How does DIA-MS proteomics empower multi-omics integration research?

Multi-omics integration analysis is one of the core trends in current systems biology, precision medicine, and translational research. It connects data from different molecular levels (such as transcriptomics, proteomics, metabolomics, epigenomics, etc.) to depict a panoramic view of biological systems from gene expression to phenotype formation. Among the various omics,proteomicsis the central layer for functional execution and holds irreplaceable biological value.

 

In recent years, with thematurity of DIA-MS (Data-Independent Acquisition Mass Spectrometry),proteomics has not only achieved breakthroughs in data coverage and quantitative stability but has also become a critical foundation for supporting multi-omics collaborative analysis. This article will delve into how DIA-MS assists multi-omics research from the perspectives of technical logic, application scenarios, and data integration strategies, advancing life sciences from 'single-point observation' to 'network understanding.'

 

Why choose DIA-MS as the protein data source in multi-omics integration analysis?

In multi-omics integration research, data comparability, coverage, and quantitative consistency are the foundation for achieving cross-omics integration. DIA-MS, with its technical characteristics, is significantly superior to DDA proteomics strategies in several dimensions:

1. High throughput and reproducibility

DIA-MS uses a systematic window acquisition strategy to ensure similar fragmentation information can be obtained from each sample. This acquisition consistency is particularly important for multi-omics studies with large sample sizes.

(1) Thousands of proteins can be stably quantified in each sample;

(2) The CV value between experiments is significantly lower than DDA, suitable for clinical or animal cohorts;

(3) Supports multi-sample tagging strategies such as TMT-DIA, enhancing quantitative throughput.

 

2. High coverage: Enhancing complementary information layers

DIA technology, combined with project-level spectral libraries and deep algorithm optimization, can stably quantify 6000–9000 proteins, significantly expanding the proteomicpathway dimension and regulatory layer information, which is critical in the following scenarios:

(1) Signal pathway analysis (PI3K/Akt, MAPK, etc.);

(2) Modeling metabolic enzyme expression and metabolite pathways;

(3) Characterizing immune cell marker expression patterns.

 

3. Traceability: Achieving long-term reuse of data assets

DIA data preserves full-spectrum fragment information and possesses strongcapabilities for data reanalysis and cross-project applications. In multi-omics research, as transcriptomics/metabolomics data is updated, DIA protein data can be re-mined and supplemented, enhancing the value density throughout the project lifecycle.

 

Howto integrate DIA proteomics data with other omics analyses? 

Multi-omics integration is not merely a simple data concatenation but requires effective collaboration across multiple dimensions (time, tissue, pathway, function, statistical structure). The following introduces how DIA proteomics data can be connected and modeled with mainstream omics types.

Proteomics × Transcriptomics

RNA-seq reveals gene expression potential, while DIA proteomics reflects functional execution. Their integration can analyze:

(1) mRNA-protein expression consistency analysis: Identifying post-transcriptional regulatory mechanisms, such as changes in translation efficiency and protein stability regulation;

（2）Co-expression network construction (WGCNA): Identifying key modules that change coordinately in both omics;

（3）Functional enrichment integration: Observing consistency or divergence in mRNA and protein at the GO/KEGG pathway level, revealing regulatory nodes.

 

Proteomics × Metabolomics

Proteins are the catalytic core of metabolic processes, and quantitative enzyme proteins by DIA-MS and changes in metabolites in metabolomics can construct 'enzyme-substrate' pathway links:

（1）Enzyme-metabolite pairing analysis: Exploring whether changes in metabolite levels are driven by the expression of key enzymes;

（2）Pathway Activity Score: Mapping proteins and metabolites to unified pathways to assess overall activation or inhibition trends;

（3）Mechanism tracing analysis: Using changes in metabolic flows to trace back to protein regulation, thereby constructing causal inference models.

 

Proteomics × Epigenomics/Single-cell omics

With the maturity of high-throughput technologies such as ChIP-seq, ATAC-seq, scRNA-seq, DIA-MS proteomics data needs to support integration with cross-resolution data:

 （1）Clustered protein expression validation: Using bulk protein data to validate scRNA classifications;

 （2）Histone modification protein quantification: Combining enriched and quantified modified histone proteins;

 （3）Transcription factor–target protein–functional enzyme three-layer regulatory construction: Achieving multi-functional reconstruction of regulatory factors.

 

Three, How to effectively utilize DIA proteomics data for integrative analysis? 

1. Pathway-based integration

• Applicable for functional pathway mapping among RNA/protein/metabolite;

• Using GO/KEGG/Reactome and other databases to standardize pathway dimensions;

• Compare whether significantly enriched functional modules are consistent across different omics.

 

2. Multi-omics Network Modeling

• Using DIA quantitative proteins as 'signal hubs' to connect upstream TF and downstream metabolic pathways;

• Construct networks using tools like Cytoscape, STRING, OmicsNet;

• Identify cross-omics key nodes (e.g., hub gene/protein/enzyme).

 

3. Multi-omics Co-expression and Latent Variable Modeling (WGCNA, MOFA, DIABLO)

• WGCNA (Weighted Gene Co-expression Network Analysis): Identify consistently regulated modules between groups;

• MOFA（Multi-Omics Factor Analysis）: Dimension reduction to extract latent variable factors;

• DIABLO: Construct predictive models across omics dimensions (especially suitable for typing and classification studies).

 

Four, How does BGI-PACT Biotech support DIA-MS multi-omics integrative research?

BGI-PACT Biotech has established anintegrated platform of technology + algorithms + bioinformatics serviceswith the following features in the direction of DIA proteomics and multi-omics integrative analysis:

1、 Multi-platform protein data generation capability

• Thermo Orbitrap Exploris 480: High-resolution DIA acquisition that supports large cohorts;

• Bruker timsTOF diaPASEF: Four-dimensional separation suitable for in-depth proteome mapping;

• Multiple enrichment strategies: Support integration with phosphoproteomics, acetylomics, glycomics, and other omics.

 

2. Standardization of bioinformatics delivery

• ProvideDIA + RNA-seq/metabolomicsintegrated analysis framework;

• Support principal component analysis, network analysis, model prediction;

• Deliver integrated charts, methods, and summaries per project.

 

3. Research outcome-oriented support

• Assist clients inproject design and omics matching scheme development；

• Output charts and data descriptions that comply with submission formats;

• ProvideSCI data packages, abstract writing suggestionsand other transformation tools.

 

The core of multi-omics research lies not only in 'data integration' but also in 'mechanism coordination.' The high-quality proteomics data generated by DIA-MS technology, with its systematic, repeatable, and structurally complete characteristics, is becoming a pivotal platform for integrating transcriptomics, metabolomics, epigenomics, and other omics information. BGI-PACT Biotech will continue to base its services on the DIA proteomics platform, linking multi-omics platforms and algorithm teams to provide you with comprehensive research solutions that span from mechanistic hypotheses to biological discoveries.

 

BGI-PACT Biotech--Characterization of biological products, premium service provider for multi-omics mass spectrometry detection

 

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