Common Reasons for Olink Experiment Failures and Solutions

In Olink's proteomics research based on PEA (Proximity Extension Assay) technology, researchers can obtain expression profiles of thousands of proteins using minimal sample volumes (1-3 μL plasma/serum). However, experiments do not always proceed smoothly, and issues such as low detection rates, poor reproducibility, or biased results may lead to unusable data or distorted conclusions. This article summarizes common causes of failure in Olink experiments and provides practical solutions to help researchers optimize project workflows and improve data quality.

 

I. Common Experimental Failures

1. Low detection rate or high proportion of LOD (Limit of Detection)

(1) Signals from many target proteins fall below LOD, leading to missing NPX data;

(2) Specific cytokines or hormones in some panels have low concentrations in samples, making them difficult to detect.

 

2. Poor reproducibility or significant batch differences

(1) NPX differences are too large for the same sample on different assay plates;

(2) Abnormal performance of standards or internal references between batches affects cross-batch comparability.

 

3. High background noise or non-specific signals

(1) Non-specific antibody binding increases background noise;

(2) Interfering substances in the sample (such as lipids, hemolysis products) affect probe pairing efficiency.

 

4. Insufficient sample or inadequate quality

(1) Insufficient plasma/serum volume or multiple freeze-thaw cycles lead to degradation;

(2) Conditions such as hemolysis or lipemia affect detection sensitivity.

 

5. Issues during data analysis

(1) Incorrect LOD handling or batch correction leads to biased statistical conclusions;

(2) Ignoring that NPX is a relative quantification and using it directly for absolute concentration comparisons.

 

II. Analysis of Main Causes

1. Sample-related factors

(1) Improper collection and storage (such as prolonged exposure to room temperature, excessive freeze-thaw cycles);

(2) Hemolysis or high lipemia increases background interference, reducing detection rates.

 

2. Operational and technical factors

(1) Concentrating samples in the same plate leads to significant batch effects;

(2) Contamination of the reaction system or operational errors cause uneven amplification efficiency.

 

3. Panel and target protein selection issues

(1) Some proteins in the selected panel have extremely low baseline concentrations in the research subject;

(2) Failure to select appropriate detection pathways based on species or disease characteristics.

 

4. Data processing issues

(1) Failure to follow Olink standard procedures for NPX standardization and LOD processing;

(2) Failure to use appropriate statistical methods (such as FDR correction) to filter differential proteins.

 

III. Solutions and Optimization Suggestions

1. Optimization of sample collection and quality control

(1) Use EDTA plasma or serum to avoid interference from hemolysis and high lipids;

(2) Separate samples promptly after collection, store at -80°C, and reduce freeze-thaw cycles;

(3) Pre-screen sample quality and eliminate or specially handle abnormal samples.

 

2. Improvement in experimental design and operations

(1) Randomly distribute different group samples in the assay plate to reduce batch effects;

(2) Use standard QC samples (quality control plasma) to monitor cross-plate consistency;

(3) Strictly follow Olink's recommended operational guidelines to avoid contamination or liquid handling errors.

 

3. Panel selection and method combination

(1) In the exploratory phase, combine Olink Explore HT (high throughput) with specific immune, inflammatory, or tumor panels to enhance detection efficiency;

(2) For low-abundance or key proteins, use targeted mass spectrometry or ELISA for supplementary validation.

 

4. Standardization of data processing and analysis

(1) Use Olink's officially recommended NPX standardization procedures;

(2) Use half-LOD imputation or elimination for proteins with high LOD proportions;

(3) Apply R packages such as limma and edgeR for batch correction and differential analysis;

(4) Differential screening should combine Fold Change (2^(ΔNPX)) and FDR correction to avoid false positives.

 

The Olink proteomics platform, with its high sensitivity and high throughput, provides powerful technical support for biomarker research. However, issues such as low detection rates, poor reproducibility, or data processing biases may affect research conclusions. Through scientific sample management, standardized experimental design and data analysis, and necessary multi-technique validation, researchers can significantly reduce the risk of failure. Biotage BioTechnology, with extensive experience in Olink projects and full-process support capabilities, assists research teams in efficiently resolving technical issues, ensuring smooth project progress and high-quality outcomes.

 

Biotage BioTechnology - Leading service provider in bioproduct characterization and multi-omics mass spectrometry detection

 

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Olink Proteomics