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- Overview
- Panels List
- Applications
- Workflow
- Why Creative Proteomics
- Demo
- Sample Requirements
- FAQ
Why Integrate Olink Proteomics with Mass Spectrometry?
The integration of Olink and mass spectrometry (MS) proteomics leverages their complementary analytical principles to achieve comprehensive protein profiling. Olink excels in detecting low-abundance proteins with ultrahigh sensitivity (down to fg/mL). In contrast, MS proteomics enabling unbiased discovery of proteins, post-translational modifications, and isoforms across diverse species. The primary goal of integrating Olink and MS is to overcome the limitations inherent in each standalone technology. While MS struggles to reliably detect low-abundance proteins (e.g., <1 ng/mL in plasma, where only ~10% of proteins are stably measurable), Olink' s antibody-dependent design restricts its scope to predefined pathways. By combining Olink' s targeted precision with MS' s untargeted depth, the integrated method bridges gaps in proteome coverage.
Olink Technology
Proximity Extension Assay (PEA)
1) Antibody pairs conjugate to DNA oligonucleotides.
2) Antibody pairs bind to target protein.
3) Upon co-localization, the DNA strands hybridize and extend into amplifiable templates.
4) Quantifiable DNA signals via qPCR
Mass Spectrometry
1) Sample Introduction: Solid/liquid samples are vaporized to enter the ion source as a gas.
2) Ionization: Gas-phase molecules are ionized.
3) Acceleration: Ions are accelerated by an electric field.
4) Deflection: Ions traverse a magnetic field, bending their paths.
5) Detection & Data Analysis: Ions are counted. m/z values are used to determine molecular weight, elemental composition, and fragmentation patterns.
Advantages
- Unparalleled Sensitivity: Olink detects fg/mL proteins inaccessible to mass spectrometry.
- Enhanced Proteome Depth: Olink can detect additional proteins low-abundance missed by MS.
- Optimized Sample and Cost Efficiency: Olink requires only 1 μL of plasma versus MS' s 5–240 μL.
- Bolstered Data Robustness: Olink' s low interplate CV and a broad dynamic range enable simultaneous quantification of high- and low-abundance proteins.
Olink & MS Platform: Selecting the Right Panel
Recommended Panels for Integrated Analysis
Olink Panels suitable for integration with MS primarily include high-density, broad-coverage Explore HT panels, specific Explore 384 panels under the Explore platform and Customized Olink Flex panels tailored to project needs. The Olink Explore platform, based on Proximity Extension Assay (PEA) technology, is designed for Discovery Science. It enables simultaneous detection of ~3,000 proteins (3072-plex), ~1,500 proteins (via 4× 384-plex panels), or ~400 proteins (single 384-plex panel). This high-throughput, broad coverage makes it ideal for the biomarker discovery phase. Integrating such large-scale proteomic data with MS-derived data through multi-omics integration analysis allows researchers to:
- Cross-validate key findings across platforms.
- Expand total protein coverage due to complementary protein detection between two technologies.
- Enhance robustness and reliability of biological conclusions via technical triangulation.
- Distinguish platform-specific signals or interference by comparing concordance/discordance.
Here is a list of recommended panels we offer:
Table. List of Olink Panels
Additional Options
In addition to the panels above, we also offer:
- Target 96 / Target 48
- Olink Target 96 Immune Response
- Olink Target 96 Inflammatory
- Olink Target 96 Cardiometabolic
- Olink Target 96 Metabolism
- Olink Target 96 Neuro Exploratory
- Olink Target 96 Neurology
- Olink Target 96 Immuno-Oncology
- Olink Target 96 Development Panel
- Olink Target 96 Organ Damage Panel
- Olink Target 96 Cell Regulation Panel
- Olink Target 96 Oncology II Panel
- Olink Target 96 Oncology III Panel
- Olink Target 96 Cardiovascular II Panel
- Olink Target 96 Cardiovascular III Panel
- Olink Target 96 Mouse Exploratory Panel
- Olink Target 48 Mouse Cytokine Panel
- Olink Target 48 Cytokine
Our team can assist in designing an integrated Olink–MS strategy tailored to your project goals.
Applications
This service excels in mechanistic disease research and therapeutic development:
Biomarker Discovery and Validation
- Cross-platform verification enhances the reliability of biomarker candidates.
- Advantage: Olink' s high sensitivity (fg/mL) captures low-abundance proteins, while MS provides broad coverage and post-translational modification (PTM) data.
Neurodegenerative Disease Studies
- Multi-omics integration: Olink + MS + genomics identified pQTLs (protein quantitative trait loci) linking genetic variants to protein dysregulation.
- Immune-endocrine crosstalk: Olink' s inflammation panels combined with MS-based exosome proteomics exposed endothelial dysfunction mechanisms.
Cancer Biology
- Tumor microenvironment: Integration of single-cell RNA-seq + Olink/MS profiled immune checkpoint proteins in melanoma, stratifying responders to immunotherapy.
- Therapeutic resistance: In bladder cancer, MS phosphoproteomics + Olink revealed RBPMS-mediated AP-1 activation as a driver of metastasis.
Target Identification
- Deconvolution of drug effects: Olink tracked dynamic changes in cytotoxic proteins in bladder cancer patients post-chemotherapy, predicting complete response.
- Mechanistic validation: MS confirmed Olink-identified targets via TCGA data integration, supporting its role as a minimally invasive diagnostic marker.
Patient Stratification Research
- Inflammatory bowel disease (IBD): Olink' s inflammation panels + MS metabolomics uncovered Th17 pathway dysregulation, guiding subtype-specific therapies.
- Obesity-linked inflammation: Olink/MS exposed TH17-skewed signatures in adipose tissue, informing targeted immunomodulatory trials.
Workflow of Olink & MS
Figure 1: Workflow of integrated Olink proteomics and mass spectrometry.
Why Creativ Proteomics
Unmatched Technical Performance and Sensitivity
- Ultra-Sensitive Detection: Capable of detecting fg/mL-Level proteins.
- Wide Dynamic Range: Covers concentrations from fg/mL to µg/mL (10 logs), enabling comprehensive profiling across high-, medium-, and low-abundance proteins in a single assay.
High-Throughput, Comprehensive Proteome Analysis
- Scalable Panels: Supports flexible workflows: Targeted Panels for focused disease pathways. Exploratory Panels for unbiased profiling.
- Ultra-low Sample Requirements: Enable high-precision protein quantification using minimal sample volumes. This is critical for rare or pediatric samples.
Rigorous Quality Control and Standardization
- Multi-Layer QC: Implements internal/external controls (e.g., inter-plate calibrators, sample-specific validations) to ensure data reproducibility.
- Diverse Sample Proficiency: Validate for challenging matrices, with strict pre-analytical controls to minimize batch effects.
Expertise-Driven Support and Customization
- End-to-End Guidance: Provides experimental design, data interpretation, and biomarker validation support.
- Cross-Omics Synergy: Integrates with genomics, metabolomics, and single-cell sequencing to unravel disease mechanisms.
- Tailored Solutions: Customized Olink panels.
Demo Results
Figure 2: Only in UC was it significantly negatively correlated with PUCAI score, fecal calprotectin, and plasma glyceric acid/threonic acid, suggesting its potential as an activity marker for UC. (Nyström, N., et al. 2022)
Figure 3: Summary of differential expression effect sizes across all cohorts and measurement technologies for DDC and DPP7. (Rutledge, J., et al. 2024)
Figure 4: Depth and overlap of proteomic coverage by three platforms in CSF and plasma. (Dammer, E.B., et al. 2022)
Sample Requirements
For Olink Analysis
- Sample Type: Plasma, serum, body fluid, tissue, cells, exosomes.
- Sample size: 40 µL/sample at least. Only 1 µL of plasma/serum suffices to quantify 92-3,000+ proteins.
- Protein concentration: 0.5mg/ml ~ 1mg/ml.
- Long-term Storage: –80°C without freeze-thaw cycles.
- Avoid detergents: e.g., SDS >0.1%, Triton >1%.
For MS Analysis
- Sample Type: Plasma/serum. Tissues/cells require digestion.
- Protein concentration: 0.5mg/ml ~ 1mg/ml.
- Long-term Storage: –80°C without freeze-thaw cycles.
- Avoid: Lipophilic solvents, strong acids/bases, high salts, or reducing agents.
FAQs
What research scenarios are suitable for integrating Olink and MS technologies?
The integrated technology is applicable to:
- Disease mechanism studies: Detecting low-abundance biomarkers.
- Precision drug development: Joint analysis of drug target expression (Olink) and protein modification dynamics (MS) optimizes therapeutic efficacy assessment.
- Early screening model construction.
How should samples be prepared and allocated?
Plasma, serum, cerebrospinal fluid (CSF), and other biofluids are compatible but require standardized preprocessing. In general, Olink analysis needs 1–3 µL per sample (Target panels) or 1–6 µL (Explore HT panels). While MS analysis requires ≥50 µL for DDA mode or 20 µL for DIA mode.
Aliquot samples should avoid freeze-thaw cycles. Residual samples after Olink detection can be used for MS analysis
How to select Olink panels and MS detection platforms?
Olink panel selection: Target 48/96 (92 proteins) is optimal for validating known biomarkers. Explore 3072 covers multiple pathways for comprehensive profiling.
MS mode selection: DDA is suitable for unknown protein discovery but with lower reproducibility. DIA/SWATH have high reproducibility and are ideal for large cohort validation.
How to resolve cross-platform data inconsistencies?
The data inconsistencies are primarily caused by inherent methodological differences. Mass spectrometry frequently fails to detect low-abundance proteins that are readily measurable by Olink due to sensitivity limitations. Furthermore, inherent differences in antibody binding specificity (for Olink) and peptide fragmentation behavior (for MS) contribute to cross-platform variations in protein identification.
To address these challenges, we recommend that critical biomarkers identified across platforms should undergo priority verification through orthogonal methods like Western Blot or ELISA. While during computational analysis, machine learning models should assign greater statistical weights to proteins demonstrating consistent cross-platform results to enhance overall reliability.
References
- Nyström N, Prast-Nielsen S, Correia M et al (2023) Mucosal and plasma metabolomes in new-onset paediatric inflammatory bowel disease: correlations with disease characteristics and plasma inflammation protein markers. Journal of Crohn's and Colitis 17(3):418-432. https://doi.org/10.1093/ecco-jcc/jjac149
- Rutledge J, Lehallier B, Zarifkar P et al (2024) Comprehensive proteomics of csf, plasma, and urine identify ddc and other biomarkers of early parkinson's disease. Acta Neuropathol 147(1):52. https://doi.org/10.1007/s00401-024-02706-0
- Dammer EB, Ping L, Duong DM et al (2022) Multi-platform proteomic analysis of alzheimer's disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome. Alzheimer's Research & Therapy 14(1):174. https://doi.org/10.1186/s13195-022-01113-5