Olink vs SomaScan: Comparative Review of Principles, Performance, and Application Contexts

Introduction

Role of High-Throughput Proteomics in Molecular Medicine

High-throughput proteomics has transformed molecular medicine by enabling simultaneous quantification of thousands of proteins in plasma, serum, or tissue samples, thereby providing insights into disease mechanisms, biomarker discovery, and translational research. By offering a system-wide view of protein expression, post-translational modifications, and protein–protein interactions, high-throughput proteomic technologies facilitate both hypothesis-driven and discovery-oriented research, bridging the gap between basic molecular biology and clinical translation.

Overview of Olink PEA and SomaScan 7K Technologies

Among the leading multiplexed proteomic platforms, Olink's Proximity Extension Assay (PEA) and SomaLogic SomaScan 7K have emerged as widely adopted tools, each distinguished by unique methodological approaches. Olink PEA relies on pairs of oligonucleotide-labeled antibodies that, upon binding to a target protein, enable proximity-dependent DNA polymerization and subsequent quantitative amplification via real-time PCR or next-generation sequencing, allowing highly specific and sensitive detection even in complex biological samples. In contrast, SomaLogic SomaScan 7K technology utilizes modified single-stranded DNA aptamers engineered to bind protein targets with high affinity and selectivity, coupled with microarray or bead-based readout systems, enabling broad proteome coverage and quantification across a wide dynamic range.

Key Technical Differences Between the Two Platforms

These platforms differ substantially in multiple technical aspects, including measurement chemistry, number and classes of detectable proteins, specificity, reproducibility, required sample volume, throughput, and data characteristics such as signal-to-noise ratio, batch effects, and quantitative accuracy. For example, Olink Explore 3072 / 1536 assays typically require smaller sample volumes and exhibit low background signal due to the dual recognition mechanism, whereas SomaScan 7K assays can detect a larger number of proteins simultaneously but may be more susceptible to nonspecific binding in complex matrices.

Considerations for Platform Selection

Consequently, researchers often evaluate platform performance based on criteria such as the total number of detectable proteins, assay specificity and sensitivity, sample input requirements, dynamic range, reproducibility across technical replicates, susceptibility to batch effects, and suitability for exploratory versus validation-focused studies. These considerations are critical for selecting the appropriate platform depending on the study design, biological sample type, and research objectives, thereby optimizing the balance between discovery breadth and measurement precision.

Technology Overview

Olink PEA: Accurate Proteomics in Small Samples

Olink PEA is based on a dual-antibody recognition system coupled with DNA amplification, enabling highly specific and sensitive quantification of proteins in complex biological matrices. For each target protein, two distinct antibodies are conjugated to complementary oligonucleotides that undergo a proximity-dependent extension reaction only when both antibodies simultaneously bind the same protein molecule. This design effectively suppresses nonspecific background signals and ensures that only true protein–antibody interactions generate measurable DNA products. The resulting amplicons are quantified using qPCR or next-generation sequencing, producing normalized protein expression (NPX) values that enable robust comparison across samples.

The platform's ability to operate with extremely small sample volumes (typically 1–3 µL) makes it highly suitable for studies involving limited biological material, such as pediatric cohorts, cerebrospinal fluid analyses, or longitudinal sampling where repeated measurements are required. Olink's high reproducibility has made it particularly valuable in large population studies and clinical research where precision is essential. For instance, in major cardiovascular cohorts—including extensions of the Framingham Heart Study—PEA-based panels have been used to monitor circulating proteins involved in inflammation, cardiac remodeling, and metabolic dysregulation, revealing proteomic signatures predictive of incident heart failure and adverse cardiovascular events. Similar applications have been reported in neurodegeneration research, where Olink profiling of cerebrospinal fluid has enabled the identification of synaptic and neuroinflammatory markers that correlate with Alzheimer's disease progression. These examples highlight the utility of Olink PEA not only for biomarker validation but also for detecting subtle, longitudinal protein changes across large cohorts.

Figure 1 : Illustration of the PEA mechanism.

SomaScan: Comprehensive Proteomics at Scale

SomaScan 7K employs chemically modified single-stranded DNA aptamers (SOMAmers) that selectively bind target proteins. These SOMAmers incorporate hydrophobic and chemically diverse side-chain modifications that enhance binding affinity and expand detectable protein classes, including low-abundance signaling molecules and traditionally challenging targets such as cytokines and transcription factors. After formation of protein–aptamer complexes, unbound molecules are removed through stringent washing steps to minimize nonspecific interactions, while bound aptamers are subsequently eluted and quantified via microarray hybridization or next-generation sequencing. The resulting relative fluorescence units (RFU) provide semi-quantitative readouts that can be normalized across samples.

A key advantage of SomaScan 7K is its exceptionally broad proteome coverage—currently exceeding ~7,000 proteins—which enables large-scale, system-level exploration of biological pathways. This breadth has made the platform particularly valuable in population-based studies aimed at uncovering molecular determinants of complex diseases. For example, SomaScan has been integrated into cardiometabolic cohorts such as the UK Biobank and the Multi-Ethnic Study of Atherosclerosis, where its extensive panel enables detection of subtle proteomic signatures associated with insulin resistance, cardiovascular risk, and early metabolic dysfunction. The high dimensionality of the assay also supports the development of predictive models that surpass conventional biomarkers in forecasting disease onset or progression.

In oncology, the wide coverage of SomaScan 7K has facilitated the identification of circulating proteins linked to tumor progression, immune escape, and treatment response. In lung, breast, and colorectal cancer studies, SOMAmer-based profiling has revealed novel protein signatures and pathway perturbations not captured by traditional immunoassays. These rich datasets enable researchers to infer protein interaction networks, characterize tumor microenvironment dynamics, and train machine learning models capable of discriminating tumor subtypes or predicting therapeutic outcomes. Through such applications, SomaScan's discovery-oriented design provides a powerful platform for comprehensive proteomic investigation across diverse biomedical fields.

Figure 2: Schematic of SOMAmer–protein binding.

Comparative Performance

Table 1 summarizes the key performance features of Olink and SomaLogic, highlighting differences in proteome coverage, specificity, sample requirements, dynamic range, batch effects, and research applicability. Comparative, head-to-head evaluations leveraging genetics and clinical traits have shown that while Olink often demonstrates higher target specificity, SomaScan 7K offers greater analytic breadth and precision. Cross-platform correlation analyses in large cohorts (e.g., ARIC) report modest concordance (median Spearman's ρ ~0.46), underscoring that the two platforms are not trivially interchangeable.

Table 1. Comparison of Olink PEA and SomaLogic SomaScan Platforms

Figure 3: Workflow comparison between Olink PEA and SomaLogic SomaScan 7K.

Applications and Discussion

Olink PEA: Precision in Targeted and Low-Volume Studies

Olink demonstrates clear advantages in targeted protein validation, studies requiring minimal sample volumes, and longitudinal analyses where high reproducibility is critical. Its dual-antibody recognition mechanism ensures that only proteins simultaneously recognized by two independent antibodies generate a signal, effectively minimizing background noise and enhancing assay specificity. This feature, combined with low sample input requirements (typically 1–3 µL of plasma or serum) and robust signal normalization (NPX values), makes Olink particularly suitable for clinical cohort studies, time-course experiments, and applications where consistent quantification across multiple experimental batches is essential. Moreover, the platform's high reproducibility allows reliable detection of subtle changes in protein abundance, which is crucial for biomarker validation and monitoring of disease progression or therapeutic response over time.

Longitudinal & Clinical Cohorts

In the Determination of temporal reproducibility and variability of cancer biomarkers in serum and EDTA plasma samples using a proximity extension assay study, Christensen et al. (2022) evaluated the reproducibility and variability of cancer-related protein biomarkers using the Olink Proximity Extension Assay (PEA). They analyzed 92 plex Immuno-Oncology panels across multiple time points and panel versions over approximately 2.5 years, including bridging samples to assess batch-to-batch and long-term stability. The study demonstrated low intra- and inter-assay variation (CVs 11–26%) and showed that normalization effectively reduced inter-study variability. Protein measurements were consistent across different sample types, time points, and panel versions, highlighting the suitability of Olink PEA for longitudinal studies, minimal sample input, and reliable biomarker monitoring.

Low-volume / challenging samples

In the Proteomic profiling of neonatal herpes simplex virus infection on dried blood spots study, Dungu et al. (2024) applied Olink Explore PEA to analyze 2,941 proteins from dried blood spot (DBS) samples collected from neonates on days 2–3 of life. By comparing infected infants (n = 53) to matched controls, they identified 20 differentially expressed proteins associated with innate and adaptive immune responses. Their work demonstrates that Olink PEA can generate biologically meaningful, high-throughput proteomic data from extremely limited and minimally invasive sample volumes.

Multi-omics integration / Proteogenomics

In the Plasma proteomic associations with genetics and health in the UK Biobank study, Sun et al. (2023) employed the Olink Proximity Extension Assay (PEA) using the Explore 3072 platform to measure ~2,923 plasma proteins in 54,219 UK Biobank participants. They conducted protein quantitative trait locus (pQTL) mapping and identified 14,287 genetic associations—of which ~81% were novel—thereby constructing a comprehensive "genetic atlas" of the plasma proteome. Their analysis revealed both cis- and trans-pQTLs, illuminated how genetic variation influences ligand–receptor networks and biological pathways, and demonstrated translational relevance by linking pQTLs to disease endpoints (e.g., ABO blood group effects, COVID-19 susceptibility). This work illustrates the power of integrating large-scale genomics and Olink-based proteomics to decode molecular mechanisms and highlight actionable biomarker and therapeutic target candidates.

Figure 4 Plasma protein profiling using Olink Explore 3072 PEA.

This figure shows plasma protein levels measured across 54,219 UK Biobank participants using the high-throughput, antibody-based Olink Explore 3072 Proximity Extension Assay (PEA). Panel a illustrates the study design and the overall protein coverage (2,923 unique proteins), highlighting Olink's broad coverage and high sensitivity for plasma proteins. Panel d shows representative protein expression levels (Normalized Protein Expression, NPX) stratified by age and sex, demonstrating the platform's quantitative capability for detecting biologically relevant differences. Adapted from Folkersen et al., Nature (2023), Fig. 1a,d, https://doi.org/10.1038/s41586-023-06592-6

Precision medicine

In the Proteomic signatures improve risk prediction for common and rare diseases study, Carrasco-Zanini et al. (2024) used the Olink Proximity Extension Assay (PEA) on plasma samples from ~41,931 UK Biobank participants (using the Explore 1536 and Explore Expansion panels) to generate sparse multi-protein models (5-20 proteins) that predict the 10-year incidence of 67 common and rare diseases. These protein predictors significantly enhanced risk stratification beyond traditional clinical risk factors and even polygenic risk scores, with a median improvement of ~7% in model performance. Validation in an independent cohort (EPIC-Norfolk) confirmed their generalizability, highlighting that Olink-based proteomic signatures can be used for personalized disease risk prediction and clinical stratification in population cohorts.

Figure 5 Disease associations of plasma proteins measured by Olink Explore.

This figure presents associations between plasma protein levels quantified using the Olink Explore platform and risk of common and rare diseases across large-scale biobank cohorts. Panel a displays a volcano plot of protein–disease associations, with effect sizes (log odds ratios) on the x-axis and statistical significance (−log₁₀(p-value)) on the y-axis; points passing genome-wide significance thresholds are labeled with protein names. Panel b highlights representative examples where inclusion of Olink-derived proteomic signatures substantially improves disease risk prediction beyond traditional clinical factors.

Adapted from Sinnott-Armstrong et al., Nature (2023), Fig. 2a,b, https://doi.org/10.1038/s41586-022-05619-w.

SomaLogic SomaScan 7K: Broad Proteome Discovery

In contrast, SomaLogic's SomaScan 7K excels in exploratory and system-wide proteomic analyses. The use of chemically modified single-stranded DNA aptamers (SOMAmers) allows simultaneous profiling of thousands of proteins, including low-abundance and previously understudied targets, across a wide dynamic range. This broad coverage facilitates unbiased discovery of novel biomarkers, comprehensive mapping of protein–protein interaction networks, and integration into high-dimensional machine learning or multi-omics analyses for systems biology applications. The platform's ability to measure a large fraction of the proteome in a single experiment makes it particularly valuable for hypothesis-generating studies where the goal is to identify unexpected pathways or protein signatures associated with disease states.

Early Detection Biomarkers

In the Multi-cohort High-Dimensional Proteomics Reveals Early Risk Markers for Lymphoid Cancer Subtypes study, Kolijn et al. (2025) used SomaScan 7K to measure over 6,400 plasma proteins in a prospective EPIC cohort. They identified >500 proteins associated with future lymphoid cancer risk, enriched in cytokine and B-cell signaling pathways. Top proteins were validated in other cohorts, showing high concordance. Some proteins exhibited altered levels years before diagnosis, highlighting their potential as early biomarkers. This study demonstrates SomaScan 7K's broad coverage, high sensitivity, and suitability for population-based discovery.

Systems biology / Protein–protein network analysis

In SomaModules: A Pathway Enrichment Approach Tailored to SomaScan Data , Candia et al. (2025) developed a novel method to identify "SomaModules"—highly intercorrelated groups of SOMAmers—from SomaScan data. They applied this to large-scale SomaScan datasets (e.g., 11K) and showed that these modules better capture biologically coherent protein modules than traditional gene-set pathways. Their approach enabled more sensitive and accurate pathway enrichment, highlighting protein co-regulation networks that reflect underlying system biology. This study demonstrates how SomaScan's broad proteomic coverage combined with custom network methods can reveal functional modules and pathways not easily detected by transcript-based analysis.

Pharmacoproteomics

In Unbiased Human Kidney Tissue Proteomics Identifies Matrix Metalloproteinase 7 as a Kidney Disease Biomarker , Hirohama et al. (2023) used the SomaScan platform to profile 1,305 proteins in kidney tissue from diabetic kidney disease (DKD) patients and controls, identifying 14 proteins associated with eGFR and 152 with interstitial fibrosis. Among them, MMP-7 showed the strongest association with both fibrosis and kidney function. Single-cell RNA-seq localized MMP-7 expression predominantly to proximal tubular cells. Crucially, they validated that plasma levels of MMP-7 (measured by SomaScan) in a large independent cohort (~11,030 subjects) predicted future decline in renal function. This work not only pinpoints MMP-7 as a biomarker for fibrosis and kidney decline, but also highlights its potential as a therapeutic target in fibrotic kidney disease.

Complementary Strengths and Strategic Integration

Importantly, these platforms should be viewed as complementary rather than mutually exclusive. Strategic integration of Olink and SomaLogic measurements can capitalize on the strengths of both approaches: Olink provides precise and reproducible quantification for validation and longitudinal studies, while SomaLogic enables expansive discovery and network-level insights. By combining these datasets, researchers can maximize both specificity and proteome coverage, improving confidence in biomarker identification and functional interpretation. It should be noted that both platforms produce relative quantification metrics—NPX for Olink and RFU for SomaLogic—and direct numerical comparison across platforms is not feasible due to differences in chemistry and normalization methods. Nevertheless, both approaches are widely accepted in the field and have been extensively incorporated into high-impact research publications.

Conclusion

Olink and SomaLogic exemplify two distinct high-throughput proteomic strategies. Olink focuses on specificity, minimal sample volume, and reproducibility, rendering it optimal for validation studies and small-sample research. SomaLogic emphasizes proteome breadth and system-level exploratory capacity, making it suitable for discovery research and network analyses. Researchers should select platforms based on experimental objectives, sample availability, and analytical requirements. Combining both platforms can exploit complementary strengths, enhancing both scientific insight and the efficiency of proteomic research.

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