IgG Adhesion on Hydrophobic Surfaces: Theory, Modelling, and Application to ELISA

TL;DR

This paper combines thermodynamics, numerical modeling, and experiments to understand and improve IgG adhesion on hydrophobic surfaces like polystyrene, enhancing ELISA performance through insights into protein immobilization mechanisms.

Contribution

It develops a theoretical and numerical framework for IgG adhesion on hydrophobic surfaces, integrating thermodynamics, RSA modeling, and experimental validation to optimize monolayer formation for ELISA.

Findings

Thermodynamics explains IgG adhesion on polystyrene.

RSA modeling predicts surface saturation and IgG orientation.

Experimental AFM and ELISA confirm theoretical predictions.

Abstract

Enzyme-Linked ImmunoSorbent Assays (ELISA) are a range of widely used analytical methods whose implementation requires to build antibodies (IgG) thin films onto surfaces predominantly made of polystyrene. The high hydrophobicity of polystyrene ensures a spontaneous and strong adhesion of proteins allowing to easily build IgG monolayers. Since the ELISA improvements definitely lie in the comprehension of physico-chemical mechanisms on which IgG immobilization on hydrophobic surfaces are relied, this work develops a theorization essay (thermodynamics of the so-called hydrophobic effect and of thin films building) emphasized by numerical modelling (random sequential additions model, i.e. RSA) and experimental estimations by atomic force microscopy (AFM) and ELISA. Keeping in mind the hydrophobic effect, thermodynamics (of irreversible processes) allows to explain why IgG adhesion on polystyrene occurs whereas numerical modelling approaches show a way of surface saturation leading to promote IgG orientations expected for ELISA. Finally, an improvement of RSA in a RSA+R model taking into account orientational and/or structural changes throughout relaxation phenomena shows an obvious relationship between deposition conditions and obtained monolayer structure. Such theoretical results seem to be strongly correlated with experimental facts, i.e. AFM and ELISA.