Integrating Proteomics with Metabolomics: A Multi-Omics Strategy for Systems Biology

1. Why Integrate Proteomics with Metabolomics in Systems Biology?

In recent years, multi-omics integration has become one of the most powerful strategies in modern life sciences. Instead of relying on a single layer of biological information, researchers are now combining multiple omics data types to gain a holistic view of complex biological systems. Among these approaches, the integration of proteomics (the large-scale study of proteins and post-translational modifications) with metabolomics (the comprehensive profiling of small-molecule metabolites) has proven especially valuable for advancing systems biology and precision medicine.

Proteins and metabolites form the functional backbone of cellular processes. Proteins act as enzymes, structural elements, and signaling molecules, while metabolites represent the end products and intermediates of biochemical reactions. Studying either layer in isolation provides only a partial picture. For example, a change in protein expression does not necessarily indicate whether its enzymatic activity is altered, and shifts in metabolite concentrations may occur without clear knowledge of the upstream regulatory proteins. By integrating proteomics and metabolomics data, researchers can uncover direct links between molecular regulators and metabolic outcomes, enabling a deeper understanding of biological mechanisms.

This integrated approach is transforming multiple domains:

• Pathway analysis becomes more accurate when proteomic signals are combined with metabolomic readouts, reducing false positives in enrichment studies.

• Biomarker discovery benefits from higher sensitivity and specificity, as protein-metabolite correlations can distinguish disease states more effectively than either dataset alone.

• Predictive modeling in clinical research is strengthened by fusing proteomic and metabolomic features, leading to more robust prognostic tools.

This surge is driven by the rise of personalized medicine, where clinicians aim to tailor treatments based on a patient’s molecular profile. Multi-omics integration—particularly proteomics-metabolomics workflows—offers one of the most actionable strategies to bridge molecular research and real-world healthcare applications.

This 2025 guide provides a comprehensive overview of how proteomics and metabolomics can be integrated effectively. It explores the fundamental principles, available technologies, practical workflows, clinical applications, and emerging trends.

2. The Foundations of Multi-Omics Integration: Proteomics and Metabolomics Explained

2.1 Proteomics Basics

Proteomics is the large-scale study of proteins, the workhorses of the cell. Unlike genomics or transcriptomics, which provide static or potential information, proteomics reflects the dynamic functional state of biological systems. Proteins not only act as enzymes and structural components but also undergo post-translational modifications (PTMs) such as phosphorylation, acetylation, and ubiquitination, which dramatically alter their activity.

Modern proteomics relies heavily on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). This approach allows the identification and quantification of thousands of proteins in a single experiment. Labeling techniques such as TMT (tandem mass tags) and quantitation strategies like data-independent acquisition (DIA) have improved throughput and reproducibility. However, challenges remain, particularly the dynamic range problem: highly abundant proteins (e.g., albumin in blood plasma) can mask the detection of low-abundance yet biologically critical proteins.

For systems biology, proteomics provides a powerful but incomplete layer of information. It tells us what proteins are present and modified, but it does not reveal how those proteins affect cellular metabolism downstream.

2.2 Metabolomics Basics

Metabolomics is the systematic study of small molecules—metabolites such as amino acids, sugars, lipids, and organic acids—that represent the end products of biochemical reactions. Because metabolites change rapidly in response to environmental or physiological shifts, metabolomics offers a real-time snapshot of cellular state.

Analytical platforms such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are commonly used to profile hundreds to thousands of metabolites. GC-MS is particularly suited for volatile and derivatized compounds, while LC-MS offers broader metabolite coverage with higher sensitivity. Nuclear magnetic resonance (NMR) spectroscopy, although less sensitive, provides highly reproducible metabolite quantification.

Despite its strengths, metabolomics has limitations when used in isolation. Metabolite shifts may indicate pathway perturbations, but without knowledge of upstream proteins or enzymes, the underlying regulatory mechanisms remain unclear. This is where integration with proteomics becomes crucial.

2.3 The Power of Integration

The true value of multi-omics integration lies in combining proteomic and metabolomic datasets into a single interpretative framework. Proteins drive biochemical pathways, while metabolites reflect their outcomes. When analyzed together, they provide bidirectional insights: which proteins regulate metabolism, and how metabolic changes feedback to modulate protein function.

For example:

• Integrating proteomics with metabolomics allows more precise pathway enrichment analysis. A pathway supported by both protein abundance and metabolite concentration changes is more likely to be biologically relevant.

• In biomarker discovery, protein-metabolite correlations enhance specificity. Instead of relying on a single marker (e.g., a protein overexpression), researchers can identify combined signatures (e.g., protein + metabolite patterns) that better distinguish disease states.

• Integration helps resolve contradictions. A protein may appear upregulated in proteomics data, but without corresponding metabolite changes, the effect may be biologically insignificant.

Recent studies in cancer and metabolic disorders have demonstrated that proteomics-metabolomics integration improves the accuracy of disease classification and therapy response prediction compared to single-omics approaches. As systems biology advances, this dual-layer integration is becoming indispensable for revealing complex molecular networks.

3. Technology Selection: Choosing the Right Tools for Multi-Omics

Selecting the appropriate technologies for proteomics-metabolomics integration is a critical step in designing a successful multi-omics study. The choice depends on research goals—whether the priority is high-throughput screening, detailed pathway mapping, or clinical biomarker validation. Below, we outline the major platforms and computational tools that drive multi-omics research.

3.1 MS-Based Technologies

Mass spectrometry (MS) remains the gold standard for both proteomics and metabolomics.

Proteomics (LC-MS/MS):

• Widely used for large-scale protein identification and quantification.

• Data-Independent Acquisition (DIA) offers high reproducibility and broad proteome coverage.

• Tandem Mass Tags (TMT) enable multiplexed quantification across multiple samples, increasing throughput.

• Limitation: low-abundance proteins may remain undetected without enrichment strategies.

Metabolomics (GC-MS and LC-MS):

• GC-MS provides excellent resolution for volatile compounds and is highly reproducible.

• LC-MS offers broader metabolite coverage, including lipids and polar metabolites, with high sensitivity.

• Limitation: variability in ionization efficiency can complicate quantitative accuracy.

By combining these approaches, researchers can balance breadth of coverage with depth of detection, which is particularly important in multi-omics workflows.

3.2 Enrichment and Prep Methods

In integrated proteomics–metabolomics research, preparation is less about compiling a long list of methods and more about aligning strategies so that protein and metabolite data remain comparable. Rather than treating enrichment as a separate, parallel step for each omics layer, the focus should be on building continuity across the workflow. For example, consistent sampling protocols, careful handling to reduce degradation, and preservation techniques that stabilize both proteins and metabolites create a common baseline. Enrichment is applied selectively, only when it clarifies the biological question—such as isolating a signaling protein of interest or capturing unstable metabolic intermediates. The guiding principle is coherence: every preparatory choice should reduce variability and enhance the integration of downstream datasets.

3.3 Bioinformatics Tools

Once proteomic and metabolomic data are generated, computational integration is the next major challenge. A wide range of tools are available to facilitate cross-omics analysis:

• MixOmics (R package): Provides multivariate statistical methods, including Partial Least Squares (PLS), to uncover correlations across datasets.

• MetaboAnalyst: Popular for metabolomics data analysis and pathway mapping, with modules designed for integration with proteomic data.

• xMWAS: Performs network-based integration, allowing researchers to visualize protein–metabolite interaction networks.

• MOFA2 (Multi-Omics Factor Analysis): A machine learning framework that captures latent factors driving variation across multiple omics layers.

These tools help reveal hidden patterns, identify multi-omics biomarkers, and strengthen pathway analysis.

3.4 Normalization and Batch Effect Correction

One of the greatest challenges in multi-omics integration is data heterogeneity. Proteomic and metabolomic datasets differ in scale, dynamic range, and noise distribution. Without proper normalization, integrated analyses may produce misleading results.

• Normalization strategies: log-transformation, quantile normalization, or variance stabilization can harmonize datasets.

• Batch effect correction: Tools like ComBat are widely used to mitigate technical variation, ensuring biological signals dominate the analysis.

• Validation methods: independent cohorts, targeted PRM (parallel reaction monitoring) for proteins, and NMR for metabolites help confirm findings.

3.4 Decision-Making for Technology Selection

Choosing the right platform requires balancing research objectives, sample size, and available resources:

• For high-throughput biomarker screening, DIA-based LC-MS/MS coupled with LC-MS metabolomics provides broad coverage.

• For mechanistic studies, targeted TMT-based proteomics combined with GC-MS metabolomics allows precise correlation between enzymes and metabolites.

• For clinical translation, robust workflows with strong QC (e.g., PRM + NMR validation) are preferred to ensure reproducibility.

Ultimately, a well-designed multi-omics workflow integrates both robust experimental techniques and reliable computational frameworks, laying the foundation for meaningful biological insights.

4. Practical Workflows for Integrating Proteomics and Metabolomics

Designing and executing a multi-omics workflow requires careful planning, as proteomics and metabolomics differ in sample preparation, detection sensitivity, and data processing. Below is a step-by-step protocol commonly adopted in systems biology and biomedical research, highlighting best practices for reproducibility and reliability.

Step 1: Sample Preparation

Goal: Obtain high-quality extracts of both proteins and metabolites.

Best Practices:

• Use joint extraction protocols when possible, enabling simultaneous recovery of proteins and metabolites from the same biological material.

• Keep samples on ice and process rapidly to minimize degradation.

• Include internal standards (e.g., isotope-labeled peptides and metabolites) to allow accurate quantification across runs.

Challenge: Balancing conditions that preserve proteins (which often require denaturants) with those that stabilize metabolites (which may be heat- or solvent-sensitive).

Step 2: Data Acquisition

In the proteomics workflow, data acquisition typically involves high-resolution mass spectrometry (MS)-based techniques. Common strategies include data-dependent acquisition (DDA) and data-independent acquisition (DIA), which enable the comprehensive detection and quantification of peptides. Targeted proteomics approaches, such as parallel reaction monitoring (PRM) or selected reaction monitoring (SRM), are also employed when specific proteins or peptides of interest need to be measured with high sensitivity and reproducibility.

In the metabolomics workflow, data acquisition depends on the type of profiling strategy. Untargeted metabolomics uses platforms such as LC-MS or GC-MS to broadly capture a wide range of metabolites, providing a global view of metabolic states. Targeted metabolomics, in contrast, is designed to accurately quantify predefined metabolites or metabolite classes. This can be achieved using LC-MS/MS with multiple reaction monitoring (MRM), nuclear magnetic resonance (NMR) spectroscopy, or other application-specific technologies. Unlike untargeted profiling, targeted approaches focus on sensitivity, selectivity, and absolute quantification of metabolites relevant to the biological question.

Step 3: Data Processing and Integration

Data Preprocessing:

• Apply normalization techniques (e.g., quantile normalization, log transformation) to harmonize proteomic and metabolomic scales.

• Use batch effect correction tools such as ComBat to minimize technical variation.

Data Integration:

Employ statistical correlation analysis (e.g., Pearson/Spearman correlation, PLS regression).

Use bioinformatics platforms:

• MixOmics for multivariate integration.

• MetaboAnalyst for pathway analysis.

• xMWAS for network analysis linking proteins to metabolites.

For complex datasets, apply machine learning frameworks such as MOFA2, which can extract hidden factors driving biological variation across omics layers.

Step 4: Validation

• Proteins: Validate candidate proteins using targeted methods such as PRM (parallel reaction monitoring) or SRM (selected reaction monitoring), ensuring specificity.

• Metabolites: Confirm findings with NMR or targeted LC-MS assays.

• Cross-validation: Independent cohorts or replicates are essential to confirm biological relevance.

Step 5: Pathway and Network Analysis

• Pathway Enrichment: Use KEGG, Reactome, or MetaCyc databases to map proteins and metabolites to known pathways.

• Network Construction: Build integrated protein–metabolite interaction networks to highlight hubs (e.g., enzymes that regulate multiple metabolites).

• Biological Interpretation: Identify pathways consistently supported by both protein and metabolite data to reduce false positives and increase confidence in findings.

Step 6: Leveraging AI and Advanced Analytics

• AI-Driven Insights: Machine learning and deep learning models are increasingly used to identify subtle patterns in multi-omics datasets.

• Predictive Models: AI can build classifiers that predict disease states or therapeutic responses based on integrated proteomic-metabolomic signatures.

• Efficiency: Studies show that AI-assisted workflows can cut data analysis time significantly while improving prediction accuracy.

Step 7: Ensuring Reproducibility

Reproducibility is the cornerstone of credible multi-omics research.

• Maintain detailed standard operating procedures (SOPs).

• Use reference samples or quality control pools across batches.

• Share data according to FAIR principles (Findable, Accessible, Interoperable, Reusable), ensuring transparency and reusability in the research community.

Summary of Workflow

A successful proteomics-metabolomics integration workflow can be summarized as:

1. Careful sample preparation with joint extraction.

2. Comprehensive data acquisition (LC-MS/MS for proteins, GC-MS/LC-MS for metabolites).

3. Rigorous data normalization and integration with specialized bioinformatics tools.

4. Validation using targeted assays.

5. Pathway and network analysis for biological interpretation.

6. Incorporation of AI-driven approaches for predictive modeling.

7. Commitment to reproducibility and data sharing.

This systematic workflow enables researchers to extract maximum biological insight from multi-omics studies while ensuring that findings are both robust and clinically translatable.

5. Clinical Applications: From Biomarkers to Therapies

The integration of proteomics and metabolomics is not only a powerful research strategy but also a practical tool for clinical and translational medicine. By combining the protein-level drivers of disease with the metabolite-level outcomes, multi-omics workflows provide a systems biology perspective that can directly inform diagnostics, drug discovery, and treatment strategies.

5.1 Biomarker Discovery

Biomarker discovery remains one of the most important applications of multi-omics integration. Traditional biomarker studies often rely on a single omics layer, which may result in markers that lack robustness or fail in clinical validation. By contrast, combining proteomic and metabolomic data provides signatures that are both mechanistically informative and clinically relevant.

For example:

• In oncology, integrated analysis of phosphorylated proteins (phosphoproteomics) with altered metabolite levels has helped identify signaling–metabolism nodes that predict tumor progression and therapy response.

• In metabolic diseases, plasma proteomics combined with metabolomics has revealed early biomarkers of insulin resistance, offering predictive value before overt diabetes develops.

The dual-layer approach increases both sensitivity (detecting early changes) and specificity (distinguishing disease subtypes), which are critical for real-world clinical applications.

5.2 Kinase Inhibitor Development

Drug development is another domain where proteomics-metabolomics integration adds substantial value.

• Target identification: Proteomic analysis identifies dysregulated enzymes or signaling proteins; metabolomics confirms their downstream metabolic consequences.

• Mechanism of action studies: By integrating proteome and metabolome data, researchers can observe how a candidate drug perturbs cellular pathways at multiple levels.

• Drug resistance: In cancer research, multi-omics workflows have uncovered protein phosphorylation patterns linked with altered metabolite flux, providing insight into why certain patients develop resistance to kinase inhibitors.

In precision medicine, this information allows clinicians to select therapies based on a patient’s integrated molecular profile, rather than relying solely on genomics. This multi-omics strategy moves healthcare closer to truly personalized treatment.

5.3 Disease Focus

Cancer

Cancer cells undergo profound metabolic reprogramming to sustain growth and survival. Integrating proteomics and metabolomics enables the identification of protein–metabolite hubs that drive tumor biology.

• Example: In breast and colorectal cancer studies, integrated workflows have mapped how oncogenic kinase activity influences glucose and amino acid metabolism.

• Clinical impact: Such integrated signatures improve the prediction of therapy response and help stratify patients for targeted treatments.

Diabetes and Metabolic Disorders

Metabolic diseases are a natural application area for proteomics-metabolomics integration.

• Example: Combining proteomic data on insulin signaling proteins with metabolomic profiles of lipid and glucose pathways reveals early molecular disturbances in prediabetic individuals.

• Clinical impact: This dual-layer insight can guide preventive interventions and monitor treatment efficacy more effectively than single biomarkers.

Cardiovascular and Neurological Diseases

• In cardiovascular disease, integrated omics workflows have identified protein markers of inflammation alongside metabolic shifts in lipid pathways, offering better predictors of heart disease risk.

• In neurology, multi-omics studies have begun to reveal how protein dysfunctions and altered neurotransmitter metabolism intersect in disorders such as Alzheimer’s disease.

5.4 Building Predictive Models

Beyond individual biomarkers, the integration of proteomics and metabolomics enables the construction of multi-omics scoring models. These models combine protein and metabolite features into a unified score that can:

• Stratify patients based on risk levels.

• Predict survival outcomes.

• Guide therapy selection.

Machine learning algorithms play a central role here, as they can handle high-dimensional multi-omics data to identify patterns that may not be evident through classical statistical approaches.

5.5 Translational Value and Patient Impact

Ultimately, the goal of integrating proteomics and metabolomics is to deliver better patient outcomes. Whether through earlier detection of disease, improved therapeutic targeting, or personalized treatment strategies, multi-omics workflows bridge the gap between molecular research and clinical practice.

As clinical adoption grows, standardization, reproducibility, and regulatory validation will become key requirements. Institutions that adopt robust, validated multi-omics workflows will be best positioned to translate discoveries into actionable healthcare solutions.

6. Emerging Trends: AI, Single-Cell, and Proteogenomics

The integration of proteomics and metabolomics is a rapidly evolving field. As technology advances, researchers are moving beyond conventional workflows toward next-generation multi-omics approaches that promise even greater resolution, precision, and clinical impact. Below are the most significant trends shaping the field in 2025 and beyond.

AI-Driven Multi-Omics Analysis

Artificial intelligence (AI) and machine learning are transforming how researchers analyze complex, high-dimensional datasets. Traditional statistical approaches often struggle with the size and heterogeneity of proteomics and metabolomics data. AI provides scalable solutions by detecting nonlinear patterns and building predictive models that integrate thousands of features.

• Deep learning algorithms are being applied to predict protein–metabolite interactions that are not obvious from correlation analysis alone.

• Feature selection models help identify the most informative biomarkers while minimizing noise.

• Clinical impact: AI-driven workflows accelerate biomarker discovery, improve disease classification accuracy, and reduce analysis time significantly.

For example, AI-based factor analysis frameworks like MOFA2 can integrate multiple omics layers simultaneously, identifying shared and distinct sources of biological variation. As more clinical datasets become available, AI will be indispensable for personalized medicine applications.

Single-Cell Multi-Omics Integration

Most current proteomics and metabolomics studies analyze bulk samples, which average out signals across thousands or millions of cells. However, many diseases—especially cancer and neurological disorders—are driven by cell-to-cell heterogeneity.

Recent advances in single-cell proteomics (SCP) and single-cell metabolomics (scMetabolomics) are pushing the boundaries of resolution:

• Proteomics: Methods such as SCoPE2 allow the detection of hundreds of proteins from individual cells, revealing signaling heterogeneity in tumors.

• Metabolomics: Single-cell mass spectrometry imaging (MSI) enables profiling of metabolites in specific cell types or microenvironments.

Combining these datasets provides a unique opportunity to map protein–metabolite interactions at the single-cell level, offering unprecedented insights into tumor evolution, immune responses, and cell fate decisions.

As instrumentation sensitivity improves, single-cell multi-omics will become a cornerstone of precision oncology and regenerative medicine.

Proteogenomics: Bridging Genomics, Proteomics, and Metabolomics

Proteogenomics integrates genomic and transcriptomic data with proteomic measurements, creating a direct link between genetic variation, protein expression, and metabolic consequences.

• Applications: Identifying tumor-specific protein isoforms, characterizing novel peptides, and linking mutations to altered metabolic pathways.

• Clinical use: Proteogenomics combined with metabolomics enhances the identification of actionable targets in oncology, leading to more personalized therapeutic strategies.

When coupled with metabolomics, proteogenomics provides a comprehensive picture: from DNA to proteins to metabolites, capturing the full cascade of molecular events.

Spatial Multi-Omics

Spatial biology is one of the fastest-growing trends in systems biology. Spatial omics technologies preserve tissue architecture, allowing researchers to study proteins, metabolites, and other biomolecules within their native microenvironments.

• Spatial proteomics maps protein distribution across tissue sections using imaging mass spectrometry.

• Spatial metabolomics reveals localized metabolite gradients that correlate with physiological or pathological states.

When combined, these approaches uncover how microenvironmental niches regulate cellular behavior, such as tumor–immune interactions or neuronal signaling in the brain.

By 2030, spatial multi-omics is expected to become a standard in both research and clinical pathology, enabling tissue-level diagnostics with unprecedented precision.

Future Outlook: Toward Next-Generation Systems Biology

Looking ahead, several developments are poised to redefine multi-omics integration:

• Quantum computing could drastically reduce computation time for high-dimensional data integration, making real-time clinical analysis feasible.

• Cloud-based platforms will facilitate large-scale data sharing and collaborative research across institutions.

• Standardization efforts by consortia such as the Human Proteome Project will drive reproducibility and regulatory acceptance.

• Clinical translation will shift multi-omics from an academic tool to a mainstay in precision medicine, particularly in oncology, metabolic diseases, and neurology.

The future of proteomics-metabolomics integration lies in greater resolution (single-cell, spatial), greater connectivity (proteogenomics), and greater intelligence (AI-driven analysis). Together, these innovations promise to transform multi-omics into a core strategy for understanding complex biology and delivering precision healthcare.

8. Why Choose MetwareBio for Multi-Omics Services?

At MetwareBio, we combine scientific expertise with advanced technologies to help researchers unlock the complexity of biological systems. Our services go beyond single-omics measurements, offering integrated proteomics and metabolomics pipelines that provide a comprehensive view of molecular networks in health and disease.

• Advanced Analytical Platforms – We employ state-of-the-art LC–MS/MS systems optimized for high sensitivity, reproducibility, and broad coverage, enabling the reliable detection of both abundant and low-abundance molecules.

• Integrated Multi-Omics Design – By combining proteomic and metabolomic profiling, our workflows reveal how protein signaling and metabolic pathways interact, supporting discoveries that cannot be achieved by analyzing each omics layer in isolation.

• Data-Driven Insights – All projects undergo rigorous quality control and bioinformatics analysis, including pathway mapping, network modeling, and biomarker identification, ensuring that raw data are transformed into biologically meaningful insights.

• Collaborative Expertise – Our team of specialists works closely with clients, tailoring study designs to specific research questions in oncology, metabolic diseases, neuroscience, and beyond.

Choosing MetwareBio means gaining a trusted partner in systems biology—one that delivers accurate, reproducible, and insightful results to accelerate your research and therapeutic development.

FAQ

Q1: What is proteomics-metabolomics integration in systems biology?

Proteomics-metabolomics integration combines large-scale protein data with metabolite profiles to capture both upstream regulators and downstream biochemical outcomes. In systems biology, this dual approach enables researchers to map molecular pathways more accurately, identify key regulatory nodes, and improve biomarker discovery. It provides a holistic view that single-omics studies cannot achieve.

Q2: How does multi-omics improve biomarker discovery?

Traditional biomarkers often lack reproducibility across cohorts. By integrating proteomic signals (e.g., altered enzymes) with metabolomic shifts (e.g., pathway intermediates), multi-omics generates composite signatures that are more robust and clinically relevant. This dual-layer validation increases sensitivity and specificity, making biomarkers more reliable for early detection and disease stratification.

Q3: What are the best tools for proteomics-metabolomics data integration?

Popular tools include MixOmics for statistical correlation, MetaboAnalyst for pathway enrichment, and xMWAS for network-based integration. Advanced frameworks like MOFA2 use machine learning to uncover latent patterns across datasets. The best tool depends on study goals: exploratory discovery, mechanistic insight, or predictive modeling. Many researchers combine multiple platforms for maximum robustness.

Q4: Can AI enhance multi-omics analysis?

Yes. Artificial intelligence is increasingly applied to multi-omics datasets to identify complex protein–metabolite relationships. Deep learning models capture nonlinear associations, while machine learning classifiers improve disease prediction and patient stratification. AI also accelerates data analysis, reducing computation time and enhancing reproducibility. As datasets grow, AI will become indispensable in clinical translation of multi-omics.

Q5: What are the main challenges in proteomics-metabolomics integration?

The biggest challenges are data heterogeneity, batch effects, and validation. Proteins and metabolites differ in abundance scales, measurement noise, and temporal dynamics, complicating integration. Technical variability across instruments can introduce batch effects, which must be corrected with normalization methods like ComBat. Finally, findings require validation using independent cohorts or targeted assays to ensure reproducibility.

Q6: How can researchers start a multi-omics project in 2025?

Begin with a clearly defined biological question. Use joint extraction protocols for sample prep, apply LC-MS/MS for proteomics and GC-MS/LC-MS for metabolomics, and select integration tools suited to the study design. Plan validation with PRM (proteins) or NMR (metabolites). For efficiency and scalability, many researchers now collaborate with specialized providers offering end-to-end multi-omics workflows.

References

1. Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature. 2016;537(7620):347–355. doi:10.1038/nature19949

2. Patti GJ, Yanes O, Siuzdak G. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13(4):263–269. doi:10.1038/nrm3314

3. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18:83. doi:10.1186/s13059-017-1215-1

4. Misra BB, Langefeld CD, Olivier M, Cox LA. Integrated Omics: Tools, Advances and Future Approaches. J Mol Endocrinol. 2019;62(1):R21–R45. doi:10.1530/JME-18-0055

5. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. 2020;14:1–24. doi:10.1177/1177932219899051

6. Huang S, Chaudhary K, Garmire LX. More is better: recent progress in multi-omics data integration methods. Front Genet. 2017;8:84. doi:10.3389/fgene.2017.00084

7. Yang X, Coulombe-Huntington J, Kang S, Sheynkman GM, Hao T, Richardson A, et al. Widespread expansion of protein interaction capabilities by alternative splicing. Cell. 2016;164(4):805–817. doi:10.1016/j.cell.2016.01.029

8. Goveia J, Pircher A, Conradi LC, Kalucka J, Lagani V, Dewerchin M, et al. Meta-omics profiling of the tumor microenvironment identifies metabolic vulnerabilities. Cell. 2016;165(3):761–774. doi:10.1016/j.cell.2016.03.026

9. Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014;513(7518):382–387. doi:10.1038/nature13438