Common Reasons for Olink Experiment Failures and Corresponding Solutions
In proteomic studies using the Olink platform based on Proximity Extension Assay (PEA) technology, researchers are able to profile thousands of proteins using only minimal amounts of plasma or serum (1–3 μL). Nonetheless, experiments may encounter various obstacles. Low detection rates, limited reproducibility, and systematic bias can render data unusable or lead to inaccurate conclusions. This article summarizes the major causes of failure in Olink experiments and provides practical solutions to help researchers optimize workflows and enhance data quality.
Common Experimental Failure Problems
1. Low Detection Rate or a High Proportion of Measurements Below the Limit of Detection (Lod)
(1) Signals for many target proteins fall below the LOD, leading to missing NPX values;
(2) Certain cytokines or hormones included in specific panels are present at very low concentrations in the samples, making them difficult to detect.
2. Poor Reproducibility or Substantial Inter-Batch Variability
(1) The same sample yields large NPX discrepancies across different assay plates;
(2) Abnormal performance of standards or internal controls across batches impairs cross-batch comparability.
3. Elevated Background Noise or Non-specific Signals
(1) Non-specific antibody interactions increase background levels;
(2) Interfering substances in the sample, such as lipids or hemolysis-related components, compromise probe-pairing efficiency.
4. Insufficient Sample Volume or Inadequate Sample Quality
(1) Plasma or serum volume is insufficient, or repeated freeze-thaw cycles cause protein degradation;
(2) Hemolysis or lipemia adversely affects assay sensitivity.
5. Issues Arising During Data Analysis
(1) Incorrect handling of LOD values or improper batch correction can bias statistical conclusions;
(2) NPX values, which represent relative quantification, are mistakenly treated as absolute concentrations.
Analysis of Major Causes
1. Sample-Related Factors
(1) Improper sample collection or storage, such as prolonged exposure to room temperature or excessive freeze-thaw cycles;
(2) Hemolysis or high lipid content increases background interference and reduces detection performance.
2. Operational and Technical Factors
(1) Assigning samples from the same experimental group to a single plate leads to pronounced batch effects;
(2) Contamination of the reaction system or operational errors result in uneven amplification efficiency.
3. Issues Related to Panel Selection and Target Protein Coverage
(1) Some proteins included in the selected panel exhibit extremely low baseline concentrations in the population under study;
(2) Detection panels are not selected according to species-specific or disease-specific biological characteristics.
4. Data Processing-Related Problems
(1) NPX normalization and LOD handling are not performed in accordance with Olink-recommended procedures;
(2) Differential protein identification is carried out without appropriate statistical approaches, such as FDR correction.
Solutions and Optimization Recommendations
1. Optimization of Sample Collection and Quality Control
(1) Use EDTA plasma or serum and avoid hemolysis or lipid contamination;
(2) Separate samples promptly after collection and store at −80°C to minimize freeze-thaw cycles;
(3) Conduct preliminary quality screening and remove or specially handle samples with abnormal characteristics.
2. Improvements in Experimental Design and Operational Procedures
(1) Randomize sample allocation across assay plates to mitigate batch effects;
(2) Employ standard QC samples, such as quality-control plasma, to monitor inter-plate consistency;
(3) Strictly follow Olink-recommended procedures to avoid contamination or pipetting errors.
3. Panel Selection and Integration of Complementary Methods
(1) During the exploratory phase, combine Olink Explore HT with specialized immunology, inflammation, or oncology panels to enhance detection efficiency;
(2) For low-abundance or biologically critical proteins, validate results using targeted mass spectrometry or ELISA.
4. Standardization of Data Processing and Analysis
(1) Apply Olink-recommended NPX normalization workflows;
(2) For proteins with high proportions of LOD values, use half-LOD imputation or remove them;
(3) Use R packages such as limma or edgeR for batch correction and differential protein analysis;
(4) Differential expression analysis should consider both fold change (2^(ΔNPX)) and FDR correction to reduce false positives.
The Olink proteomics platform provides powerful support for biomarker research due to its high sensitivity and throughput. Nevertheless, low detection rates, poor reproducibility, and improper data handling may compromise research findings. By implementing rigorous sample management, standardized experimental design and analysis procedures, and complementary validation methods, researchers can substantially reduce the risk of failure. With extensive experience in Olink projects and comprehensive workflow support, MtoZ Biolabs assists research teams in efficiently resolving technical challenges, ensuring smooth project progress and the generation of high-quality results.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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