Metabolomics

Why study metabolomics? How do we study metabolites? Targeted VS Untargeted Bottlenecks in Metabolomics Looking to the future Metabolomics Metabolomics is a powerful tool to study the complex metabolic processes that occur in a cell, tissue or organism. Metabolomics has a wide set of applications and might be used to: Metabolomics workflows include analytical techniques for separation and detection, such as: Metabolomics workflows can be categorized as targeted or untargeted. Let’s explore generic targeted and untargeted workflows. The pairing of liquid chromatography with mass spectrometry (LC-MS) has become a popular tool of choice for metabolomics research, owing to the technologies’ high throughput, soft ionization and broad coverage. The analytical tools adopted in a metabolomics study will largely depend on the research question and the sample type as these factors influence the type of workflow used. Features Sample selection Sample analysis Data processing + analysis Data interpretation Features Validation Sample selection Sample analysis Data processing + analysis Untargeted used to both identify and quantify the metabolites present in a sample. This infographic will provide a visual exploration of what metabolites are, and why and how we study them. The metabolites produced by a cell, tissue or organism represent the endpoint of the omics process. Genome Transcriptome Metabolome Cell phenotype Proteome Mass spectrometry Nuclear Magnetic Resonance Spectroscopy Chromatography The study of metabolites that are present in a cell, tissue or organism. What can happen What appears to be happening What makes it happen What has happened and is happening Compared to earlier points in the “omics” cascade, such as the genome and transcriptome, the metabolome more closesly reflects the phenotype. Identify biomarkers of health and disease Monitor disease progression and treatment responses Identify novel drug targets and study a drug’s effect on metabolism, or how a drug is metabolized Explore the effect of environmental factors or exposures on metabolism Study the effects of diet on human metabolism Study the metabolic processes of plants, and how we can engineer them for optimization Engineer new metabolic systems in synthetic biology This graphic summarizes a typical LC-MS set up. • Hypothesis-driven • Used to study a set of known metabolites in a sample • Provides absolute quantification Metabolites of interest are selected based on previous knowledge or hypotheses. Sample is analyzed using a targeted analytical platform, most often a mass spectrometer in Multi Reaction Monitoring (MRM) acquisition mode. The raw data is extracted, processed and analyzed using software tools capable of quantifying the targeted metabolite(s). Statistical analysis conducted. Data is interpreted in the context of the experiment. • Hypothesis-generating • Aims to comprehensively identify and quantify as many metabolites as possible in a sample • Provides an unbiased approach Sample is extracted and purified to concentrate the metabolites present. Sample is analyzed using a highthroughput platform capable of global analysis, such as MS or NMR. The data is analyzed and processed to identify and/or quantify the metabolites discovered. Untargeted data processing workflows incorporate several steps, such as: • Noise filtering • Peak detection • Peak deconvolution • Retention time correction • Feature annotation Data can be compared to existing databases for analyte identification. Statistical analysis conducted. The unbiased approach is advantageous; however, it also carries the stipulation that sample preparation and analytical methods can have a direct impact on the qualitative results that are gathered. Data interpretation can be challenging, and there are often larger numbers of false positives and negatives with untargeted metabolomics workflows. Less false positives than untargeted workflows, but a limited number of compounds can be targeted. Targeted Sample Analytical challenges Validation Standardization Integration with other “omics” Data analysis Databases As metabolomics research methods continue to be optimized and improved, drug safety evaluation is expected to become more precise and efficient, a more comprehensive understanding of disease will be garnered and metabolomics is likely to emerge as a powerful tool for guiding human health and monitoring the environment. Sponsored by