The metabolomic sequencing process is an indispensable part of current life science research and is widely used in the fields of disease mechanism research, drug research and development, and environmental impact assessment. Metabolomics, as the science of studying metabolic substances in cells, can help us gain an in-depth understanding of the physiological and pathological state of the body. The accuracy and efficiency of the metabolomic sequencing process are important guarantees to ensure the credibility of research results.
1. Sample preparation Sample preparation is the first step in metabolomic sequencing. Its purpose is to extract metabolites from organisms (such as animals, plants, microorganisms, etc.) and prepare for subsequent instrumental testing. The specific steps of sample preparation may vary depending on the sample type (such as animal tissue, cells, plasma, serum, etc.), but usually include the following links: 1. Sample collection: collect appropriate biological samples according to the purpose of the research to ensure the representativeness and integrity of the sample. 2. Cleaning and treatment: Use appropriate solution (such as pre-cooled PBS) to remove impurities and interfering substances. Different treatment methods are needed for different types of samples (such as animal tissues, cells, etc.). 3. Freezing and storage: Put the treated samples into liquid nitrogen to quickly freeze them and store them in a -80°C refrigerator to ensure the stability of metabolites and prevent degradation.
2. Instrument detection Instrument detection is a core step in metabolomic sequencing. It uses advanced mass spectrometry analysis technology and nuclear magnetic resonance spectrum analysis technology to detect metabolites in the sample. At present, mass spectrometry analysis technology is one of the most commonly used analytical methods in metabolomics, and it has the advantages of high sensitivity, high resolution and high resolution. 1. Mass spectrometry analysis: Introduce the sample into a mass spectrometer, and perform qualitative and quantitative analysis of metabolites in the sample through steps such as ionization, mass screening and detection. Commonly used mass spectrometry techniques include liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), etc. 2. Nuclear magnetic resonance spectroscopy analysis: Another important metabolomic analysis technology, which uses the phenomenon of nuclear magnetic resonance to detect metabolites in the sample. Nuclear magnetic resonance spectroscopy has the ability to elucidate molecular structure and plays an important role in metabolomics.
3. Data analysis Data analysis is an indispensable step in the metabolomic sequencing process. It involves the processing, analysis and mining of the original data to extract meaningful metabolites information. The main steps of data analysis include: 1. Data preprocessing: converting the original data into an analytical format, such as converting the mass spectrometry data into a peak list or a table corresponding to the mass-to-charge ratio (m/z) and intensity. At the same time, data filtering, normalization and conversion are also required to improve data quality and reliability. 2. Peak extraction and annotation: Use computer programs to extract peaks in mass spectrometry data, and annotate the peaks through database comparison and peering to determine the identity of metabolites. 3. Statistical analysis: Statistical analysis of the extracted metabolites data, such as univariate analysis, multivariate statistical analysis, etc., to discover the differences and correlations of metabolites between different samples. 4. Screening and identification of differential metabolites: Based on the statistical analysis results, differential metabolites are screened out and identified through database search and other methods.
4. Results presentation and biological interpretation The last step is to present and biologically interpret the results of the data analysis. This usually includes making visual charts such as cluster heat maps, metabolic pathway maps, and functional annotation and pathway analysis of differential metabolites based on biological background knowledge. Through these steps, we can gain an in-depth understanding of the metabolic status of organisms, disease occurrence mechanisms, and drug action mechanisms.
The metabolomic sequencing process is a complex process involving multiple steps such as sample preparation, instrumentation detection, data analysis, and result presentation and biological interpretation. Each step requires rigorous operation and precise analysis to ensure the accuracy and reliability of the final result.
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