DIA Quantitative Proteomics Analysis Service

Technical Principles

DIA quantitative proteomics is a mass spectrometry acquisition strategy that systematically collects fragment ion information across predefined precursor isolation windows. Unlike DDA, which selects individual precursor ions for fragmentation, DIA fragments multiple precursor ions within each window and generates multiplexed MS/MS data. These complex fragment spectra are computationally analyzed to identify peptides and extract quantitative signals for protein-level relative abundance measurement.

Pappireddi, N. et al. ChemBioChem, 2019.

Figure 1. DIA Mass Spectrometry Acquisition Principle

Choose the Right  DIA Proteomics Workflow

DIA Quantitative Proteomics Workflow	Suitable Project Scenario	Advantages
Library-Based DIA Workflow	Projects requiring high-confidence peptide identification, customized spectral references, or established spectral library resources.	Experimental spectral libraries provide additional peptide fragment information to support DIA data interpretation and quantitative analysis.
Library-Free DIA Workflow	Large-scale quantitative proteomics studies requiring flexible DIA data processing without building an experimental spectral library.	Reduces dependence on experimental library generation and supports scalable analysis of large DIA datasets.
4D-DIA Quantitative Proteomics Workflow	Complex samples, deep proteome profiling projects, and studies requiring enhanced peptide separation and improved quantitative performance.	Integrates additional ion mobility separation with DIA acquisition to improve peptide separation, reduce interference, and enhance proteome coverage in complex samples.
Large-Scale Quantitative DIA Workflow	Large cohorts, multi-group comparisons, longitudinal studies, and projects requiring consistent protein abundance comparison across many samples.	DIA systematic acquisition helps reduce missing quantitative values and improves comparability among multiple samples.
DIA Discovery with PRM/MRM Validation Workflow	Research projects that require broad protein profiling followed by targeted verification of selected candidate proteins.	Combines the broad discovery capability of DIA with the targeted measurement capability of PRM/MRM for candidate protein verification.

Why Choose MtoZ Biolabs?

1. Advanced Mass Spectrometry Platforms

MtoZ Biolabs uses high-resolution platforms including Bruker timsTOF Pro 2, Orbitrap Exploris 480, and Fusion Lumos. timsTOF Pro 2 supports TIMS and dia-PASEF for added ion mobility separation, while Orbitrap platforms can support standard DIA and downstream PRM verification.

2. Rigorous Quality Control

Quality control is implemented from sample preparation through data analysis. BCA assays and SDS-PAGE are used to assess protein quantity and quality, while peptide length distributions, identification scores, and related metrics are evaluated during data processing.

3. In-Depth Bioinformatics Analysis

In addition to standard differential, functional, and pathway analyses, MtoZ Biolabs provides customized bioinformatics support. Dedicated bioinformatics specialists can assist with multi-omics integration and customized result visualization based on research objectives.

Analysis Workflow

1. Project Design Review

Confirm sample type, species, group structure, sample number, research objective, database requirements, and expected downstream analyses before laboratory processing.

2. Protein Extraction and Sample Preparation

Apply matrix-appropriate protein extraction and cleanup, followed by protein quality assessment and preparation for enzymatic digestion.

3. Protein Digestion and Peptide Cleanup

Digest proteins into peptides, remove components that may interfere with LC-MS/MS, and prepare comparable peptide inputs for analysis.

4. LC-MS/MS DIA Acquisition

Separate peptides chromatographically and acquire DIA data by sequentially scanning predefined precursor isolation windows across the selected mass range.

5. DIA Data Processing and Quantification

Perform peptide/protein identification, interference-aware signal extraction, quantitative normalization, and protein abundance estimation using a validated DIA processing workflow.

6. Statistical and Bioinformatics Analysis

Compare groups, visualize quantitative patterns, identify differentially abundant proteins, and perform functional annotation and enrichment analyses according to the project objective.

Sample Submission Requirements

Item	Requirement
Supported Sample Types	Animal tissues, plant tissues, cultured cells, microorganisms, serum, plasma, and other biological samples suitable for proteomics analysis.
Species Information	Species information should be provided before analysis. For non-model organisms, reference sequence information or a suitable FASTA database is required for protein identification.
Recommended Protein Input	At least 20 μg protein input is recommended. Approximately 50 μg protein input is preferred for improved quantitative performance.
Protein Concentration	Protein concentration above 0.5 μg/μL is recommended for routine DIA quantitative proteomics analysis.
Sample Quality Requirements	Samples should avoid severe protein degradation, high salt contamination, and residual detergents or other components that may interfere with LC-MS/MS analysis.
Storage and Shipping	Samples should be stored under appropriate conditions and shipped on dry ice to maintain sample integrity and minimize degradation.
Project Information Required	Sample type, species, experimental groups, sample number, research objectives, and expected downstream analysis requirements should be provided for workflow evaluation.

Deliverables

1. Protein Identification and Quantification Results

2. Quality Control Results

3. Differential Protein Analysis

4. Functional Annotation and Enrichment Analysis

5. Protein-Protein Interaction (PPI) Analysis

6. Data Report

7. Raw Data Files (Optional)

FAQs

Q1. Is DIA the same as label-free quantitative proteomics?

No. DIA describes how precursor ions are selected and fragmented during mass spectrometry acquisition, whereas label-free describes a quantification strategy that does not use isotopic or isobaric labels. In MtoZ Biolabs’ routine DIA quantitative proteomics service, DIA is used in a label-free relative quantification workflow, but the two terms are not academically interchangeable.

Q2. How does DIA differ from DDA for quantitative proteomics?

DDA selects a subset of precursor ions for fragmentation during each cycle, while DIA systematically fragments ions within predefined precursor windows. DIA can provide more consistent detection across repeated sample runs, which is useful for multi-sample quantitative studies. DDA remains valuable for many discovery and label-based workflows, so the best choice depends on the project design.

Q3. Does DIA analysis require an experimental spectral library?

Not always. Modern DIA workflows may use experimental spectral libraries, predicted libraries, or library-free/direct analysis strategies. The appropriate approach depends on the instrument data, sample type, database, depth requirements, and project design.

Q4. Can DIA be used for biomarker validation?

DIA is well suited to broad candidate discovery and comparative profiling. When a defined set of candidate proteins requires focused verification, targeted methods such as PRM or MRM may be more appropriate for the validation stage.

Start Your DIA Quantitative Proteomics Project

Planning a DIA quantitative proteomics study? Send us your sample type, species, group design, total sample number, available sample amount, and research objective. Our technical team can review sample compatibility, workflow design, database requirements, and downstream analysis options before project initiation.