蛋白质组学研究中的无标记定量方法

蛋白质定量是探索疾病发生发展状况和寻找新药靶标的重要手段.该领域最常用的技术是比较染色后的二维凝胶上蛋白点的光密度值或综合同位素标记后的质谱峰强度方法.但此二者的样品处理方法都比较麻烦,不利于进行大规模蛋白质组的定量研究.最近几年出现了利用质谱数据进行无标记定量的方法, 根据数据类型这些方法可以分为2类：基于鉴定蛋白的肽段数的方法和基于质谱峰强度的方法,在高通量大规模蛋白组定量研究中有很大优势.本综述主要介绍了这2类无标记定量方法的模型及优缺点,并比较了2类方法的灵敏度和准确度.肽段计数方法在检测蛋白丰度变化时更灵敏,而峰面积强度在评估蛋白比率时更准确.     

Critical assessment of proteome‐wide label‐free absolute abundance estimation strategies

There is a great interest in reliable ways to obtain absolute protein abundances at a proteome‐wide scale. To this end, label‐free LC‐MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low‐cost and easily implemented quantification methods (Absolute Protein Expression, Exponentially Modified Protein Abundance Index, Intensity‐Based Absolute Quantification Index, Top3, and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; that is, using spiked in stable isotope labeled standard peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of miscleaved, modified, and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy.     

Spectral index for assessment of differential protein expression in shotgun proteomics

Detecting differentially expressed proteins is a key goal of proteomics. We describe a label-free method, the spectral index, for analyzing relative protein abundance in large-scale data sets derived from biological samples by shotgun proteomics. The spectral index is comprised of two biochemically plausible features: relative protein abundance (assessed by spectral counts) and the number of samples within a group with detectable peptides. We combined the spectral index with permutation analysis to establish confidence intervals for assessing differential protein expression in bronchoalveolar lavage fluid from cystic fibrosis and control subjects. Significant differences in protein abundance determined by the spectral index agreed well with independent biochemical measurements. When used to analyze simulated data sets, the spectral index outperformed four other statistical tests (Student's t-test, G-test, Bayesian t-test, and Significance Analysis of Microarrays) by correctly identifying the largest number of differentially expressed proteins. Correspondence analysis and functional annotation analysis indicated that the spectral index improves the identification of enriched proteins corresponding to clinical phenotypes. The spectral index is easily implemented and statistically robust, and its results are readily interpreted graphically. Therefore, it should be useful for biomarker discovery and comparisons of protein expression between normal and disease states.     

Toward the complete characterization of host cell proteins in biotherapeutics via affinity depletions,LC-MS/MS,and multivariate analysis

Host cell protein (HCP) impurities are generated by the host organism during the production of therapeutic recombinant proteins, and are difficult to remove completely. Though commonly present in small quantities, if levels are not controlled, HCPs can potentially reduce drug efficacy and cause adverse patient reactions. A high resolution approach for thorough HCP characterization of therapeutic monoclonal antibodies is presented herein. In this method, antibody samples are first depleted via affinity enrichment (e.g., Protein A, Protein L) using milligram quantities of material. The HCP-containing flow-through is then enzymatically digested, analyzed using nano-UPLC-MS/MS, and proteins are identified through database searching. Nearly 700 HCPs were identified from samples with very low total HCP levels (< 1 ppm to ～10 ppm) using this method. Quantitation of individual HCPs was performed using normalized spectral counting as the number of peptide spectrum matches (PSMs) per protein is proportional to protein abundance. Multivariate analysis tools were utilized to assess similarities between HCP profiles by: 1) quantifying overlaps between HCP identities; and 2) comparing correlations between individual protein abundances as calculated by spectral counts. Clustering analysis using these measures of dissimilarity between HCP profiles enabled high resolution differentiation of commercial grade monoclonal antibody samples generated from different cell lines, cell culture, and purification processes.     

Toward the complete characterization of host cell proteins in biotherapeutics via affinity depletions,LC-MS/MS,and multivariate analysis

Host cell protein (HCP) impurities are generated by the host organism during the production of therapeutic recombinant proteins, and are difficult to remove completely. Though commonly present in small quantities, if levels are not controlled, HCPs can potentially reduce drug efficacy and cause adverse patient reactions. A high resolution approach for thorough HCP characterization of therapeutic monoclonal antibodies is presented herein. In this method, antibody samples are first depleted via affinity enrichment (e.g., Protein A, Protein L) using milligram quantities of material. The HCP-containing flow-through is then enzymatically digested, analyzed using nano-UPLC-MS/MS, and proteins are identified through database searching. Nearly 700 HCPs were identified from samples with very low total HCP levels (< 1 ppm to ∼10 ppm) using this method. Quantitation of individual HCPs was performed using normalized spectral counting as the number of peptide spectrum matches (PSMs) per protein is proportional to protein abundance. Multivariate analysis tools were utilized to assess similarities between HCP profiles by: 1) quantifying overlaps between HCP identities; and 2) comparing correlations between individual protein abundances as calculated by spectral counts. Clustering analysis using these measures of dissimilarity between HCP profiles enabled high resolution differentiation of commercial grade monoclonal antibody samples generated from different cell lines, cell culture, and purification processes.     

Development of glycoprotein capture-based label-free method for the high-throughput screening of differential glycoproteins in hepatocellular carcinoma

A robust, reproducible, and high throughput method was developed for the relative quantitative analysis of glycoprotein abundances in human serum. Instead of quantifying glycoproteins by glycopeptides in conventional quantitative glycoproteomics, glycoproteins were quantified by nonglycosylated peptides derived from the glycoprotein digest, which consists of the capture of glycoproteins in serum samples and the release of nonglycopeptides by trypsin digestion of captured glycoproteins followed by two-dimensional liquid chromatography-tandem MS analysis of released peptides. Protein quantification was achieved by comparing the spectrum counts of identified nonglycosylated peptides of glycoproteins between different samples. This method was demonstrated to have almost the same specificity and sensitivity in glycoproteins quantification as capture at glycopeptides level. The differential abundance of proteins present at as low as nanogram per milliliter levels was quantified with high confidence. The established method was applied to the analysis of human serum samples from healthy people and patients with hepatocellular carcinoma (HCC) to screen differential glycoproteins in HCC. Thirty eight glycoproteins were found with substantial concentration changes between normal and HCC serum samples, including α-fetoprotein, the only clinically used marker for HCC diagnosis. The abundance changes of three glycoproteins, i.e. galectin-3 binding protein, insulin-like growth factor binding protein 3, and thrombospondin 1, which were associated with the development of HCC, were further confirmed by enzyme-linked immunosorbent assay. In conclusion, the developed method was an effective approach to quantitatively analyze glycoproteins in human serum and could be further applied in the biomarker discovery for HCC and other cancers.     

Label-free quantitative analysis of one-dimensional PAGE LC/MS/MS proteome: application on angiotensin II-stimulated smooth muscle cells secretome

A widely used method for protein identification couples prefractionation of protein samples by one-dimensional (1D) PAGE with LC/MS/MS. We developed a new label-free quantitative algorithm by combining measurements of spectral counting, ion intensity, and peak area on 1D PAGE-based proteomics. This algorithm has several improvements over other label-free quantitative algorithms: (i) Errors in peak detection are reduced because the retention time is based on each LC/MS/MS run and actual precursor m/z. (ii) Detection sensitivity is increased because protein quantification is based on the combination of peptide count, ion intensity, and peak area. (iii) Peak intensity and peak area are calculated in each LC/MS/MS run for all slices from 1D PAGE for every single identified protein and visualized as a Western blot image. The sensitivity and accuracy of this algorithm were demonstrated by using standard curves (17.4 fmol to 8.7 pmol), complex protein mixtures (30 fmol to 1.16 pmol) of known composition, and spiked protein (34.8 fmol to 17.4 pmol) in complex proteins. We studied the feasibility of this approach using the secretome of angiotensin II (Ang II)-stimulated vascular smooth muscle cells (VSMCs). From the VSMC-conditioned medium, 629 proteins were identified including 212 putative secreted proteins. 26 proteins were differently expressed in control and Ang II-stimulated VSMCs, including 18 proteins not previously reported. Proteins related to cell growth (CYR61, protein NOV, and clusterin) were increased, whereas growth arrest-specific 6 (GAS6) and growth/differentiation factor 6 were decreased by Ang II stimulation. Ang II-stimulated changes of plasminogen activator inhibitor-1, GAS6, cathepsin B, and periostin were validated by Western blot. In conclusion, a novel label-free quantitative analysis of 1D PAGE-LC/MS/MS-based proteomics has been successfully applied to the identification of new potential mediators of Ang II action and may provide an alternative to traditional protein staining methods.     

Analysis of seminal plasma from patients with non-obstructive azoospermia and identification of candidate biomarkers of male infertility

Infertility affects approximately 15% of couples with equivalent male and female contribution. Absence of sperm in semen, referred to as azoospermia, accounts for 5-20% of male infertility cases and can result from pretesticular azoospermia, non-obstructive azoospermia (NOA), and obstructive azoospermia (OA). The current clinical methods of differentiating NOA cases from OA ones are indeterminate and often require surgical intervention for a conclusive diagnosis. We catalogued 2048 proteins in seminal plasma from men presented with NOA. Using spectral-counting, we compared the NOA proteome to our previously published proteomes of fertile control men and postvasectomy (PV) men and identified proteins at differential abundance levels among these clinical groups. To verify spectral counting ratios for candidate proteins, extracted ion current (XIC) intensities were also used to calculate abundance ratios. The Pearson correlation coefficient between spectral counting and XIC ratios for the Control-NOA and NOA-PV data sets is 0.83 and 0.80, respectively. Proteins that showed inconsistent spectral counting and XIC ratios were removed from analysis. There are 34 proteins elevated in Control relative to NOA, 18 decreased in Control relative to NOA, 59 elevated in NOA relative to PV, and 16 decreased in NOA relative to PV. Many of these proteins have expression in the testis and the epididymis and are linked to fertility. Some of these proteins may be useful as noninvasive biomarkers in discriminating NOA cases from OA.     

Interaction of ferricytochrome c with zwitterionic phospholipid bilayers: a Raman spectroscopic study

The vibrational Raman spectra of both pure L-alpha-dipalmitoylphosphatidylcholine (DPPC) liposomes and DPPC multilayers reconstituted with ferricytochrome c under varying conditions of pH and ionic strength are reported as a function of temperature. Total integrated band intensities and relative peak height intensity ratios, two spectral scattering parameters used to determine bilayer disorder, are invariant to changes in pH and ionic strength but exhibit a sensitivity to the bilayer concentration of the ferricytochrome c. Protein concentrations were estimated by comparing the 1636 cm-1 resonance Raman line of known ferricytochrome c solutions to intensity values for the reconstituted multilayer samples. Temperature-dependent profiles of the 3100-2800 cm-1 C-H stretching, 1150-1000 cm-1 C-C stretching, 1440 cm-1 CH2 deformation, and 1295 cm-1 CH2 twisting mode regions characteristic of acyl chain vibrations reflect bilayer perturbations due to the weak interactions of ferricytochrome c. The DPPC multilamellar gel to liquid-crystalline phase transition temperature, TM, defined by either the C-H stretching mode I2935/I2880 or the C-C stretching mode I1061/I1090 peak height intensity ratios, is decreased by approximately 4 degrees C for the approximately 10(-4) M ferricytochrome c reconstituted DPPC liposomes. Other spectral features, such as the increase in the 2935 cm-1 C-H stretching mode region and the enhancement of higher frequency CH2 twisting modes, which arise in bilayers containing approximately 10(-4) M protein, are interpreted in terms of protein penetration into the hydrophobic region of the bilayer.     

Differential analysis of membrane proteins in mouse fore- and hindbrain using a label-free approach

The ability to quantitatively compare protein levels across different regions of the brain to identify disease mechanisms remains a fundamental research challenge. It requires both a robust method to efficiently isolate proteins from small amounts of tissue and a differential technique that provides a sensitive and comprehensive analysis of these proteins. Here, we describe a proteomic approach for the quantitative mapping of membrane proteins between mouse fore- and hindbrain regions. The approach focuses primarily on a recently developed method for the fractionation of membranes and on-membrane protein digestion, but incorporates off-line SCX-fractionation of the peptide mixture and nano-LC-MS/MS analysis using an LTQ-FT-ICR instrument as part of the analytical method. Comparison of mass spectral peak intensities between samples, mapping of peaks to peptides and protein sequences, and statistical analysis were performed using in-house differential analysis software (DAS). In total, 1213 proteins were identified and 967 were quantified; 81% of the identified proteins were known membrane proteins and 38% of the protein sequences were predicted to contain transmembrane helices. Although this paper focuses primarily on characterizing the efficiency of this purification method from a typical sample set, for many of the quantified proteins such as glutamate receptors, GABA receptors, calcium channel subunits, and ATPases, the observed ratios of protein abundance were in good agreement with the known mRNA expression levels and/or intensities of immunostaining in rostral and caudal regions of murine brain. This suggests that the approach would be well-suited for incorporation in more rigorous, larger scale quantitative analysis designed to achieve biological significance.     

DeepQuanTR: MALDI‐MS‐based label‐free quantification of proteins in complex biological samples

The quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and quantification of differentially expressed proteins using LC‐MALDI‐MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI‐MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter‐sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative quantification of proteins derived from a wide variety of different samples.     

Increased Power for the Analysis of Label-free LC-MS/MS Proteomics Data by Combining Spectral Counts and Peptide Peak Attributes

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics provides a wealth of information about proteins present in biological samples. In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level. If a protein-level analysis is desired, the peptide-level information must be rolled up into protein-level information. We propose a principal component analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data that incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume, or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented. We show that ProPCA outperformed existing quantitative methods for peptide-protein roll-up, including spectral counting methods and other methods for combining LC-MS peptide peak attributes. The performance of ProPCA was validated using a data set derived from the LC-MS/MS analysis of a mixture of protein standards (the UPS2 proteomic dynamic range standard introduced by The Association of Biomolecular Resource Facilities Proteomics Standards Research Group in 2006). Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total cell lysates prepared for LC-MS/MS analysis by alternative lysis methods and show that ProPCA identified more differentially abundant proteins than competing methods.One of the fundamental goals of proteomics methods for the biological sciences is to identify and quantify all proteins present in a sample. LC-MS/MS-based proteomics methodologies offer a promising approach to this problem (1–3). These methodologies allow for the acquisition of a vast amount of information about the proteins present in a sample. However, extracting reliable protein abundance information from LC-MS/MS data remains challenging. In this work, we were primarily concerned with the analysis of data acquired using bottom-up label-free LC-MS/MS-based proteomics techniques where “bottom-up” refers to the fact that proteins are enzymatically digested into peptides prior to query by the LC-MS/MS instrument platform (4), and “label-free” indicates that analyses are performed without the aid of stable isotope labels. One challenge inherent in the bottom-up approach to proteomics is that information directly available from the LC-MS/MS data is at the peptide level. When a protein-level analysis is desired, as is often the case with discovery-driven LC-MS research, peptide-level information must be rolled up into protein-level information.Spectral counting (5–10) is a straightforward and widely used example of peptide-protein roll-up for LC-MS/MS data. Information experimentally acquired in single stage (MS) and tandem (MS/MS) spectra may lead to the assignment of MS/MS spectra to peptide sequences in a database-driven or database-free manner using various peptide identification software platforms (SEQUEST (11) and Mascot (12), for instance); the identified peptide sequences correspond, in turn, to proteins. In principle, the number of tandem spectra matched to peptides corresponding to a certain protein, the spectral count (SC),1 is positively associated with the abundance of a protein (5). In spectral counting techniques, raw or normalized SCs are used as a surrogate for protein abundance. Spectral counting methods have been moderately successful in quantifying protein abundance and identifying significant proteins in various settings. However, SC-based methods do not make full use of information available from peaks in the LC-MS domain, and this surely leads to loss of efficiency.Peaks in the LC-MS domain corresponding to peptide ion species are highly sensitive to differences in protein abundance (13, 14). Identifying LC-MS peaks that correspond to detected peptides and measuring quantitative attributes of these peaks (such as height, area, or volume) offers a promising alternative to spectral counting methods. These methods have become especially popular in applications using stable isotope labeling (15). However, challenges remain, especially in the label-free analysis of complex proteomics samples where complications in peak detection, alignment, and integration are a significant obstacle. In practice, alignment, identification, and quantification of LC-MS peptide peak attributes (PPAs) may be accomplished using recently developed peak matching platforms (16–18). A highly sensitive indicator of protein abundance may be obtained by rolling up PPA measurements into protein-level information (16, 19, 20). Existing peptide-protein roll-up procedures based on PPAs typically involve taking the mean of (possibly normalized) PPA measurements over all peptides corresponding to a protein to obtain a protein-level estimate of abundance. Despite the promise of PPA-based procedures for protein quantification, the performance of PPA-based methods may vary widely depending on the particular roll-up procedure used; furthermore, PPA-based procedures are limited by difficulties in accurately identifying and measuring peptide peak attributes. These two issues are related as the latter issue affects the robustness of PPA-based roll-up methods. Indeed, existing peak matching and quantification platforms tend to result in PPA measurement data sets with substantial missingness (16, 19, 21), especially when working with very complex samples where substantial dynamic ranges and ion suppression are difficulties that must be overcome. Missingness may, in turn, lead to instability in protein-level abundance estimates. A good peptide-protein roll-up procedure that utilizes PPAs should account for this missingness and the resulting instability in a principled way. However, even in the absence of missingness, there is no consensus in the existing literature on peptide-protein roll-up for PPA measurements.In this work, we propose ProPCA, a peptide-protein roll-up method for efficiently extracting protein abundance information from bottom-up label-free LC-MS/MS data. ProPCA is an easily implemented, unsupervised method that is related to principle component analysis (PCA) (22). ProPCA optimally combines SC and PPA data to obtain estimates of relative protein abundance. ProPCA addresses missingness in PPA measurement data in a unified way while capitalizing on strengths of both SCs and PPA-based roll-up methods. In particular, ProPCA adapts to the quality of the available PPA measurement data. If the PPA measurement data are poor and, in the extreme case, no PPA measurements are available, then ProPCA is equivalent to spectral counting. On the other hand, if there is no missingness in the PPA measurement data set, then the ProPCA estimate is a weighted mean of PPA measurements and spectral counts where the weights are chosen to reflect the ability of spectral counts and each peptide to predict protein abundance.Below, we assess the performance of ProPCA using a data set obtained from the LC-MS/MS analysis of protein standards (UPS2 proteomic dynamic range standard set² manufactured by Sigma-Aldrich) and show that ProPCA outperformed other existing roll-up methods by multiple metrics. The applicability of ProPCA is not limited by the quantification platform used to obtain SCs and PPA measurements. To demonstrate this, we show that ProPCA continued to perform well when used with an alternative quantification platform. Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total human hepatocellular carcinoma (HepG2) cell lysates prepared for LC-MS/MS analysis by alternative lysis methods. We show that ProPCA identified more differentially abundant proteins than competing methods.     

Proteomic analysis of eccrine sweat: implications for the discovery of schizophrenia biomarker proteins

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately 2-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers.     

Faecal egg counts provide a reliable measure of Trichostrongylus tenuis intensities in free-living red grouse Lagopus lagopus scoticus

The reliability of different egg counting methods for estimating the intensity of Trichostrongylus tenuis infections in red grouse, Lagopus lagopus scoticus, was investigated in the autumn, when grouse may harbour high parasite intensities. Possible limitations to the use of these methods were also examined. Faecal egg counts were found to accurately estimate T. tenuis worm intensities, at least up to an observed maximum of c. 8000 worms. Two egg counting methods (smear and McMaster) gave consistent results, although the exact relationship with worm intensity differed according to the method used. Faecal egg counts significantly decreased with increasing length of sample storage time, but egg counts were reliable for estimating worm intensity for three weeks. The concentration of eggs in the caecum was also found to reliably estimate worm intensity. However, egg counts from frozen gut samples cannot be used to estimate worm intensities. These results conclude that, despite some limitations, faecal and caecum egg counts provide useful and reliable ways of measuring T. tenuis intensities in red grouse.     

Approaches for the quantification of protein concentration ratios

The function of a protein is modulated by its abundance and its degree of specific post-translational modifications such as phosphorylation, glycosylation or truncation. Consequently, changes of protein concentration and the extent of their post-translational modifications has a great influence on the activity of intracellular substrate degradation processes, on the activity of intracellular biosynthetic pathways, on the cell cycle or on the function of a single cell in a whole organism. Defects in this area lead to diseases like cancer or neurodegeneration. Therefore, it is a challenge to quantify changes within the proteome in the diseased state or between developmental stages and to use the results obtained for the maximization of product yields in biotechnology or for the development of new drug targets to fight against diseases. In order to determine the intracellular concentration of a protein it is necessary to spike the cell sample with the same protein in a pure form. If the concentration changes of many proteins have to be determined, it takes a long time to obtain all these proteins in a pure form. Therefore, most approaches in this field are restricted to the determination of protein abundance ratios between two states such as diseased or healthy tissues. In this case the proteins in the sample of state A function as an internal standard for the proteins in the sample of state B and vice versa. The most common techniques in this field are the comparison of two-dimensional gel spot intensities after staining or the integration of mass spectrometric peak intensities after stable isotope labelling with (13)C, (15)N, (18)O or deuterium. The results, advantages and drawbacks of these approaches are discussed. Stable isotope labelling in combination with mass spectrometry is more accurate than the comparison of spot intensities and has the potential for the investigation of highly complex tissue samples.     

Improvements in proteomic metrics of low abundance proteins through proteome equalization using ProteoMiner prior to MudPIT

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.     

Two-dimensional mass spectra generated from the analysis of 15N-labeled and unlabeled peptides for efficient protein identification and de novo peptide sequencing

Protein identification has been greatly facilitated by database searches against protein sequences derived from product ion spectra of peptides. This approach is primarily based on the use of fragment ion mass information contained in a MS/MS spectrum. Unambiguous protein identification from a spectrum with low sequence coverage or poor spectral quality can be a major challenge. We present a two-dimensional (2D) mass spectrometric method in which the numbers of nitrogen atoms in the molecular ion and the fragment ions are used to provide additional discriminating power for much improved protein identification and de novo peptide sequencing. The nitrogen number is determined by analyzing the mass difference of corresponding peak pairs in overlaid spectra of (15)N-labeled and unlabeled peptides. These peptides are produced by enzymatic or chemical cleavage of proteins from cells grown in (15)N-enriched and normal media, respectively. It is demonstrated that, using 2D information, i.e., m/z and its associated nitrogen number, this method can, not only confirm protein identification results generated by MS/MS database searching, but also identify peptides that are not possible to identify by database searching alone. Examples are presented of analyzing Escherichia coli K12 extracts that yielded relatively poor MS/MS spectra, presumably from the digests of low abundance proteins, which can still give positive protein identification using this method. Additionally, this 2D MS method can facilitate spectral interpretation for de novo peptide sequencing and identification of posttranslational or other chemical modifications. We envision that this method should be particularly useful for proteome expression profiling of organelles or cells that can be grown in (15)N-enriched media.     

Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.     

Sources of Variability among Replicate Samples Separated by Two-Dimensional Gel Electrophoresis

Two-dimensional gel electrophoresis (2DE) offers high-resolution separation for intact proteins. However, variability in the appearance of spots can limit the ability to identify true differences between conditions. Variability can occur at a number of levels. Individual samples can differ because of biological variability. Technical variability can occur during protein extraction, processing, or storage. Another potential source of variability occurs during analysis of the gels and is not a result of any of the causes of variability named above. We performed a study designed to focus only on the variability caused by analysis. We separated three aliquots of rat left ventricle and analyzed differences in protein abundance on the replicate 2D gels. As the samples loaded on each gel were identical, differences in protein abundance are caused by variability in separation or interpretation of the gels. Protein spots were compared across gels by quantile values to determine differences. Fourteen percent of spots had a maximum difference in intensity of 0.4 quantile values or more between replicates. We then looked individually at the spots to determine the cause of differences between the measured intensities. Reasons for differences were: failure to identify a spot (59%), differences in spot boundaries (13%), difference in the peak height (6%), and a combination of these factors (21). This study demonstrates that spot identification and characterization make major contributions to variability seen with 2DE. Methods to highlight why measured protein spot abundance is different could reduce these errors.     

Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label‐free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium‐complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre‐fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity‐ and identification‐based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC‐MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open‐source license.