How to perform joint analysis of lipidomics and transcriptomics?
Integrated analysis of lipidomics and transcriptomics typically includes the following steps:
1. Sample Preparation
Divide the same biological sample into two parts, one for lipidomics analysis and the other for transcriptomics analysis.
2. Lipidomics Analysis
1. Lipid Extraction: Use organic solvents (such as chloroform-methanol) to extract lipids.
2. Analytical Techniques: Use mass spectrometry (e.g., LC-MS, GC-MS) to qualitatively and quantitatively analyze the extracted lipids.
3. Data Processing: Use specialized software for peak identification, quantification, normalization, and statistical analysis to obtain lipid profile data.
3. Transcriptomics Analysis
1. RNA Extraction: Use Trizol reagent or an RNA extraction kit to extract total RNA from the sample.
2. Sequencing: Use high-throughput sequencing technology (e.g., Illumina RNA-seq) to sequence RNA and obtain gene expression profile data.
3. Data Processing: Use bioinformatics tools (e.g., Hisat2, StringTie) to align, quantify RNA-seq data, and calculate gene expression levels.
4. Data Integration and Joint Analysis
1. Data Preprocessing: Normalize and correct batch effects for both lipidomics and transcriptomics data.
2. Correlation Analysis: Use correlation analysis (such as Pearson or Spearman correlation coefficients) to explore the relationship between lipids and gene expression, identifying significantly correlated lipids and genes.
3. Network Analysis: Construct metabolic or gene regulatory networks and use tools (such as Cytoscape) to analyze interactions between lipids and genes.
4. Functional Enrichment Analysis: Use GO or KEGG analysis to explore biological pathways and functions related to differential lipids or genes.
5. Interpretation of Results
1. Biological Significance: Interpret how lipid metabolism affects gene expression based on the joint analysis results and the roles these changes may play in biological processes (such as diseases, development, etc.).
2. Hypothesis Generation: Propose new hypotheses based on the results, which may include mechanisms of specific lipids regulating specific gene expression or the role of transcription factors in lipid metabolism.
6. Validation Experiments
1. Functional Experiments: Conduct gene knockout, overexpression, or lipid metabolism intervention experiments to verify the causal relationship between lipids and gene expression.
2. Integration with Other Omics: If necessary, combine with proteomics or metabolomics data to further validate and expand findings.
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Related Services:
Integration of Transcriptomics and Metabolomics
Integration of Transcriptomics and Proteomics
Integration of Lipidomics and Proteomics
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