Metabolomics FAQ

• • 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......

• • What Reagents and Their Names Are Required in Metabolomics?

  In metabolomics, the reagents commonly employed are typically associated with sample preparation, metabolite extraction, derivatization, and analysis. Below are some widely used reagents at each stage of metabolomics research:   Sample Preparation and Extraction 1. Methanol Frequently used in sample pretreatment and extraction.   2. Acetonitrile Utilized for metabolite extraction and protein precipitation.   3. Chloroform Commonly employed for extracting lipid metabolites.   Derivatization Reagents 1.......

• • How Should One Interpret an OPLS-DA Plot in MetaboAnalyst?

  An OPLS-DA plot typically comprises a score plot and a loading plot. The score plot illustrates how samples are projected onto the principal components of the model and is commonly utilized to depict the separation between different groups, such as disease and control groups. The loading plot, on the other hand, indicates the contribution of individual variables (e.g., metabolites) in distinguishing between these groups.   Interpretation of the Score Plot 1. Sample Clustering Assess whether samples fr......

• • How to Perform WGCNA on a Pathway of Interest and Identify Hub Genes?

  1. Data Preparation To begin, a high-quality gene expression dataset is required, typically derived from RNA-seq or microarray experiments.   2. Data Preprocessing Standardize the dataset, eliminate batch effects (if applicable), and filter out genes with minimal expression levels to reduce noise.   3. Selection of Pathways of Interest Based on the research objectives, select one or more relevant pathways from well-established biological databases such as KEGG and Reactome.   4. Construction of the Ex......

• • Which Software Is Commonly Used for Generating PLS-DA/OPLS-DA 2D Plots?

  PLS-DA/OPLS-DA 2D plots are typically generated using specialized statistical and data analysis software. Commonly used tools include:   R Language R is a powerful statistical computing and visualization tool. By utilizing packages such as mixOmics or ropls, users can conduct PLS-DA and OPLS-DA analyses and generate corresponding 2D plots.   MetaboAnalyst MetaboAnalyst is a web-based platform that provides a comprehensive suite of tools specifically designed for metabolomics data analysis. It includes......

• • Is an OPLS-DA Model with a Q2Y Value of Around 0.2 Considered Reliable in the Case of a Small Sample Size?

  Q2Y Value as a Critical Metric for Evaluating the Predictive Performance of OPLS-DA Models 1. Understanding the Q2Y Value The Q2Y value is widely used in OPLS-DA to assess the predictive performance of a model. A higher Q2Y value (close to 1) indicates strong predictive reliability, whereas a lower Q2Y value suggests poor predictive performance. Typically, a Q2Y value above 0.5 is deemed acceptable for model validation.   2. Impact of Small Sample Sizes on Q2Y Values When working with small sample siz......

• • How to Calculate Polysaccharide Purity?

  The purity of polysaccharides is typically determined using analytical techniques such as high-performance liquid chromatography (HPLC) or gel permeation chromatography (GPC). Different analytical methods may employ distinct calculation approaches. Here, we describe a purity calculation method based on chromatographic analysis:   Sample Preparation and Analysis The polysaccharide sample is subjected to analysis using an appropriate chromatographic system.   Peak Area Calculation In the chromatogram, p......

• • What Is the Optimal Concentration Method for High-Volume Solutions in Polysaccharide Extraction and Purification?

  During the extraction and purification of polysaccharides, if the solution volume is large, the following concentration methods can be considered:   Filtration-Based Concentration An appropriate filter membrane or filter paper can be used to remove macromolecules and impurities from the polysaccharide solution, yielding a relatively pure filtrate. The filtrate is then subjected to reduced temperature or vacuum conditions to facilitate solvent evaporation, thereby achieving concentration.   Membrane Se......