Mass spectrometry label-free quantitative analysis of proteins

The dependence of the intensity of a mass spectrometric signal on protein concentration has been investigated using individual purified proteins and complex protein mixtures. Linearity of this dependence has been determined within the concentration ranges from 1 nM to 1 μM. In this range the dependence of the mass spectrometric signal intensity on concentration possesses a slope (tangent) characteristic for each protein studied. The efficacy of the method has been evaluated using the other methods (AQUA and ¹⁸O-labelling) employed in quantitative proteomics.     

Nonionic detergent phase extraction for the proteomic analysis of heart membrane proteins using label-free LC-MS

Heart diseases resulting in heart failure are among the leading causes of morbidity and mortality in the Western world and can result from either systemic disease (e.g., hypertensive heart disease, ischemic heart disease) or specific heart muscle disease (e.g., dilated cardiomyopathy/DCM). Subproteome analysis of such disease subsets affords a reduction in sample complexity, potentially revealing biomarkers of cardiac failure that would otherwise remain undiscovered in proteome wide studies. Label-free nanoscale LC-MS has been applied in this study to validate a Triton X-114-based phase enrichment method for cardiac membrane proteins. Annotation of the subcellular location combined with GRAVY score analysis indicates a clear separation between soluble and membrane-bound proteins with an enrichment of over 62% for this protein subset. LC-MS allowed confident identification and annotation of hydrophobic proteins in this control sample pilot study and demonstrates the power of the proposed technique to extract integral membrane-bound proteins. This approach should be applicable to a wider scale study of disease-associated changes in the cardiac membrane subproteome.     

Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis

BACKGROUND: Massive Parallel Sequencing methods (MPS) can extend and improve the knowledge obtained by conventional microarray technology, both for mRNAs and short non-coding RNAs, e.g. miRNAs. The processing methods used to extract and interpret the information are an important aspect of dealing with the vast amounts of data generated from short read sequencing. Although the number of computational tools for MPS data analysis is constantly growing, their strengths and weaknesses as part of a complex analytical pipe-line have not yet been well investigated. PRIMARY FINDINGS: A benchmark MPS miRNA dataset, resembling a situation in which miRNAs are spiked in biological replication experiments was assembled by merging a publicly available MPS spike-in miRNAs data set with MPS data derived from healthy donor peripheral blood mononuclear cells. Using this data set we observed that short reads counts estimation is strongly under estimated in case of duplicates miRNAs, if whole genome is used as reference. Furthermore, the sensitivity of miRNAs detection is strongly dependent by the primary tool used in the analysis. Within the six aligners tested, specifically devoted to miRNA detection, SHRiMP and MicroRazerS show the highest sensitivity. Differential expression estimation is quite efficient. Within the five tools investigated, two of them (DESseq, baySeq) show a very good specificity and sensitivity in the detection of differential expression. CONCLUSIONS: The results provided by our analysis allow the definition of a clear and simple analytical optimized workflow for miRNAs digital quantitative analysis.     

Metaprotein expression modeling for label-free quantitative proteomics

Background

Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.

Results

We describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides??a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.

Conclusions

Mass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.     

Design and analysis of quantitative differential proteomics investigations using LC-MS technology

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.     

Proton NMR quantitative profiling for quality assessment of greenhouse-grown tomato fruit

Tomato is an essential crop in terms of economic importance and nutritional quality. In France, the third most important region for tomato (Solanum lycopersicum L.) production is Aquitaine where the major part of production is now grown soilless under greenhouse conditions with harvest from March to November. Tomato fruit quality at harvest is a direct function of its metabolite content at that time. The aim of this work was to use a global approach to characterize changes in the fruit organoleptic quality at harvest under commercial culture conditions during an entire season for two varieties and two different fertilization practices (with or without recycling of the nutrient solution) for one variety. Absolute quantification data of 32 major compounds in fruit without seeds were obtained through untargeted (proton nuclear magnetic resonance, ¹H-NMR) quantitative profiling. These data were complemented by colorimetric analysis of ascorbate and total phenolics. They were analyzed with chemometric approaches. Principal component analysis (PCA) or partial least square analyses (PLS) revealed more discriminant metabolites for season than for variety and showed that nutrient solution recycling had very little effect on fruit composition. These tendencies were confirmed with univariate analyses. ¹H-NMR profiling complemented with colorimetric analyses therefore provided a diagnostic tool to follow the changes in organoleptic and nutritional quality of tomato. In addition the quantitative information generated will help to increase our knowledge on the mechanisms of plant response to environmental modifications. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters

Background  

Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins.     

A quantitative LC-MS/MS method for comparative analysis of capture-antibody affinity toward protein antigens

A mass spectrometry-based antibody selection procedure was developed to evaluate optimal 'capture' monoclonal antibodies that can be used in a variety of analytical measurement applications. The isotope-dilution liquid chromatography-tandem mass spectrometry (ID LC-MS/MS) methodology is based on the use of multiple-reaction monitoring of tryptic peptide fragments derived from protein antigens. A panel of monoclonal antibodies (mAb) was evaluated based on a quantitative determination of relative binding affinity to human cardiac troponin I following immunoprecipitation. Dissociation constants (K(d)) were determined for 'bound mAb-antigen' vs. 'unbound antigen' using non-linear regression analysis. Relative quantification of both antigen and antibody was based on the use of stable isotope-labeled synthetic peptides as internal standards. Optimal 'capture' mAbs were determined through evaluation of relative K(d) constants of all monitored peptide transitions. A panel of six pre-screened candidate capture mAbs was concluded to consist of two subsets of mAbs, each with statistically equivalent K(d) constants as determined using NIST Standard Reference Material (SRM) 2921 - Human Cardiac Troponin Complex. This ID LC-MS/MS method is shown to be capable of quantitatively differentiating mAbs based on relative binding affinities. Selection of optimal capture mAbs can be applied toward a number of analytical applications which require metrological traceability and unbiased quantification.     

Generic quality of life assessment in dementia patients: a prospective cohort study

Background  

Quality of life (QoL) is increasingly used to characterize the impact of disease and the efficacy of interventions.     

Targeted label-free quantitative analysis of secretory proteins from adipocytes in response to oxidative stress

Adipocytes are well known to release regulation factors associated with metabolic disorders. In particular, increased oxidative stress in adipocytes contributes to dysregulation of adipokine production. In this study, we applied relative quantitative proteomic analysis based on label-free multiple reaction monitoring (MRM) to discover biological changes of adipokines under oxidative stress. Among a total of 194 identified proteins, 8 proteins were selected and quantified between control and hydrogen peroxide (H₂O₂)-treated groups by label-free MRM quantification. The secretion levels of matrix metalloproteinase-2 (MMP-2), stromal cell-derived factor-1 (SDF-1, CXCL12), resistin, and complement factor D (CFD, adipsin) decreased, whereas the secretion levels of tissue inhibitor of metalloproteinase-2 (TIMP-2) and aldolase A increased. Here we suggest that our study with label-free quantitative analysis will contribute to the efficient quantitative analysis of targeted proteins in complex mixtures and specifically to a better understanding of changes of adipokines under oxidative stress.     

Identification of vascular breast tumor markers by laser capture microdissection and label-free LC-MS

Blood vessels in tumors frequently show abnormal characteristics, such as tortuous morphology or leakiness, but very little is known about protein expression in tumor vessels. In this study, we have used laser capture microdissection (LCM) to isolate microvessels from clinical samples of invasive ductal carcinoma (IDC), the most common form of malignant breast cancer, and from patient-matched adjacent nonmalignant tissue. This approach eliminates many of the problems associated with the heterogeneity of clinical tumor tissues by controlling for differences in protein expression between both individual patients and different cell types. Proteins from the microvessels were trypsinized and the resulting peptides were quantified by a label-free nanoLC-MS method. A total of 86 proteins were identified that are overexpressed in tumor vessels relative to vessels isolated from the adjacent nonmalignant tissue. These proteins include well-known breast tumor markers such as Periostin and Tenascin C but also proteins with lesser-known or emerging roles in breast cancer and tumor angiogenesis (i.e., Serpin H1, Clic-1, and Transgelin 2). We also identified 40 proteins that were relatively under-expressed in IDC tumor vessels, including several components of the basement membrane whose lower expression could be responsible for weakening tumor vessels. Lastly, we show that a subset of 29 proteins, derived from our list of differentially expressed proteins, is able to predict survival in three publicly available clinical breast cancer microarray data sets, which suggests that this subset of proteins likely plays a functional role in cancer progression and outcome.     

Rapid and quantitative assessment of cell quality, identity, and functionality for cell-based assays using real-time cellular analysis

Strict quality control of cells is required for the standardization and interpretation of results in all areas of cell-based research, especially in drug discovery. Real-time cellular analysis using electrical impedance as a readout offers a rapid and highly reproducible method for quality control as it provides a quantitative measure of overall cell morphology and growth. In a case study, the authors demonstrate that samples of a single cell line obtained from several different labs show clear differences in their impedance profiles when compared with the corresponding standard cell line. A number of kinetic parameters were derived from the impedance profiles and used to quantify the differences among these cell lines. Our findings indicate that this methodology can detect cell line differences including mix-ups or contaminations, genetic alterations, and potential epigenetic changes occurring during passaging, all of which can occur in the time scale of a screening campaign. Finally, we provide evidence that these impedance profile differences can be predictive of different outcomes in cell-based functional assays for the effects of small molecules on otherwise seemingly identical cell lines.     

An integrated computational workflow for efficient and quantitative modeling of renin inhibitors

A new integrated computational workflow that couples the strength of the molecular overlay methods to achieve rapid and automated alignments along with 3D-QSAR techniques like CoMFA and CoMSIA for quantitative binding affinity prediction is presented. The results obtained from such techniques are compared with rule-based Topomer CoMFA method, where possible. The developed 3D-QSAR models were prospectively used to predict the affinities of new compounds designed through R-group deconvolution starting from the core chemical scaffold and subsequent virtual combinatorial library enumeration. The general applicability of the seamless in silico modeling workflow is demonstrated using several datasets reported for small molecule inhibitors of renin.     

Biomedical applications of ion mobility-enhanced data-independent acquisition-based label-free quantitative proteomics

Mass spectrometry-based proteomics greatly benefited from recent improvements in instrument performance and the development of bioinformatics solutions facilitating the high-throughput quantification of proteins in complex biological samples. In addition to quantification approaches using stable isotope labeling, label-free quantification has emerged as the method of choice for many laboratories. Over the last years, data-independent acquisition approaches have gained increasing popularity. The integration of ion mobility separation into commercial instruments enabled researchers to achieve deep proteome coverage from limiting sample amounts. Additionally, ion mobility provides a new dimension of separation for the quantitative assessment of complex proteomes, facilitating precise label-free quantification even of highly complex samples. The present work provides a thorough overview of the combination of ion mobility and data-independent acquisition-based label-free quantification LC-MS and its applications in biomedical research.     

Piezoelectric quartz crystal based label-free analysis for allergy disease

This work presents a piezoelectric (Pz) quartz crystal based label-free quantification of total IgE and allergen-specific IgE in human sera for allergy testing. An evaluation of the different brands of crystals was first initiated with respect to variability in mass sensitivity, frequency measurement reliability and stability, and surface roughness. Thereafter, for total IgE quantification. a direct assay format was adopted. By means of thioctic acid (TA) and coupling reagents, anti-human IgE antibodies were immobilized on AT-cut Pz crystals (10 MHz). The modified crystals could detect serum IgE directly corresponding to a downward frequency shift. The results showed that silver-coated crystals as compared with their gold-coated counterparts provided approximately 1.5 times higher mass detection sensitivity for total IgE in the range of 5-300 IU/ml with a linear regression line, y = 1.8957 x + 1.5603, R2 = 0.995. For the detection of allergen-specific IgE, a sandwiched assay format was used. As the allergen-modified sensor surface captured various classes of associated antibodies (IgE, IgG, etc) and interfering serum proteins as well, the initial frequency shift downwards caused by sera sample incubation would not be proportional to specific IgE levels. Thus, following sample incubation, a second incubation step with secondary anti-human IgE was added to recognize IgE from other bound substances. The frequency shift after secondary antibody binding reflected the amount of allergen-specific IgE proportionally. Compared with 10 MHz crystals, the 20 MHz counterparts provided approximately four times higher mass detection sensitivity for allergen specific IgE in the range of 0.15-17.5 IU/ml with a linear regression line, y = 50.525 x + 107.777, R2 = 0.954. Total IgE and allergen specific IgE assay results of real patients' sera using the Pz sensors agreed well with those obtained by commercially available test kits with correlation coefficient 0.96-0.98. The possibility of regenerating the quartz crystals for further re-use was also dealt with.     

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements

Background  

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.     

A microfluidic-based electrochemical biochip for label-free diffusion-restricted DNA hybridization analysis

DNA hybridization detection in microfluidic devices can reduce sample volumes, processing times, and can be integrated with other measurements. However, as device footprints decrease and their complexity increase, the signal-to-noise ratio in these systems also decreases and the sensitivity is thereby compromised. Device miniaturization produces distinct properties and phenomena with greater influence at the micro-scale than at the macro-scale. Here, a diffusion-restriction model was applied to a miniaturized biochip nanovolume reactor to accurately characterize DNA hybridization events that contribute to shifts in both charge transfer resistance and diffusional resistance. These effects are shown to play a significant role in electrochemical impedance spectroscopy (EIS) analyses at these length scales. Our highly functional microfluidic biosensor enables the detection of ssDNA targets selectively, with a calculated detection limit of 3.8 nM, and cross-reactivity of 13% following 20 min incubation with the target. This new biosensing approach can be further modeled and tested elucidating diffusion behavior in miniaturized devices and improving the performance of biosensors.     

Stoichiometry of chromatin-associated protein complexes revealed by label-free quantitative mass spectrometry-based proteomics

Many cellular proteins assemble into macromolecular protein complexes. The identification of protein–protein interactions and quantification of their stoichiometry is therefore crucial to understand the molecular function of protein complexes. Determining the stoichiometry of protein complexes is usually achieved by mass spectrometry-based methods that rely on introducing stable isotope-labeled reference peptides into the sample of interest. However, these approaches are laborious and not suitable for high-throughput screenings. Here, we describe a robust and easy to implement label-free relative quantification approach that combines the detection of high-confidence protein–protein interactions with an accurate determination of the stoichiometry of the identified protein–protein interactions in a single experiment. We applied this method to two chromatin-associated protein complexes for which the stoichiometry thus far remained elusive: the MBD3/NuRD and PRC2 complex. For each of these complexes, we accurately determined the stoichiometry of the core subunits while at the same time identifying novel interactors and their stoichiometry.