Design and application of a data-independent precursor and product ion repository.

The functional design and application of a data-independent LC-MS precursor and product ion repository for protein identification, quantification, and validation is conceptually described. The ion repository was constructed from the sequence search results of a broad range of discovery experiments investigating various tissue types of two closely related mammalian species. The relative high degree of similarity in protein complement, ion detection, and peptide and protein identification allows for the analysis of normalized precursor and product ion intensity values, as well as standardized retention times, creating a multidimensional/orthogonal queryable, qualitative, and quantitative space. Peptide ion map selection for identification and quantification is primarily based on replication and limited variation. The information is stored in a relational database and is used to create peptide- and protein-specific fragment ion maps that can be queried in a targeted fashion against the raw or time aligned ion detections. These queries can be conducted either individually or as groups, where the latter affords pathway and molecular machinery analysis of the protein complement. The presented results also suggest that peptide ionization and fragmentation efficiencies are highly conserved between experiments and practically independent of the analyzed biological sample when using similar instrumentation. Moreover, the data illustrate only minor variation in ionization efficiency with amino acid sequence substitutions occurring between species. Finally, the data and the presented results illustrate how LC-MS performance metrics can be extracted and utilized to ensure optimal performance of the employed analytical workflows.

Using ion purity scores for enhancing quantitative accuracy and precision in complex proteomics samples.

To accurately determine the quantitative change of peptides and proteins in complex proteomics samples requires knowledge of how well each ion has been measured. The precision of each ions' calculated area is predicated on how uniquely it occupies its own space in m/z and elution time. Given an initial assumption that prior to the addition of the "heavy" label, all other ion detections are unique, which is arguably untrue, an initial attempt at quantifying the pervasiveness of ion interference events in a representative binary SILAC experiment was made by comparing the centered m/z and retention time of the ion detections from the "light" variant to its "heavy" companion. Ion interference rates were determined for LC-MS data acquired at mass resolving powers of 20 and 40 K with and without ion mobility separation activated. An ion interference event was recorded, if present in the companion dataset was an ion within ± its Δ mass at half-height, ±15 s of its apex retention time and if utilized by ±1 drift bin. Data are presented illustrating a definitive decrease in the frequency of ion interference events with each additional increase in selectivity of the analytical workflow. Regardless of whether the quantitative experiment is a composite of labeled samples or label free, how well each ion is measured can be determined given knowledge of the instruments mass resolving power across the entire m/z scale and the ion detection algorithm reporting both the centered m/z and Δ mass at half-height for each detected ion. Given these measurements, an effective resolution can be calculated and compared with the expected instrument performance value providing a purity score for the calculated ions' area based on mass resolution. Similarly, chromatographic and drift purity scores can be calculated. In these instances, the error associated to an ions' calculated peak area is estimated by examining the variation in each measured width to that of their respective experimental median. Detail will be disclosed as to how a final ion purity score was established, providing a first measure of how accurately each ions' area was determined as well as how precise the calculated quantitative change between labeled or unlabelled pairs were determined. Presented is how common ion interference events are in quantitative proteomics LC-MS experiments and how ion purity filters can be utilized to overcome and address them, providing ultimately more accurate and precise quantification results across a wider dynamic range.

Software for quantitative proteomic analysis using stable isotope labeling and data independent acquisition.

Many software tools have been developed for analyzing stable isotope labeling (SIL)-based quantitative proteomic data using data dependent acquisition (DDA). However, programs for analyzing SIL-based quantitative proteomics data obtained with data independent acquisition (DIA) have yet to be reported. Here, we demonstrated the development of a new software for analyzing SIL data using the DIA method. Performance of the DIA on SYNAPT G2MS was evaluated using SIL-labeled complex proteome mixtures with known heavy/light ratios (H/L = 1:1, 1:5, and 1:10) and compared with the DDA on linear ion trap (LTQ)-Orbitrap MS. The DIA displays relatively high quantitation accuracy for peptides cross all intensity regions, while the DDA shows an intensity dependent distribution of H/L ratios. For the three proteome mixtures, the number of detected SIL-peptide pairs and dynamic range of protein intensities using DIA drop stepwise, whereas no significant changes in these aspects using DDA were observed. The new software was applied to investigate the proteome difference between mouse embryonic fibroblasts (MEFs) and MEF-derived induced pluripotent stem cells (iPSCs) using (16)O/(18)O labeling. Our study expanded the capacities of our UNiquant software pipeline and provided valuable insight into the performance of the two cutting-edge MS platforms for SIL-based quantitative proteomic analysis today.

Simulating and validating proteomics data and search results.

The computational simulation of complete proteomic data sets and their utility to validate detection and interpretation algorithms, to aid in the design of experiments and to assess protein and peptide false discovery rates is presented. The simulation software has been developed for emulating data originating from data-dependent and data-independent LC-MS workflows. Data from all types of commonly used hybrid mass spectrometers can be simulated. The algorithms are based on empirically derived physicochemical liquid and gas phase models for proteins and peptides. Sample composition in terms of complexity and dynamic range, as well as chromatographic, experimental and MS conditions, can be controlled and adjusted independently. The effect of on-column amounts, gradient length, mass resolution and ion mobility on search specificity will be demonstrated using tryptic peptides from human and yeast cellular lysates simulated over five orders of magnitude in dynamic range. Initial justification of the simulated data sets is achieved by comparing and contrasting the in silico simulated data to experimentally derived results from a 48 protein mixture, spanning a similar magnitude of five orders of magnitude. Additionally, experimental data from replicate and dilutions series experiments will be utilized to determine error rates at the peptide and protein level with respect to mass, area, retention and drift time. The data presented reveal a high degree of similarity at the ion detection, peptide and protein level when analyzed under similar conditions.

Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures.

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC-MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four-protein mixture, the same four-protein mixture spiked into a complex biological background, and a variety of other "system" type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.

The detection, correlation, and comparison of peptide precursor and product ions from data independent LC-MS with data dependant LC-MS/MS.

The detection, correlation, and comparison of peptide and product ions from a data independent LC-MS acquisition strategy with data dependent LC-MS/MS is described. The data independent mode of acquisition differs from an LC-MS/MS data acquisition since no ion transmission window is applied with the first mass analyzer prior to collision induced disassociation. Alternating the energy applied to the collision cell, between low and elevated energy, on a scan-to-scan basis, provides accurate mass precursor and associated product ion spectra from every ion above the LOD of the mass spectrometer. The method therefore provides a near 100% duty cycle, with an inherent increase in signal intensity due to the fact that both precursor and product ion data are collected on all isotopes of every charge-state across the entire chromatographic peak width. The correlation of product to precursor ions, after deconvolution, is achieved by using reconstructed retention time apices and chromatographic peak shapes. Presented are the results from the comparison of a simple four protein mixture, in the presence and absence of an enzymatically digested protein extract from Escherichia coli. The samples were run in triplicate by both data dependant analysis (DDA) LC-MS/MS and data-independent, alternate scanning LC-MS. The detection and identification of precursor and product ions from the combined DDA search results of the four protein mixture were used for comparison to all other data. Each individual set of data-independent LC-MS data provides a more comprehensive set of detected ions than the combined peptide identifications from the DDA LC-MS/MS experiments. In the presence of the complex E. coli background, over 90% of the monoisotopic masses from the combined LC-MS/MS identifications were detected at the appropriate retention time. Moreover, the fragmentation pattern and number of associated elevated energy product ions in each replicate experiment was found to be very similar to the DDA identifications. In the case of the corresponding individual DDA LC-MS/MS experiment, 43% of the possible detectable peptides of interest were identified. The presented data illustrates that the time-aligned data from data-independent alternate scanning LC-MS experiments is highly comparable to the data obtained via DDA. The obtained information can therefore be effectively and correctly deconvolved to correlate product ions with parent precursor ions. The ability to generate precursor-product ion tables from this information and subsequently identify the correct parent precursor peptide will be illustrated in a companion manuscript.

Comparison of 1-D and 2-D LC MS/MS methods for proteomic analysis of human serum.

1-D and 2-D LC methods were utilized for proteome analysis of undepleted human serum. Separation of peptides in 2-D LC was performed either with strong cation exchange (SCX)-RP chromatography or with an RP-RP 2-D LC approach. Peptides were identified by MS/MS using a data-independent acquisition approach. A peptide retention prediction model was used to highlight the potential false-positive peptide identifications. When applying selected data filtration, we identified 52 proteins based on 316 peptides in serum in 1-D LC setup. One hundred and eighty-four proteins/1036 peptides and 142 proteins/905 peptides were identified in RP-RP and SCX-RP 2-D LC, respectively. The performance of both 2-D LC methods for proteomic analysis is critically compared.