How DIA-MS Proteomics Data Empowers Multi-Omics Integration

Multi-omics integration is a central theme in contemporary systems biology, precision medicine, and translational research. By bridging data across distinct molecular layers—such as transcriptomics, proteomics, metabolomics, and epigenomics—it enables a comprehensive understanding of biological systems, from gene expression through to phenotypic outcomes. Among these layers, proteomics plays a pivotal role in functional execution and holds unique biological significance.

 

With the maturation of Data-Independent Acquisition Mass Spectrometry (DIA-MS), proteomics has advanced markedly in both data coverage and quantitative robustness, establishing itself as a critical foundation for integrated multi-omics analysis. This article explores how DIA-MS facilitates multi-omics research through its technical principles, application domains, and data integration strategies, thereby supporting a shift in life sciences from single-point measurements to network-level insights.

 

Why Use DIA-MS as the Proteomics Backbone in Multi-Omics Integration?

Successful multi-omics integration requires high data comparability, broad molecular coverage, and consistent quantification. Owing to its inherent technical strengths, DIA-MS outperforms traditional Data-Dependent Acquisition (DDA) approaches in several key dimensions:

1. High Throughput and Reproducibility

DIA-MS employs a systematic windowed acquisition strategy that ensures consistent fragment ion profiles across all samples—a critical feature for studies involving large cohorts.

(1) Enables the stable quantification of thousands of proteins per sample;

(2) Exhibits significantly lower inter-experimental coefficient of variation (CV) than DDA, making it suitable for clinical or animal cohort studies;

(3) Supports multiplexed labeling strategies such as TMT-DIA, enhancing quantitative throughput.

 

2. Broad Coverage: Enhancing Multi-Layer Information Complementarity

By combining project-specific spectral libraries with advanced computational algorithms, DIA-MS can reliably quantify 6,000 to 9,000 proteins. This expanded depth enriches pathway-level and regulatory network insights, especially in the following applications:

(1) Signal transduction analysis (e.g., PI3K/Akt, MAPK pathways);

(2) Modeling of metabolic enzyme expression and metabolite flux pathways;

(3) Profiling immune cell marker expression patterns.

 

3. Data Traceability: Enabling Long-Term Reusability

DIA-MS captures full-spectrum fragment data, facilitating robust re-analysis and reuse across studies. As transcriptomic or metabolomic datasets evolve, DIA-based proteomic data can be re-mined or re-integrated to validate and extend previous findings, thereby increasing the value density throughout the project lifecycle.

 

Integration of DIA Proteomics Data with Other Omics

Multi-omics integration goes beyond the simple concatenation of datasets and instead requires coordinated alignment across multiple dimensions, including time, tissue types, biological pathways, functional layers, and statistical structures. The following sections describe how DIA proteomics data can be linked and modeled with mainstream types of omics data.

1. Proteomics × Transcriptomics

RNA-seq captures the potential for gene expression, whereas DIA-based proteomics reflects the actual execution of cellular functions. Their integration enables the following analyses:

(1) mRNA–Protein Concordance Analysis: Identifies post-transcriptional regulatory mechanisms, such as changes in translational efficiency and the regulation of protein stability.

(2) Co-expression Network Analysis (WGCNA): Detects key modules exhibiting coordinated or co-regulated expression patterns across both transcriptomic and proteomic layers.

(3) Integrated Functional Enrichment: Compares mRNA and protein-level enrichment in GO and KEGG pathways, highlighting convergences and divergences to uncover regulatory nodes.

 

2. Proteomics × Metabolomics

Proteins, particularly enzymes, serve as the catalytic backbone of metabolic processes. DIA-MS–quantified enzymatic proteins and metabolomic profiles of downstream products together support the construction of enzyme–substrate metabolic pathway linkages:

(1) Enzyme–Metabolite Association Analysis: Investigates whether changes in metabolite levels are driven by the expression of corresponding catalytic enzymes.

(2) Pathway Activity Scoring: Maps proteins and metabolites onto shared biological pathways to evaluate overall pathway activation or suppression trends.

(3) Mechanistic Backtracking Analysis: Traces metabolic flux alterations back to upstream protein regulatory mechanisms, enabling the development of causal inference models.

 

3. Proteomics × Epigenomics / Single-Cell Omics

With the advancement of high-throughput technologies such as ChIP-seq, ATAC-seq, and scRNA-seq, DIA-MS proteomics must be capable of integrating with omics data across varying resolutions:

(1) Cluster-Based Protein Expression Validation: Uses bulk proteomic data to validate cell subtypes identified via scRNA-seq clustering.

(2) Quantitative Profiling of Histone-Modifying Proteins: Integrates enrichment data from histone modification profiling with DIA-based quantification of histone-modifying enzymes.

(3) Reconstruction of a Three-Layer Regulatory Axis (Transcription Factor–Target Protein–Functional Enzyme): Enables multi-dimensional functional reconstruction of key regulatory factors.

 

Utilizing DIA Proteomics Data for Integrated Analysis

1. Pathway-Based Integration

This approach is suitable for mapping functional pathways across RNA, protein, and metabolite layers. Databases such as GO, KEGG, and Reactome are employed to harmonize pathway definitions. Comparative analyses can then be conducted to evaluate whether significantly enriched functional modules are concordant across the different omics layers.

 

2. Multi-Omics Network Modeling

DIA-quantified proteins can serve as central signal hubs that link upstream transcription factors with downstream metabolic pathways. Tools such as Cytoscape, STRING, and OmicsNet are used to construct interaction networks. This enables the identification of cross-omics key nodes, such as hub genes, proteins, or enzymes.

 

3. Multi-Omics Co-Expression and Latent Variable Modeling (WGCNA, MOFA, DIABLO)

• WGCNA (Weighted Gene Co-Expression Network Analysis) is used to identify modules of co-regulated features that are preserved across sample groups.

• MOFA (Multi-Omics Factor Analysis) performs dimensionality reduction to uncover shared latent factors that explain variation across omics.

• DIABLO constructs predictive models that integrate omics data by capturing correlated components across datasets, particularly effective for subtyping and classification tasks.

 

MtoZ Biolabs’ Approach to Supporting DIA-MS-Based Multi-Omics Integration

MtoZ Biolabs has established a unified platform integrating advanced instrumentation, algorithm development, and bioinformatics services to facilitate DIA-MS proteomics and multi-omics integration research. This platform offers the following key features:

1. Multi-Platform Protein Data Generation

• Thermo Orbitrap Exploris 480: Enables high-resolution DIA acquisition suitable for large-scale cohort studies.

• Bruker timsTOF diaPASEF: Offers four-dimensional separation, ideal for achieving in-depth and comprehensive proteomic coverage.

• Diverse enrichment strategies: Compatible with the integration of post-translational modification (PTM)-based omics such as phosphoproteomics, acetylomics, and glycoproteomics, among others.

 

2. Standardized Bioinformatics Delivery

• Provides an integrated analysis framework that combines DIA proteomics with transcriptomic (RNA-seq) and metabolomic data.

• Supports advanced statistical analyses including principal component analysis (PCA), network-based modeling, and predictive modeling approaches.

• Delivers project-specific outputs including integrated visualizations, methodological details, and abstract content tailored for scientific communication.

 

3. Research-Oriented Support for Scientific Output

• Assists researchers in experimental design and the development of matched multi-omics strategies aligned with research objectives.

• Generates publication-ready figures and detailed data annotations in accordance with target journal submission standards.

• Offers supplementary deliverables such as SCI-compliant data packages and guidance for scientific abstract preparation and manuscript drafting.

 

The core of multi-omics research lies not only in data integration but also in mechanistic coherence. DIA-MS technology, by virtue of its systematic acquisition, high reproducibility, and structural comprehensiveness, is emerging as a central platform for the integration of transcriptomic, metabolomic, epigenomic, and other omics datasets. Building on its robust DIA-MS platform, MtoZ Biolabs collaborates across omics platforms and algorithm development teams to provide comprehensive research solutions that bridge mechanistic hypotheses and biological discovery.

 

MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.

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