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Novel Bead-Based Epitope Assay is a sensitive and reliable tool for profiling epitope-specific antibody repertoire in food allergy

Suprun, Maria, Getts, Robert, Raghunathan, Rohit, Grishina, Galina, Witmer, Marc, Gimenez, Gustavo, Sampson, Hugh A., Suárez-Fariñas, Mayte·2019·Scientific Reports
DOI 10.1038/s41598-019-54868-7PMC PMC6895130
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Abstract

Identification of allergenic IgE epitopes is instrumental for the development of novel diagnostic and prognostic methods in food allergy. In this work, we present the quantification and validation of a Bead-Based Epitope Assay (BBEA) that through multiplexing of epitopes and multiple sample processing enables completion of large experiments in a short period of time, using minimal quantities of patients’ blood. Peptides that are uniquely coupled to beads are incubated with serum or plasma samples, and after a secondary fluorophore-labeled antibody is added, the level of fluorescence is quantified with a Luminex reader. The signal is then normalized and converted to epitope-specific antibody binding values. We show that the effect of technical artifacts, i.e. well position or reading order, is minimal; and batch effects - different individual microplate runs - can be easily estimated and eliminated from the data. Epitope-specific antibody binding quantified with BBEA is highly reliable, reproducible and has greater sensitivity of epitope detection compared to peptide microarrays. IgE directed at allergenic epitopes is a sensitive biomarker of food allergy and can be used to predict allergy severity and phenotypes; and quantification of the relationship between epitope-specific IgE and IgG4 can further improve our understanding of the immune mechanisms behind allergic sensitization.

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Figure 1 Bead-Based Epitope Assay (BBEA) set up and quantification. (A) Selected epitopes are uniquely coupled to beads, incubated with a patient’s serum or plasma sample and secondary antibodies. Then the 96-well plate is read on the Luminex-200 instrument, which uses dual-laser classification to detect beads and secondary antibodies, and the signal is quantified as a Median Fluorescence Intensity (MFI). Illustration by Jill Gregory. Printed with permission from Mount Sinai Health System, licensed under CC BY-ND. (B) Image of the 96-well plate with MFI is then transformed to binding scores using log2 normalization and subtraction of the non-specific binding (NSB) wells. (C) Histograms and Cullen-Frey plots of the MFIs and binding scores indicate that MFI distribution has large skewness and kurtosis, while binding scores are closer to normal or log-normal distributions.
Figure 1 Bead-Based Epitope Assay (BBEA) set up and quantification. (A) Selected epitopes are uniquely coupled to beads, incubated with a patient’s serum or plasma sample and secondary antibodies. Then the 96-well plate is read on the Luminex-200 instrument, which uses dual-laser classification to detect beads and secondary antibodies, and the signal is quantified as a Median Fluorescence Intensity (MFI). Illustration by Jill Gregory. Printed with permission from Mount Sinai Health System, licensed under CC BY-ND. (B) Image of the 96-well plate with MFI is then transformed to binding scores using log2 normalization and subtraction of the non-specific binding (NSB) wells. (C) Histograms and Cullen-Frey plots of the MFIs and binding scores indicate that MFI distribution has large skewness and kurtosis, while binding scores are closer to normal or log-normal distributions.CC BY · PubMed Central
Figure 2 Detection and elimination of technical artifacts. (A) Images of the 2 plates with a randomized position of technical triplicates, and, on the right, the image of the coefficients for each well estimated with the linear mixed-effects model of those two plates (a coefficient of zero is indicative of no effect). (B) Estimated coefficients with 95% CIs for each well, presented in the order they were read by Luminex-200. With the majority of the coefficients centered around zero, no systematic bias attributable to the well position is observed, with slightly higher effect of the A1 and B1 wells. (C,D) On the left, PCA and PVCA plots for the 4 milk and 22 peanut IgE plates, showing that 8% and 13% (quantified as the weighted average proportion variance, WAPV) of the variability in the data is attributable to the plate effect. PCA plots on the right show that after the adjustment, this effect is responsible for <1% and <3%, for milk and peanut respectively. On the PCA plots points represent individual samples done in triplicates colored by the plates (batches).
Figure 2 Detection and elimination of technical artifacts. (A) Images of the 2 plates with a randomized position of technical triplicates, and, on the right, the image of the coefficients for each well estimated with the linear mixed-effects model of those two plates (a coefficient of zero is indicative of no effect). (B) Estimated coefficients with 95% CIs for each well, presented in the order they were read by Luminex-200. With the majority of the coefficients centered around zero, no systematic bias attributable to the well position is observed, with slightly higher effect of the A1 and B1 wells. (C,D) On the left, PCA and PVCA plots for the 4 milk and 22 peanut IgE plates, showing that 8% and 13% (quantified as the weighted average proportion variance, WAPV) of the variability in the data is attributable to the plate effect. PCA plots on the right show that after the adjustment, this effect is responsible for <1% and <3%, for milk and peanut respectively. On the PCA plots points represent individual samples done in triplicates colored by the plates (batches).CC BY · PubMed Central
Figure 3 Reliability and reproducibility of the IgE binding to peanut epitopes. (A) PCA plot of the technical triplicates colored by the sample type. (B) Phylogenetic tree of the technical triplicates constructed with the Spearman correlation as distance metric and “average” agglomeration algorithm. (C) Average agreement ICC with 95% CI across triplicates for all samples within three centers. These results indicate high reliability of the BBEA with triplicates from the same samples clustering together and having high agreement; with the exception of the NC that has MFI and binding scores close to 0. (D) PVCA plot shows that the center contributes to a small proportion of total variance (WAPV <1%), with the majority of variance explained by the sample type. (E) Average consistency ICC with 95% CI across the 3 centers for all 8 samples. (F) Average consistency ICC with 95% CI across the 3 centers for all 50 epitopes. These results show that BBEA has high reproducibility across independent laboratories.
Figure 3 Reliability and reproducibility of the IgE binding to peanut epitopes. (A) PCA plot of the technical triplicates colored by the sample type. (B) Phylogenetic tree of the technical triplicates constructed with the Spearman correlation as distance metric and “average” agglomeration algorithm. (C) Average agreement ICC with 95% CI across triplicates for all samples within three centers. These results indicate high reliability of the BBEA with triplicates from the same samples clustering together and having high agreement; with the exception of the NC that has MFI and binding scores close to 0. (D) PVCA plot shows that the center contributes to a small proportion of total variance (WAPV <1%), with the majority of variance explained by the sample type. (E) Average consistency ICC with 95% CI across the 3 centers for all 8 samples. (F) Average consistency ICC with 95% CI across the 3 centers for all 50 epitopes. These results show that BBEA has high reproducibility across independent laboratories.CC BY · PubMed Central
Figure 4 Benchmarking of BBEA against MIA using milk epitope-specific IgE. (A) Design of the experiment: for both BBEA and MIA same experiment was repeated on two separate days, as represented by pink and grey background colors. Each MIA experiment included two chips for the same PP and one for the NC. Each BBEA run had 4 technical replicates of both PP and NC. (B) Spearman correlation between replicates with higher color intensity representing stronger correlation (red - positive, blue - negative; all correlations were significant, p < 0.05). On average, correlation between replicates is close to 1.0 for the BBEA and around 0.6 for MIA. (C) Average agreement ICC with 95% CI across the two days is higher for BBEA (orange color) compared to MIA (grey color). (D) Boxplot of the effect sizes shows that BBEA can detect smaller difference. (F) Scatter plot of the effect sizes show that BBEA detected 66/66 milk epitopes compared to 11/66 detected by MIA. Epitope detection threshold was set to p < 0.05.
Figure 4 Benchmarking of BBEA against MIA using milk epitope-specific IgE. (A) Design of the experiment: for both BBEA and MIA same experiment was repeated on two separate days, as represented by pink and grey background colors. Each MIA experiment included two chips for the same PP and one for the NC. Each BBEA run had 4 technical replicates of both PP and NC. (B) Spearman correlation between replicates with higher color intensity representing stronger correlation (red - positive, blue - negative; all correlations were significant, p < 0.05). On average, correlation between replicates is close to 1.0 for the BBEA and around 0.6 for MIA. (C) Average agreement ICC with 95% CI across the two days is higher for BBEA (orange color) compared to MIA (grey color). (D) Boxplot of the effect sizes shows that BBEA can detect smaller difference. (F) Scatter plot of the effect sizes show that BBEA detected 66/66 milk epitopes compared to 11/66 detected by MIA. Epitope detection threshold was set to p < 0.05.CC BY · PubMed Central
Figure 5 Relationship between epitope-specific IgE and IgG4 antibodies. (A) Boxplots of the average of 7 milk epitopes show dose-depended relationship between IgE and the OFC dose at month 32 of 44 patients; while the relationship is less clear for either IgG4 or IgG4-IgE ratio. (B) Scatterplot with the loess smoothed line of the average IgE and IgG4 to 7 milk epitopes; each point represents an individual patient, colored by the amount of protein they were able to consume during the OFC at month 32. (C) A classification tree determining best relationship between IgE and IgG4 and the binary outcome of the OFC, shows that having very high or low levels of IgE is enough to classify the OFC outcome, while at the intermediate IgE levels (0.81–2.4), high IgG4 (>7.81) can further improve classification. (D) Heatmaps of 73 peanut allergic (red) and 83 non-allergic (grey) patients and their IgE, IgG4, and IgG4-IgE binding to epitopes from 3 peanut proteins. The epitopes with FCH≥1.5 and FDR <0.05 are presented. All IgG4-IgE epitopes are also present among the IgE ones, indicating that IgE binding alone is most informative in identifying differences among allergic and non-allergic individuals.
Figure 5 Relationship between epitope-specific IgE and IgG4 antibodies. (A) Boxplots of the average of 7 milk epitopes show dose-depended relationship between IgE and the OFC dose at month 32 of 44 patients; while the relationship is less clear for either IgG4 or IgG4-IgE ratio. (B) Scatterplot with the loess smoothed line of the average IgE and IgG4 to 7 milk epitopes; each point represents an individual patient, colored by the amount of protein they were able to consume during the OFC at month 32. (C) A classification tree determining best relationship between IgE and IgG4 and the binary outcome of the OFC, shows that having very high or low levels of IgE is enough to classify the OFC outcome, while at the intermediate IgE levels (0.81–2.4), high IgG4 (>7.81) can further improve classification. (D) Heatmaps of 73 peanut allergic (red) and 83 non-allergic (grey) patients and their IgE, IgG4, and IgG4-IgE binding to epitopes from 3 peanut proteins. The epitopes with FCH≥1.5 and FDR <0.05 are presented. All IgG4-IgE epitopes are also present among the IgE ones, indicating that IgE binding alone is most informative in identifying differences among allergic and non-allergic individuals.CC BY · PubMed Central

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Metadata

Authors
Suprun, M., Getts, R., Raghunathan, R., Grishina, G., Witmer, M., Gimenez, G., Sampson, H. A., & Suárez-Fariñas, M.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2019-12-05
Volume
9
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-019-54868-7
PMC
PMC6895130
Cited by
64
References
4
Language
en
Update notices
correction (2020-02-13)
Licence
Funders
  • Division of Intramural Research, National Institute of Allergy and Infectious Diseases (AI-66738)
  • David H. and Julia Koch Research Program in Food Allergy Therapeutics; AllerGenis LLC

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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