Liquid chromatography–mass spectrometry based global metabolite profiling: A review

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

To date most global approaches to functional genomics have centred on genomics, transcriptomics and proteomics. However, since a number of high-profile publications, interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increasing. A range of analytical techniques, including 1H NMR spectroscopy, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), Fourier Transform mass spectrometry (FT-MS), high performance liquid chromatography (HPLC) and electrochemical array (EC-array), are required in order to maximize the number of metabolites that can be identified in a matrix. Applications have included phenotyping of yeast, mice and plants, understanding drug toxicity in pharmaceutical drug safety assessment, monitoring tumour treatment regimes and disease diagnosis in human populations. These successes are likely to be built on as other analytical and bioinformatic approaches are developed to fully exploit the information obtained in metabolic profiles. To assist in this process, databases of metabolomic data will be necessary to allow the passage of information between laboratories. In this prospective review, the capabilities of metabolomics in the field of medicine will be assessed in an attempt to predict the impact this 'Cinderella approach' will have at the 'functional genomic ball'.

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state. 1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression. 2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF), 3,4 cardiac hypertrophy, 5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact,

Oligonucleotide Bioanalysis: Sensitivity Versus Specificity

LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics

Data quality in global metabolomics is of great importance for biomarker discovery and system biology studies. However, comprehensive metrics and methods to evaluate and compare the data quality of global metabolomics data sets are lacking. In this work, we combine newly developed metrics, along with well-known measures, to comprehensively and quantitatively characterize the data quality across two similar liquid chromatography coupled with mass spectrometry (LC-MS) platforms, with the goal of providing an efficient and improved ability to evaluate the data quality in global metabolite profiling experiments. A pooled human serum sample was run 50 times on two high-resolution LC-QTOF-MS platforms to provide profile and centroid MS data. These data were processed using Progenesis QI software and then analyzed using five important data quality measures, including retention time drift, the number of compounds detected, missing values, and MS reproducibility (2 measures). The detected compounds were fit to a γ distribution versus compound abundance, which was normalized to allow comparison of different platforms. To evaluate missing values, characteristic curves were obtained by plotting the compound detection percentage versus extraction frequency. To characterize reproducibility, the accumulative coefficient of variation (CV) versus the percentage of total compounds detected and intraclass correlation coefficient (ICC) versus compound abundance were investigated. Key findings include significantly better performance using profile mode data compared to centroid mode as well quantitatively better performance from the newer, higher resolution instrument. A summary table of results gives a snapshot of the experimental results and provides a template to evaluate the global metabolite profiling workflow. In total, these measures give a good overall view of data quality in global profiling and allow comparisons of data acquisition strategies and platforms as well as optimization of parameters.

Metabolic Signature of CKD: The Search Continues

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples.

Small biochemicals are the end result of all the regulatory complexity present in a cell, tissue, or organism, including transcriptional regulation, translational regulation, and posttranslational modifications. Metabolic changes are thus the most proximal reporters of the body’s response to a disease process or drug therapy. In 1971, Robinson and coworkers1 conceived the core idea that information-rich data reflecting the functional status of a complex biological system resides in the quantitative and qualitative pattern of metabolites in body fluids. In the same year, Horning and Horning2 first used the term metabolic profiling to describe the output of a gas chromatogram from a patient sample. This new approach to the quantitative metabolic profiling of large numbers of small molecules in biofluids was ultimately termed “metabonomics” by Nicholson et al3 and “metabolomics” by others. Article see p 207 Two core technologies are used to perform metabolic profiling: nuclear magnetic resonance and tandem mass spectrometry (MS/MS), as previously reviewed in Circulation: Cardiovascular Genetics. 4 Nuclear magnetic resonance requires relatively little sample preparation and is nondestructive, allowing for subsequent structural analyses. However, the method tends to have low sensitivity and can detect only highly abundant analytes. Tandem mass spectrometry (MS/MS), coupled with liquid chromatography, on the other hand, has much higher sensitivity for small molecules and is also applicable to a wide range of biological fluids (including serum, plasma, and urine). Recent advances in MS technology now enable researchers to determine analyte masses with such high precision and accuracy that metabolites can be identified unambiguously even in complex fluids. These technologies can be used to characterize biological samples either in a targeted manner or in a pattern discovery manner. In the former, the investigator targets a predefined …

Oral manifestations can be the initial sign of systemic diseases such as Behcet's disease (BD) and Sjogren's syndrome (PSS). Their frequency and morphology vary widely, and recurrent aphthous stomatitis (RAS) often mimics BD ulcers, complicating differentiation on clinical grounds. Nonspecific aphthoid lesions are also seen in PSS. This study aimed to identify distinctive metabolic patterns in saliva that could discriminate BD, PSS, and RAS using global metabolite profiling. Saliva samples were collected from 43 patients (BD, n = 24; PSS, n = 10; RAS, n = 9) after fasting and abstaining from oral activities for at least 90 minutes. Gas chromatography-mass spectrometry (GC/MS) was employed for metabolite profiling. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were used to distinguish groups. Variable importance in projection (VIP) scores were calculated from partial least squares discriminant analysis (PLS-DA), and ANOVA was applied to compare metabolite abundance. Forty-two metabolites were identified and categorized into amino acids, organic acids, sugars, sugar alcohols, and others. PCA and HCA demonstrated clear dis-crimination among BD, PSS, and RAS. Metabolites with VIP > 1 included malonic acid sorbitol, pyroglutamic acid, aspartic acid, decanoic acid, hexadecenoic acid, and L-proline. Notably, malonic acid and aspartic acid were significantly elevated in BD compared to PSS and RAS, suggesting their diagnostic potential. Global salivary metabolite profiling by GC/MS provides distinct metabolic signatures enabling discrimination among BD, PSS, and RAS. This approach may enhance understanding of oral mucosal manifestations in systemic autoimmune diseases.

We describe the application of LC-MS without the use of stable isotope labeling for differential quantitative proteomic analysis of whole cell lysates of Shewanella oneidensis MR-1 cultured under aerobic and suboxic conditions. LC-MS/MS was used to initially identify peptide sequences, and LC-FTICR was used to confirm these identifications as well as measure relative peptide abundances. 2343 peptides covering 668 proteins were identified with high confidence and quantified. Among these proteins, a subset of 56 changed significantly using statistical approaches such as statistical analysis of microarrays, whereas another subset of 56 that were annotated as performing housekeeping functions remained essentially unchanged in relative abundance. Numerous proteins involved in anaerobic energy metabolism exhibited up to a 10-fold increase in relative abundance when S. oneidensis was transitioned from aerobic to suboxic conditions.

Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4-5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.

Kidney disease(KD) is a major challenge for global public healthcare systems. Traditional biomarkers (such as serum creatinine and urea) for measuring kidney function are not sufficient sensitivity and specificity. They are not only affected by many factors, but also were only increase significantly in severe KD. Therefore, more sensitive biomarkers for evaluation of KD is essential. Metabolomics is a promising tool that identify non-targeted, global small-molecule metabolite profiles of complex samples, such as biofluids and tissues extract. The application of metabolomics in KD studies has developed rapidly in the past years. Many benefits have been shown from the use of metabolomics in identifying biomarkers for KD. In particular, metabolomic approaches have the potential to diagnose KD with a higher accuracy than traditional diagnostic methods. Metabolomics in KD research has been expanded from experimental study into clinical applications.(Chin J Lab Med, 2018, 41: 246-250) Key words: Kidney disease; Biomarker; Metabonome/metabolome; Metabonomics/metabolomics

There is intense interest in comprehensive proteomic approaches for analyzing integral membrane proteins and lipoproteins. Key features of mass spectrometric analysis center on enriching biological material for proteins of interest, efficiently digesting them, extracting the resulting peptides, and using fractionation methods to comprehensively sample proteins or peptides by tandem mass spectrometry. However, lipid-associated proteins are generally rich in hydrophobic domains and are often low in abundance. These features, together with the associated lipid, make their mass spectrometric analysis technically challenging. In this article, we review analytical strategies for successful proteomic analysis of lipid-associated proteins.

Workflow methodology for rat brain metabolome exploration using NMR, LC–MS and GC–MS analytical platforms