DIA Quantitative Proteomics
DIA Quantitative Proteomics
DlA Proteomics Service Overview
MetwareBio’s DIA-based quantitative proteomics service is powered by the Bruker timsTOF HT mass spectrometer, featuring a dual TIMS (Trapped Ion Mobility Spectrometry) design and the advanced diaPASEF® (Parallel Accumulation–Serial Fragmentation) acquisition mode. This architecture significantly improves ion utilization and transmission efficiency while accelerating scan speed. Through the integration of ion mobility separation, the platform simultaneously captures four-dimensional data—retention time, m/z, ion mobility, and signal intensity—enabling precise peptide identification and quantification. This powerful setup offers exceptional proteome depth, sensitivity, and reproducibility, making it particularly suitable for large-scale studies, biomarker discovery, and the analysis of complex biological systems, even with limited sample input.
The diaPASEF acquisition method (Meier et al., 2020)
Why Choose Our DlA-Based Protein Analysis Service
DIA Proteomics Service Deliverables: From Protein Analysis to Bioinformatics Reporting
Proven Expertise in DIA Proteomics Service
Number of proteins identified from various medical and plant samples via DIA quantitative proteomics
Applications of Our DIA Proteomics Services
Mass spectrometry-based quantitative proteomics enables in-depth profiling of protein expression changes in human diseases. It supports biomarker discovery, mechanism-of-action studies, and therapeutic target identification in oncology, neurology, metabolic disorders, and precision medicine.
Proteomics reveals the functional dynamics of microbial systems, including protein expression under stress, virulence factor regulation, and metabolic adaptation. It is also a powerful tool for studying host–pathogen interactions, immune evasion, and microbial responses in infection models.
In plant systems, proteomics facilitates the investigation of developmental processes, plant hormone signaling, and stress responses to drought, salinity, and pathogens. These insights aid in molecular breeding, trait improvement, and plant systems biology.
Quantitative proteomics is increasingly used in environmental biology to study organismal responses to pollutants, climate stress, and habitat changes. It provides valuable molecular indicators for ecotoxicology, climate adaptation, and ecosystem monitoring.
By integrating proteome profiling with genomics and transcriptomics, proteomics enables systems-level analysis of gene function, regulatory networks, and biological pathways across different species, tissues, and conditions.
Case Study: DIA Proteomics Service in Cardiovascular Research
DIA Proteomics Uncovers Anti-Aging Pathways Modulated by Empagliflozin in HfpEF
In a study recently published in Cardiovascular Diabetology, researchers explored the protective effects of Empagliflozin (EMPA) on heart failure with preserved ejection fraction (HFpEF) and its potential role in modulating anti-aging pathways. Using a well-established mouse model, the team applied MetwareBio’s DIA-based quantitative proteomics service to investigate proteomic alterations across control, HFpEF, and EMPA-treated groups. The analysis identified 5,702 proteins, with over 300 differentially expressed proteins (DEPs) between HFpEF and control hearts, and 173 DEPs between the EMPA-treated and untreated HFpEF groups. Further K-means clustering revealed U-shaped and reverse U-shaped expression profiles among key regulatory proteins, implicating the STAT1–STING senescence axis in HFpEF pathology and its attenuation by EMPA.
This case highlights the value of MetwareBio’s DIA proteomics platform in revealing complex disease mechanisms and therapeutic responses. With deep proteome coverage, high reproducibility, and robust quantification, DIA is an essential tool for advancing cardiovascular research, drug mechanism studies, and systems-level biological discovery.
Effects of EMPA on the protein expression profile of HFpEF mice (Shi et al., 2024)
Sample Requirements of DIA Quantitative Proteomics
| Sample Type | Samples | Recommended Sample Size | Minimum Sample Size |
| Human/Animal Tissue | Normal tissues (heart, liver, spleen, lungs, intestines, kidneys, etc.) | 50mg | 5mg |
| Fatty tissue | 200mg | 100mg | |
| Brain tissue | 50mg | 5mg | |
| Bone | 1g | 200mg | |
| Hair | 500mg | 200mg | |
| Skin | 200mg | 100mg | |
| Plant Tissue | Young tissue (young leaf, seedling, petal, etc.) | 200mg | 100mg |
| Mature tissue (root, stem, fruit, pericarp, etc.) | 1g | 500mg | |
| Pollen | 40mg | 15mg | |
| Liquid Samples | Serum/Plasma (without removing high abundance proteins) | 20μL | 5μL |
| Serum/Plasma (remove high abundance proteins) | 200μL | 100μL | |
| Joint fluid, Lymph fluid | 200μL | 100μL | |
| Aqueous humor, Vitreous body | 300μL | 200μL | |
| Cerebrospinal fluid | 200μL | 100μL | |
| Ascites, Follicular fluid | 100μL | 50μL | |
| Alveolar lavage fluid (BALF) | 1ml | 500μL | |
| Amniotic fluid | 1ml | 500μL | |
| Milk | 20μL | 5μL | |
| Urine | 10mL | 5mL | |
| Saliva (mammals) | 1ml | 500μL | |
| Fermentation broth, Bacterial solution | 10ml | 5ml | |
| Cellular supernatant | 25mL | 10ml | |
| Exosome (sediment) | 25μl | 15μL | |
| Microorganisms | Bacteria | 200mg | 100mg |
| Fungi | 300mg | 150mg | |
| Cells | Primary Cells | 3×10^6 | 1×10^6 |
| Transmissible cells | 2×10^6 | 1×10^6 | |
| Sperm, Platelets | 2×10^7 | 1×10^7 | |
| Protein | Protein | 100μg | 50μg |
FAQ on DIA Quantitative Proteomics
DIA is a mass spectrometry-based strategy that systematically fragments all precursor ions within defined m/z windows, enabling unbiased, reproducible, and high-throughput protein quantification across complex biological samples.
Depending on sample type and database annotation, our DIA workflow can identify over 12,000 proteins, providing exceptional proteome depth and enabling detection of low-abundance targets missed by traditional DDA or label-based methods.
Typically, 50–100 µg of total protein per sample is sufficient. We accept a wide range of biological materials, including tissues, cells, serum/plasma, and model organisms. Please contact us for species-specific or sample-type recommendations.
DIA offers greater consistency and data completeness by capturing fragment data from all ions in each run. This reduces missing values and improves inter-sample reproducibility, especially in large-scale or longitudinal studies.
Our platform uses diaPASEF® technology, which combines DIA with Parallel Accumulation–Serial Fragmentation (PASEF) and Trapped Ion Mobility Spectrometry (TIMS). This enables 4D separation (retention time, m/z, ion mobility, and intensity), improving sensitivity, resolution, and acquisition speed.
We utilize industry-recognized DIA data analysis platforms such as Spectronaut, DIA-NN, and OpenSWATH for accurate protein identification and quantification. Both library-based and library-free (direct-DIA) workflows are supported, depending on the experimental design. The analysis includes precursor and fragment-level quantification, false discovery rate (FDR) control, and statistical modeling for differential expression, ensuring high-confidence results.
We provide a comprehensive analysis pipeline, including data quality assessment, differential expression analysis, functional enrichment (GO/KEGG), PPI network construction, and WGCNA, along with expert interpretation support.
Yes. As a multi-omics CRO, MetwareBio supports seamless integration of DIA proteomics with metabolomics, lipidomics, and transcriptomics, enabling multi-omics analysis and systems biology insights across research applications
Reference
1. Shi, Y., Zhao, L., Wang, J., Liu, X., Bai, Y., Cong, H., & Li, X. (2024). Empagliflozin protects against heart failure with preserved ejection fraction partly by inhibiting the senescence-associated STAT1-STING axis. Cardiovascular diabetology, 23(1), 269.https://doi.org/10.1186/s12933-024-02366-0
2. Meier, F., Brunner, A. D., Frank, M., Ha, A., Bludau, I., Voytik, E., Kaspar-Schoenefeld, S., Lubeck, M., Raether, O., Bache, N., Aebersold, R., Collins, B. C., Röst, H. L., & Mann, M. (2020). diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nature methods, 17(12), 1229–1236. https://doi.org/10.1038/s41592-020-00998-0
Next-Generation Omics Solutions:
Proteomics & Metabolomics
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