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What is the Olink Explore 384 Inflammation Panel
Customized panel for human
The Olink Explore 384 Inflammation Panel leverages Proximity Extension Assay (PEA) technology to simultaneously quantify 368 inflammation-related protein biomarkers from just 1 μL of plasma or serum across 88 samples per run, delivering high-sensitivity measurements (<1 pg/mL) with excellent reproducibility (inter-assay CV <5% for most biomarkers) through its NPX (Normalized Protein eXpression) data normalization system. This innovative platform combines eight systematically curated subpanels with minimal target overlap, enabling researchers to either perform comprehensive immune profiling or create customized configurations for focused pathway analysis, while maintaining rigorous quality control through NIST-traceable standards, making it ideal for applications biomarker discovery.
Features of the pane
- Species: Exclusively validated for human proteome applications (non-human species untested).
- Proteins: 384 parallel measurements of inflammation-associated protein biomarkers
- Sample: Minimal 1 µL input volume (plasma/serum compatible)
- Readout: Quantitative NPX-normalized relative abundance values.
- Platform: Designed specifically for Olink Signature Q100 instrumentation
List of 384 human derived biomarkers
Protein category
The Olink Explore 384 Inflammation Panel includes 368 proteins categorized into six main groups: chemokines, cell adhesion molecules, growth factor binding proteins, enzymes, receptors, and other functional proteins (see Table. List of Olink Explore 384 Inflammation Panel). The biomarkers were rigorously curated according to their sample dynamic range and Inflammatory response pathway significance, with classification via Uniprot, Human Protein Atlas, Gene Ontology, and DisGeNET databases demonstrating functional roles in critical biological mechanisms such as metabolic regulation, cellular adhesion, immunological activity, and complement system modulation.
Protein Functions
Biological process
Mainly associated with immune system, cytokine signaling in immune system, and cytokine- cytokine receptor interactive.
Disease area
Demonstrates strong associations with Rheumatoid arthritis, asthma, and.infection.
The Application of Olink Explore 384 Inflammation Panel.
The Olink Explore 384 Inflammation Panel enables simultaneous quantification of 384 protein biomarkers associated with Inflammatory response, providing researchers with a powerful tool for:
- Comprehensive Biomarker Discovery in Chronic Diseases;
- Longitudinal Monitoring of Immune Responses;
- Investigating the Link Between Inflammation & Cancer.
Workflow of Olink Proteomics
Why CPR
Rigorous Quality Assurance
Implements 14-stage QC protocols with inter-plate normalization and sample integrity validation, ensuring <5% CV for 95% of inflammatory biomarkers.
Disease-Specific Analytical Frameworks
Preconfigured workflows for autoimmune diseases (RA, IBD), infectious diseases (sepsis, COVID-19), and chronic inflammation with 68 validated biomarker ratios.
Adaptive Panel Configuration
42 customizable assay slots complementing 342 fixed inflammatory targets for study-specific needs.
Researcher Capacity Building
Comprehensive training ecosystem including inflammation-specific data interpretation workshops and visualization toolkits.
Demo Results of Olink Data
Principal component analysis (PCA) double plots of protein expression profiles by clinical, biological, and technical variables. (Zhang, Z, et al. 2024)
Sample Requirements
| Sample Type | Recommended Sample Size | Sample Quality | Pre-treatment and Storage | Sample Transport |
| Plasma/Serum/Body Fluid | 40µL/sample | Protein concentration: 0.5mg/ml ~ 1mg/ml | Transfer to sterile tubes, aliquot into 1.5 mL EP tubes or 96-well plates, and store at -80°C | All specimens shall be hermetically sealed with aluminum foil and transported in dry ice at -80°C |
| Tissue | ||||
| Cells | ||||
| Exosomes | ||||
| Other |
Case Study
A serum B-lymphocyte activation signature is a key distinguishing feature of the immune response in sarcoidosis compared to tuberculosis
Journal: Communications biology
Year: 2024
- Background
- Methods
- Results
Sarcoidosis and tuberculosis (TB) are two multisystem granulomatous inflammatory diseases that most commonly affect the lungs but can also affect multiple organs, including the eyes. Sarcoidosis is a disease of unknown etiology with complex interactions between host, genetic, and environmental factors. This is thought to result in abnormal immune activation to unknown antigens. In addition, sarcoidosis is associated with both autoinflammatory and autoimmune features. On the other hand, tuberculosis is an infectious disease caused by infection with the bacterium Mycobacterium tuberculosis. Therefore, it is crucial to diagnose tuberculosis and sarcoidosis accurately and in a timely manner, as the treatment of these two diseases is different. Sarcoidosis relies on immunosuppressive therapy, whereas tuberculosis relies on antituberculous therapy. However, in many clinical situations, it may not be straightforward to determine the diagnosis of sarcoidosis and TB using existing diagnostic modalities.
The research utilizes the Olink Explore inflammation panel (list of proteins measured in the inflammation panel: Open in a new tab). Olink provides standardized protein expression (NPX) values as an alternative marker for protein abundance, used for further analysis. Proteins detected in more than 25% of all samples were included in further analysis, allowing for 357 out of 368 proteins from the Olink inflammation panel to be included. Data values below the detection limits of the included proteins were replaced by the manufacturer's recommended fixed value (0). All 368 proteins from one SS patient did not pass quality control and were excluded from further analysis. Proteomic analyses between sarcoidosis and DHC, as well as active pulmonary tuberculosis and IHC, were conducted independently, as all included serum samples were measured in two independent runs. Therefore, for direct comparative analysis between the disease (case) group and the control group, the sarcoidosis cohort was directly compared only with DHC. We applied a similar approach to TB and compared the TB cohort with IHC. Inter-group comparisons were conducted using the Mann-Whitney U test, and the Benjamini-Hochberg (B-H) method was applied for multiple testing correction, i.e., false discovery rate (FDR) correction. An FDR adjusted p-value of p < 0.05 was considered statistically significant. Statistical analysis was performed in R (v4.2.2, R Core Team 2021).
Heat maps generated from all the proteins assayed in each cohort show that sarcoidosis cases show different patterns of assay proteins compared to DHC cases. Similarly, the protein expression pattern in TB cases is different from that in IHC. Notably, there is no clear pattern associated with uveitis in patients with sarcoidosis or TB compared to those without uveitis (Figure 1).
Figure 1. Proteomic analysis of serum highlights differential protein expression between sarcoidosis and tuberculosis. ( Putera, I., et al. 2024)
FAQs
Which normalization method should I use for Olink Explore 3072/384?
Intensity normalization is typically recommended for well-randomized multi-plate projects, whereas Plate Control normalization is typically useful for single plate and non-randomized projects. It is always recommended to randomize samples in projects when possible.
What is the hands-on time of the Olink Explore 3072/384?
For Olink Explore 3072/384, results were obtained within 36 hours; Operating time: <5 hours
Do the different Explore 384 panels have overlap detections and why?
IL6 and IL8 (CXCL8) are included in the CARDIO, ONC, NEURO and INF panels, while IDO1, LMOD1 and SCRIB are included in the CARDIO II, ONC II, NEURO II and INF II panels. These assays were used as additional controls for the comparison panel. In the Olink Explore 3072 run, 4 measurements were taken for each overlap detection.
References
- Zhang, Z., Chen, L., Sun, B., et al. (2024). Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration. Nature communications, 15(1), 9028. https://doi.org/10.1038/s41467-024-53239-9
- Putera, I., Schrijver, B., Kolijn, P. M., et al. (2024). A serum B-lymphocyte activation signature is a key distinguishing feature of the immune response in sarcoidosis compared to tuberculosis. Communications biology, 7(1), 1114. https://doi.org/10.1038/s42003-024-06822-1