Creative Proteomics offers a one-stop biomarker research service covering cohort design, multi-omics detection, biomarker screening, and data analysis. Our multi-omics integrated biomarker discovery approach delivers multi-dimensional biological insights, representing the most advanced direction in biomarker research.
What are Disease Biomarkers
A disease diagnostic biomarker is a measurable indicator that shows whether a disease is present, how advanced it is, or how it is progressing. These biomarkers can include:
- Protein biomarkers: detectable in blood, tissues, or other biological fluids.
- Nucleic acid biomarkers: such as specific DNA mutations, RNA transcripts, or epigenetic modifications.
- Metabolite biomarkers: small molecules indicating altered metabolic pathways.
- Microbial biomarkers: microbiome changes associated with disease onset or severity.
Multi-omics biomarkers, which combine proteomics, metabolomics, lipidomics, and genomics data, can significantly enhance diagnostic accuracy and reliability compared with single-biomarker approaches.
Technical Workflow for Multi-Omics Disease Biomarker Research Solutions
Technical Advantages of Our Multi-Omics Disease Biomarker Research Solutions
- Professional One-Stop Service: Clients receive a fully coordinated workflow, from study design through to data interpretation, all managed by a single team.
- Advanced Instrumentation: Utilizes state-of-the-art LC-MS/MS, next-generation sequencing (NGS), and high-throughput screening.
- Integrated Machine Learning: Combines multiple algorithms to select consistently reliable features, improving biomarker stability and reproducibility.
- Multi-Omics Integration: Connects different molecular data layers to provide a clearer, more accurate picture for diagnostics.
Multi-Omics Analysis Service We Provide
We offer a comprehensive portfolio of omics technologies that can be combined or applied individually, depending on project goals.
Proteomics: Quantitative and qualitative protein profiling for disease-specific protein biomarker discovery.
Metabolomics: Identification of small-molecule metabolic signatures linked to disease processes.
Lipidomics: Characterization of lipid species and lipid metabolism alterations in health and disease.
Microbiomics: Profiling of microbial communities to identify microbial-derived disease indicators.
Data Analysis and Interpretation
- Molecular Profiling and Expression Patterns: Map the disease's molecular landscape by analyzing genes, proteins, metabolites, and lipids.
- Clustering and Disease Subtyping: Group patients into molecular subtypes using unsupervised clustering of omics data.
- Subtype-Specific Target and Drug Sensitivity Screening: Identify potential therapeutic targets and predict drug responses for each subtype.
- Immune Profiling: Examine immune cell composition, checkpoint activity, and inflammation to understand immune variations.
- Multi-Omics Expression Correlation: Link data across proteomics, metabolomics, lipidomics, and genomics to reveal key molecular relationships.
- Functional Mechanism Exploration of Key Molecules: Study the biological roles and pathways of candidate biomarkers to guide translational research and therapy development.
Application for Multi-Omics Disease Biomarker Research
- Disease Mechanism Studies: Uncovering pathways and biological processes underlying disease.
- Predictive Biomarkers: Identifying individuals likely to respond to specific therapies.
- Disease subtyping: Distinguishing between disease subtypes.
- Prognostic Biomarkers: Predicting disease progression and patient outcomes.
Sample Requirements (Recommended)
| Sample Type | Recommended Volume | Storage & Handling |
|---|---|---|
| Plasma / Serum | 500 µL–2 mL per sample | Snap-freeze in liquid nitrogen; store at –80°C |
| Tissue | 50–100 mg per sample | Snap-freeze immediately; store at –80°C |
| Urine | 10–50 mL | Store at –80°C; avoid repeated freeze-thaw cycles |
| Swabs / Microbiome Samples | 1 swab or 0.5–1 g material | Freeze at –80°C immediately |
| Cell Lysates / Cytokine Samples | 1–2 million cells or 200 µL supernatant | Snap-freeze; store at –80°C |
Why Choose Creative Proteomics
- Rich Omics Expertise: Proven track record in complex multi-omics research projects.
- State-of-the-Art Infrastructure: Equipped with advanced proteomics, metabolomics, and genomics platforms.
- Customizable Project Design: Tailored workflows to meet unique research and regulatory requirements.
- Comprehensive Data Analysis: In-depth bioinformatics interpretation with actionable insights.
- Publication-Ready Deliverables: High-quality figures, statistical outputs, and fully annotated datasets.
References
- Kumar P, Kanchan S, Kesheri M. Multi-omics in human disease biomarker discovery. Microbial omics in environment and health, 2024, 205-239.
- Chen C, et al. Applications of multi‐omics analysis in human diseases. MedComm, 2023, 4(4): e315.
- Olivier M, et al. The need for multi-omics biomarker signatures in precision medicine. International journal of molecular sciences, 2019, 20(19): 4781.
Large-Scale Multi-omic Analysis of COVID-19 Severity.
Journal: Cell Systems
Impact factor: 7.7
Published: 2021
DOI: 10.1016/j.cels.2020.10.003
Backgrounds
Clinical outcomes in COVID-19 are highly heterogeneous, and prior work suggested distinct molecular signatures underlie severity (inflammation, coagulopathy, lipid dysregulation). The authors motivate a broad multi-omic survey to map molecular correlates of disease severity and provide a resource for hypothesis generation.
Materials & Methods
RNA-seq (leukocytes); proteomics, metabolomics, lipidomics in plasma via mass spectrometry
| Transcriptomics (RNA-seq) | Identified differentially expressed genes (DEGs) between HCC tissues and adjacent non-tumor tissues | Detected 1,248 DEGs (FDR < 0.05), including upregulation of AFP, GPC3, and SPP1, and downregulation of CYP3A4 |
| Proteomics (LC-MS/MS) | Quantify protein abundance differences between groups | Identified 832 proteins with significant differential abundance; SPP1 and AFP showed >2.5-fold increase in HCC tissues |
| Metabolomics (LC-MS) | Detect metabolic alterations associated with disease state | Found 75 significantly altered metabolites (p < 0.05), including elevated bile acids and decreased polyunsaturated fatty acids |
| Integrated Pathway Analysis | Correlate transcriptomic, proteomic, and metabolomic data to identify key pathways | Revealed enrichment in glycolysis/gluconeogenesis, bile acid metabolism, and PI3K–Akt signaling pathway |
| Biomarker Panel Development | Combine multi-omics markers to improve diagnostic accuracy | Combined AFP, SPP1, and bile acid levels achieved AUC = 0.93 for HCC diagnosis, outperforming AFP alone (AUC = 0.82) |
Results
- The study quantified >17,000 molecular features across omic layers and identified 219 biomolecules strongly associated with COVID-19 status and severity (transcripts, proteins, lipids, metabolites).
- Multi-omics integration identified consistent signatures of disrupted lipid transport, activation of neutrophils, increased blood clotting and platelet activity, and a systemic acute-phase inflammatory response. Notable leads included decreased plasma gelsolin (pGSN), altered plasmalogen and apolipoprotein levels, and elevated coagulation markers.
- A multi-omics classifier significantly outperformed traditional clinical comorbidity scores in predicting disease severity. Key features identified included biologically relevant molecules such as plasmalogens, S100 proteins, and kynurenine metabolites.
Fig. 1 Multi-omics analysis reveals strong molecular signatures associated with COVID-19 status and severity.
Fig 2. Leveraging the value of multi-omic data through cross-ome correlation analysis.
Conclusions
The study shows that comprehensive multi-omics profiling of patient blood can identify biologically relevant biomarkers and improve clinical outcome prediction beyond standard scoring methods. The authors also share a dataset and web tool to enable reproducibility and support further validation and translational research.
What is the difference between single-omics and multi-omics biomarker research?
Single-omics analysis examines a single molecular layer, such as proteins or metabolites, which can offer only a partial view of complex biological processes. In contrast, multi-omics combines two or more molecular datasets—proteins, metabolites, lipids, or microbiome data—to uncover interactions across biological layers, enhancing the accuracy and interpretability of biomarkers.
How should multi-omics data be integrated analytically?
Integration strategies can occur at different stages, from combining features early in the workflow to merging modality-specific models later. Incorporating networks and biological pathways adds meaningful context, while multi-block methods (e.g., MOFA, DIABLO) and graph-based approaches reveal relationships across data types. The choice of method depends on dataset size, missing data patterns, and whether the goal is prediction or understanding underlying mechanisms.
How does machine learning improve biomarker selection?
Machine learning examines complex datasets to find patterns and highlight the most predictive biomarkers. Using ensemble methods, which combine multiple models, improves reliability and reduces false positives, making biomarker discovery more robust.
How large should cohorts be for discovery vs. validation stages?
There is no one-size-fits-all rule for sample size. The number of samples needed depends on the strength of the effect, biological variability, and the desired confidence in the results. Small, well-controlled cohorts are often sufficient for initial discovery, but larger, independent cohorts are necessary to confirm clinical relevance. Pilot studies and statistical power calculations should guide sample size decisions.
Demo: Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients' blood
Fig 3. Cell-free multi-omics data summary and quality control (Tao Y H, et al., 2023).
Fig 4. Detection of the cancer genes using different types of variations (Tao Y H, et al., 2023).