Large-scale Protein Quantification Research Articles

Differential Tissue Abundance of Membrane-Bound Drug Metabolizing Enzymes and Transporter Proteins by Global Proteomics.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are useful for scaling in vitro and animal data to humans for accurate prediction and interpretation of drug clearance and toxicity. Targeted DMET proteomics that relies on synthetic stable isotope-labeled surrogate peptides as calibrators is routinely used for the quantification of selected proteins; however, the technique is limited to the quantification of a small number of proteins. Although the global proteomics-based total protein approach (TPA) is emerging as a better alternative for large-scale protein quantification, the conventional TPA does not consider differential sequence coverage by identifying unique peptides across proteins. Here, we optimized the TPA approach by correcting protein abundance data by the sequence coverage, which was applied to quantify 54 DMETs for characterization of 1) differential tissue DMET abundance in the human liver, kidney, and intestine, and 2) interindividual variability of DMET proteins in individual intestinal samples (n = 13). Uridine diphosphate-glucuronosyltransferase 2B7 (UGT2B7), microsomal glutathione S-transferases (MGST1, MGST2, and MGST3) carboxylesterase 2 (CES2), and multidrug resistance-associated protein 2 (MRP2) were expressed in all three tissues, whereas, as expected, four cytochrome P450s (CYP3A4, CYP3A5, CYP2C9, and CYP4F2), UGT1A1, UGT2B17, CES1, flavin-containing monooxygenase 5, MRP3, and P-glycoprotein were present in the liver and intestine. The top three DMET proteins in individual tissues were: CES1>CYP2E1>UGT2B7 (liver), CES2>UGT2B17>CYP3A4 (intestine), and MGST1>UGT1A6>MGST2 (kidney). CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2 showed high interindividual variability in the intestine. These data are relevant for enhancing in vitro to in vivo extrapolation of drug absorption and disposition and can be used to enhance the accuracy of physiologically based pharmacokinetic prediction of systemic and tissue concentration of drugs. SIGNIFICANCE STATEMENT: This study quantified the abundance and compositions of drug-metabolizing enzymes and transporters in pooled human liver, intestine, and kidney microsomes as well as individual intestinal microsomes using an optimized global proteomics approach. The data revealed large intertissue differences in the abundance of these proteins and high intestinal interindividual variability in the levels of cytochrome P450s (e.g., CYP3A4 and CYP3A5), uridine diphosphate-glucuronosyltransferase 2B17, carboxylesterase 2, and microsomal glutathione S-transferase 2. These data are applicable for the prediction of first-pass metabolism and tissue-specific drug clearance.

CancerProteome: a resource to functionally decipher the proteome landscape in cancer.

Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.

Perspectives and opinions from scientific leaders on the evolution of data-independent acquisition for quantitative proteomics and novel biological applications

The methodology of data-independent acquisition (DIA) within mass spectrometry (MS) was developed into a method of choice for quantitative proteomics, to capture the depth and dynamics of biological systems, and to perform large-scale protein quantification. DIA provides deep quantitative proteome coverage with high sensitivity, high quantitative accuracy, and excellent acquisition-to-acquisition reproducibility. DIA workflows benefited from the latest advancements in MS instrumentation, acquisition/isolation schemes, and computational algorithms, which have further improved data quality and sample throughput. This powerful DIA-MS scan type selects all precursor ions contained in pre-determined isolation windows, and systematically fragments all precursor ions from each window by tandem mass spectrometry, subsequently covering the entire precursor ion m/z range. Comprehensive proteolytic peptide identification and label-free quantification are achieved post-acquisition using spectral library-based or library-free approaches. To celebrate the > 10 years of success of this quantitative DIA workflow, we interviewed some of the scientific leaders who have provided crucial improvements to DIA, to the quantification accuracy and proteome depth achieved, and who have explored DIA applications across a wide range of biology. We discuss acquisition strategies that improve specificity using different isolation schemes, and that reduce complexity by combining DIA with sophisticated chromatography or ion mobility separation. Significant leaps forward were achieved by evolving data processing strategies, such as library-free processing, and machine learning to interrogate data more deeply. Finally, we highlight some of the diverse biological applications that use DIA-MS methods, including large-scale quantitative proteomics, post-translational modification studies, single-cell analysis, food science, forensics, and small molecule analysis.

Abstract 119: Multi-Omics Investigation of Cardiomyocyte-to-Fibroblast Crosstalk in Human iPSC Models

Introduction: Cardiac cells communicate with each other in part through secreted proteins known as cardiokines. The total repertoire of proteins secreted by cardiac cells is unknown. We aim to apply human iPSC models and large-scale multi-omics to identify secreted proteins and the crosstalk signals they mediate. Method: We optimized an experimental protocol to recover and identify cell-specific secreted proteins from human iPSC-cardiac cells. Human iPSCs were differentiated into cardiomyocyte (CM) and endothelial cells (EC) using established protocols, after which secreted proteins were extracted from the conditioned medium for analysis. We combined multiplexed aptamer-based large-scale protein quantification platform and high-resolution mass spectrometry to identify secreted proteins from iPSC-CM, iPSC-ECs, and primary ventricular fibroblasts. An in-silico filter was implemented to prioritize bona fide cardiokines over proteins externalized by passive lysis. Result: We identified 146 candidate cardiokines at 1% false discovery rate, including cell-specific cardiokines as well as a common core secretome of three cardiac cells. We analyzed the data to identify potential signals secreted by cardiomyocytes to mediate fibroblast function, using a ligand-receptor model based on proteomics and single-cell transcriptomics data to prioritize cardiokines secreted by iPSC-CMs and which bind to fibroblast-expressed receptors. To examine their roles in fibrosis regulation, we selected three candidate cardiokines (PLAU, FGF7, and CXCL12) for verification using immunodetection, and exposed their recombinant proteins at multiple concentrations to human ventricular fibroblasts. The results nominated cardiokine-specific effects on recipient cell gene expression including evidence of modulation of fibroblast transcription and translation pathways. Conclusion: We demonstrate a multi-omics approach in iPSC models to explore the large-scale human secretome of multiple cardiac cell types. The approach holds promise for identifying disease markers and understanding intercellular communication in cardiac development and diseases.

Estimating the Reliability of Low-Abundant Signals and Limited Replicate Measurements through MS2 Peak Area in SWATH.

Sequential windows acquisition of all theoretical fragment ions mass spectrometry (SWATH-MS) provides large-scale protein quantification with high accuracy and selectivity. Nevertheless, reliable quantification of low-abundant signals in complex samples remains challenging, as recently illustrated in a multicenter benchmark study of different label-free software tools. Here, the SWATH Replicates Analysis 2.0 template from Sciex is used to highlight that the relationship between the MS2 peak area and the variability can be described by a function. This functional relationship appears to be largely insensitive to variation in samples or acquisition conditions, suggesting a device-intrinsic property. By using a power regression, it is shown that the MS2 peak area can be used to predict the quantification repeatability without relying on replicate injections, thus contributing to high-throughput confident quantification of low-abundant signals with SWATH-MS.

Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry

Quantitative proteomics employing mass spectrometry is an indispensable tool in life science research. Targeted proteomics has emerged as a powerful approach for reproducible quantification but is limited in the number of proteins quantified. SWATH-mass spectrometry consists of data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, and selectivity) of targeted proteomics at large scale. While previous SWATH-mass spectrometry studies have shown high intra-lab reproducibility, this has not been evaluated between labs. In this multi-laboratory evaluation study including 11 sites worldwide, we demonstrate that using SWATH-mass spectrometry data acquisition we can consistently detect and reproducibly quantify >4000 proteins from HEK293 cells. Using synthetic peptide dilution series, we show that the sensitivity, dynamic range and reproducibility established with SWATH-mass spectrometry are uniformly achieved. This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification.

Current trends in quantitative proteomics - an update.

Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.

Label-Free Phosphoproteomic Approach for Kinase Signaling Analysis.

Phosphoproteomics is a powerful platform for the unbiased profiling of kinase-driven signaling pathways. Quantitation of phosphorylation can be performed by means of either labeling or label-free mass spectrometry (MS) methods. Because of their simplicity and universality, label-free methodology is gaining acceptance and popularity in molecular biology research. Analytical workflows for label-free quantification of phosphorylation, however, need to overcome several hurdles for the technique to be accurate and precise. These include the use of biochemical extraction procedures that efficiently and reproducibly isolate phosphopeptides from complex peptide matrices and an analytical strategy that can cope with missing MS/MS phosphopeptide spectra in a subset of the samples being compared. Testing the accuracy of the developed workflows is an essential prerequisite in the analysis of small molecules by MS, and this is achieved by constructing calibration curves to demonstrate linearity of quantification for each analyte. This level of analytical rigor is rarely shown in large-scale quantification of proteins using either label-based or label-free techniques. In this chapter we show an approach to test linearity of quantification of each phosphopeptide quantified by liquid chromatography (LC)-MS without the need to synthesize standards or label proteins. We further describe the appropriate sample handling techniques required for the reproducible recovery of phosphopeptides and explore the essential algorithmic features that enable the handling of missing MS/MS spectra and thus make label-free data suitable for such analyses. The combined technology described in this chapter expands the applicability of phosphoproteomics to questions not previously tractable with other methodologies.

Global Protein Expression Profiling of Zebrafish Organs Based on in Vivo Incorporation of Stable Isotopes

The zebrafish has become a widely used model organism employed for developmental studies, live cell imaging, and genetic screens. High-resolution transcriptional profiles of different developmental and adult stages of the fish and of its various organs were generated, which are readily accessible via the ZFIN database. In contrast, quantitative proteomic studies of zebrafish organs are still in their infancy. Here, we used the SILAC (stable isotope labeling by amino acids in cell culture) zebrafish as a "spike-in" reference to generate a protein atlas of nine organs including gills, brain, heart, muscle, liver, spleen, skin, swim bladder, and testis. Single-shot 4 h LC gradients coupled to a Quadrupole-Orbitrap (QExactive) instrument allowed identification of over 5000 proteins in less than 5 days, of which more than 70% were quantified in triplicate. Identified proteins were subjected to BLAST searches and Gene Ontology classification to improve annotation of zebrafish proteins and obtain insights into potential functions. Comparison to mouse tissue proteome data sets revealed differences and similarities in the protein composition of zebrafish versus mouse organs. We reason that the data set will be helpful for the proteomic characterization of zebrafish organs and identification of tissue-specific proteins that might serve as biomarkers. Our approach provides a complementary view into the biochemistry of zebrafish models and will assist large-scale protein quantification in zebrafish disease models.

Quantification of proteins by label-free LC-MS(E.).

Quantitative proteomics by LC-MS/MS is a widely used approach for quantifying a significant portion of any complex proteome. Among the different techniques used for this purpose, one is by use of Data Independent Acquisition (DIA). We present a descriptive protocol for label-free quantitation of proteins by one DIA method termed LC-MS(E), which facilitates large-scale quantification of proteins without the need for isotopic labelling and with no theoretical limit to the number of samples included in an experiment.

A Fast Workflow for Identification and Quantification of Proteomes

The current in-depth proteomics makes use of long chromatography gradient to get access to more peptides for protein identification, resulting in covering of as many as 8000 mammalian gene products in 3 days of mass spectrometer running time. Here we report a fast sequencing (Fast-seq) workflow of the use of dual reverse phase high performance liquid chromatography - mass spectrometry (HPLC-MS) with a short gradient to achieve the same proteome coverage in 0.5 day. We adapted this workflow to a quantitative version (Fast quantification, Fast-quan) that was compatible to large-scale protein quantification. We subjected two identical samples to the Fast-quan workflow, which allowed us to systematically evaluate different parameters that impact the sensitivity and accuracy of the workflow. Using the statistics of significant test, we unraveled the existence of substantial falsely quantified differential proteins and estimated correlation of false quantification rate and parameters that are applied in label-free quantification. We optimized the setting of parameters that may substantially minimize the rate of falsely quantified differential proteins, and further applied them on a real biological process. With improved efficiency and throughput, we expect that the Fast-seq/Fast-quan workflow, allowing pair wise comparison of two proteomes in 1 day may make MS available to the masses and impact biomedical research in a positive way.

The effect of peptide adsorption on signal linearity and a simple approach to improve reliability of quantification

Peptide quantification using MS often relies on the comparison of peptide signal intensities between different samples, which is based on the assumption that observed signal intensity has a linear relationship to peptide abundance. A typical proteomics experiment is subject to multiple sources of variance, so we focussed here on properties affecting peptide linearity under simple, well-defined conditions. Peptides from a standard protein digest were analysed by multiple reaction monitoring (MRM) MS to determine peptide linearity over a range of concentrations. We show that many peptides do not display a linear relationship between signal intensity and amount under standard conditions. Increasing the organic content of the sample solvent increased peptide linearity by increasing the accuracy and precision of quantification, which suggests that peptide non-linearity is due to concentration-dependent surface adsorption. Using multiple peptides at various dilutions, we show that peptide non-linearity is related to observed retention time and predicted hydrophobicity. Whereas the effect of adsorption on peptide storage has been investigated previously, here we demonstrate the deleterious effect of peptide adsorption on the quantification of fresh samples, highlight aspects of sample preparation that can minimise the effect, and suggest bioinformatic approaches to enhance the selection of peptides for quantification.Biological significanceAccurate quantification is central to many aspects of science, especially those examining dynamic processes or comparing molecular stoichiometries. In biological research, the quantification of proteins is an important yet challenging objective. Large-scale quantification of proteins using MS often depends on the comparison of peptide intensities with only a single-level calibrant (as in stable isotope labelling and absolute quantification approaches) or no calibrants at all (as in label-free approaches). For these approaches to be reliable, it is essential that the relationship between signal intensity and concentration is linear, without a significant intercept. Here, we show that peptide adsorption can severely affect this relationship, even under controlled conditions, and we demonstrate simple methodologies that can be used to moderate and predict this effect. These findings thus enable the quantification of proteins with increased robustness and reliability.

Proteomic and phosphoproteomic comparison of human ES and iPS cells

Combining high-mass-accuracy mass spectrometry, isobaric tagging and software for multiplexed, large-scale protein quantification, we report deep proteomic coverage of four human embryonic stem cell and four induced pluripotent stem cell lines in biological triplicate. This 24-sample comparison resulted in a very large set of identified proteins and phosphorylation sites in pluripotent cells. The statistical analysis afforded by our approach revealed subtle but reproducible differences in protein expression and protein phosphorylation between embryonic stem cells and induced pluripotent cells. Merging these results with RNA-seq analysis data, we found functionally related differences across each tier of regulation. We also introduce the Stem Cell-Omics Repository (SCOR), a resource to collate and display quantitative information across multiple planes of measurement, including mRNA, protein and post-translational modifications.

The Green Proteome: Challenges in Plant Proteomics

An organism is an expression of its underlying molecular composition that reacts and responds to a variety of stimuli. Central to this response are proteins, which undertake a multitude of functional and structural roles throughout an organism. The ability to readily identify and analyze protein populations (proteomics) represents a recent technological advance in the field of biological sciences. Although the term proteomics was coined in the mid 1990’s (Wilkins, 2009), the technique was significantly hampered by a lack of cohesive genomics data and advanced instrumentation. The field of proteomics has expanded rapidly in recent years due to the completion of genomes, access and improvements to mass spectrometers and the development of new techniques all of which have increased its role in biological research (Han et al., 2008).

Advances in quantitative proteomics

Large-scale protein quantification has become a major proteomics application in many areas of biological and medical research. During the past years, different techniques have been developed, including gel-based such as differential in-gel electrophoresis (DIGE) and liquid chromatography-based such as isotope labeling and label-free quantification. These quantitative proteomics tools hold significant promise for biomarker discovery, diagnostic and therapeutic applications. They are also important for research in functional genomics and systems biology towards basic understanding of molecular networks and pathway interactions. In this review, we summarize current technologies in quantitative proteomics and discuss recent applications of the technologies.

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.

In vivo measurement of synthesis rate of multiple plasma proteins in humans

Advances in quantitative proteomics have facilitated the measurement of large-scale protein quantification, which represents net changes in protein synthesis and breakdown. However, measuring the rate of protein synthesis is the only way to determine the translational rate of gene transcripts. Here, we report a technique to measure the rate of incorporation of amino acids from ingested protein labeled with stable isotope into individual plasma proteins. This approach involves three steps: 1) production of stable isotope-labeled milk whey protein, oral administration of this intrinsically labeled protein, and subsequent collection of blood samples; 2) fractionation of the plasma and separation of the individual plasma proteins by a combination of anion exchange high-pressure liquid chromatography and gel electrophoresis; and 3) identification of individual plasma proteins by tandem mass spectrometry and measurement of stable isotopic enrichment of these proteins by gas chromatography-mass spectrometry. This method allowed the measurement of the fractional synthesis rate (FSR) of 29 different plasma proteins by using the same precursor pool. We noted a 30-fold difference in FSR of different plasma proteins with a wide range of physiological functions. This approach offers a tremendous opportunity to study the regulation of plasma proteins in humans in many physiological and pathological states.