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Why Integrate Olink Proteomics with Metagome and Metabolome?

The integration of Olink and transcriptome provides a transformative multi-omics framework for deciphering complex biological systems. This integrated service addresses limitations of single-omics approaches by capturing functional interactions across host molecular pathways, microbiome activities, and metabolic states. Single-omics studies reveal potential biological states, but the Olink-metabolome-metagome triad quantifies active physiological processes.

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 or NGS.

Metagenome sequencing

1) Microbial sample collection.

2) DNA extraction.

3) Sequencing.

4) De novo assembly or align to reference genome.

5) Metagenome amplicon sequencing or metagenomics random shortgun sequencing.

Metabolome

1) Metabolite Detection and Quantification:

Mass Spectrometry (MS) coupled with chromatography.
Nuclear Magnetic Resonance (NMR)

2) Data Processing and Metabolite Identification.

3) Analytical Strategies: Untargeted metabolomics, targeted metabolomics and widely targeted metabolomics.

4) Biological Interpretation: Pathway analysis and multi-omics Integration.

Advantages

Minimal Sample Requirements: Requires only 1–6 µL of sample (e.g., serum, plasma, CSF) for simultaneous quantification of 1,500–3,000 proteins.
Unprecedented Sensitivity and Specificity: Detects low-abundance proteins at fg/mL levels in complex biofluids, overcoming limitations of mass spectrometry. Dual antibody-DNA recognition minimizes cross-reactivity and false positives.
Cross-omics validation: Olink proteins and metabolome metabolites co-validate pathways.
Holistic insights: Links host proteome, metabolome, and microbiome in diseases.

Olink & Metagome & Metabolome Platform: Selecting the Right Panel

Recommended Panels for Integrated Analysis

We recommend prioritizing the Target 96 Metabolism, Immuno-Response, Inflammatory, and Organ Damage panels, complemented by targeted Custom Flex panels like the UKB T2D Panel or Inflammation in Aging Panel when disease-specific. Because these options provide focused, high-quality data on proteins intimately involved in metabolic regulation, immune response modulation, inflammation, and tissue homeostasis.

Here is a list of recommended panels we offer:

Table. List of Olink Panels

Platforms	Recommended Olink Panels
Explore 384	Olink Explore 384 Inflammation
Explore 384	Olink Explore 384 / Inflammation Ⅱ
Target 96	Olink Target 96 Immuno-Oncology
	Olink Target 96 Metabolism
	Olink Target 96 Inflammatory
	Olink Target 96 Organ Damage Panel
Custom Olink Flex systems	Inflammation in aging Panel
	UKB type 2 diabetes Panel
	Cytokine storm Panel

Additional Options

In addition to the panels above, we also offer:

Our team can assist in designing an integrated Olink–Metagome-Metabolome strategy tailored to your project goals.

Applications

These applications leverage multi-omics synergy to map interactions across biological scales, advancing mechanistic research in precision health and disease biology:

Host-Microbiome Interaction Networks

This approach deciphers how gut microbial communities (metagenome) influence host physiology by producing metabolites that modulate protein expression (proteomics).

Multi-Omic Drivers of Complex Traits

Integrated analysis identifies how genetic factors and microbial features jointly explain individual variations in circulating protein levels. Genetic variants primarily regulate immune-related proteins, while microbes dominate metabolic protein profiles.

Functional Biomarker Discovery

Combining proteomics with metagenome/metabolome uncovers early-prediction signatures for diseases like Parkinson' s or cancer.

Causal Target Prioritization

Mendelian randomization integrates genomic data with proteomics to identify proteins causally linked to diseases. Metabolome and metagenome data then validate whether microbial or metabolic pathways regulate these targets, accelerating therapeutic development.

Intervention Response Profiling

Monitoring protein, metabolite, and microbial dynamics during dietary or therapeutic interventions quantifies biological impact.

Workflow of Olink & Metagome & Metabolome

Bioinformatics multi-omics workflow: genomics, proteomics, metabolomics integration. Figure 1: Workflow of integrated Olink proteomics, Metagome and Metabolome.

Why Creativ Proteomics

Advanced Multi-Omics Integration Capabilities

Cross-Platform Data Harmonization: Ability to integrate data from Olink (targeted proteomics), metabolomics, and proteomics into a unified analysis pipeline. This includes reconciling technical differences.
Joint Pathway and Network Analysis: Expertise in identifying multi-layer molecular interactions (e.g., protein-metabolite correlations in disease mechanisms).

Cutting-Edge Technology and Platforms

High-Sensitivity Platforms: Proficiency in Explore HT (5,300+ proteins, 2μl sample volume) or Target 48/96 for focused studies. Use of high-resolution mass spectrometry for broad metabolite coverage. Standard workflows for deep proteome profiling.

Robust Bioinformatics

Integrated Knowledge Bases: Use of biomedical knowledge graphs to annotate proteins/metabolites and prioritize biomarkers.
Data Reproducibility

End-to-End Quality Control & Standardization

Cross-Platform QC: NPX normalization and sample/plate controls for Olink.
Orthogonal Validation: Use of multiple platforms to verify key biomarkers.

Domain-Specific Expertise & Customization

Disease-Focused Panels: Customized multi-omics panels for diseases like cancer, cardiovascular disorders, or neurodegeneration (e.g., combining Olink Inflammation Panel with metabolomics for stroke biomarker discovery).
Sample Type Versatility: Experience with diverse samples (serum, plasma, CSF, tissue) and low-volume handling (critical for pediatric or longitudinal studies)

Demo Results

Diagram linking immune/endocrine/metabolic/digestive systems with medical terms and symbols. Figure 2: Causal networks between the 5 MetBAGs and 525 Des. (The MULTI consortium., et al. 2025)

Molecular interactions for KLK7, glutamine & fructose in human biosys. Figure 3: Multiomics interaction network of protein and metabolite signatures in MARD-associated clusters. (Halama, A., et al. 2024)

Heatmap of microbiome data clustered by bacterial (pink) and viral (purple) stimuli. Figure 4: Co-expression of significantly altered lipids, metabolites and proteinss. (Rischke, S., et al. 2025)

Sample Requirements

Step	Olink Proteomics	Metagenomics (Shotgun Sequencing)	Metabolomics
Sample Types	Serum, plasma, CSF, urine, tissue lysates, cell supernatants	Body fluids (BALF, CSF, blood), tissue, sterile swabs	Serum, plasma, urine, CSF
Volume	Standard: ≥40 μL (serum/plasma) Minimum: ≥20 μL (critical for low-volume samples like aqueous humor or tears)	Blood: ≥1.5 mL (adults), ≥1 mL (children) BALF/CSF: ≥2 mL Tissue: ≥20 mg	≥50–100 μL (serum/plasma); ≥200 μL (urine)
Avoid	Hemolysis, repeated freeze-thaw, or samples exposed to fluorescein (interferes with qPCR)	Hemolysis (increases human DNA background), delays in processing, or non-sterile collection	Additives (e.g., sodium azide), hemolysis, or room-temperature exposure >30min
Storage Temperature	–80°C (no dry ice sublimation)	–80°C (vapor-phase LN₂ preferred)	–80°C (avoid frost-free freezers)
Transport	Ship on dry ice

Notes

Cross-Contamination: Use aerosol-resistant tips during aliquoting.
Inconsistent Collection: Use identical anticoagulants (e.g., EDTA for all plasma).
Thawing for Sub-aliquoting: Pre-plan aliquot sizes to avoid thawing main stock.

FAQs

What sample types are compatible across these technologies?

The Olink platform is validated for EDTA plasma and serum, with broad compatibility extending to CSF, saliva, tissue lysates, and exosomal extracts. Metabolomics supports diverse sample types including serum/plasma, cells, tissues, and microbial cultures. In contrast, metagenomics specifically requires microbial genomic DNA derived from stool, environmental samples, or biopsy tissues to reconstruct genome-scale metabolic models for predicting microbial community interactions. For integrated multi-omics workflows, sample fractionation is critical. Biofluids can be aliquoted for metabolomics (prioritizing immediate metabolite extraction), Olink proteomics (stable proteins), and metagenomics (DNA from microbial-enriched fractions), ensuring analyte integrity across assays.

How to minimize batch effects in multi-omics studies?

To minimize batch effects in multi-omics studies, a three-pronged approach is essential:

Reference sample-based normalization: For Olink and metagenome, use 8–16 bridging reference samples across batches to convert absolute feature values into ratios relative to the reference. This can effectively decouple batch-specific technical variations from biological signals.
Quality control (QC) integration in metabolomics: Include pooled QC samples processed alongside study samples in each batch. These QCs enable normalization via probabilistic quotient normalization or similar methods, correcting for instrumental drift and matrix effects.
Automated workflows: Adopt standardized, high-throughput platforms such as EMR-lipid 96-well plates for metabolomics to ensure consistent sample handling, extraction, and analysis. Automation reduces human-induced variability and enhances reproducibility across batches.

What is the optimal order of sample processing?

The optimal order for processing multi-omics samples prioritizes metabolomics first due to the extreme lability of metabolites, requiring immediate quenching to halt enzymatic activity and prevent degradation. Olink proteomics follows, as proteins remain relatively stable during short-term storage at –80°C. Microbial genomics is processed last, since microbial DNA retains integrity in preservative buffers for weeks, allowing flexible storage at 4°C or room temperature with stabilization agents.

This sequential workflow (metabolites → proteins → microbial DNA) minimizes analyte degradation, leverages differential stability across biomolecule classes, and ensures data fidelity for integrated multi-omics analysis.

How to integrate heterogeneous data types?

Effective integration of heterogeneous data types requires platform-specific normalization followed by advanced cross-omics modeling.

For normalization:

Olink proteomics employs NPX on a log₂ scale to minimize plate- and run-specific biases while preserving relative quantification.
Metabolomics leverages PQN to correct for sample dilution variations or uses internal standards to calibrate absolute concentrations against technical noise.
Metagenomics normalizes microbial gene counts via TPM to account for sequencing depth and gene length biases, enabling accurate comparisons across microbiome samples.

References

The MULTI consortium., Anagnostakis, F., Ko, S. et al. Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk. Nat Commun 16, 4871 (2025).
Halama, A., Zaghlool, S., Thareja, G. et al. A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes. Nat Commun 15, 7111 (2024).
Rischke, S., Gurke, R., Zielbauer, AS. et al. Proteomic, metabolomic and lipidomic profiles in community acquired pneumonia for differentiating viral and bacterial infections. Sci Rep 15, 1922 (2025).

* For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.

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Integrated Olink Proteomics & Metagome & Metabolome Service