High-throughput, multiplexed IgG subclassing of antigen-specific antibodies from clinical samples

Abstract

In vivo, the activity of antibodies relies critically on properties of both the variable domain, responsible for antigen recognition, and the constant domain, responsible for innate immune recognition. Here, we describe a flexible, microsphere-based array format for capturing information about both functional ends of disease-specific antibodies from complex, polyclonal clinical serum samples. Using minimal serum, we demonstrate IgG subclass profiling of multiple antibody specificities. We further capture and determine the subclass of epitope-specific antibodies. The data generated in this array provides a profile of the humoral immune response with multi-dimensional metrics regarding properties of both variable and constant IgG domains. Significantly, these properties are assessed simultaneously, and therefore information about the relationship between variable and constant domain characteristics is captured, and can be used to predict functions such as antibody effector activity.

Figure 1

Assay schematic and representative data. A) Each antigen of interest is covalently conjugated to a coded microsphere. Microspheres are coded via differential incorporation of 2 internal fluorescent dyes, allowing identification of each bead, and therefore, antigen type. Microspheres are mixed with plasma antibodies to allow capture of antigen-specific antibodies, resulting in “on-bead” affinity purification. Beads are washed, split into replicate wells, and labeled for IgG subclass identification, and subsequently acquired on a Luminex flow cytometer. B) Representative data for quantification of total IgG bound to 4 different antigen bead sets, presented as relative MFI for a series of HIV positive (+) and negative (−) subjects, as well as 2 control monoclonal antibodies. C–D) Analysis of epitope-specific antibodies. C) IgG subclass was determined for antibodies recognizing the CD4bs as defined by recognition of resurfaced stabilized core (RSC). HIV positive subjects included: controllers (ctr), treated (tx), and untreated (untx) progressors. D) Relative signal of RSC-specific (CD4bs) IgG1 antibodies, as compared to total gp120-specific IgG1.

Figure 2

Assay Quality Control data. A) Subclassing replicate data (MFI) showing low inter-assay variation with different operators on different days with the gp120 bead set. B) Alignment of ELISA titer data (OD) with Luminex titer (MFI) for purified IgG on the gp120 bead set. Error bars represent standard deviation between duplicate measurements. C) Saturation MFI showing how antibody epitope breadth plays an important role in the total signal observed on the gp120 bead set. D) Additive Effects of combining monoclonal anti-gp120 antibodies with different epitope specificities. E) Comparison of subclassing data using either serum or Melon-Gel purified IgG samples from the same patients across multiple bead sets. F) Monoclonal and polyclonal antibody samples were diluted from saturating concentrations until the assay MFI reached baseline (shaded box) on the gp120 bead set. Plotting on a log-log scale indicates a large useful dynamic range for this assay (roughly 10⁻¹⁰ to 10⁻⁶ M for polyclonal and 10⁻¹² to 10⁻⁸ M for monoclonal antibodies).

Figure 3

Using array signatures to characterize function. A) A cluster plot of array data generates groups of samples with similar humoral immune profiles on the y-axis, and groups antibody features that co-vary on the x-axis. B) Antibody dependent cellular viral inhibition (ADCVI) activity for subjects in array-based clusters. C) Relationship of ADCVI activity with IgG1 responses to gp120. D) ADCVI activity for subjects with high or low titers of gp120-specific antibody. E) Relationship of ADCVI activity with titer.