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Online hyphenation of size-exclusion chromatography and gas-phase electrophoresis facilitates the characterization of protein aggregates.

Victor U WeissNatalia DenderzGuenter AllmaierMartina Marchetti-Deschmann
Published in: Electrophoresis (2021)
Gas-phase electrophoresis yields size distributions of polydisperse, aerosolized analytes based on electrophoretic principles. Nanometer-sized, surface-dry, single-charged particles are separated in a high laminar sheath flow of particle-free air and an orthogonal tunable electric field. Additionally, nano Electrospray Gas-Phase Electrophoretic Mobility Molecular Analyzer (nES GEMMA) data are particle-number based. Therefore, small particles can be detected next to larger ones without a bias, for example, native proteins next to their aggregates. Analyte transition from the liquid to the gas phase is a method inherent prerequisite. In this context, nonvolatile sample buffers influence results. In the worst case, the (bio-)nanoparticle signal is lost due to an increased baseline and unspecific clustering of nonvolatile components. We present a novel online hyphenation of liquid chromatography and gas-phase electrophoresis, coupling a size-exclusion chromatography (SEC) column to an advanced nES GEMMA. Via this novel approach, it is possible to (i) separate analyte multimers already present in liquid phase from aggregates formed during the nES process, (ii) differentiate liquid phase and spray-induced multimers, and (iii) to remove nonvolatile buffer components online before SEC-nES GEMMA analysis. Due to these findings, SEC-nES GEMMA has the high potential to help to understand aggregation processes in biological buffers adding the benefit of actual size determination for noncovalent assemblies formed in solution. As detection and characterization of protein aggregation in large-scale pharmaceutical production or sizing of noncovalently bound proteins are findings directly related to technologically and biologically relevant situations, we proposed the presented method to be a valuable addition to LC-MS approaches.
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