LFAQ: towards unbiased label-free absolute protein quantification by predicting peptide quantitative factors
Cheng ChangZhiqiang GaoWantao YingYan ZhaoYan FuSongfeng WuMengjie LiGuibin WangXiaohong QianYunping ZhuFuchu He
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Abstract Mass spectrometry (MS) has become a prominent choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose a novel algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on datasets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins.Keywords:
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Abstract In order to assess the biological function of proteins and their modifications for understanding signaling mechanisms within cells as well as specific biomarkers to disease, it is important that quantitative information be obtained under different experimental conditions. Stable isotope labeling is a powerful method for accurately determining changes in the levels of proteins and PTMs; however, isotope labeling experiments suffer from limited dynamic range resulting in signal change ratios of less than ∼20:1 using most commercial mass spectrometers. Label‐free approaches to relative quantification in proteomics such as spectral counting have gained popularity since no additional chemistries are needed. Here, we show a label‐free method for relative quantification based on the TIC from peptide MS/MS spectra collected from data‐dependent runs can be used effectively as a quantitative measure and expands the dynamic range over isotope labeling experiments allowing for abundance differences up to ∼60:1 in a screen for proteins that bind to phosphotyrosine residues.
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Quantitative data is an excellent resource in any proteomics study but is essential in many. In recent years this area has expanded from relative to absolute quantification with a wide range of methods available for absolute quantitative proteomics. In general protein quantification is based on either label-mediated or label-free strategies. Common label-mediated approaches are isotope dilution strategies, such as AQUA, coupled with mass spectrometry, where analyte signal is compared to a stable isotope labelled standard added in known abundance. These methods are suited to small-scale studies but increasing demand for large-scale proteome quantification exposed the need for alternative quantification methodologies. The QconCAT technology, first published in 2005, is a label mediated approach which utilises the principle of surrogacy to quantify analyte proteins based on a signature peptide, or peptides, for each protein. QconCATs are concatenations of quantotypic peptides for a group of proteins, the QconCAT gene is designed in silico and expressed heterologously in E.coli with [13C6]arg and [13C6]lys to elicit a stable isotope labelled multiplexed absolute quantification standard. In this thesis I describe several developments to the QconCAT production protocol. These developments reduce the production time from ~19d, using the initial method, to less than 7d. Time gains have been made across the whole workflow in the areas of protein expression, cell lysis, and product purification. Moreover verification of the QconCAT is delayed until the final product is synthesised, made possible by evidence of high quality reproducible expression. I explain how these alterations allow for production of several QconCATs in parallel, giving added efficiency. The success of the method is demonstrated through the use of multiple QconCATs. As a result of this work it is now possible to make at least eight QconCATs per week and the rate-limiting step of the quantification workflow has migrated from standard preparation to data processing. The final study in this thesis discusses methods for accurate quantification of the QconCAT protein and additional applications of QconCATs for testing mass spectrometer performance.
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(1) Background: Mass spectrometry-based quantitative proteome profiling is most commonly performed by label-free quantification (LFQ), stable isotopic labeling with amino acids in cell culture (SILAC), and reporter ion-based isobaric labeling methods (TMT and iTRAQ). Isobaric peptide termini labeling (IPTL) was described as an alternative to these methods and is based on crosswise labeling of both peptide termini and MS2 quantification. High quantification accuracy was assumed for IPTL because multiple quantification points are obtained per identified MS2 spectrum. A direct comparison of IPTL with other quantification methods has not been performed yet because IPTL commonly requires digestion with endoproteinase Lys-C. (2) Methods: To enable tryptic digestion of IPTL samples, a novel labeling for IPTL was developed that combines metabolic labeling (Arg-0/Lys-0 and Arg-d4/Lys-d4, respectively) with crosswise N-terminal dimethylation (d4 and d0, respectively). (3) Results: The comparison of IPTL with LFQ revealed significantly more protein identifications for LFQ above homology ion scores but not above identity ion scores. (4) Conclusions: The quantification accuracy was superior for LFQ despite the many quantification points obtained with IPTL.
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Quantitative proteome analysis has become a versatile tool to understand biological functions. Although stable isotope labeling is the most reliable method for quantitative mass spectrometry, preparation of isotope-labeled compounds is time-consuming and expensive. Simple label-free approaches have been introduced, but intensity-based quantitation without standards is not generally accepted as reliable, especially for small molecules. We have developed a novel label-free quantitative proteome analysis using pseudo internal standards (PISs). This idea was derived from northern blotting analysis, in which housekeeping genes are used as standards to normalize and compare target gene expression levels in different samples. In many proteomics studies, most proteins do not change their expression levels under different conditions, and therefore, these proteins can be employed as pseudo internal standards. This new approach is simple and does not require additional standards or labeling reagents. The PIS method represents a novel approach for mass spectrometry-based comprehensive quantitatitation and may also be applicable to quantitative metabolome analysis.
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Absolute protein quantification methods based on molecular mass spectrometry usually require stable isotope-labeled analogous standards for each target protein or peptide under study, which in turn must be certified using natural standards. In this work, we report a direct and accurate methodology based on capLC-ICP-QQQ and online isotope dilution analysis for the absolute and sensitive quantification of intact proteins. The combination of the postcolumn addition of 34S and a generic S-containing internal standard spiked to the sample provides full compound independent detector response and thus protein quantification without the need for specific standards. Quantitative recoveries, using a chromatographic core-shell C4 column for the various protein species assayed were obtained (96-100%). Thus, the proposed strategy enables the accurate quantification of proteins even if no specific standards are available for them. In addition, to the best of our knowledge, we obtained the lowest detection limits reported in the quantitative analysis of intact proteins by direct measurement of sulfur with ICPMS (358 fmol) and protein (ranging from 7 to 15 fmol depending on the assayed protein). The quantitative results for individual and simple mixtures of model proteins were statistically indistinguishable from the manufacturer's values. Finally, the suitability of the strategy for real sample analysis (including quantitative protein recovery from the column) was illustrated for the individual absolute quantification of the proteins and whole protein content in a venom sample. Parallel capLC-ESI-QTOF analysis was employed to identify the proteins, a prerequisite to translate the mass of quantified S for each chromatographic peak into individual protein mass.
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MS-based proteomics has emerged as a powerful tool in biological studies. The shotgun proteomics strategy, in which proteolytic peptides are analyzed in data-dependent mode, enables a detection of the most comprehensive proteome (>10 000 proteins from whole-cell lysate). The quantitative proteomics uses stable isotopes or label-free method to measure relative protein abundance. The isotope labeling strategies are more precise and accurate compared to label-free methods, but labeling procedures are complicated and expensive, and the sample number and types are also limited. Sequential window acquisition of all theoretical mass spectra (SWATH) is a recently developed technique, in which data-independent acquisition is coupled with peptide spectral library match. In principle SWATH method is able to do label-free quantification in an MRM-like manner, which has higher quantification accuracy and precision. Previous data have demonstrated that SWATH can be used to quantify less complex systems, such as spiked-in peptide mixture or protein complex. Our study first time assessed the quantification performance of SWATH method on proteome scale using a complex mouse-cell lysate sample. In total 3600 proteins got identified and quantified without sample prefractionation. The SWATH method shows outstanding quantification precision, whereas the quantification accuracy becomes less perfect when protein abundances differ greatly. However, this inaccuracy does not prevent discovering biological correlates, because the measured signal intensities had linear relationship to the sample loading amounts; thus the SWATH method can predict precisely the significance of a protein. Our results prove that SWATH can provide precise label-free quantification on proteome scale.
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Mass spectrometry (MS)-based proteomics has become an integral analytical technology in life science research. Multiple quantification strategies for MS-based proteomics have been reported. They can be categorized into two main regimes: absolute and relative quantification. The basic principle of stable isotope-based labeling for peptide and protein quantification is that the physicochemical characteristics of the differentially labeled peptides are nearly identical. Labeling via stable isotopes by amino acids in cell culture (SILAC) is a strategy that facilitates complete and proteome-wide labeling. Label-free quantification strategies are based on either spectral counting or precursor ion signal intensity. Several studies have focused on head-to head comparisons between spectral counting-based label-free quantification and metabolic labeling. The chapter presents a systematic global comparison of SILAC, dimethyl labeling, and isobaric tagging (TMT). The ideal quantitative proteomics approach would enable reproducible, comprehensive, sensitive and unbiased analysis that provides accurate and precise quantitative data with a high dynamic range and within reasonable analysis time.
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In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies.
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