Immune Complexes by Size-Exclusion Chromatography Multi-Angle Light Scattering, Native Charge Detection Mass Spectrometry, and Mass Photometry

[Image: see text] Qualitative and quantitative mass analysis of antibodies and related macromolecular immune complexes is a prerequisite for determining their identity, binding partners, stoichiometries, and affinities. A plethora of bioanalytical technologies exist to determine such characteristics, typically based on size, interaction with functionalized surfaces, light scattering, or direct mass measurements. While these methods are highly complementary, they also exhibit unique strengths and weaknesses. Here, we benchmark mass photometry (MP), a recently introduced technology for mass measurement, against native mass spectrometry (MS) and size exclusion chromatography multi-angle light scattering (SEC-MALS). We examine samples of variable complexity, namely, IgG4Δhinge dimerizing half-bodies, IgG-RGY hexamers, heterogeneously glycosylated IgG:sEGFR antibody–antigen complexes, and finally megadalton assemblies involved in complement activation. We thereby assess the ability to determine (1) binding affinities and stoichiometries, (2) accurate masses, for extensively glycosylated species, and (3) assembly pathways of large heterogeneous immune complexes. We find that MP provides a sensitive approach for characterizing antibodies and stable assemblies, with dissociation correction enabling us to expand the measurable affinity range. In terms of mass resolution and accuracy, native MS performs the best but is occasionally hampered by artifacts induced by electrospray ionization, and its resolving power diminishes when analyzing extensively glycosylated proteins. In the latter cases, MP performs well, but single-particle charge detection MS can also be useful in this respect, measuring masses of heterogeneous assemblies even more accurately. Both methods perform well compared to SEC-MALS, still being the most established method in biopharma. Together, our data highlight the complementarity of these approaches, each having its unique strengths and weaknesses.