This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.

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Mass spectrometry-based metabolomics.

Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.

Protein biomarker discovery and validation: the long and uncertain path to clinical utility.

The quantification of differences between two or more physiological states of a biological system is among the most important but also most challenging technical tasks in proteomics. In addition to the classical methods of differential protein gel or blot staining by dyes and fluorophores, mass-spectrometry-based quantification methods have gained increasing popularity over the past five years. Most of these methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides (i) metabolically, (ii) by chemical means, (iii) enzymatically, or (iv) provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly. In this review, we critically examine the more commonly used quantitative mass spectrometry methods for their individual merits and discuss challenges in arriving at meaningful interpretations of quantitative proteomic data.

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Quantitative mass spectrometry in proteomics: a critical review

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/j.febslet.2009.10.036

Correlation of mRNA and protein in complex biological samples

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.

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The Human Serum Metabolome

A new method is presented for quantifying proteomic and metabolomic profile data by liquid chromatography−mass spectrometry (LC−MS) with electrospray ionization. This biotechnology provides differential expression measurements and enables the discovery of biological markers (biomarkers). Work presented here uses human serum but is applicable to any fluid or tissue. The approach relies on linearity of signal versus molecular concentration and reproducibility of sample processing. There is no use of isotopic labeling or chemically similar standard materials. Linear standard curves are reported for a variety of compounds introduced into human serum. As a measure of analytical reproducibility for proteome and metabolome sampling, median coefficients of variation of 25.7 and 23.8%, respectively, were determined for ∼3400 molecular ions (not counting their numerous isotopes) from 25 independently processed human serum samples, corresponding to a total of 85 000 individual molecular ion measurements.

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Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards

The high frequency of single nucleotide polymorphisms (SNPs) in the human genome makes them a valuable source of genetic markers for identity testing, genome mapping, and medical diagnostics. Conventional technologies for detecting SNPs are laborious and time-consuming, often prohibiting large-scale analysis. A rapid, accurate, and cost-effective method is needed to meet the demands of a high-throughput DNA assay. We demonstrate here that analysis of these genetic markers can now be performed routinely in a rapid, automated, and high-throughput fashion using time-of-flight mass spectrometry and a primer extension assay with a novel cleavable primer. SNP genotyping by mass spectrometry involves detection of single-base extension products of a primer immediately adjacent to the SNP site. Measurement of the mass difference between the SNP primer and the extension peak reveals which nucleotide is present at the polymorphic site. The primer is designed such that its extension products can be purified and chemically released from the primer in an automated format. The reduction in size of the products as a result of this chemical cleavage allows more accurate identification of the polymorphic base, especially in samples from a heterozygotic population. All six possible heterozygotes are resolved unambiguously, including an A/T heterozygote with extension products differing by only 9 Da. Multiplex SNP determination is demonstrated by simultaneously probing multiple SNP sites from a single polymerase chain reaction (PCR) product as well as from multiplexed PCR amplicons. Samples are processed in parallel on a robotic workstation, and analyzed serially in an automated mass spectrometer with analysis times of only a few seconds per sample, making it possible to process thousands of samples per day.

Single nucleotide polymorphism determination using primer extension and time-of-flight mass spectrometry.

Summary: Using replicated human serum samples, we applied an error model for proteomic differential expression profiling for a high-resolution liquid chromatography-mass spectrometry (LC-MS) platform. The detailed noise analysis presented here uses an experimental design that separates variance caused by sample preparation from variance due to analytical equipment. An analytic approach based on a two-component error model was applied, and in combination with an existing data driven technique that utilizes local sample averaging, we characterized and quantified the noise variance as a function of mean peak intensity. The results indicate that for processed LC-MS data a constant coefficient of variation is dominant for high intensities, whereas a model for low intensities explains Poisson-like variations. This result leads to a quadratic variance model which is used for the estimation of sample preparation noise present in LC-MS data.

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Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum

A sample preparation method is disclosed for volatilization and mass spectrometric analysis of nonvolatile high molecular weight molecules. Photoabsorbing molecules having significant sublimation rates at room temperature under vacuum, and preferably containing hydroxy functionalities, are disclosed for use as matrices in matrix-assisted laser desorption/ionization mass spectrometry. The samples are typically cooled in the mass spectrometer to temperatures significantly below room temperature.

Hua Lin

Papers

Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards

Single nucleotide polymorphism determination using primer extension and time-of-flight mass spectrometry.

Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum

Volatile matrices for matrix-assisted laser desorption/ionization mass spectrometry

Differential expression profiling of serum proteins and metabolites for biomarker discovery