Marc V. Gorenstein

Absolute Quantification of Proteins by LCMSE A Virtue of Parallel ms Acquisition

Relative quantification methods have dominated the quantitative proteomics field. There is a need, however, to conduct absolute quantification studies to accurately model and understand the complex molecular biology that results in proteome variability among biological samples. A new method of absolute quantification of proteins is described. This method is based on the discovery of an unexpected relationship between MS signal response and protein concentration: the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than ±10%. Given an internal standard, this relationship is used to calculate a universal signal response factor. The universal signal response factor (counts/mol) was shown to be the same for all proteins tested in this study. A controlled set of six exogenous proteins of varying concentrations was studied in the absence and presence of human serum. The absolute quantity of the standard proteins was determined with a relative error of less than ±15%. The average MS signal responses of the three most intense peptides from each protein were plotted against their calculated protein concentrations, and this plot resulted in a linear relationship with an R2 value of 0.9939. The analyses were applied to determine the absolute concentration of 11 common serum proteins, and these concentrations were then compared with known values available in the literature. Additionally within an unfractionated Escherichia coli lysate, a subset of identified proteins known to exist as functional complexes was studied. The calculated absolute quantities were used to accurately determine their stoichiometry.

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.

Simultaneous Qualitative and Quantitative Analysis of theEscherichia coli Proteome A Sweet Tale

We describe a novel LCMS approach to the relative quantitation and simultaneous identification of proteins within the complex milieu of unfractionated Escherichia coli. This label-free, LCMS acquisition method observes all detectable, eluting peptides and their corresponding fragment ions. Postacquisition data analysis methods extract both the chromatographic and the mass spectrometric information on the tryptic peptides to provide time-resolved, accurate mass measurements, which are subsequently used for quantitation and identification of constituent proteins. The response of E. coli to carbon source variation is well understood, and it is thus commonly used as a model biological system when validating an analytical method. Using this LCMS approach, we characterized proteins isolated from E. coli grown in glucose, lactose, and acetate. The change in relative abundance of the corresponding proteins was measured from peptides common to both conditions. Protein identities were also determined for those peptides that were unique to each condition, and these identities were found to be consistent with the underlying biochemical restrictions imposed by the growth conditions. The relative change in abundance of the characterized proteins ranged from 0.1- to 90-fold among the three binary comparisons. The overall coverage of the characterized proteins ranged from 10 to 80%, consisting of one to 34 peptides per protein. The quantitative results obtained from our study were comparable to other existing proteomic and transcriptional profiling approaches. This study illustrates the robustness of this novel LCMS approach for the simultaneous quantitative and comprehensive qualitative analysis of proteins in complex mixtures.

Simultaneous qualitative and quantitative analysis of the E. coli proteome: A sweet tale

We describe a novel LCMS approach to the relative quantitation and simultaneous identification of proteins within the complex milieu of unfractionated Escherichia coli. This label-free, LCMS acquisition method observes all detectable, eluting peptides and their corresponding fragment ions. Postacquisition data analysis methods extract both the chromatographic and the mass spectrometric information on the tryptic peptides to provide time-resolved, accurate mass measurements, which are subsequently used for quantitation and identification of constituent proteins. The response of E. coli to carbon source variation is well understood, and it is thus commonly used as a model biological system when validating an analytical method. Using this LCMS approach, we characterized proteins isolated from E. coli grown in glucose, lactose, and acetate. The change in relative abundance of the corresponding proteins was measured from peptides common to both conditions. Protein identities were also determined for those peptides that were unique to each condition, and these identities were found to be consistent with the underlying biochemical restrictions imposed by the growth conditions. The relative change in abundance of the characterized proteins ranged from 0.1- to 90-fold among the three binary comparisons. The overall coverage of the characterized proteins ranged from 10 to 80%, consisting of one to 34 peptides per protein. The quantitative results obtained from our study were comparable to other existing proteomic and transcriptional profiling approaches. This study illustrates the robustness of this novel LCMS approach for the simultaneous quantitative and comprehensive qualitative analysis of proteins in complex mixtures.

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.