How to Efficiently Conduct Olink Proteomics?
In biomarker discovery, clinical cohort investigations, and pharmaceutical development, the Olink proteomics platform, based on Proximity Extension Assay (PEA) technology, offers reliable data support with minimal sample input, high sensitivity, and substantial high-throughput capacity. To ensure both research efficiency and data reliability, research teams must make informed decisions at multiple stages, including study design, experimental execution, and data analysis. This article systematically outlines strategies for conducting Olink proteomics studies effectively, aiming to assist researchers in avoiding common pitfalls and expediting the translation of scientific findings.
Strategic Planning of Experimental Design
1. Define Research Objectives and Grouping
(1) Clinical cohort studies: emphasize large-scale biomarker screening and validation.
(2) Mechanistic studies: focus on alterations in signaling pathways and the dynamics of low-abundance proteins.
(3) Drug development: convert NPX data into absolute concentrations to facilitate efficacy and safety evaluation.
2. Determine Sample Size and Statistical Power
(1) For adequate statistical sensitivity, a minimum of 30 samples per group is recommended.
(2) For proteins with small effect sizes or studies involving comparisons across multiple experimental groups, conduct a priori power analysis.
3. Select the Appropriate Platform
(1) Olink Explore: suited for medium-scale studies involving several hundred samples and exploratory objectives.
(2) Olink Explore HT: optimized for large-scale cohorts with thousands of samples and multi-center clinical trials.
(3) Integration with mass spectrometry: apply absolute quantification or modification identification for proteins exhibiting significant NPX differences.
Optimization of Sample Handling and Quality Control
1. Sample Collection and Storage
(1) Use EDTA plasma or serum, ensuring the avoidance of hemolysis.
(2) Process samples immediately after collection, store at −80°C, and minimize freeze–thaw cycles.
2. Sample Batching and Randomization
(1) Distribute grouped samples across different assay plates to mitigate batch effects.
(2) In large-scale projects, include QC samples and internal controls to enable cross-batch normalization.
3. Prevention of Common Issues
(1) Insufficient sample volume: although Olink assays require only 1–3 μL per measurement, prepare additional volume for repeat testing;
(2) Hyperlipidemic or hemolyzed samples: these may interfere with antibody binding and should be screened beforehand.
Standardized Data Analysis Workflow
1. NPX Computation and Quality Control
(1) NPX (Normalized Protein eXpression) values are reported on a log₂ scale of relative abundance.
(2) Key preprocessing steps include cross-plate normalization, handling of measurements below the limit of detection (LOD), and outlier exclusion.
(3) Retain proteins with <20% of values below LOD for statistical analysis.
2. Differential Analysis and Statistical Approaches
(1) Between-group comparisons can be conducted using t-tests, ANOVA, or non-parametric equivalents, with false discovery rate (FDR) adjustment.
(2) ΔNPX values can be transformed to fold change using 2^(ΔNPX) to aid interpretation.
3. Biological Interpretation and Visualization
(1) Pathway enrichment analyses (e.g., KEGG, Reactome) can elucidate disease-associated molecular networks.
(2) Significant differences can be visualized through volcano plots and heatmaps.
(3) Integrate with transcriptomic and metabolomic datasets for comprehensive multi-omics analysis reports.
Validation and Translational Applications
1. Absolute Quantification and Experimental Validation
(1) For key proteins with significant differences, confirm findings via ELISA or targeted mass spectrometry (PRM/MRM).
(2) Provide reportable absolute concentration units (pg/mL, ng/mL) for clinical and drug development contexts.
2. Research and Industrial Translation
(1) Cohort studies: support predictive disease models and early diagnostic biomarker screening.
(2) Drug development: facilitate efficacy assessment, target validation, and safety monitoring.
(3) Clinical translation: accelerate the implementation of findings in grant applications or industry partnerships.
Efficient execution of Olink proteomics studies requires meticulous planning across all stages, from experimental design and sample management to platform selection, data analysis, and validation. Leveraging extensive expertise, integrated mass spectrometry validation, and robust bioinformatics support, MtoZ Biolabs is committed to enabling research teams to acquire high-quality proteomics data efficiently and to expedite the conversion of scientific discoveries into clinical and industrial value.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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