Data-Dependent Acquisition
Data-Dependent Acquisition (DDA) is one of the most established and widely adopted data acquisition strategies in proteomics mass spectrometry analysis. In this approach, a full scan (MS1) of all ions present in the sample is first performed, followed by the selection of several precursor ions with the highest intensities for fragmentation scanning (MS2), enabling the derivation of their peptide sequence information for subsequent protein identification and quantification. Data-Dependent Acquisition operates as a dynamic and selective acquisition mode, driven by real-time scan results, offering high generalizability and adaptability. In proteomics experiments, Data-Dependent Acquisition serves as a critical bridge between complex sample profiling and downstream bioinformatics analysis. It not only enables researchers to extract representative information from heterogeneous samples but also provides high-quality input data for subsequent computational workflows. Beyond protein identification, Data-Dependent Acquisition plays a pivotal role in differential protein screening, post-translational modification studies, and signaling pathway analysis. By conducting parallel acquisitions and comparing samples under varying experimental conditions, proteins with significantly altered expression levels can be identified, offering insights into disease mechanisms, drug responses, or biological processes. Thus, Data-Dependent Acquisition is not only a cornerstone of mass spectrometry-based workflows but also a foundational element for ensuring the accuracy and interpretability of proteomic data.
The fundamental principle underlying Data-Dependent Acquisition is the “intensity-based selection” mechanism. In the MS1 spectrum, a predetermined number (e.g., 10 or 20) of the most intense precursor ions are selected based on signal intensity or other defined criteria and subjected to fragmentation in the collision cell (MS2). Each fragmented ion yields a specific set of peptide sequences, which collectively form the basis for constructing comprehensive proteomic maps. During a standard Data-Dependent Acquisition workflow, the mass spectrometer continuously alternates between MS1 and MS2 scans until the analysis is complete. This real-time switching allows Data-Dependent Acquisition to dynamically capture the predominant components in a sample, thereby facilitating accurate and high-resolution profiling of sample composition.
While Data-Dependent Acquisition provides broad proteomic coverage and operational flexibility, it is inherently biased toward high-abundance peptides, which introduces certain limitations. As the mass spectrometer can only select a fixed number of ions for MS2 scanning in each acquisition cycle, low-abundance peptides—especially in complex biological matrices—are often underrepresented or entirely missed. This phenomenon, known as “identification bias,” becomes particularly pronounced when investigating subtle proteomic changes or low-abundance targets. To mitigate these shortcomings, researchers often implement strategies such as dynamic exclusion, elevated ion intensity thresholds, or complementary acquisition methods to enhance the detection of under-sampled species.
To further improve the reproducibility and coverage of Data-Dependent Acquisition, ongoing efforts have focused on refining acquisition parameters and optimizing experimental designs. For instance, extending chromatographic gradient durations, enhancing LC-MS interface conditions, and employing replicate scan strategies have been shown to significantly improve inter-sample consistency. In parallel, the integration of high-resolution, high-sensitivity mass spectrometers has broadened the applicability of Data-Dependent Acquisition to include low-abundance protein detection and comprehensive post-translational modification profiling. Advances in bioinformatics have also driven substantial improvements in Data-Dependent Acquisition data processing, including the development of machine learning-based peptide identification algorithms and spectral library-based reanalysis approaches, both of which enhance identification depth and precision.
MtoZ Biolabs possesses extensive expertise and experience in quantitative proteomics analysis. We place strong emphasis on the biological relevance of each dataset and are committed to delivering stable, efficient, and reliable analytical services through rigorous and scientifically validated workflows, supporting your proteomics research with the highest standards of data integrity and reproducibility.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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