Untargeted Metabolomics-An Overview

What is Untargeted Metabolomics?

Untargeted metabolomics involves the global profiling of metabolites without prior knowledge of their identities or quantities. This comprehensive analysis contrasts with targeted metabolomics, which focuses on quantifying predefined sets of metabolites. By surveying the entire metabolome, untargeted metabolomics can reveal unexpected metabolic changes and novel biomarkers, providing a more holistic view of metabolic processes.

Methodologies in Untargeted Metabolomics

Two principal analytical techniques underpin untargeted metabolomics: mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Each method offers unique strengths and capabilities, making them complementary tools in metabolomics research.

Mass Spectrometry (MS)

Mass spectrometry (MS) is renowned for its high sensitivity, specificity, and ability to analyze a wide range of metabolites, from small organic acids to large lipids. The MS process begins with ionizing the sample's chemical compounds, creating charged molecules or molecular fragments. These ions are then separated based on their mass-to-charge ratios (m/z) and detected, producing a spectrum that represents the sample's molecular composition.

Various MS techniques are employed in untargeted metabolomics, each suited to different types of metabolites and analytical needs:

Liquid Chromatography-Mass Spectrometry (LC-MS): LC-MS combines the separation capabilities of liquid chromatography with the detection power of MS. This technique is particularly effective for analyzing polar and non-volatile compounds. Liquid chromatography separates metabolites based on their chemical properties, such as polarity and size, before they enter the mass spectrometer. This separation enhances the detection and quantification of complex mixtures, making LC-MS a versatile and widely used technique in metabolomics.

Gas Chromatography-Mass Spectrometry (GC-MS): GC-MS integrates gas chromatography with MS, making it ideal for volatile and semi-volatile organic compounds. In this method, metabolites are vaporized and separated in a gas chromatograph before entering the mass spectrometer. GC-MS provides high resolution and sensitivity, making it valuable for analyzing small and thermally stable molecules, such as fatty acids and hydrocarbons.

Nuclear Magnetic Resonance (NMR) Spectroscopy

NMR spectroscopy is a powerful analytical technique that provides detailed structural information about metabolites in a non-destructive manner. NMR exploits the magnetic properties of certain atomic nuclei. When placed in a strong magnetic field and exposed to a specific radiofrequency pulse, these nuclei resonate at characteristic frequencies, providing information about their chemical environment.

Key advantages of NMR in untargeted metabolomics include:

Structural Elucidation: NMR excels at elucidating the chemical structures of metabolites, identifying functional groups, and determining molecular conformations. This structural information complements the mass-based data from MS, offering a more comprehensive understanding of metabolite composition.

Quantitative Analysis: NMR can provide absolute quantification of metabolites without the need for external standards. The intensity of NMR signals directly correlates with metabolite concentrations, enabling precise quantification of a wide range of compounds.

Non-Destructive Analysis: Unlike MS, NMR is non-destructive, allowing for the recovery and further analysis of samples. This feature is particularly valuable when sample availability is limited or when subsequent analyses are required.

Workflow: Sample Preparation, Data Acquisition, and Data Processing

Sample Preparation

Effective sample preparation is critical for untargeted metabolomics. It involves steps such as quenching metabolic activity, extracting metabolites, and sometimes derivatization to enhance detection. The goal is to preserve the metabolite profile accurately and consistently.

Data Acquisition

Data acquisition in untargeted metabolomics is performed using MS or NMR. High-resolution data are generated, capturing the full spectrum of metabolites in the sample. This step is crucial for ensuring comprehensive coverage and accurate quantification.

Data Processing

Processing the large volumes of data generated is a complex task. It involves peak detection, deconvolution, alignment, normalization, and annotation. Advanced bioinformatics tools and databases are employed to interpret the data, identify metabolites, and integrate them into metabolic pathways.

Data Analysis in Untargeted Metabolomics

Data analysis in untargeted metabolomics is a multi-step process that transforms raw data into meaningful biological insights. This involves preprocessing, normalization, peak detection, identification, and interpretation.

Preprocessing

Preprocessing involves preparing the raw data for analysis by reducing noise and correcting for technical variations. This typically starts with noise reduction to filter out background signals and irrelevant noise, enhancing the quality of the data. Next, peak detection algorithms are employed to identify and quantify the peaks corresponding to metabolites. Following this, peak alignment corrects for any retention time shifts (in LC-MS/GC-MS) or chemical shift variations (in NMR) across different samples, ensuring consistent peak matching.

Normalization

Normalization adjusts for variations in sample concentration, extraction efficiency, and instrument response. This step is crucial for ensuring that the data is comparable across different samples. Common normalization methods include total ion current (TIC) normalization, probabilistic quotient normalization (PQN), and internal standard normalization. These methods correct systematic biases and allow for accurate quantitative comparisons.

Peak Detection

Peak detection involves identifying the presence and intensity of metabolite peaks in the chromatograms or spectra. Sophisticated algorithms, such as those implemented in software like XCMS, MZmine, or MetAlign, are used to detect and quantify peaks. These tools can handle the complexity of metabolomics data, accurately distinguishing between true metabolite signals and background noise.

Metabolite Identification

Identifying metabolites from detected peaks is a challenging yet crucial step. This involves matching the observed data (e.g., mass-to-charge ratios, retention times, NMR chemical shifts) with known metabolite databases such as HMDB, METLIN, or KEGG. Tandem mass spectrometry (MS/MS) and NMR spectroscopy can provide additional structural information to aid in the accurate identification of metabolites. Software tools like MetaboAnalyst and GNPS are often used for this purpose.

Data Interpretation

Interpreting the metabolomics data involves mapping identified metabolites to metabolic pathways to understand their biological significance. This can be done using pathway analysis tools such as MetaboAnalyst, Ingenuity Pathway Analysis (IPA), or KEGG pathway mapping. These tools help visualize the metabolic networks and identify key pathways and metabolites that are significantly altered in different conditions. Statistical analysis, including multivariate techniques like PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares Discriminant Analysis), can be used to identify patterns and correlations in the data, providing insights into the underlying biological processes and disease mechanisms.

Untargeted Metabolomics Methods to Analyze Blood-Derived Samples (Dudzik et al., 2021)

What are the Advantages of Untargeted Metabolomics?

Comprehensive Metabolic Profiling: Untargeted metabolomics provides a broad overview of the entire metabolome, capturing a wide array of metabolites, including those that are unknown or unexpected. This comprehensive profiling enables a holistic understanding of metabolic states and interactions.

Discovery of Novel Metabolites: The unbiased nature of untargeted metabolomics allows for the discovery of previously unknown metabolites. This can lead to the identification of new biomarkers, therapeutic targets, and insights into novel metabolic pathways.

Data-Driven Insights: By generating extensive datasets, untargeted metabolomics facilitates the discovery of patterns and correlations that can lead to data-driven hypotheses and discoveries. This approach can reveal complex relationships between metabolites and biological processes.

Broad Applicability: Untargeted metabolomics can be applied to a wide range of biological samples, including tissues, biofluids, and cells, making it versatile for various fields such as disease research, drug development, nutrition, and environmental studies.

Pathway Elucidation: The comprehensive data obtained can be mapped onto metabolic pathways, helping to elucidate complex biochemical networks and understand the underlying mechanisms of diseases and physiological responses.

Flexibility in Experimental Design: Without the need for prior selection of target metabolites, untargeted metabolomics offers flexibility in experimental design. This adaptability is beneficial for exploratory studies and when little is known about the metabolic changes involved in a condition or treatment.

What is the Difference Between Targeted and Untargeted Metabolomics?

Targeted and untargeted metabolomics differ primarily in their scope and objectives. Targeted metabolomics focuses on the quantification of a predefined set of known metabolites, providing high sensitivity and specificity for those selected compounds. In contrast, untargeted metabolomics aims to profile all detectable metabolites in a sample without prior knowledge, offering a comprehensive and unbiased overview of the metabolome. While targeted approaches are used for hypothesis-driven studies and validation, untargeted approaches are exploratory, enabling the discovery of novel metabolites and insights into global metabolic changes.

Applications of Untargeted Metabolomics

Discovery of Novel Biomarkers for Disease Diagnosis and Prognosis

Cancer Biomarkers

In oncology, untargeted metabolomics has revolutionized biomarker discovery, aiding early diagnosis and prognosis. For example, research has identified unique metabolic signatures associated with different cancers, such as altered levels of specific amino acids, lipids, and nucleotides. These signatures often reflect changes in cellular metabolism that occur before clinical symptoms, offering potential for early detection.

• Breast Cancer: Studies have shown that breast cancer patients exhibit distinct metabolomic profiles compared to healthy controls, with significant alterations in metabolites involved in lipid metabolism and oxidative stress.
• Prostate Cancer: Metabolomic analysis of urine samples from prostate cancer patients has revealed biomarkers like sarcosine, which is associated with tumor progression and metastasis.

Cardiovascular Disease Biomarkers

Cardiovascular diseases (CVD) benefit significantly from untargeted metabolomics, which helps identify metabolites linked to disease risk and progression. Metabolic profiling has uncovered several biomarkers that correlate with CVD outcomes.

• Heart Failure: Elevated levels of metabolites such as trimethylamine N-oxide (TMAO) have been associated with increased risk of heart failure and mortality, providing potential targets for intervention.
• Atherosclerosis: Metabolites involved in lipid metabolism, like certain bile acids, have been linked to the development and progression of atherosclerosis.

Metabolic Disorders

Metabolic diseases, including diabetes and obesity, have complex etiologies that untargeted metabolomics can help unravel. By analyzing the metabolic profiles of affected individuals, researchers can identify biomarkers indicative of disease states and potential therapeutic targets.

• Type 2 Diabetes: Studies have identified branched-chain amino acids (BCAAs) and acylcarnitines as biomarkers for insulin resistance and diabetes risk. These metabolites offer insights into the metabolic dysregulation occurring in diabetes.
• Obesity: Metabolomic profiling has revealed altered lipid and amino acid metabolism in obese individuals, helping to understand the metabolic shifts associated with obesity.

Insights into Metabolic Pathways and Mechanisms

Untargeted metabolomics provides a comprehensive view of metabolic pathways, aiding in the understanding of how various biochemical processes are interconnected.

Pathway Analysis

By mapping detected metabolites onto known biochemical pathways, researchers can identify perturbations and flux changes in metabolic networks. This helps elucidate the underlying mechanisms of diseases and physiological responses.

• Cancer Metabolism: Untargeted metabolomics has been instrumental in studying the Warburg effect in cancer cells, where there is a shift from oxidative phosphorylation to glycolysis. This metabolic reprogramming supports rapid cell growth and proliferation.
• Inflammatory Diseases: Metabolomic studies have identified key metabolic pathways involved in inflammation, such as arachidonic acid metabolism, which produces pro-inflammatory eicosanoids.

Drug Mechanism of Action

Understanding how drugs affect metabolic pathways is crucial for developing effective therapies. Untargeted metabolomics can reveal the biochemical impact of drugs, offering insights into their mechanisms of action and potential side effects.

• Chemotherapeutics: Metabolomic profiling has shown how certain chemotherapeutic agents alter amino acid and nucleotide metabolism, providing clues about their cytotoxic effects on cancer cells.
• Antibiotics: Studies have demonstrated how antibiotics impact microbial and host metabolism, revealing potential metabolic side effects and guiding the development of more targeted therapies.

Nutritional Studies

Untargeted metabolomics is invaluable in nutrition research, helping to understand how diet influences metabolism and health.

• Dietary Interventions: By analyzing metabolic changes in response to different diets (e.g., high-fat, ketogenic), researchers can identify beneficial or harmful metabolic effects, informing dietary guidelines and interventions.
• Nutritional Deficiencies: Metabolomic profiling can detect metabolic signatures of nutrient deficiencies, aiding in the diagnosis and treatment of conditions like vitamin and mineral deficiencies.

Reference

1. Dudzik, Danuta, and Antonia García. "Untargeted metabolomics methods to analyze blood-derived samples." Metabolomics (2021): 173-187.