Metabolomics FAQ

• • What Are the Common Ethanol-Based Methods for Crude Polysaccharide Extraction?

  Ethanol-based extraction of crude polysaccharides primarily relies on the solubility differences between polysaccharides and other soluble components in the extraction medium. When the ethanol concentration reaches a specific threshold, polysaccharides precipitate out of solution, whereas other soluble substances—such as non-polar compounds and small molecular weight substances—remain dissolved. Two commonly employed ethanol precipitation strategies for crude polysaccharide extraction are outlined bel......

• • How to Perform Normalization and Peak Alignment Using XCMS for Metabolomics Data Preprocessing

  When performing metabolomics data preprocessing with XCMS, normalization and peak alignment are critical steps. Normalization eliminates technical variation between samples, allowing for comparison and analysis of metabolite peak intensities across different samples. Peak alignment addresses the drift and shift of metabolite peaks across samples, ensuring consistent peak positions for the same metabolite in different samples. The general steps are as follows: 1. First, use XCMS to extract peaks from raw....

• • Steps for Visualizing Lipidomics Data Analysis

  Here are the steps for several common visualization methods: Principal Component Analysis (PCA) Plot Creation 1. Data Preparation Obtain the standardized dataset from your statistical analysis, ensuring the data format is suitable for PCA analysis. 2. Plotting with Python Import necessary libraries like pandas for data reading, scikit-learn for PCA analysis, and matplotlib or seaborn for plotting. Read the dataset into a DataFrame, setting lipid types as rows and samples as columns. Perform PCA analysis....

• • What is PLS-DA Analysis

  PLS-DA (Partial Least Squares Discriminant Analysis) is a statistical method primarily used for classification and discriminant analysis of high-dimensional data. This method is particularly useful in fields such as bioinformatics, chemometrics, and metabolomics, where it helps extract and identify patterns from complex datasets. PLS-DA is based on Partial Least Squares Regression (PLS), but unlike PLS, it focuses on classification problems. Key Features and Applications of PLS-DA: 1. Classification and....

• • Can Origin Draw PLS-DA

  Origin is a widely used data analysis and graphing software in scientific and engineering fields, known for its powerful graphing capabilities and data analysis functions. However, Origin does not natively support PLS-DA (Partial Least Squares Discriminant Analysis). PLS-DA is a complex statistical method for high-dimensional data processing and pattern recognition, typically implemented in specialized statistical or data analysis software such as MATLAB, R, SIMCA, or specific libraries in Python (e.g......

• • How to Interpret Metabolomics Results

  When you have metabolomics analysis results in hand, the key is how to extract meaningful information and interpret it. You can approach this from the following aspects: 1. Significantly Different Metabolites Typically, you will obtain a list of metabolites that show significant differences between experimental groups or conditions. Pay attention to each metabolite's p-value, adjusted p-value (e.g., FDR), and fold change. 2. Biological Significance (1) Metabolic Pathway Mapping: Map the significantly.......

• • What Are the Standard Procedures for Serum Sample Preparation in Metabolomics Studies?

  The preparation of serum samples for metabolomics analysis typically involves the following steps:   1. Blood Collection Venous blood is collected without the use of anticoagulants to allow for natural coagulation, which facilitates subsequent serum separation.   2. Clotting and Centrifugation The collected blood is left undisturbed at room temperature for 30 to 60 minutes to ensure complete clotting. It is then centrifuged at approximately 2000–3000 × g for 10 minutes to separate the serum from the c......

• • What Are the Next Steps After Identifying Differential Metabolite Pathways? How to Identify Pathway Targets Beyond WB?

  In metabolomics research, once the pathways of differential metabolites are identified, further experiments are typically required to validate these findings and to understand how these metabolites influence the physiological state of the organism through specific biological pathways.   Identification of Key Enzymes and Proteins Each metabolic pathway is regulated by certain key enzymes or proteins that play pivotal roles in the pathway. These key molecules often act as catalysts for critical steps wi......

• • What are the Reference Ranges for Total Bile Acids in the Serum, Liver, and Feces of C57BL/6 Mice?

  The reference concentration ranges for total bile acids in the serum, liver, and feces of C57BL/6 mice can vary due to factors such as experimental conditions, housing environment, methods, and sources of literature. Below are some general reference ranges, which should be adapted based on specific experimental setups and relevant literature:   Total Bile Acids in Serum Typically between 5-30 µmol/L, although this range can differ depending on experimental conditions and literature sources.   Total Bi......

• • How to Generate Clustering Analysis Plots for Differential Metabolites?

  Clustering analysis of differential metabolites is commonly visualized using heatmaps or cluster dendrograms. These two plot types are particularly useful for displaying the expression patterns of metabolites across various samples or conditions, along with their clustering relationships. Below is an overview of the typical steps involved in creating these visualizations:   1. Data Preparation Begin by collecting metabolite data from all samples. To minimize the impact of varying measurement scales on......