An approach towards method development for untargeted urinary metabolite profiling in metabonomic research using UPLC/QToF MS

The application of LC-MS for untargeted urinary metabolite profiling in metabonomic research has gained much interest in recent years. However, the effects of varying sample pre-treatments and LC conditions on generic metabolite profiling have not been studied. We aimed to evaluate the effects of varying experimental conditions on data acquisition in untargeted urinary metabolite profiling using UPLC/QToF MS. In-house QC sample clustering was used to monitor the performance of the analytical platform. In terms of sample pre-treatment, results showed that untreated filtered urine yielded the highest number of features but dilution with methanol provided a more homogenous urinary metabolic profile with less variation in number of features and feature intensities. An increased cycle time with a lower flow rate (400mul/min vs 600mul/min) also resulted in a higher number of features with less variability. The step elution gradient yielded the highest number of features and the best chromatographic resolution among three different elution gradients tested. The maximum retention time and mass shift were only 0.03min and 0.0015Da respectively over 600 injections. The analytical platform also showed excellent robustness as evident by tight QC sample clustering. To conclude, we have investigated LC conditions by studying variability and repeatability of LC-MS data for untargeted urinary metabolite profiling.     

A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry

Background

Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study.

Results

We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools.

Conclusions

AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0376-0) contains supplementary material, which is available to authorized users.     

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.     

An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data

Over the past decade, a series of experimental strategies for mass spectrometry based quantitative proteomics and corresponding computational methodology for the processing of the resulting data have been generated. We provide here an overview of the main quantification principles and available software solutions for the analysis of data generated by liquid chromatography coupled to mass spectrometry (LC-MS). Three conceptually different methods to perform quantitative LC-MS experiments have been introduced. In the first, quantification is achieved by spectral counting, in the second via differential stable isotopic labeling, and in the third by using the ion current in label-free LC-MS measurements. We discuss here advantages and challenges of each quantification approach and assess available software solutions with respect to their instrument compatibility and processing functionality. This review therefore serves as a starting point for researchers to choose an appropriate software solution for quantitative proteomic experiments based on their experimental and analytical requirements.     

Serum Metabolomics of Slow vs. Rapid Motor Progression Parkinson’s Disease: a Pilot Study

Progression of Parkinson’s disease (PD) is highly variable, indicating that differences between slow and rapid progression forms could provide valuable information for improved early detection and management. Unfortunately, this represents a complex problem due to the heterogeneous nature of humans in regards to demographic characteristics, genetics, diet, environmental exposures and health behaviors. In this pilot study, we employed high resolution mass spectrometry-based metabolic profiling to investigate the metabolic signatures of slow versus rapidly progressing PD present in human serum. Archival serum samples from PD patients obtained within 3 years of disease onset were analyzed via dual chromatography-high resolution mass spectrometry, with data extraction by xMSanalyzer and used to predict rapid or slow motor progression of these patients during follow-up. Statistical analyses, such as false discovery rate analysis and partial least squares discriminant analysis, yielded a list of statistically significant metabolic features and further investigation revealed potential biomarkers. In particular, N8-acetyl spermidine was found to be significantly elevated in the rapid progressors compared to both control subjects and slow progressors. Our exploratory data indicate that a fast motor progression disease phenotype can be distinguished early in disease using high resolution mass spectrometry-based metabolic profiling and that altered polyamine metabolism may be a predictive marker of rapidly progressing PD.     

SVM model for quality assessment of medium resolution mass spectra from 18O-water labeling experiments

We describe a method for assessing the quality of mass spectra and improving reliability of relative ratio estimations from (18)O-water labeling experiments acquired from low resolution mass spectrometers. The mass profiles of heavy and light peptide pairs are often affected by artifacts, including coeluting contaminant species, noise signal, instrumental fluctuations in measuring ion position and abundance levels. Such artifacts distort the profiles, leading to erroneous ratio estimations, thus reducing the reliability of ratio estimations in high throughput quantification experiments. We used support vector machines (SVMs) to filter out mass spectra that deviated significantly from expected theoretical isotope distributions. We built an SVM classifier with a decision function that assigns a score to every mass profile based on such spectral features as mass accuracy, signal-to-noise ratio, and differences between experimental and theoretical isotopic distributions. The classifier was trained using a data set obtained from samples of mouse renal cortex. We then tested it on protein samples (bovine serum albumin) mixed in five different ratios of labeled and unlabeled species. We demonstrated that filtering the data using our SVM classifier results in as much as a 9-fold reduction in the coefficient of variance of peptide ratios, thus significantly improving the reliability of ratio estimations.     

Profiling of Arabidopsis secondary metabolites by capillary liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry

Large-scale metabolic profiling is expected to develop into an integral part of functional genomics and systems biology. The metabolome of a cell or an organism is chemically highly complex. Therefore, comprehensive biochemical phenotyping requires a multitude of analytical techniques. Here, we describe a profiling approach that combines separation by capillary liquid chromatography with the high resolution, high sensitivity, and high mass accuracy of quadrupole time-of-flight mass spectrometry. About 2000 different mass signals can be detected in extracts of Arabidopsis roots and leaves. Many of these originate from Arabidopsis secondary metabolites. Detection based on retention times and exact masses is robust and reproducible. The dynamic range is sufficient for the quantification of metabolites. Assessment of the reproducibility of the analysis showed that biological variability exceeds technical variability. Tools were optimized or established for the automatic data deconvolution and data processing. Subtle differences between samples can be detected as tested with the chalcone synthase deficient tt4 mutant. The accuracy of time-of-flight mass analysis allows to calculate elemental compositions and to tentatively identify metabolites. In-source fragmentation and tandem mass spectrometry can be used to gain structural information. This approach has the potential to significantly contribute to establishing the metabolome of Arabidopsis and other model systems. The principles of separation and mass analysis of this technique, together with its sensitivity and resolving power, greatly expand the range of metabolic profiling.     

液质联用多反应监测法定量目标多肽或蛋白质

为建立优化的血浆内源性多肽提取方法,并且构建目标多肽和蛋白质的质谱定量方 法,本研究考察了超滤法、有机溶剂沉淀法和固相萃取法对血浆内源性多肽的提取效果 ,并通过Tricine-SDS-PAGE对提取效果进行比较.通过液相色谱串联质谱多反应监测 （MRM）分析,建立了多肽标准品ESAT-6定量方法,并将ESAT-6定量建立的液相色谱和质谱条件应用于蛋白质的定量,对多肽和蛋白质MRM定量的标准曲线进行了考 察.Tricine-SDS-PAGE结果表明,乙腈沉淀法是最佳的血浆内源性多肽提取方法,低分子量的多肽可以得到很好的富集,且能有效地去除高分子蛋白质的污染.液相色谱串联 质谱MRM法检测血浆内提取的多肽,标准曲线的线性较好,相关系数为0.999.另外,采 用MRM法对胶内分离的蛋白质进行定量,标准曲线的线性相关系数为0.995.综上所述, 本研究构建了一种简单有效的血浆多肽提取方法,通过液质联用MRM法成功地实现了目标多肽和蛋白质定量测定.该定量方法可以推广应用于复杂样品中的多肽和蛋白质的定 量分析.     

Improving mass and liquid chromatography based identification of proteins using bayesian scoring

We present a method for peptide and protein identification based on LC-MS profiling. The method identified peptides at high-throughput without expending the sequencing time necessary for CID spectra based identification. The measurable peptide properties of mass and liquid chromatographic elution conditions are used to characterize and differentiate peptide features, and these peptide features are matched to a reference database from previously acquired and archived LC-MS/MS experiments to generate sequence assignments. The matches are scored according to the probability of an overlap between the peptide feature and the database peptides resulting in a ranked list of possible peptide sequences for each peptide submitted. This method resulted in 6 times more peptide sequence identifications from a single LC-MS analysis of yeast than from shotgun peptide sequencing using LC-MS/MS.     

Tissue profiling by mass spectrometry: a review of methodology and applications

Matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has become a valuable tool to address a broad range of questions in many areas of biomedical research. One such application allows spectra to be obtained directly from intact tissues, termed "profiling" (low resolution) and "imaging" (high resolution). In light of the fact that MALDI tissue profiling allows over a thousand peptides and proteins to be rapidly detected from a variety of tissues, its application to disease processes is of special interest. For example, protein profiles from tumors may allow accurate prediction of tumor behavior, diagnosis, and prognosis and uncover etiologies underlying idiopathic diseases. MALDI MS, in conjunction with laser capture microdissection, is able to produce protein expression profiles from a relatively small number of cells from specific regions of heterogeneous tissue architectures. Imaging mass spectrometry enables the investigator to assess the spatial distribution of proteins, drugs, and their metabolites in intact tissues. This article provides an overview of several tissue profiling and imaging applications performed by MALDI MS, including sample preparation, matrix selection and application, histological staining prior to MALDI analysis, tissue profiling, imaging, and data analysis. Several applications represent direct translation of this technology to clinically relevant problems.     

Cross-platform analysis of longitudinal data in metabolomics

Metabolic profiling is considered to be a very promising tool for diagnostic purposes, for assessing nutritional status and response to drugs. However, it is also evident that human metabolic profiles have a complex nature, influenced by many external factors. This, together with the understanding of the difficulty to assign people to distinct groups and a general move in clinical science towards personalized medicine, raises the interest to explore individual and variable metabolic features for each individual separately in longitudinal study design. In the current paper we have analyzed a set of metabolic profiles of a selection of six urine samples per person from a set of healthy individuals by (1)H NMR and reversed-phase UPLC-MS. We have demonstrated that the method for recovery of individual metabolic phenotypes can give complementary information to another established method for analysis of longitudinal data--multilevel component analysis. We also show that individual metabolic signatures can be found not only in (1)H NMR data, as has been demonstrated before, but also even more strongly in LC-MS data.     

Use of online-dual-column extraction in conjunction with chiral liquid chromatography tandem mass spectrometry for determination of terbutaline enantiomers in human plasma

An online sample extraction chiral bioanalytical method was developed and validated for the quantification of terbutaline, a beta2-selective adrenoceptor agonist, spiked into human plasma by using two extraction columns and a chiral stationary phase (CSP) in conjunction with liquid chromatography tandem mass spectrometry (LC-MS/MS). In this method, two Oasis HLB extraction columns were used in parallel for plasma sample purification and a Chirobiotic T CSP was used for enantiomeric separation. Atmospheric pressure chemical ionization MS/MS was employed in multiple reaction monitoring mode for the detection and quantification. Subsequent to the addition of an internal standard solution, the plasma samples were directly injected onto the system for extraction and analysis. This method allowed the use of one of the extraction columns for purification while the other was being equilibrated. Hence, the time required for reconditioning the extraction columns did not contribute to the total analysis time per sample, which resulted in a shorter run time and higher throughput. A lower limit of quantification of 1.0 ng/mL was achieved using only 50 microliter of human plasma. The method was validated with a dynamic range of 1.0-200 ng/mL. The intra- and interday precision was no more than 11% CV and the assay accuracy was between 94-106%.     

A statistical method for chromatographic alignment of LC-MS data

Integrated liquid-chromatography mass-spectrometry (LC-MS) is becoming a widely used approach for quantifying the protein composition of complex samples. The output of the LC-MS system measures the intensity of a peptide with a specific mass-charge ratio and retention time. In the last few years, this technology has been used to compare complex biological samples across multiple conditions. One challenge for comparative proteomic profiling with LC-MS is to match corresponding peptide features from different experiments. In this paper, we propose a new method--Peptide Element Alignment (PETAL) that uses raw spectrum data and detected peak to simultaneously align features from multiple LC-MS experiments. PETAL creates spectrum elements, each of which represents the mass spectrum of a single peptide in a single scan. Peptides detected in different LC-MS data are aligned if they can be represented by the same elements. By considering each peptide separately, PETAL enjoys greater flexibility than time warping methods. While most existing methods process multiple data sets by sequentially aligning each data set to an arbitrarily chosen template data set, PETAL treats all experiments symmetrically and can analyze all experiments simultaneously. We illustrate the performance of PETAL on example data sets.     

An automated method for scanning LC-MS data sets for significant peptides and proteins, including quantitative profiling and interactive confirmation

Differential quantification of proteins and peptides by LC-MS is a promising method to acquire knowledge about biological processes, and for finding drug targets and biomarkers. However, differential protein analysis using LC-MS has been held back by the lack of suitable software tools. Large amounts of experimental data are easily generated in protein and peptide profiling experiments, but data analysis is time-consuming and labor-intensive. Here, we present a fully automated method for scanning LC-MS/MS data for biologically significant peptides and proteins, including support for interactive confirmation and further profiling. By studying peptide mixtures of known composition, we demonstrate that peptides present in different amounts in different groups of samples can be automatically screened for using statistical tests. A linear response can be obtained over almost 3 orders of magnitude, facilitating further profiling of peptides and proteins of interest. Furthermore, we apply the method to study the changes of endogenous peptide levels in mouse brain striatum after administration of reserpine, a classical model drug for inducing Parkinson disease symptoms.     

Evaluation of the repeatability of ultra-performance liquid chromatography-TOF-MS for global metabolic profiling of human urine samples

The application of reversed-phase ultra-performance liquid chromatography, based on the use of sub 2 microm particles, combined with time-of-flight mass spectrometry has been investigated for the production of global metabolite profiles from human urine. The stability and repeatability of the methodology, which employed gradient elution, was determined by the repeat analysis of a pooled quality control (QC) sample. As seen in previous studies conducted with conventional LC-MS an element of system conditioning was required to obtain reproducible data, as the initial injections were unrepresentative. However, once the system had equilibrated excellent repeatability in terms of retention time, signal intensity and mass accuracy was seen providing confidence that for this matrix, the within-day repeatability of UPLC-TOF-MS was sufficient to assure data quality in global metabolic profiling applications.     

Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles

Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.     

MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments

Background  

Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.     

Multiplexed complexome profiling using tandem mass tags

Complexome profiling is a rapidly spreading, powerful technique to gain insight into the nature of protein complexes. It identifies and quantifies protein complexes separated into multiple fractions of increasing molecular mass using mass spectrometry-based, label-free bottom-up proteomics. Complexome profiling enables a sophisticated and thorough characterization of the composition, molecular mass, assembly, and interactions of protein complexes. However, in practice, its application is limited by the large number of samples it generates and the related time of mass spectrometry analyses. Here, we report an improved process workflow that implements tandem mass tags for multiplexing complexome profiling. This workflow substantially reduces the number of samples and measuring time without compromising protein identification or quantification reliability. In profiles from mitochondrial fractions of cells recovering from chloramphenicol treatment, tandem mass tags-multiplexed complexome profiling exhibited migration patterns of mature ATP synthase (complex V) and assembly intermediates that were consistent in composition and abundance with profiles obtained by the label-free approach. Reporter ion quantifications of proteins and complexes unaffected by the chloramphenicol treatment presented less variation in comparison to the label-free method. Incorporation of tandem mass tags enabled an efficient and robust complexome profiling analysis and may foster broader application for protein complex profiling in biomedical research and diagnostics.     

Comprehensive profiling of the low molecular weight proteins and peptides in weak cation exchange beads human serum retentate

Mass spectrometric profiling using ProteinChip and magnetic beads has rapidly grown over the past years, particularly to generate serum profiles for cancer diagnosis. The molecular weights of these distinguishing peaks are usually under 30 kDa. To identify those low molecular weight proteins and peptides is important for specific assays to be developed and increases biological insight. In this study, low molecular weight proteins and peptides from serum were purified by a combination of weak cation exchange magnetic beads and high performance liquid chromatography. The purified proteins and peptides were analyzed by 1D SDS PAGE, SELDI and LC-MS/MS. 246 proteins were identified from the HPLC fractions by LC-MS/MS. 95(38.62%) proteins were first identified in serum compare with Sys-BodyFluid database. 11(11/96) proteins were documented cancer associated proteins. We also observed about 109 proteins/peptides in SELDI mass spectrum, and 13 of the SELDI features were identified.