Fig 1: Proof of concept.(A, B, C) Simulation of the competition of two receptor-binding domain (RBD)–reactive antibodies, A and B, with KA = 10-9 M and KB = 10-7 M, respectively. (A) For the case in which [A] < [B], there are two sub-regimes: if the RBD concentration is approximately equal to or lower than KA, the combined behaviour resembles that of the stronger binding species A (top). If RBD is present around or above the higher equilibrium constant, KB, then the behaviour resembles that of the weaker binding species (bottom). In between, the behaviour is intermediate. (A, B) For the situation where [A] ˜ [B], the combined response is dominated by the tighter binder (A) in both high and low RBD concentration. (A, C) For [A] > [B], the signal measured is also determined by the tightly binding antibody (A), regardless of the RBD concentration. (D) Binding curve of commercial antibody CR3022 IgG (ab273073, Abcam) in PBS-T (containing 5% HSA [wt/vol]) with RBD yielding a dissociation constant Kd = 35 [5, 98] nM, and Kd = 46 [10,117] nM in human serum. This is in good agreement with literature values (24). (E, F) Binding curve of human-derived anti-SARS-CoV-2 S2 antibody B4 with (E) spike ectodomain using surface-plasmon resonance (Kd = 1.46 ± 0.01 nM) and (F) spike ectodomain with microfluidic antibody affinity profiling, yielding a Kd = 27 [12,46] nM. The anti-SARS-CoV-2 S2 domain antibody B4 was labelled with Alexa 647 for the Microfluidic Antibody Affinity Profiling experiment. Data in d and f are represented as mean ± SD of replicate measurements.