Data-Dependent Acquisition DDA

Detection, characterization, and identification of metabolites require multiple mass spectrometry experiments such as full scan LC/MS and LC/MS/MS. The process becomes very time consuming, particularly when a large number of metabolites are formed. The interpretation of LC/MS/MS data is very laborious and inefficient, and can be a rate-limiting step in the metabolite identification process. Therefore, new approaches to data acquisition that would minimize the need for multiple experiments, and data processing tools that would simplify mass spectral interpretation, are highly desired. 

SCHEME 11.4 Knowledge-based metabolic prediction of indinavir. Adapted from Anari et al. (2004) with permission of the American Chemical Society. 

---

MS/MS Duty Cycle Typical MS/MS analysis is a serial process, relying on the selection of precursors (peptides) in MS mode, followed by high-energy fragmentation in MS/MS. This process is termed data dependent acquisition (DDA). The duty cycle for the completion of MS and MS/MS cycles (the time necessary for MS/MS spectrum acquisition) is of primary importance. When the separation performance is viewed from the mass spectrometry perspective, the peak capacity can be characterized by the number of MS/MS scans, yielding successful... 

When working with non-radiolabeled drugs the major challenge is to find metabolites in the biological matrices. Because the enzymes responsible for metabolism are quite well characterized metabolic changes can partially be predicted. For example hydroxylation of the parent drug is in many cases the principal metabolic pathway. From a mass spectrometric point of view it results in an increase of 16 units in the mass spectrum. In the full-scan mode an extracted ion current profile can be used to screen for potential metabolites. In a second step a product ion spectrum is recorded for structural interpretation. Ideally, one would like to obtain relative molecular mass information and the corresponding product ion spectrum in the same LC-MS run. This information can be obtained by data dependant acquisition (DDA), as illustrated in Fig. 1.39. 

An important issue is the need to acquire both MS and MS-MS data for the unknowns. In general, this requires two injections with data-processing in between. The first run is done in full-spectrum LC-MS mode. Precmsor /w/z-values for relevant peaks in this chromatogram have to be determined in (manual) data processing. The /w/z-values found are then used in a time-scheduled product-ion MS-MS procedure using multiple precmsor-ions. Alternatively, data-dependent acquisition (DDA, Ch. 2.4.2) can be used, as demonstrated by Drexler et al. [106]. An alternative to DDA in a triple-quadrupole instrument is the RF product-ion analysis mode (RFD), proposed by Kienhuis and Geerdink [107]. 

Data-dependent acquisition (DDA) on an ion-trap instrument was applied in combination with in-source CID for the analysis of C-glycosyhlavone-O-glycosides in plant extracts [38], DDA controlled switching between a survey MS scan and MS-MS allowed automated LC-MS analysis of plant extracts. Differentiation between 6- and 8-C-glycosylation is made via the in-source CID generation of X-ions and subsequent MS-MS analysis. 

Data Dependent Acquisition (DDA) and CID Fragmentation by Voltage Switching (Data Independent Acquisition, DIA)... 

Data dependent acquisition (DDA) is switched on automaticaUy when an MS data threshold is reached. MS/MS data is coUected until a preselected signal is accumulated, after which the instrument returns to MS operation. The mass selection (MS) fragmentation (MS/MS) cycle is continued as long as the chromatographic peak intensity exceeds a predetermined threshold. 

Figure 3.1 Process control for HX-MS. Data from individual proteins or collections of proteins (in any state of assembly) are grouped according to the labeling conditions applied (e.g., different time points) and associated with the feature lists assembled from data-dependent acquisitions (DDA) of LC-MS/MS runs. Software should automate the extraction of features (retention time and m/z pairs) from the full set, generate the essential deuteration data, and provide strong methodology for validating the data and extracting knowledge...

---