Sample Acquisition: Data Independent LC-MS versus Data

Mass spectrometry has evolved into a powerful tool for the analysis of protein mixtures due to its speed of analysis, sensitivity, and accuracy. As biological sample complexity increases, dynamic range and molecular weight limits the use of protein mass fingerprinting (PMF) for protein identification. As a result, samples are typically analyzed by electrospray LC-MSMS using a precursor selected data dependent acquisition (DDA) method. The major advantage of this approach is the generation of primary structural

information from the peptide precursor ion selected for fragmentation. The added specificity provided by the fragment ion information increases the quality of peptide identification in complex protein mixtures.

A DDA experiment is a cyclical process. The series starts by acquiring an MS survey over a designated mass range in which the quadrupole is set to pass all ions to the TOF analyzer unaffected by ion fragmentation. Set thresholds of ion intensity, charge state, or adduct formation can be configured such that when an ion meets the conditions determined, the instrument switches to MSMS mode applying an increase in collision energy and thus resulting in peptide fragmentation. The number of selected precursor ions and the allocated MSMS acquisition time are optimized for a given sample type and complexity. For DDA experiments conducted in this work, a collision energy profile was employed where

fragmentation was primarily selected by ion intensity. When ions breach a current threshold, the selected precursor ion is ramped through a series of three collision energies until the intensity of the precursor ion drops below a given threshold or after a period of 3.3 seconds

has passed. After fragmentation, the system switches to continue in MS mode until

conditions for precursor fragmentation are again met. At any given time there can be up to two precursor ions being selected for fragmentation. An experimental peptide digest of a DDA acquired chromatogram is shown in Figure 2.5. Eluting precursor ions are recorded in a MS survey scan (A) with corresponding MSMS component chromatograms in (B and C).

An inherent disadvantage of DDA intensity selection is that precursor ions are selected for fragmentation immediately after passing a predetermined ion current threshold, which may or may not be at their chromatographic apex. Typically the number of selected precursor ions will increase and the MSMS acquisition time for each ion will decrease with increasing sample complexity. This creates a fundamental problem for the identification of proteins over a wide dynamic range or generation of extensive sequence coverage for a single protein. Fast separations with narrow, rapidly eluting peaks result in significant loss of data in MS mode when MSMS data is being acquired. As a result, precursor ion sampling drastically suffers. While DDA is sufficient in providing sequence information for identification of proteins, topology peptide mapping for individual peptides is severely under-sampled since only the two most intense ions at a given time are selected for MSMS fragmentation. Many of these limitations described have a direct effect on the lack of reproducibility, low sequence coverage, and large number of single peptide-based protein identifications present in the literature.55, 56

To overcome some of these problems, a data independent mode of acquisition was introduced by Waters Corporation for label-free quantitative LC-MS studies.52, 53 _This

process, termed MSE, takes advantage of a high-peak capacity chromatographic separation coupled with the high sampling-rate of an orthogonal acceleration TOF mass spectrometer

providing a rapid and parallel generation of peptide precursor and product ion detections on

all eluting species within a chromatographic separation. Maximizing the duty cycle, MSE is

programmed in such a way to acquire two functions during alternating scans. The first function is set at a low collision energy during which no fragmentation occurs to the precursor ions. Contrastingly, the second function is acquired during which the collision energy is linearly ramped between two user defined energies inducing fragmentation of any species present in the gas cell at that time. The resulting low collision energy and elevated collision energy chromatograms for a peptide digest are shown in Figure 2.6 (A and B) respectively. For optimal determination of m/z values, retention times, and peak volumes of all detectable ions, the acquisition time of the scan speed is set in proportion to the

chromatographic peak width allowing a sufficient number (approximately 10) of scans to be collected from each precursor ion. Figure 2.7 compares preservation of the chromatographic fidelity between the low collision energy scans in MSE (A) mode compared to DDA (B) mode for the same peptide digest sample. Here one can note the preservation in peak geometry in the MSE sample chromatogram as low and high collision energy functions are acquired simultaneously by alternating 0.7 second scans. Product ion information is obtained from all the isotopes and charge states of a given precursor peptide as fragmentation occurs in parallel. The DDA chromatogram, on the other hand, loses the ability to accurately determine chromatographic peak profiling parameters as data acquisition is spread over three collection channels. The DDA MS chromatogram, therefore, is depicted as a smoothed representation of only the ions present when the collision energy is switched low. Precursor ions in the low intensity regime of a peptide digest not only lack in MSMS sequence

fragmentation periods. Both methods of precursor fragmentation allows for improvements in peptide identification compared to peptide mass fingerprinting. While DDA allows for highly confident selected precursor fragmentation with minimal product ion impurities, MSE results in complex fragmentation patterns of all precursor ions at a given time. Data

processing algorithms, therefore, must be designed to appropriately interpret peptide identification for complex sample analysis.

2.4.2 Peptide Detection: Data Independent LC-MS versus Data Dependent LC-MSMS