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What is Data-Independent Acquisition (DIA)?
Data-Independent Acquisition (DIA) is a novel mass spectrometry data acquisition method developed in recent years. Current popular proteomics research techniques such as iTRAQ, TMT, Label-free, and SILAC utilize data-dependent acquisition (DDA), a method in which secondary mass spectrometry signals are collected based on the intensity of primary mass spectrometry signals. Stronger ion signals are selected for secondary acquisition, while weaker signals are discarded, resulting in the loss of secondary information from weak signals.
DIA technology combines the high-throughput nature of traditional proteomics shotgun methods with the accurate quantification capabilities of the gold standard PRM (Parallel Reaction Monitoring) method. It was recognized by Nature Methods as a noteworthy technology in 2015. Presently, DIA quantitative proteomics includes two detection techniques: 3D DIA and 4D DIA. 3D DIA employs the Thermo Scientific QE series mass spectrometers, a classic choice for proteomics analysis due to its high peptide on-column loading capacity (ranging from 1-5 μg of peptides). On the other hand, 4D DIA employs the TIMS TOF mass spectrometers from Bruker, which enhances sensitivity and detection capabilities compared to the classical 3D technology, resulting in higher protein and peptide detection yields from the same sample.
Data-Independent Acquisition (DIA) proteomics technology is applicable to nearly all sample types, allowing unbiased collection of all information within samples. It offers a superior solution for achieving high coverage, accuracy, and in-depth analysis of proteomes. DIA is particularly well-suited for analyzing disease-related samples rich in biological information, providing a significant resource for biomarker discovery, molecular typing, disease mechanism investigation, and more. By comparing differences in protein composition among different groups or states, this approach enables systematic studies of diseases, facilitating the development of precision therapies.
Principles of Data-Independent Acquisition (DIA) Scanning
In the DIA scanning mode, the mass range of the mass spectrometry full scan is divided into several windows. Subsequently, all ions within each window undergo fragmentation and secondary mass spectrometry detection, ensuring comprehensive and unbiased acquisition of information from all ions in the sample. As shown in Figure 1B, during the pre-defined mass range MS2 scan, DIA guarantees sampling of all peptides within the selected mass range, enabling the identification of abundant peptide information.
In all DIA methods, each MS2 scan contains fragments of every peptide from one or multiple pre-defined precursor m/z windows. Each window is sampled multiple times, resulting in multiple fragmentation events per peptide. The earliest and most common DIA method employs a sequential sampling strategy (Figure 1B), where the m/z range covers most of the peptides of interest, divided into a sequence of non-overlapping windows. These windows are typically of equal size, but sometimes vary based on the m/z distribution of the peptides of interest. For each window in the sequence, all precursor ions entering that window are fragmented together and measured in the MS2 scan. The instrument repeats this sequence throughout the gradient elution, with the time required for completing this sequence of scans being a few seconds. This allows each peptide to be scanned multiple times during its elution, enhancing coverage and accuracy.
(Nishant Pappireddi et al,. ChemBioChem 2019)
DIA VS DDA
|Data Acquisition Mode||DIA||DDA|
|MS1||Enzymatically digested peptides from the sample are directly subjected to MS1 without filtering based on m/z range.||Same as DIA, no difference in MS1 acquisition.|
|MS2||Fragmentation of all peptides within set windows and subsequent MS2 analysis.||Sequential fragmentation of the top-ranking peptides with the strongest signals from the MS1for MS2.|
|Advantages||Uniform collection of peptides from each window, no omission, no limitation based on precursor ions; high integrity, reproducibility, and stability.||Peptide information in MS2 largely originates from individual peptides, simplifying subsequent database searches; simple experimental procedures; mature method with abundant references and lower costs.|
|Disadvantages||Peptide information in MS2 originates from all peptides within set windows, making data processing complex; library-based DIA has higher costs and is suitable for large samples. Direct DIA requires no library and is suitable for samples of all sizes.||MS2 captures information from only the top 10-40 peptides based on signal intensity ranking, potentially missing low-abundance protein information; precursor ions for MS2 are chosen randomly based on signal intensity sorting in the MS1.|
Our DIA Technology Platform
Creative Proteomics offers DIA/4D-DIA quantitative proteomics services using the TIMS TOF mass spectrometer from Bruker. This platform is particularly suited for large-scale sample proteomic analysis, such as disease-related samples, clinical cohort analyses, and studies of crop population traits.
The platform is capable of detecting a wide range of sample types, including but not limited to blood, FFPE, urine, exosomes, feces, and single cells. It integrates machine learning algorithms and multi-omics analysis strategies, enabling in-depth data mining and supporting precision medicine research.
Advantages of Our DIA Technology
Improved Stability: Compared to traditional Data-Dependent Acquisition (DDA) modes, DIA provides unbiased comprehensive scans, yielding richer information and greater protein resolution than standard Label-Free Quantification (LFQ) methods. It demonstrates good stability and relatively fewer missing values.
High Quantitative Accuracy: Achieves quantitative accuracy comparable to Parallel Reaction Monitoring (PRM) technology.
Broad Applicability: Suitable for individual sample analysis, capable of partial identification or non-identification, and facilitates protein expression profile comparisons across different tissue types.
Wide Time Span: Incorporates standard peptide segments with retention time calibration in each sample, allowing unbiased analysis across different analysis times. This approach is well-suited for large sample sizes and disease-related studies.
Suitable for Common Sample Types: Applicable for the analysis of commonly used sample types in disease research, including blood, exosomes, formalin-fixed paraffin-embedded (FFPE) tissues, and urine.
High Sensitivity: Our 4D-DIA platform achieves nearly 100% ion utilization, enhancing detection sensitivity.
Establishment of a Customized Spectral Library: In practical projects, the need for library establishment is eliminated, greatly reducing the cost and time required for sample library construction, while significantly enhancing the number of identified proteins.
|Standard Data Analysis Content|
|Mass spectrometry data analysis||Spectral peptide quality deviation distribution, peptide length distribution, unique peptide number distribution, protein coverage distribution|
|Protein Expression Analysis||Protein abundance value distribution of PTMs, protein abundance ratio distribution of PTMs between samples, PCA analysis, statistical analysis of significant differences|
|Protein Functional Analysis||Total protein and differential protein GO secondary classification, COG function classification, KEGG annotation, subcellular organelle location, domain annotation, signal peptide prediction and PPI prediction; differential protein GO, KEGG, domain enrichment analysis|
|Advanced Data Analysis Content|
|Protein Gene Chromosome Localization||Obtain the distribution of genes encoding proteins on chromosomes|
|WGCNA Analysis||Predict functional clustering and network interactions by protein expression level; correlate with phenotype data to obtain key proteins or protein complexes that influence phenotype|
|Trend Cluster Analysis||Obtain protein expression trend patterns|
|Molecular Typing||Molecular typing for large cohort samples|
|Survival Curve Analysis||Study the relationship between influencing factors and survival time and outcome|
|ROC Curves||Evaluate predictive accuracy by combining specificity and sensitivity, such as biomarker impact assessment on tumor grade|
Association of Complement and MAPK Activation With SARS-CoV-2–Associated Myocardial Inflammation
Journal: JAMA Cardiol.
Impact Factor: 14.7
Sample Type: FFPE
Research Strategy: 4D-DIA Proteomics
Inflammatory cardiomyopathy refers to myocarditis accompanied by heart dysfunction. Myocarditis, in the strict sense, typically results from infectious agents such as viruses, bacteria, mycoplasmas, or fungi. Recent findings indicate that patients infected with SARS-CoV-2 also exhibit inflammatory cardiomyopathy; however, the precise molecular characteristics remain elusive. In this study, a cohort of four groups of suspected myocarditis patients was examined: Group 1 with 5 cases of SARS-CoV-2 infection, Group 2 with 4 cases of virus-related myocarditis, Group 3 with 5 cases of immune-mediated myocarditis, and Group 4 as a non-inflammatory control. Utilizing 4D-DIA proteomics and transcriptomics techniques, FFPE samples from these groups were analyzed to evaluate the biological pathways associated with cardiac inflammation linked to SARS-CoV-2 infection.
Clustered Heatmap of MAPK Pathway-Associated Proteins
- Leon Bichmann, Shubham Gupta, George Rosenberger, Leon Kuchenbecker, Timo Sachsenberg, Phil Ewels, Oliver Alka, Julianus Pfeuffer, Oliver Kohlbacher, and Hannes Röst.DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics.Journal of Proteome Research 2021 20 (7), 3758-3766.
- Florian Meier, et al. (2019) Parallel accumulation – serial fragmentation combined with data-independent acquisition (diaPASEF): Bottom-up proteomics with near optimal ion usage. BioRxiv.
- Weckbach LT, Schweizer L, Kraechan A, et al. Association of Complement and MAPK Activation With SARS-CoV-2–Associated Myocardial Inflammation. JAMA Cardiol. 2022;7(3):286–297.