Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides

Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets

High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool "Normalyzer". It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly influences the outcome of downstream quantitative comparisons. Normalyzer is an R package and can be used locally or through the online implementation at http://quantitativeproteomics.org/normalyzer .

Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN)

Researchers are increasingly turning to label-free MS1 intensity-based quantification strategies within HPLC-ESI-MS/MS workflows to reveal biological variation at the molecule level. Unfortunately, HPLC-ESI-MS/MS workflows using these strategies produce results with poor repeatability and reproducibility, primarily due to systematic bias and complex variability. While current global normalization strategies can mitigate systematic bias, they fail when faced with complex variability stemming from transient stochastic events during HPLC-ESI-MS/MS analysis. To address these problems, we developed a novel local normalization method, proximity-based intensity normalization (PIN), based on the analysis of compositional data. We evaluated PIN against common normalization strategies. PIN outperforms them in dramatically reducing variance and in identifying 20% more proteins with statistically significant abundance differences that other strategies missed. Our results show the PIN enables the discovery of statistically significant biological variation that otherwise is falsely reported or missed.

Improved normalization of systematic biases affecting ion current measurements in label-free proteomics data

Normalization is an important step in the analysis of quantitative proteomics data. If this step is ignored, systematic biases can lead to incorrect assumptions about regulation. Most statistical procedures for normalizing proteomics data have been borrowed from genomics where their development has focused on the removal of so-called 'batch effects.' In general, a typical normalization step in proteomics works under the assumption that most peptides/proteins do not change; scaling is then used to give a median log-ratio of 0. The focus of this work was to identify other factors, derived from knowledge of the variables in proteomics, which might be used to improve normalization. Here we have examined the multi-laboratory data sets from Phase I of the NCI's CPTAC program. Surprisingly, the most important bias variables affecting peptide intensities within labs were retention time and charge state. The magnitude of these observations was exaggerated in samples of unequal concentrations or "spike-in" levels, presumably because the average precursor charge for peptides with higher charge state potentials is lower at higher relative sample concentrations. These effects are consistent with reduced protonation during electrospray and demonstrate that the physical properties of the peptides themselves can serve as good reporters of systematic biases. Between labs, retention time, precursor m/z, and peptide length were most commonly the top-ranked bias variables, over the standardly used average intensity (A). A larger set of variables was then used to develop a stepwise normalization procedure. This statistical model was found to perform as well or better on the CPTAC mock biomarker data than other commonly used methods. Furthermore, the method described here does not require a priori knowledge of the systematic biases in a given data set. These improvements can be attributed to the inclusion of variables other than average intensity during normalization.

Gaussian process regression model for normalization of LC-MS data using scan-level information

Background

Differences in sample collection, biomolecule extraction, and instrument variability introduce bias to data generated by liquid chromatography coupled with mass spectrometry (LC-MS). Normalization is used to address these issues. In this paper, we introduce a new normalization method using the Gaussian process regression model (GPRM) that utilizes information from individual scans within an extracted ion chromatogram (EIC) of a peak. The proposed method is particularly applicable for normalization based on analysis order of LC-MS runs. Our method uses measurement variabilities estimated through LC-MS data acquired from quality control samples to correct for bias caused by instrument drift. Maximum likelihood approach is used to find the optimal parameters for the fitted GPRM. We review several normalization methods and compare their performance with GPRM.

Results

To evaluate the performance of different normalization methods, we consider LC-MS data from a study where metabolomic approach is utilized to discover biomarkers for liver cancer. The LC-MS data were acquired by analysis of sera from liver cancer patients and cirrhotic controls. In addition, LC-MS runs from a quality control (QC) sample are included to assess the run to run variability and to evaluate the ability of various normalization method in reducing this undesired variability. Also, ANOVA models are applied to the normalized LC-MS data to identify ions with intensity measurements that are significantly different between cases and controls.

Conclusions

One of the challenges in using label-free LC-MS for quantitation of biomolecules is systematic bias in measurements. Several normalization methods have been introduced to overcome this issue, but there is no universally applicable approach at the present time. Each data set should be carefully examined to determine the most appropriate normalization method. We review here several existing methods and introduce the GPRM for normalization of LC-MS data. Through our in-house data set, we show that the GPRM outperforms other normalization methods considered here, in terms of decreasing the variability of ion intensities among quality control runs.

Data processing methods and quality control strategies for label-free LC-MS protein quantification

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Neurotoxin-induced neuropeptide perturbations in striatum of neonatal rats

The cyanobacterial toxin β-N-methylamino-l-alanine (BMAA) is suggested to play a role in neurodegenerative disease. We have previously shown that although the selective uptake of BMAA in the rodent neonatal striatum does not cause neuronal cell death, exposure during the neonatal development leads to cognitive impairments in adult rats. The aim of the present study was to characterize the changes in the striatal neuropeptide systems of male and female rat pups treated neonatally (postnatal days 9-10) with BMAA (40-460 mg/kg). The label-free quantification of the relative levels of endogenous neuropeptides using mass spectrometry revealed that 25 peptides from 13 neuropeptide precursors were significantly changed in the rat neonatal striatum. The exposure to noncytotoxic doses of BMAA induced a dose-dependent increase of neurosecretory protein VGF-derived peptides, and changes in the relative levels of cholecystokinin, chromogranin, secretogranin, MCH, somatostatin and cortistatin-derived peptides were observed at the highest dose. In addition, the results revealed a sex-dependent increase in the relative level of peptides derived from the proenkephalin-A and protachykinin-1 precursors, including substance P and neurokinin A, in female pups. Because several of these peptides play a critical role in the development and survival of neurons, the observed neuropeptide changes might be possible mediators of BMAA-induced behavioral changes. Moreover, some neuropeptide changes suggest potential sex-related differences in susceptibility toward this neurotoxin. The present study also suggests that neuropeptide profiling might provide a sensitive characterization of the BMAA-induced noncytotoxic effects on the developing brain.

Quantitative peptidomics for discovery of circadian-related peptides from the rat suprachiasmatic nucleus

In mammals the suprachiasmatic nucleus (SCN), the master circadian clock, is sensitive to light input via the optic chiasm and synchronizes many daily biological rhythms. Here we explore variations in the expression levels of neuropeptides present in the SCN of rats using a label-free quantification approach that is based on integrating peak intensities between daytime, Zeitgeber time (ZT) 6, and nighttime, ZT 18. From nine analyses comparing the levels between these two time points, 10 endogenous peptides derived from eight prohormones exhibited significant differences in their expression levels (adjusted p-value <0.05). Of these, seven peptides derived from six prohormones, including GRP, PACAP, and CART, exhibited ≥ 30% increases at ZT 18, and the VGRPEWWMDYQ peptide derived from proenkephalin A showed a >50% increase at nighttime. Several endogenous peptides showing statistically significant changes in this study have not been previously reported to alter their levels as a function of time of day, nor have they been implicated in prior functional SCN studies. This information on peptide expression changes serves as a resource for discovering unknown peptide regulators that affect circadian rhythms in the SCN.

Tools for label-free peptide quantification

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

Longitudinal characterization of the brain proteomes for the tg2576 amyloid mouse model using shotgun based mass spectrometry

Neurodegenerative disorders are often defined pathologically by the presence of protein aggregates, such as amyloid plaques composed of β-amyloid (Aβ) peptide in Alzheimer's disease. Such aggregates are the result of abnormal protein accumulation and may lead to neuronal dysfunction and cell death. In this study, APPSWE transgenic mice (Tg2576), which overexpress the Swedish mutated form of human amyloid precursor protein (APP), were used to study the brain proteome associated with amyloid plaque deposition. The major aim of the study was to map and compare the Tg2576 model brain proteome profiles during pathology progression using a shotgun approach based on label free quantification with mass spectrometry. Overall, 1085 proteins were identified and longitudinally quantified. Principal component analysis (PCA) showed the appearance of the pathology onset between twelve and fifteen months, correlating with sharp amyloid plaque accumulation within the same ages. Cluster analysis followed by protein-protein interaction analysis revealed an age-dependent decrease in mitochondrial protein expression. We identified 57 significantly affected mitochondrial proteins, several of which have been reported to alter expression in neurological diseases. We also found ten proteins that are upregulated early in the amyloid driven pathology progression with high confidence, some of which are directly involved in the onset of mitochondrial apoptosis and may represent potential markers for use in human neurological diseases prognosis. Our results further contribute to identifying common pathological pathways involved in both aging and progressive neurodegenerative disorders enhancing the understanding of disease pathogenesis.

Proteomics wants cRacker: automated standardized data analysis of LC-MS derived proteomic data

The large-scale analysis of thousands of proteins under various experimental conditions or in mutant lines has gained more and more importance in hypothesis-driven scientific research and systems biology in the past years. Quantitative analysis by large scale proteomics using modern mass spectrometry usually results in long lists of peptide ion intensities. The main interest for most researchers, however, is to draw conclusions on the protein level. Postprocessing and combining peptide intensities of a proteomic data set requires expert knowledge, and the often repetitive and standardized manual calculations can be time-consuming. The analysis of complex samples can result in very large data sets (lists with several 1000s to 100,000 entries of different peptides) that cannot easily be analyzed using standard spreadsheet programs. To improve speed and consistency of the data analysis of LC-MS derived proteomic data, we developed cRacker. cRacker is an R-based program for automated downstream proteomic data analysis including data normalization strategies for metabolic labeling and label free quantitation. In addition, cRacker includes basic statistical analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in editable graphic formats and in list files.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

Key issues in the acquisition and analysis of qualitative and quantitative mass spectrometry data for peptide-centric proteomic experiments

Proteomic technologies have matured to a level enabling accurate and reproducible quantitation of peptides and proteins from complex biological matrices. Analysis of samples as diverse as assembled protein complexes, whole cell lysates or sub-cellular proteomes from cell cultures, and direct analysis of animal and human tissues and fluids demonstrate the incredible versatility of the fundamental nature of the technique that forms the basis of most proteomic applications today (mass spectrometry). Determining the mass of biomolecules and their fragments or related products with high accuracy can convey a highly specific assay for detection and identification. Importantly, ion currents representative of these specifically identified analytes can be accurately quantified with the correct application of smart isobaric tagging chemistries, heavy and light isotopically derivatised samples or standards, or by careful application of workflows to compare unlabelled samples in so-called 'label-free' and targeted selected reaction monitoring experiments. In terms of exploring biology, a myriad of protein changes and modifications are being increasingly probed and quantified, including diverse chemical changes from relatively decisive modifications such as protein splicing and truncation, to more transient dynamic modifications such as phosphorylation, acetylation and ubiquitination. Proteomic workflows can be complex beasts and several key considerations to ensure effective applications have been outlined in the recent literature. The past year has witnessed the publication of several excellent reviews that thoroughly describe the fundamental principles underlying the state of the art. This review further elaborates on specific critical issues introduced by these publications and raises other important unaddressed considerations and new developments that directly impact on the effectiveness of proteomic technologies, in particular for, but not necessarily exclusive to peptide-centric experiments. These factors are discussed both in terms of qualitative analyses, including dynamic range and sampling issues, and developments to improve the translation of peptide fragmentation data into peptide and protein identities, as well as quantitative analyses, including data normalisation and the utility of ontology or functional annotation, the effects of modified peptides, and considered experimental design to facilitate the use of robust statistical methods.

Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics

Shotgun proteomics has become the standard proteomics technique for the large-scale measurement of protein abundances in biological samples. Despite quantitative proteomics has been usually performed using label-based approaches, label-free quantitation offers advantages related to the avoidance of labeling steps, no limitation in the number of samples to be compared, and the gain in protein detection sensitivity. However, since samples are analyzed separately, experimental design becomes critical. The exploration of spectral counting quantitation based on LC-MS presented here gathers experimental evidence of the influence of batch effects on comparative proteomics. The batch effects shown with spiking experiments clearly interfere with the biological signal. In order to minimize the interferences from batch effects, a statistical correction is proposed and implemented. Our results show that batch effects can be attenuated statistically when proper experimental design is used. Furthermore, the batch effect correction implemented leads to a substantial increase in the sensitivity of statistical tests. Finally, the applicability of our batch effects correction is shown on two different biomarker discovery projects involving cancer secretomes. We think that our findings will allow designing and executing better comparative proteomics projects and will help to avoid reaching false conclusions in the field of proteomics biomarker discovery.

Broad characterization of endogenous peptides in the tree shrew visual system

Endogenous neuropeptides, acting as neurotransmitters or hormones in the brain, carry out important functions including neural plasticity, metabolism and angiogenesis. Previous neuropeptide studies have focused on peptide-rich brain regions such as the striatum or hypothalamus. Here we present an investigation of peptides in the visual system, composed of brain regions that are generally less rich in peptides, with the aim of providing the first broad overview of peptides involved in mammalian visual functions. We target three important parts of the visual system: the primary visual cortex (V1), lateral geniculate nucleus (LGN) and superior colliculus (SC). Our study is performed in the tree shrew, a close relative of primates. Using a combination of data dependent acquisition and targeted LC-MS/MS based neuropeptidomics; we identified a total of 52 peptides from the tree shrew visual system. A total of 26 peptides, for example GAV and neuropeptide K were identified in the visual system for the first time. Out of the total 52 peptides, 27 peptides with high signal-to-noise-ratio (>10) in extracted ion chromatograms (EIC) were subjected to label-free quantitation. We observed generally lower abundance of peptides in the LGN compared to V1 and SC. Consistently, a number of individual peptides showed high abundance in V1 (such as neuropeptide Y or somatostatin 28) and in SC (such as somatostatin 28 AA1-12). This study provides the first in-depth characterization of peptides in the mammalian visual system. These findings now permit the investigation of neuropeptide-regulated mechanisms of visual perception.

Sample collection and handling considerations for peptidomic studies in whole saliva; implications for biomarker discovery

BACKGROUND

Proteomic studies in saliva have demonstrated its potential as a diagnostic biofluid, however the salivary peptidome is less studied. Here we study the effects of several sample collection and handling factors on salivary peptide abundance levels.

METHODS

Salivary peptides were isolated using an ultrafiltration device and analyzed by tandem mass spectrometry. A panel of 41 peptides common after various treatments were quantified and normalized. We evaluated the effects of freezing rate of the samples, nutritional status of the donors (fed vs. fasted), and room-temperature sample degradation on peptide abundance levels. Repeatability of our sample processing method and our instrumental analysis method were investigated.

RESULTS

Increased sample freezing rate produced higher levels of peptides. Donor nutritional status had no influence on the levels of measured peptides. No significant difference was detected in donors' saliva following 5, 10 and 15 min of room-temperature degradation. Sample processing and instrumental variability were relatively small, with median CVs of 9.6 and 6.6.

CONCLUSIONS

Peptide abundance levels in saliva are rather forgiving towards variations in sample handling and donor nutritional status. Differences in freezing methods may affect peptide abundance, so consistency in freezing samples is preferred. Our results are valuable for standardizing sample collection and handling methods for peptidomic-based biomarker studies in saliva.

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities.

Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery

Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

Biomarkers of disease and post-mortem changes - Heat stabilization, a necessary tool for measurement of protein regulation

This review focuses on post sampling changes and how the Stabilizor system has been used to control this natural biological process and potential implications on cancer-specific biomarkers due to post sampling changes. Tissue sampling is a major traumatic event that can have drastic effects within a very short timeframe at the molecular level [1] resulting in loss of sample quality due to post-mortem changes. A heat-stabilization technology, using the Stabilizor system, has been developed to quickly and permanently abolish the enzymatic activity that causes these changes post-sampling and so preserve sample quality. The Stabilizor system has been shown to give better sample quality when analyzing a variety of tissues in various proteomic workflows. In this paper we discuss the impact of using heat-stabilized tissue in different proteomic applications. Based on our observations regarding the overlap between commonly changing proteins and proteins found to change post-mortem we also highlight a group of proteins of particular interest in cancer studies.

Generic workflow for quality assessment of quantitative label-free LC-MS analysis

As high-resolution instruments are becoming standard in proteomics laboratories, label-free quantification using precursor measurements is becoming a viable option, and is consequently rapidly gaining popularity. Several software solutions have been presented for label-free analysis, but to our knowledge no conclusive studies regarding the sensitivity and reliability of each step of the analysis procedure has been described. Here, we use real complex samples to assess the reliability of label-free quantification using four different software solutions. A generic approach to quality test quantitative label-free LC-MS is introduced. Measures for evaluation are defined for feature detection, alignment and quantification. All steps of the analysis could be considered adequately performed by the utilized software solutions, although differences and possibilities for improvement could be identified. The described method provides an effective testing procedure, which can help the user to quickly pinpoint where in the workflow changes are needed.

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.

The emerging process of Top Down mass spectrometry for protein analysis: biomarkers, protein-therapeutics, and achieving high throughput

Top Down mass spectrometry (MS) has emerged as an alternative to common Bottom Up strategies for protein analysis. In the Top Down approach, intact proteins are fragmented directly in the mass spectrometer to achieve both protein identification and characterization, even capturing information on combinatorial post-translational modifications. Just in the past two years, Top Down MS has seen incremental advances in instrumentation and dedicated software, and has also experienced a major boost from refined separations of whole proteins in complex mixtures that have both high recovery and reproducibility. Combined with steadily advancing commercial MS instrumentation and data processing, a high-throughput workflow covering intact proteins and polypeptides up to 70 kDa is directly visible in the near future.

Neuropeptidomic analysis of the embryonic Japanese quail diencephalon

Background

Endogenous peptides such as neuropeptides are involved in numerous biological processes in the fully developed brain but very little is known about their role in brain development. Japanese quail is a commonly used bird model for studying sexual dimorphic brain development, especially adult male copulatory behavior in relation to manipulations of the embryonic endocrine system. This study uses a label-free liquid chromatography mass spectrometry approach to analyze the influence of age (embryonic days 12 vs 17), sex and embryonic day 3 ethinylestradiol exposure on the expression of multiple endogenous peptides in the developing diencephalon.

Results

We identified a total of 65 peptides whereof 38 were sufficiently present in all groups for statistical analysis. Age was the most defining variable in the data and sex had the least impact. Most identified peptides were more highly expressed in embryonic day 17. The top candidates for EE₂ exposure and sex effects were neuropeptide K (downregulated by EE₂ in males and females), gastrin-releasing peptide (more highly expressed in control and EE₂ exposed males) and gonadotropin-inhibiting hormone related protein 2 (more highly expressed in control males and displaying interaction effects between age and sex). We also report a new potential secretogranin-2 derived neuropeptide and previously unknown phosphorylations in the C-terminal flanking protachykinin 1 neuropeptide.

Conclusions

This study is the first larger study on endogenous peptides in the developing brain and implies a previously unknown role for a number of neuropeptides in middle to late avian embryogenesis. It demonstrates the power of label-free liquid chromatography mass spectrometry to analyze the expression of multiple endogenous peptides and the potential to detect new putative peptide candidates in a developmental model.

Quantitation in mass-spectrometry-based proteomics

Mass-spectrometry-based proteomics, the large-scale analysis of proteins by mass spectrometry, has emerged as a new technology over the last decade and become routine in many plant biology laboratories. While early work consisted merely of listing proteins identified in a given organ or under different conditions of interest, there is a growing need to apply comparative and quantitative proteomics strategies toward gaining novel insights into functional aspects of plant proteins and their dynamics. However, during the transition from qualitative to quantitative protein analysis, the potential and challenges will be tightly coupled. Several strategies for differential proteomics that involve stable isotopes or label-free comparisons and their statistical assessment are possible, each having specific strengths and limitations. Furthermore, incomplete proteome coverage and restricted dynamic range still impose the strongest limitations to data throughput and precise quantitative analysis. This review gives an overview of the current state of the art in differential proteomics and possible strategies in data processing.

A quantitative peptidomic analysis of peptides related to the endogenous opioid and tachykinin systems in nucleus accumbens of rats following naloxone-precipitated morphine withdrawal

We have applied a recently developed label-free mass spectrometry based peptidomic approach to identify and quantify a variety of endogenous peptides from rat nucleus accumbens following withdrawal in naloxone-precipitated, morphine-dependent rats of two separate strains. We focused on maturated, partially processed and truncated peptides derived from the peptide precursors proenkephalin, prodynorphin and preprotachykinin. The expression of several identified peptides was dependent on strain and was affected during morphine withdrawal.