What is the normal analysis sequence for metabolomics?
The typical workflow for metabolomics analysis generally includes multiple steps from sample collection to data analysis and interpretation. Below is a common metabolomics analysis workflow:
I. Sample Collection and Preservation
1. The sample collection method should be chosen based on the research objective, such as blood, urine, or tissue samples; consistency in sampling time is necessary to reduce errors caused by metabolic fluctuations.
2. Samples should be immediately frozen (e.g., at -80°C) or preserved with appropriate stabilizers to prevent metabolite degradation; storage conditions are crucial for metabolite stability.
II. Sample Pretreatment
Sample extraction, purification, or concentration should be performed according to the requirements of the analysis platform; common pretreatment methods include protein precipitation, liquid-liquid extraction, and solid-phase extraction.
III. Metabolite Detection and Analysis
Common detection platforms include gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance spectroscopy (NMR). The appropriate platform is chosen based on the nature of the metabolites and research objectives.
- LC-MS is suitable for a wide range of metabolite detection, especially small to medium-sized molecules.
- GC-MS is more suitable for detecting volatile and low molecular weight metabolites.
- NMR does not require sample separation and is suitable for structural identification and quantitative analysis.
IV. Data Acquisition and Quality Control
After data acquisition, strict quality control is necessary, including repeatability of the analytical tests, standard detection, and internal standard calibration.
V. Data Processing and Preprocessing
Raw data undergoes peak extraction, normalization, noise reduction, alignment, and standardization to obtain a stable metabolite data matrix.
VI. Statistical Analysis and Multivariate Analysis
1. Common statistical analysis methods include Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) to help identify differences between metabolites and relationships between sample groups.
2. Differential metabolite screening typically uses t-tests, ANOVA, etc., combined with False Discovery Rate (FDR) for multiple correction to ensure the reliability of the screening results.
VII. Metabolite Identification and Pathway Analysis
1. Differential metabolites are structurally identified by comparing them with databases such as HMDB, KEGG, and Metlin.
2. Identified metabolites undergo pathway enrichment analysis to reveal their roles in biological pathways and their relevance to the research objectives.
VIII. Result Interpretation and Biological Significance Analysis
1. Integrate the results of pathway analysis with biological background knowledge to interpret the biological significance of metabolite changes.
2. Based on the experimental design, explore the relationship between metabolite changes and the research subjects (e.g., disease, drug effects, environmental impacts).
IX. Report Writing and Data Storage
1. Organize the experimental results and write an analysis report, detailing the experimental steps, data analysis process, and interpretation of results.
2. Store raw and analysis data in appropriate data warehouses or databases to ensure data reproducibility and traceability.
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