What is Targeted Metabolomics?
Targeted metabolomics is an analytical strategy within the field of metabolomics, contrasting with untargeted metabolomics. In targeted metabolomics, the focus is on specific metabolites with known chemical structures or biochemical markers. This approach allows for the quantification or semi-quantification of molecular metabolites using standard references.
Unlike untargeted metabolomics, targeted metabolomics does not encompass the analysis of all measurable metabolites but rather concentrates on a selected set of metabolites associated with particular metabolic pathways or molecules. By comprehensively understanding the dynamics of metabolic enzymes, their kinetics, end products, and established biochemical pathways, targeted metabolomics provides in-depth and targeted insights.
In targeted metabolomics, optimized sample preparation methods can be employed to minimize the impact of high-abundance metabolites during analysis. This approach proves beneficial in discovering abnormal correlations within metabolite profiles when analyzing predefined sets of known metabolites under specific physiological conditions.
Overall, targeted metabolomics emphasizes the selective measurement of known metabolites, offering a profound understanding of specific biochemical pathways and metabolic networks. In comparison to untargeted metabolomics, targeted metabolomics holds advantages in terms of quantification and specificity. However, its limitation lies in the potential oversight of unknown metabolites.
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Targeted metabolomics (Astarita et al., 2014).
Targeted Metabolomics Research Process
The research process of targeted metabolomics involves several steps, from sample preparation to data analysis. Each step significantly influences the reliability and depth of the final results. Here is a detailed description of the targeted metabolomics research process:
1. Sample Collection and Storage: Collect the required biological samples, such as blood, urine, or tissues. Use standardized methods for sample collection and storage to ensure sample integrity and repeatability.
2. Sample Preprocessing and Extraction: Perform sample preprocessing, including steps like protein removal or centrifugation, to enhance the sensitivity of metabolite detection. Use suitable extraction methods, such as solvent extraction or solid-phase microextraction, to extract metabolites from the samples.
3. Standard Preparation: Prepare standards for quantitative or semi-quantitative analysis. These standards are samples of known concentrations of known metabolites used to establish standard curves.
4. Instrumental Analysis: Employ techniques like high-performance liquid chromatography-mass spectrometry (HPLC-MS) or gas chromatography-mass spectrometry (GC-MS) for metabolite analysis. Achieve high-sensitivity detection of target metabolites by setting appropriate separation conditions and mass spectrometry parameters.
5. Data Acquisition and Processing: Collect raw data from the mass spectrometer. Preprocess raw data, including peak identification, alignment, and noise removal, to obtain a high-quality dataset.
6. Quantitative Analysis: Use standard curves and known concentrations of standard samples to quantitatively or semi-quantitatively analyze metabolites. Ensure accurate determination of the concentration of each target metabolite.
7. Bioinformatics Analysis: Conduct statistical analysis of metabolite data to identify differences between different groups. Utilize bioinformatics tools for pathway analysis to understand the biological functions and relationships of metabolites.
8. Results Interpretation and Validation: Interpret the biological significance of differential metabolites, potentially involving in-depth research on metabolic pathways and biomarkers. Conduct validation experiments, such as quantitative PCR or Western blot, to ensure the reliability of metabolomic results.
LC-MS in Targeted Metabolomics
When conducting targeted metabolomic analysis using LC-MS, a range of factors that could potentially impact the accuracy of experimental data must be considered. Choosing the appropriate ion source is crucial, and electrospray ionization (ESI) is the most commonly used method for detecting small molecules in LC-MS-based metabolomic studies. ESI is a soft ionization technique that enables mass spectrometry to detect non-volatile and high-molecular-weight compounds. Its primary advantage lies in allowing researchers to detect non-volatile compounds without the need for derivatization to enhance volatility, reducing the generation of fragment ions, and simplifying the spectral interpretation of complex compounds. However, ESI also has limitations, with one prominent issue being the strong ion suppression effect observed when analyzing complex biological samples.
During ionization, different molecules compete for charges, leading to ion suppression, and the ionization efficiency is correlated with the chemical characteristics of the analyte itself. Therefore, the observed ion counts for a specific ion may vary due to differences in co-ionization with other analytes or background ions, even if no interference signals are visible in the mass spectrum. Hence, the entire analysis must be based on the premise that the composition of compounds in all tested samples is roughly similar. Therefore, selecting biological samples with the same matrix for analysis and comparison is crucial; for example, it is advisable not to compare plasma samples with tissue samples. In targeted metabolomic analysis, internal standards are often required to control ion suppression and matrix effects.
Most LC-MS platforms used in metabolomic studies can operate in both positive and negative ion modes for sample detection. Therefore, it is essential to choose the appropriate detection mode based on the physicochemical properties of the metabolites in the experiment, ensuring efficient ionization of the target compounds.
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Advantages of Targeted Analysis
In mass spectrometry-based metabolomic analysis, it is essential to be aware that both ion intensity and chromatographic retention time may drift over time. Therefore, in experiments, samples should be detected in a random order, and data collection is preferably done on the same day and batch to minimize errors.
The advantages of targeted analysis are evident in two aspects:
Each sample is supplemented with an internal standard, allowing normalization of metabolite concentrations across different batches and sample groups. This is particularly useful in large sample analyses and experiments lasting several days or weeks.
By using the Multiple Reaction Monitoring (MRM) scan mode, it is possible to precisely define each detected compound and include it in the subsequent analysis. As illustrated in the diagram below, a triple quadrupole mass spectrometer is used, where metabolites are ionized in ESI, selected and isolated in Q1, fragmented in Q2 (collision cell), and the resulting fragments are further selected and detected in Q3.
Each metabolite has a specific precursor ion → product ion pair (also known as a channel), ensuring accurate monitoring of each compound. When combined with retention time information, accuracy is further enhanced. However, before targeted analysis, optimization of parameters such as monitoring channels, retention times, concentration dynamic range, collision energy, etc., for the compounds of interest is a tedious but necessary task. To obtain meaningful results, biological replicates are needed, followed by statistical analysis to determine whether differences in a particular compound between different groups are statistically significant.
Metabolite Extraction
Metabolite extraction is crucial for any metabolomics experiment. In metabolomic research, researchers aim to minimize sample pre-processing steps as much as possible, as an increasing number of steps can introduce uncontrollable losses of metabolites. Additionally, extraction itself is selective, leading to bias in comprehensive metabolite analysis. For untargeted metabolomics, the extraction method needs to ensure the extraction of as many metabolites as possible from the sample. On the other hand, for targeted metabolomics, where only specific metabolites are analyzed, the extraction method focuses on improving the efficiency of extracting the target analytes. Therefore, extraction methods need adjustments based on the physical and chemical properties of the analytes and their relative intensities, excluding large molecules such as proteins.
Several parameters need consideration, including whether it is a single-phase or two-phase extraction, the properties, quantities, and ratios of the aqueous and organic phases used in the extraction, as well as the pH and temperature of the extraction. The extraction method can influence the quantity, nature, and reproducibility of the extracted metabolites. It is crucial to optimize these parameters to achieve the desired balance and enhance the overall efficiency of the extraction process while excluding interfering large molecules like proteins.
Reference
- Astarita, Giuseppe, and James Langridge. "An emerging role for metabolomics in nutrition science." Lifestyle Genomics 6.4-5 (2014): 181-200.
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