How to perform metabolomics combined with 16S analysis? Is 3 samples per group enough?
Metabolomics combined with 16S rRNA gene sequencing can be used to simultaneously reveal changes in metabolites and the structure of microbial communities. Below is a detailed step-by-step guide and experimental design recommendations.
I. Study Design and Sample Size
1. Sample Size
Three samples per group are typically considered the minimum number for replication. While this is suitable for preliminary exploratory studies, it may have low statistical power and might not capture all biological variability. Ideally, each group should have at least 5-6 biological replicates to improve the reliability of statistical analyses and the credibility of conclusions.
2. Experimental Group Design
Determine experimental and control groups.
II. Sample Collection and Processing
1. Sample Types
(1) Metabolomics analysis typically uses biological samples such as blood, urine, tissues, or cell cultures.
(2) 16S rRNA gene sequencing typically uses feces, gut contents, oral swabs, or environmental samples.
2. Sample Collection
(1) Ensure strict control of conditions during sample collection to avoid cross-contamination.
(2) Samples should be rapidly frozen (-80°C) after collection to preserve their integrity.
3. Sample Processing
(1) Metabolomics: Typically involves metabolite extraction (e.g., using solvents like methanol or acetonitrile) followed by liquid chromatography-mass spectrometry (LC-MS) or gas chromatography-mass spectrometry (GC-MS) analysis.
(2) 16S rRNA gene sequencing: After DNA extraction, perform PCR amplification of specific 16S rRNA gene regions (usually V3-V4 or V4 regions), then conduct high-throughput sequencing (e.g., Illumina MiSeq).
III. Data Acquisition and Analysis
1. Metabolomics Data Analysis
(1) Data Preprocessing: Raw data undergo quality control, peak detection, alignment, normalization, and annotation.
(2) Statistical Analysis: Employ multivariate analysis (e.g., PCA, PLS-DA) and univariate analysis (e.g., t-test, ANOVA) to identify significantly altered metabolites.
(3) Metabolic Pathway Analysis: Map significantly altered metabolites to metabolic pathways to understand potential biological significance.
2. 16S rRNA Gene Sequencing Data Analysis
(1) Data Preprocessing: Raw sequence data undergo quality control, denoising, merging, and classification.
(2) Microbial Community Analysis: Analyze community diversity (α-diversity and β-diversity) and composition through OTU or ASV clustering.
(3) Differential Analysis: Use statistical methods (e.g., LEfSe, ANCOM) to identify significantly different microbial taxa.
IV. Integrated Analysis
1. Data Integration
(1) Combine metabolomics and 16S rRNA gene sequencing data using multivariate statistical methods (e.g., co-network analysis, CCA, PLS) to explore associations between metabolites and microbes.
(2) Use systems biology approaches to build association networks between metabolites and microbes, revealing potential metabolic pathways and microbial regulation mechanisms.
2. Biological Interpretation
(1) Elucidate the biological relationship between metabolite changes and microbial community structure changes by combining metabolic pathway and microbial functional analyses.
(2) Utilize literature and databases (e.g., KEGG, MetaboAnalyst) for functional annotation to interpret the biological significance of results.
BiotechPack, A Biopharmaceutical Characterization and Multi-Omics Mass Spectrometry (MS) Services Provider
Related Services:
How to order?
