Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods

Abstract

High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.

Fig. 1.. Unique proteins identified by each platform in analysis of JHS.

Venn diagram depicts the overlap between unique UniProt IDs targeted by the Olink Explore and Soma1.3K platforms. Pairing Olink and Soma1.3K reagents based on UniProt target identifies 616 unique reagent pairs.

Fig. 2.. Intra- and inter-assay CVs.

(A) CVs shown are for each reagent on each platform. Intra-assay CVs were calculated using two standard pooled plasma samples included on each plate of a given profiling batch and averaged across all plates. Inter-assay CVs were calculated using 14 pooled plasma samples from seven Olink plates and 10 calibrator samples from five Soma1.3K plates (batch 1 samples only). The CV corresponding to each percentile is shown in the table below the plot. Reagents that have overlapping protein targets are highlighted in darker blue. (B) Shown are a family of curves for each platform showing the relationship between the difference in mean protein level between two groups and the sample size required to detect that difference for a given CV. The mean inter-assay CV for each platform is indicated by the solid line, and the 5th percentile CV and 95th percentile CVs are indicated by the limits of the shaded regions. As CV increases, the required sample size to detect a given percent difference in mean protein levels between groups also increases.

Fig. 3.. Spearman correlations between Olink and Soma1.3K reagents, which measure the same protein.

(A) K-means clustering of correlations into three levels of correlation. (B) Colored bars indicate number of proteins on each platform in that correlation bin that have cis pQTLs, defined as a variant-protein association with P < 1 × 10^–5 within 1 Mb of the transcription start site for the cognate gene.

Fig. 4.. Proteins on each platform by PANTHER protein classification.

The number of proteins on each platform in the top 20 subcategories across four classification systems. Soma1.3K is shown in red, Olink is shown in blue. Distribution for all subcategories can be viewed in fig. S2.

Fig. 5.. PCA of each platform.

(A) Total platform variance explained by each of the top 10 PCs on each platform. (B) Total cumulative variance explained with 95% variance marked by the black horizontal line. (C) Scatterplot of each participant showing their top 2 PCs and overlaid with eGFR, age, or sex.

Fig. 6.. Phenotypic associations by platform.

(A) Number of associations on each platform across eight phenotypes and at three different significance thresholds. (B) Associations at P < 0.05 for the same eight phenotypes but limited to overlapping proteins from each platform. The associations are shown on the same distribution of Spearman correlations as seen in Fig. 3. ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index (kg/m²); eGFR, estimated glomerular filtration rate (ml/min/1.73 m²); FEV1, Forced expiratory volume in the first second (L); HbA1c, hemoglobin A1c (%); SBP, systolic blood pressure (mmHg), total cholesterol/HDL, total cholesterol divided by high-density lipoprotein cholesterol; FDR, false discovery rate.

Fig. 7.. Comparison between Soma5K and Olink Explore in HERITAGE.

Plasma profiling was performed on a random subset of HERITAGE (N = 219). (A) Overlap between unique UniProt targets between the two platforms. (B) Spearman correlations between overlapping reagents on Olink and Soma5K. K-means clustering divided the distribution into three clusters. (C) Phenotypic associations between all reagents on each platform and four phenotypes at P < 0.05. (D) Associations at P < 0.05 for the same four phenotypes but limited to overlapping proteins from each platform. The associations are shown on the same distribution of Spearman correlations as seen in (B).

Fig. 8.. Correlations between ELISA and both Olink and Soma.

In 60 random samples from either JHS or HERITAGE (according to sample availability), protein levels were assayed by ELISA and compared to measurements from each affinity platform. Shown here are the normalized data and Spearman correlations for (A) CD97, (B) mesothelin, (C) HSP70, and (D and E) ANGPTL3. In the case of (A) to (D), aptamers are those featured on the Soma1.3K, while (E) features a new ANGPTL3 aptamer, upgraded on the Soma5K platform. Absolute concentrations by ELISA are shown on a log scale axis, while affinity reagent measurements are log₂-transformed and scaled.