Untargeted Metabolomics Analysis workflow

Untargeted metabolomics is a powerful, hypothesis-free approach that measures all small molecules—or metabolites—present in a biological sample, such as blood, urine, or tissue, without prior knowledge of their identity. Unlike targeted metabolomics, which focuses on a predefined set of known compounds, untargeted metabolomics casts a wide net, making it ideal for discovery-driven research. This method excels in identifying novel biomarkers, uncovering unexpected metabolic changes, and providing a holistic view of biological systems. Whether you're a beginner exploring metabolomics or an expert refining your workflow, this guide offers a comprehensive roadmap to understanding and implementing untargeted metabolomics analysis.

MetwareBio Metabolomics Services - Client Focused, Unparalleled Quality

Workflow Overview

The untargeted metabolomics workflow is a multi-step process designed to capture, analyze, and interpret the vast array of metabolites in a sample. It begins with thoughtful experimental design and progresses through sample preparation, data acquisition, processing, statistical analysis, metabolite identification, and biological interpretation. Each stage relies on advanced technologies—like Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), and Nuclear Magnetic Resonance (NMR)—and specialized software tools to transform raw data into meaningful insights.

Detailed Steps and Tools

1. Experimental Design

By defining the scope and parameters of the study, the foundation is laid for obtaining reliable and reproducible results. Researchers need to consider sample size, control groups, experimental conditions (e.g., disease state vs. healthy state), and potential confounders to ensure that the study has adequate statistical power and minimal variability. For those new to the field, a simple case-control design may be a viable starting point, while experienced researchers may choose a longitudinal study approach to capture dynamic metabolic changes over time.

2. Sample Collection and Preparation

Once the experiment is designed, sample collection and preparation come next, where biofluids like plasma or urine, or tissues, are gathered and processed to extract metabolites. This involves using solvents such as methanol or acetonitrile to isolate the metabolites while preserving their integrity, with consistency across all samples being critical to reduce technical noise and ensure the data reflects true biological differences. Beginners can benefit from standardized extraction kits or protocols available from companies like Thermo Fisher Scientific, whereas experts might tailor the preparation method to suit specific sample types or analytical goals.

3. Data Acquisition

With samples prepared, data acquisition follows, utilizing advanced analytical techniques to detect metabolites.

• LC-MS: Liquid Chromatography-Mass Spectrometry (LC-MS) is commonly employed for its high sensitivity and ability to analyze polar and semi-polar metabolites, often using high-resolution tools like Orbitrap mass spectrometers.

• GC-MS: Gas Chromatography-Mass Spectrometry (GC-MS) is preferred for volatile compounds and provides structural data through electron ionization.

• NMR: Nuclear Magnetic Resonance (NMR) offers detailed structural insights but is less sensitive, making it a complementary option for confirmation.

High-resolution accurate mass (HRAM) instruments are essential in this step to distinguish closely related compounds, and beginners might find LC-MS a versatile entry point, while experts could combine multiple techniques for comprehensive metabolite coverage.

4. Data Processing

After acquiring raw data, the next step is data processing, where spectral data is transformed into a usable format for analysis. This involves correcting baselines and reducing noise with software like Compound Discoverer or XCMS, followed by identifying peaks that represent metabolites and aligning them across samples to account for slight variations in retention times. Normalization is then applied to adjust for systematic biases, often using stable endogenous metabolites like creatinine or the total spectral area, ensuring the data is comparable. These tools offer user-friendly templates, such as “Untargeted Metabolomics with Statistics Detect Unknowns,” making the process accessible to novices, while providing flexibility for experts to fine-tune parameters.

5. Statistical Analysis

With processed data in hand, statistical analysis is conducted to uncover significant patterns or differences in metabolite profiles. Researchers might use univariate methods like t-tests or ANOVA to identify individual metabolite changes, or multivariate approaches such as Principal Component Analysis (PCA) to explore data structure and detect outliers, and Partial Least Squares-Discriminant Analysis (PLS-DA) to classify samples into groups like diseased versus healthy. Tools like MetaboAnalyst provide an intuitive interface for beginners to perform these analyses, while experts can leverage R-based packages for more advanced modeling, ultimately aiming to extract biologically relevant insights from the data.

6. Metabolite Identification

Following statistical analysis, metabolite identification is undertaken to assign identities to the detected peaks by matching spectral data against databases such as mzCloud, METLIN, or HMDB for LC-MS, or NIST for GC-MS. For unknown compounds, high-resolution accurate mass MS^n analysis can provide structural clues, though this remains a challenging and time-intensive task due to the prevalence of novel metabolites not yet cataloged. Beginners can rely on automated identification tools integrated into software platforms, while experts might combine multiple databases and manual validation to improve accuracy and confidence in their annotations.

7. Biological Interpretation

The final step is biological interpretation, where identified metabolites are mapped to biological pathways using resources like KEGG or MetaCyc to understand their roles in processes such as disease mechanisms or metabolic regulation. This stage often involves integrating metabolomics data with other omics datasets, such as genomics or proteomics, to build a systems-level understanding of the biology. Interactive visualization platforms, like those offered by Thermo Fisher’s Compound Discoverer, help make these insights accessible by displaying metabolic networks, enabling both novices and experts to translate raw data into meaningful scientific conclusions.

Applications and Case Studies

Metwarebio Metabolomics

This guide introduces you to the complete workflow of untargeted metabolomics, providing a clear path from experimental design to biological insights. Whether you are new to the field or an experienced researcher, the steps, tools, and applications outlined in this article will help you better use this cutting-edge technology. For users seeking expert support, MetwareBio provides comprehensive protein and metabolite detection services, focusing on untargeted metabolomics research. With advanced liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) platforms, MetwareBio can provide accurate and reliable data based on your research needs to help you advance the next breakthrough.

Read More

LC-MS VS GC-MS: What's the Difference

Applications of Untargeted Metabolomics

Targeted vs Untargeted vs Widely-targeted Metabolomics