Immuno-qPCR detection of the tandem affinity purification (TAP)-tag as a sensitive and accurate tool suitable for large-scale protein quantification

The tandem affinity purification (TAP)-tag has rapidly gained a wide popularity, mostly in studies on protein interactions, but lately also in large-scale protein quantification studies. We have developed an immuno-quantitative real-time PCR (qPCR) method to achieve rapid, sensitive and accurate quantification of TAP-tagged (and protein A-tagged) proteins in yeast with a detection range between 10(7) and 10(10) molecules. The immuno-qPCR protein quantification showed an excellent correlation to the published in vivo fluorescent protein (GFP)-based large-scale protein quantifications, but allowed for a much higher sensitivity. The correlation with published data from the large-scale Western blotting-based quantification of the TAP-tag was lower, but the sensitivity of detection was on roughly the same level. The practical use of the immuno-qPCR approach was demonstrated by analysis of osmo-regulated proteins, where the 2000-fold increase in expression of Catalase (Ctt1p), from an extremely low basal expression, could be accurately quantified. All steps of the method, from cell growth, to protein extraction and determination and the immuno-qPCR reaction itself are potentially amenable to automatization. Therefore, since the TAP-tag and protein A are useful in most model organisms, the immuno-qPCR method is both generic and suitable for large-scale studies.     

A rapid method for liquid β-galactosidase reporter assay in Saccharomyces cerevisiae

Although β-galactosidase assay is widely used for various studies in yeast a quantitative estimation of low enzyme activities with standard reactives remains hampered. It requires long reaction time and large amounts of cells. To overcome existing limitations we developed protocol, which incorporates realization reaction in miniaturized format, cell lysis in reaction buffer and simplification the normalization of β-galactosidase activity. These features allow faster reaction kinetics, accurate and simple quantification of low enzyme activities. To perform studies in vivo conditions we constructed a reporter plasmids based on the low copy yeast vector Ycp50. We adapted our assay on the yeast protein Rpn4 which is highly unstable with a half-life of only 2 min. We demonstrated that detection of Rpn4–LacZ fusion is achieved in 40 min in our method, whereas in standard assay it requires 4–5 h. Moreover, we implemented our approach for promoter dissection investigation. Thus, we present rapid, convenient and less labor-intensive method for assessment β-galactosidase activity.     

Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics

A crucial issue in comparative proteomics is the accurate quantification of differences in protein expression levels. To achieve this, several methods have been developed in which proteins are labeled with stable isotopes either in vivo via metabolic labeling or in vitro by protein derivatization. Although metabolic labeling is the only way to obtain labeling of all proteins, it has thus far only been applied to single- celled organisms and cells in culture. Here we describe quantitative 15N metabolic labeling of the multicellular organisms Caenorhabditis elegans, a nematode, and Drosophila melanogaster, the common fruit fly, achieved by feeding them on 15N-labeled Escherichia coli and yeast, respectively. The relative abundance of individual proteins obtained from different samples can then be determined by mass spectrometry (MS). The applicability of the method is exemplified by the comparison of protein expression levels in two C. elegans strains, one with and one without a germ line. The methodology described provides tools for accurate quantitative proteomic studies in these model organisms.     

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.     

Dual display of proteins on the yeast cell surface simplifies quantification of binding interactions and enzymatic bioconjugation reactions

Yeast surface display, a well‐established technology for protein analysis and engineering, involves expressing a protein of interest as a genetic fusion to either the N‐ or C‐terminus of the yeast Aga2p mating protein. Historically, yeast‐displayed protein variants are flanked by peptide epitope tags that enable flow cytometric measurement of construct expression using fluorescent primary or secondary antibodies. Here, we built upon this technology to develop a new yeast display strategy that comprises fusion of two different proteins to Aga2p, one to the N‐terminus and one to the C‐terminus. This approach allows an antibody fragment, ligand, or receptor to be directly coupled to expression of a fluorescent protein readout, eliminating the need for antibody‐staining of epitope tags to quantify yeast protein expression levels. We show that this system simplifies quantification of protein‐protein binding interactions measured on the yeast cell surface. Moreover, we show that this system facilitates co‐expression of a bioconjugation enzyme and its corresponding peptide substrate on the same Aga2p construct, enabling enzyme expression and catalytic activity to be measured on the surface of yeast.     

CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches

Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.     

Difference in mass analysis using labeled lysines (DIMAL-K): a new, efficient proteomic quantification method applied to the analysis of astrocytic secretomes

Here we describe an original strategy for unbiased quantification of protein expression called difference in mass analysis using labeled lysine (K) (DIMAL-K). DIMAL-K is based on the differential predigestion labeling of lysine residues in complex protein mixtures. The method is relevant for proteomic analysis by two-dimensional electrophoresis and MALDI-TOF mass spectrometry. Protein labeling on lysine residues uses two closely related chemical reagents, S-methyl thioacetimidate and S-methyl thiopropionimidate. Using protein standards, we demonstrated that 1) the chemical labeling was quantitative, specific, and rapid; 2) the differentially labeled proteins co-migrated on two-dimensional gels; and 3) the identification by mass fingerprinting and the relative quantification of the proteins were possible from a single MALDI-TOF mass spectrum. The power of the method was tested by comparing and quantifying the secretion of proteins in normal and proinflammatory astrocytic secretomes (20 microg). We showed that DIMAL-K was more sensitive and accurate than densitometric image analysis and allowed the detection and quantification of novel proteins.     

The role of bioinformatics in two-dimensional gel electrophoresis

Over the last two decades, two-dimensional electrophoresis (2-DE) gel has established itself as the de facto approach to separating proteins from cell and tissue samples. Due to the sheer volume of data and its experimental geometric and expression uncertainties, quantitative analysis of these data with image processing and modelling has become an actively pursued research topic. The results of these analyses include accurate protein quantification, isoelectric point and relative molecular mass estimation, and the detection of differential expression between samples run on different gels. Systematic errors such as current leakage and regional expression inhomogeneities are corrected for, followed by each protein spot in the gel being segmented and modelled for quantification. To assess differential expression of protein spots in different samples run on a series of two-dimensional gels, a number of image registration techniques for correcting geometric distortion have been proposed. This paper provides a comprehensive review of the computation techniques used in the analysis of 2-DE gels, together with a discussion of current and future trends in large scale analysis. We examine the pitfalls of existing techniques and highlight some of the key areas that need to be developed in the coming years, especially those related to statistical approaches based on multiple gel runs and image mining techniques through the use of parallel processing based on cluster computing and the grid technology.     

Quantification of proteins using data-independent analysis (MSE) in simple andcomplex samples: a systematic evaluation

A MS‐based method for the quantification of proteins termed data‐independent analysis (or MS^E) has been introduced recently. Although this method has been applied to the analysis of various types of biological samples, a thorough evaluation to assess the performance of this approach has yet to be conducted. Presented here is the first systematic and comprehensive study investigating the MS^E approach for quantitative analysis of low‐, medium‐, and high‐complexity samples. We demonstrate that this method has a linear dynamic range spanning three orders of magnitude with a limit of quantification of 61 amol/uL in low‐complexity samples and 488 amol/uL in high‐complexity samples. In addition, comprehensive sequence coverage was obtained and accurate quantification achieved for expression ratios ranging from 1:1.5 to 1:6. However, underestimation of ratios was detected independent of sample type, consistent with other quantitative proteomic methods. The present study provides validation of the MS^E approach for accurate quantitative proteomic analysis of biological samples while, at the same time, proving high sequence coverage of target proteins.     

Robust estimation of protein expression ratios with lysate microarray technology

MOTIVATION: The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points. RESULTS: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray. AVAILABILITY: http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm     

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

The quantification of gene expression at the single cell level uncovers novel regulatory mechanisms obscured in measurements performed at the population level. Two methods based on microscopy and flow cytometry are presented to demonstrate how such data can be acquired. The expression of a fluorescent reporter induced upon activation of the high osmolarity glycerol MAPK pathway in yeast is used as an example. The specific advantages of each method are highlighted. Flow cytometry measures a large number of cells (10,000) and provides a direct measure of the dynamics of protein expression independent of the slow maturation kinetics of the fluorescent protein. Imaging of living cells by microscopy is by contrast limited to the measurement of the matured form of the reporter in fewer cells. However, the data sets generated by this technique can be extremely rich thanks to the combinations of multiple reporters and to the spatial and temporal information obtained from individual cells. The combination of these two measurement methods can deliver new insights on the regulation of protein expression by signaling pathways.     

Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines

Within the past decade numerous methods for quantitative proteome analysis have been developed of which all exhibit particular advantages and disadvantages. Here, we present the results of a study aiming for a comprehensive comparison of ion-intensity based label-free proteomics and two label-based approaches using isobaric tags incorporated at the peptide and protein levels, respectively. As model system for our quantitative analysis we used the three hepatoma cell lines HepG2, Hep3B and SK-Hep-1. Four biological replicates of each cell line were quantitatively analyzed using an RPLC–MS/MS setup. Each quantification experiment was performed twice to determine technical variances of the different quantification techniques. We were able to show that the label-free approach by far outperforms both TMT methods regarding proteome coverage, as up to threefold more proteins were reproducibly identified in replicate measurements. Furthermore, we could demonstrate that all three methods show comparable reproducibility concerning protein quantification, but slightly differ in terms of accuracy. Here, label-free was found to be less accurate than both TMT approaches. It was also observed that the introduction of TMT labels at the protein level reduces the effect of underestimation of protein ratios, which is commonly monitored in case of TMT peptide labeling. Previously reported differences in protein expression between the particular cell lines were furthermore reproduced, which confirms the applicability of each investigated quantification method to study proteomic differences in such biological systems. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Probabilistic Approach to Predicting Substrate Specificity of Methyltransferases

We present a general probabilistic framework for predicting the substrate specificity of enzymes. We designed this approach to be easily applicable to different organisms and enzymes. Therefore, our predictive models do not rely on species-specific properties and use mostly sequence-derived data. Maximum Likelihood optimization is used to fine-tune model parameters and the Akaike Information Criterion is employed to overcome the issue of correlated variables. As a proof-of-principle, we apply our approach to predicting general substrate specificity of yeast methyltransferases (MTases). As input, we use several physico-chemical and biological properties of MTases: structural fold, isoelectric point, expression pattern and cellular localization. Our method accurately predicts whether a yeast MTase methylates a protein, RNA or another molecule. Among our experimentally tested predictions, 89% were confirmed, including the surprising prediction that YOR021C is the first known MTase with a SPOUT fold that methylates a substrate other than RNA (protein). Our approach not only allows for highly accurate prediction of functional specificity of MTases, but also provides insight into general rules governing MTase substrate specificity.     

A novel strategy for quantitative proteomics using isotope-coded protein labels

Stable isotope labelling in combination with mass spectrometry has emerged as a powerful tool to identify and relatively quantify thousands of proteins within complex protein mixtures. Here we describe a novel method, termed isotope-coded protein label (ICPL), which is capable of high-throughput quantitative proteome profiling on a global scale. Since ICPL is based on stable isotope tagging at the frequent free amino groups of isolated intact proteins, it is applicable to any protein sample, including extracts from tissues or body fluids, and compatible to all separation methods currently employed in proteome studies. The method showed highly accurate and reproducible quantification of proteins and yielded high sequence coverage, indispensable for the detection of post-translational modifications and protein isoforms. The efficiency (e.g. accuracy, dynamic range, sensitivity, speed) of the approach is demonstrated by comparative analysis of two differentially spiked proteomes.     

In vivo determination of organellar pH using a universal wavelength-based confocal microscopy approach

Many essential cellular processes are affected by transmembrane H(+) gradients and intracellular pH (pHi). The research of such metabolic events calls for a non-invasive method to monitor pHi within individual subcellular compartments. We present a novel confocal microscopy approach for the determination of organellar pHi in living cells expressing pH-dependent ratiometric fluorescent proteins. Unlike conventional intensity-based fluorometry, our method relies on emission wavelength scans at single-organelle resolution to produce wavelength-based pH estimates both accurate and robust to low-signal artifacts. Analyses of Ato1p-pHluorin and Ato1p-mCherry yeast cells revealed previously unreported wavelength shifts in pHluorin emission which, together with ratiometric mCherry, allowed for high-precision quantification of actual physiological pH values and evidenced dynamic pHi changes throughout the different stages of yeast colony development. Additionally, comparative pH quantification of Ato1p-pHluorin and Met17p-pHluorin cells implied the existence of a significant pHi gradient between peripheral and internal cytoplasm of cells from colonies occurring in the ammonia-producing alkali developmental phase. Results represent a step forward in the study of pHi regulation and subcellular metabolic functions beyond the scope of this study.     

A simplified procedure for antibody engineering by yeast surface display: Coupling display levels and target binding by ribosomal skipping

Yeast surface display is a valuable, widely used method for protein engineering. However, current yeast display applications rely on the staining of epitope tags in order to verify full‐length presentation of the protein of interest on the cell surface. We aimed at developing a modified yeast display approach that relies on ribosomal skipping, thereby enabling the translation of two proteins from one open reading frame and, in that manner, generating an intracellular fluorescence signal. This improved setup is based on a 2A sequence that is encoded between the protein to be displayed and a gene for green fluorescent protein (GFP). The intracellular GFP fluorescence signal of yeast cells correlates with full‐length protein presentation and omits the need for the immunofluorescence detection of epitope tags. For method validation, shark‐derived IgNAR variable domains (vNAR) were subjected to affinity maturation using the 2A‐GFP system. Yeast library screening of full‐length vNAR variants which were detected via GFP expression yielded the same high‐affinity binder that had previously been isolated by our group using the conventional epitope tag‐based display format. The presented method obviates the need for additional immunofluorescence cell staining, offering an easy and cost‐friendly alternative to conventional epitope tag detections.     

Comparison of protein quantification and extraction methods suitable for E. coli cultures

Many different extraction and analysis methods exist to determine the protein fraction of microbial cells. For metabolic engineering purposes it is important to have precise and accurate measurements. Therefore six different protein extraction protocols and seven protein quantification methods were tested and compared. Comparison was based on the reliability of the methods and boxplots of the normalized residuals. Some extraction techniques (SDS/chloroform and toluene) should never be used: the measurements are neither precise nor accurate. Bugbuster extraction combined with UV280 quantification gives the best results, followed by the combinations Sonication-UV280 and EasyLyse-UV280. However, if one does not want to use the quantification method UV280, one can opt to use Bugbuster, EasyLyse or sonication extraction combined with any quantification method with exception of the EasyLyse-BCA_P and Sonication-BCA_P combinations.     

18O labeling over a coffee break: a rapid strategy for quantitative proteomics

Proteomics-based quantification methods for differential protein expression measurements are among the most important and challenging techniques in the field of mass spectrometry. Though numerous quantification methods have been established, no method meets all the demands for measuring accurate protein expression levels. Of the various relative quantification methods by isotopic labeling, (18)O labeling method has been shown to be simple, specific, cost-effective and applicable to a wide range of analyses. However, some researchers refrain from using the method due to long incubation periods required during the labeling process. To address this problem, we demonstrate a method by which the labeling procedure can be completed in 15 min. We digested and labeled samples using immobilized trypsin on micro-spin columns to speed up the enzyme-mediated oxygen substitution, thereby completing the labeling process within 15 min with high labeling efficiency. We demonstrate the efficiency and accuracy of the method using a four protein mixture and whole cell lysate from rat vascular endothelial cells.