How to Efficiently Conduct Olink Proteomics
In biomarker research, clinical cohort projects, and drug development, Olink's proteomics platform based on PEA (Proximity Extension Assay) technology provides researchers with reliable data support through its low sample requirement, high sensitivity, and large-scale throughput. However, to ensure research efficiency and result reliability, research teams need to make correct decisions at various stages such as project design, experimental execution, and data analysis. This article systematically introduces how to efficiently conduct Olink proteomics research, helping researchers avoid common issues and accelerate the translation of scientific findings.
1. Rational Planning of Experimental Design
1. Clarify Research Objectives and Grouping
(1) Clinical cohort study: Focus on biomarker screening and validation in large-scale samples;
(2) Mechanism research: Emphasize changes in signaling pathways and dynamics of low-abundance proteins;
(3) Drug development: Requires converting NPX data into absolute concentrations to evaluate efficacy and safety.
2. Determine Sample Size and Statistical Power
(1) It is recommended that each group has a sample size of ≥30 to ensure the ability to detect differences;
(2) For proteins with low effect values or projects with multiple group comparisons, power analysis should be conducted in advance.
3. Choose the Appropriate Platform
(1) Olink Explore platform: Suitable for medium-scale (hundreds of samples), exploratory research;
(2) Olink Explore HT platform: Suitable for large-scale cohorts and multi-center clinical research involving thousands of samples;
(3) Combine with mass spectrometry: Perform absolute quantification or modification identification on proteins with significant NPX differences.
2. Optimize Sample Handling and Quality Control
1. Sample Collection and Storage
(1) It is recommended to use EDTA plasma or serum and avoid hemolysis;
(2) Samples should be isolated immediately after collection and stored at -80°C to avoid repeated freeze-thaw cycles.
2. Sample Batch and Randomization
(1) Avoid concentrating grouped samples on the same assay plate to reduce batch effects;
(2) For large projects, it is recommended to introduce QC samples and internal references for cross-batch correction.
3. Avoid Common Problems
(1) Insufficient sample volume: Olink requires only 1-3 μL/sample, but additional volume should be prepared for retesting;
(2) High lipemia or hemolyzed samples: May interfere with antibody binding and should be screened in advance.
3. Standardized Data Analysis Process
1. NPX Calculation and Quality Control
(1) Olink outputs NPX (Normalized Protein eXpression) as log2 scale relative abundance;
(2) Cross-plate correction, handling of LOD (Limit of Detection), and outlier screening are core steps;
(3) It is recommended to retain proteins with LOD proportion <20% for statistical analysis.
2. Differential Analysis and Statistical Methods
(1) Inter-group comparisons can use t-tests, ANOVA, or non-parametric tests, combined with FDR correction;
(2) ΔNPX can be converted to Fold Change using 2^(ΔNPX) for easier interpretation of fold differences.
3. Biological Interpretation and Visualization
(1) Pathway enrichment (KEGG, Reactome) reveals disease-related networks;
(2) Use volcano plots and heatmaps to present significant differences;
(3) Integrate with transcriptomics and metabolomics to form a multi-omics analysis report.
4. Integration of Validation and Result Translation
1. Absolute Quantification and Validation
(1) For key proteins with significant differences, confirmation through ELISA or targeted mass spectrometry (PRM/MRM) is recommended;
(2) Provide reportable concentration units (pg/mL, ng/mL) for clinical research or drug projects.
2. Research and Industrial Application
(1) Cohort study: Supports disease prediction models and early diagnostic biomarker screening;
(2) Drug development: Used for efficacy evaluation, target validation, and safety monitoring;
(3) Clinical translation: Helps accelerate the transition of research findings into project applications or collaborations with pharmaceutical companies.
To efficiently conduct Olink proteomics research, research teams need to meticulously plan each stage from experimental design, sample handling, platform selection to data analysis and validation.Bytpeck Biotechnology, with its rich experience, mass spectrometry combined validation capabilities, and bioinformatics support, is committed to helping research teams efficiently obtain high-quality proteomics data and accelerate the translation of scientific findings into clinical and industrial value.
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