Reliable Profile Detection in Comparative Metabolomics

Investigation of the derivatization conditions for GC-MS metabolomics of biological samples

AIM

Metabolomics applications represent an emerging field where significant efforts are directed. Derivatization consists prerequisite for GC-MS metabolomics analysis.

METHODS

Common silylation agents were tested for the derivatization of blood plasma. Optimization of methoxyamination and silylation reactions was performed on a mixture of reference standards, consisting of 46 different metabolites. Stability of derivatized metabolites was tested at 4°C.

RESULTS

Optimum results were achieved using N-methyl-N-(trimethylsilyl)trifluoroacetamide. Methoxyamination at room temperature for 24 h followed by 2-h silylation at high temperature lead to efficient derivatization.

CONCLUSION

Formation and stability of derivatives among metabolites differ greatly, so derivatization should be studied before application in metabolomics studies.

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments

Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes of molecules, with its scope rapidly expanding in the areas of post-translational modifications, MS instrumentation, and many others. Yet integration of novel analyte preparatory and purification methods with existing or novel mass spectrometers can introduce new challenges for MS sensitivity. The mechanisms that govern detection by MS are particularly complex and interdependent, including ionization efficiency, ion suppression, and transmission. Performance of both off-line and MS methods can be optimized separately or, when appropriate, simultaneously through statistical designs, broadly referred to as "design of experiments" (DOE). The following review provides a tutorial-like guide into the selection of DOE for MS experiments, the practices for modeling and optimization of response variables, and the available software tools that support DOE implementation in any laboratory. This review comes 3 years after the latest DOE review (Hibbert DB, 2012), which provided a comprehensive overview on the types of designs available and their statistical construction. Since that time, new classes of DOE, such as the definitive screening design, have emerged and new calls have been made for mass spectrometrists to adopt the practice. Rather than exhaustively cover all possible designs, we have highlighted the three most practical DOE classes available to mass spectrometrists. This review further differentiates itself by providing expert recommendations for experimental setup and defining DOE entirely in the context of three case-studies that highlight the utility of different designs to achieve different goals. A step-by-step tutorial is also provided.

Validated and predictive processing of gas chromatography-mass spectrometry based metabolomics data for large scale screening studies, diagnostics and metabolite pattern verification

The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

Pair-wise multicomparison and OPLS analyses of cold-acclimation phases in Siberian spruce

Analysis of metabolomics data often goes beyond the task of discovering biomarkers and can be aimed at recovering other important characteristics of observed metabolomic changes. In this paper we explore different methods to detect the presence of distinctive phases in seasonal-responsive changes of metabolomic patterns of Siberian spruce (Picea obovata) during cold acclimation occurred in the period from mid-August to January. Multivariate analysis, specifically orthogonal projection to latent structures discriminant analysis (OPLS-DA), identified time points where the metabolomic patterns underwent substantial modifications as a whole, revealing four distinctive phases during acclimation. This conclusion was re-examined by a univariate analysis consisting of multiple pair-wise comparisons to identify homogeneity intervals for each metabolite. These tests complemented OPLS-DA, clarifying biological interpretation of the classification: about 60% of metabolites found responsive to the cold stress indeed changed at one or more of the time points predicted by OPLS-DA. However, the univariate approach did not support the proposed division of the acclimation period into four phases: less than 10% of metabolites altered during the acclimation had homogeneous levels predicted by OPLS-DA. This demonstrates that coupling the classification found by OPLS-DA and the analysis of dynamics of individual metabolites obtained by pair-wise multicomparisons reveals a more correct characterization of biochemical processes in freezing tolerant trees and leads to interpretations that cannot be deduced by either method alone. The combined analysis can be used in other 'omics'-studies, where response factors have a causal dependence (like the time in the present work) and pair-wise multicomparisons are not conservative. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0304-5) contains supplementary material, which is available to authorized users.

Method for liver tissue metabolic profiling study and its application in type 2 diabetic rats based on ultra performance liquid chromatography-mass spectrometry

A protocol for the metabolic profiling of rat liver was developed based on ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to explore metabolic state directly. Methanol/water (4:1, v:v) was selected as the optimal extraction solvent. The established method was validated with a linearity over the 10-5000 ng/mL for internal standards (IS) and got an average correlation coefficient of 0.9986. The intra-day and inter-day RSD for most endogenous compounds were below 15%. And the absolute recovery of IS was from 84.8% to 109.1%. Liver tissues from diabetic and control rats were enrolled in the subsequent study to show the usefulness of the method. A clear classification between the control and model animals was achieved, some significant metabolites were successfully filtered. These metabolites reflected the abnormal metabolism of diabetic rats. This initial application indicated that the method is suitable and reliable for liver tissue metabolic profiling. It is expected this protocol could also be extended to metabonomic studies of other tissues.

The evolution of metabolic profiling in parasitology

The uses of metabolic profiling technologies such as mass spectrometry and nuclear magnetic resonance spectroscopy in parasitology have been multi-faceted. Traditional uses of spectroscopic platforms focused on determining the chemical composition of drugs or natural products used for treatment of parasitic infection. A natural progression of the use of these tools led to the generation of chemical profiles of the parasite in in vitro systems, monitoring the response of the parasite to chemotherapeutics, profiling metabolic consequences in the host organism and to deriving host-parasite interactions. With the dawn of the post-genomic era the paradigm in many research areas shifted towards Systems Biology and the integration of biomolecular interactions at the level of the gene, protein and metabolite. Although these technologies have yet to deliver their full potential, metabolic profiling has a key role to play in defining diagnostic or even prognostic metabolic signatures of parasitic infection and in deciphering the molecular mechanisms underpinning the development of parasite-induced pathologies. The strengths and weaknesses of the various spectroscopic technologies and analytical strategies are summarized here with respect to achieving these goals.

Ultra-high performance liquid chromatography-mass spectrometry for the metabolomic analysis of urine in colorectal cancer

We report here the results of a pilot study in which ultra-high performance liquid chromatography/time-of- flight-mass spectrometry (UPLC/TOF-MS) and multivariate statistical analysis (supervised partial least squares discriminant analysis, PLS-DA) were applied for urinary metabolite profiling and data interpretation. The results of the PLS-DA indicated that the metabolic pattern as a whole was significantly different between the groups of preoperative colorectal cancer (CRC) patients, postoperative CRC patients, and healthy volunteers, respectively. The preoperative group of patients showed significantly increased levels of low-molecular weight compounds (LMC) MW 283 and MW 234 in comparison to the group of healthy volunteers group. After the operation, the levels of these two LMC significantly decreased. These preliminary results suggest that the UPLC-MS-based method coupled with pattern recognition will likely lead to procedures with the potential to be clinically applicable for the diagnosis of CRC and, consequently, to an improvement in patient prognosis.

Metabolomics in biomarker discovery: future uses for cancer prevention

Metabolomics is the systematic study of small-molecular-weight substances in cells, tissues and/or whole organisms as influenced by multiple factors including genetics, diet, lifestyle and pharmaceutical interventions. These substances may directly or indirectly interact with molecular targets and thereby influence the risk and complications associated with various diseases, including cancer. Since the interaction between metabolites and specific targets is dynamic, knowledge regarding genetics, susceptibility factors, timing, and degree of exposure to an agent (drug or food component) is fundamental to understanding the metabolome and its potential use for predicting and preventing early phenotypic changes. The future of metabolomics rests with its ability to monitor subtle changes in the metabolome that occur prior to the detection of a gross phenotypic change reflecting disease. The integrated analysis of metabolomics and other 'omics' may provide more sensitive ways to detect changes related to disease and discover novel biomarkers. Knowledge regarding these multivariant characteristics is critical for establishing validated and predictive metabolomic models for cancer prevention. Understanding the metabolome will not only provide insights into the critical sites of regulation in health promotion, but will also assist in identifying intermediate or surrogate cancer biomarkers for establishing preemptive/preventative or therapeutic approaches for health. While unraveling the metabolome will not be simple, the societal implications are enormous.