Inquiry
Quantitative Lipidomics Data Analysis for Animal

Quantitative Lipidomics Data Analysis for Animal

Online Inquiry

Lipids can be divided into eight categories, namely fatty acids, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides. Lipidomics is now widely used in important fields such as drug discovery, molecular physiology, molecular pathology, functional genomics, nutrition, and environment and health.

Introduction of Quantitative Lipidomics Data Analysis for Animal

Lipidomics analysis aims to comprehensively and systematically analyze and identify lipids and the molecules interacting with them in organisms, tissues, or cells, so as to understand the structure and function of lipids and thus reveal the relationship between lipid metabolism and the physiological and pathological processes of cells, organs and even the organism. Quantitative lipidomics analysis services include lipid extraction, isolation, analytical identification, and corresponding bioinformatics analysis. CD Genomics has established a lipidome-wide quantitative library based on stable isotope-labeled internal standards and response factors, which enables quantitative analysis of a wide range of lipid types using LC-MS or GC-MS techniques. Our quantitative lipidomics data analysis services cover the following two main directions.

  • Cell lipidomics. Similar to the transcriptome and proteome, the cellular lipidome undergoes remodeling in response to various stimuli and physiological conditions.
  • Plasma lipidomics. Daily physiological conditions, genetic background, and diseases can alter plasma lipid concentrations and composition, and we can provide quantitative analysis of the plasma lipidome.

Advantages of integrating metabolomics and lipidomics.Figure 1. Advantages of integrating metabolomics and lipidomics. (Wang R, et al., 2020)

Project Workflow of Quantitative Lipidomics Data Analysis for Animal

Quantitative Lipidomics Data Analysis for Animal

Deliverables

We can provide the following analysis results, including but not limited to.

  • Heat map analysis of differential lipid clustering.
  • KEGG functional annotation of differential lipids.
  • Enrichment analysis of differential lipids.
  • Volcano diagram of differential lipids.

How Can We Help You

Choosing our quantitative lipidomics data analysis for animals can help you solve the following problems.

  • External stimuli can cause changes in lipids, and we can use quantitative lipidomics analysis to understand the patterns of lipid changes and thus help our customers screen for biomarkers of disease.
  • A single drug usually has multiple arms of action that can cause changes in the biological lipidome and metabolome, while lipids and small metabolites are highly interconnected in the functional network of the biological organism. We can use quantitative lipidomic analysis to evaluate the mechanism of action of the drug.
  • Using quantitative lipidomics to analyze the changes in lipid composition of different metabolic pathways in various metabolic diseases, we can help our customers to study the function of lipid composition during disease development and progression.

Can CD Genomics help me generate raw data?

Yes, we can. With advanced instrumentation, a high-coverage self-built database, and experienced metabolomics experts, we can provide you with quantitative lipidomics detection services.

How should I prepare and send my samples?

If you require our quantitative lipidomics detection services, please send samples as requested in the form below and contact us.

Sample Type Minimum requirement per sample Storage and transportation
Serum 2 mL Snap freeze in liquid nitrogen.
Store at -80 ℃.
Ship with dry ice.
Animal tissue 2 g
Cell 1 x 108 cells
Exosome 330 ul

References

  1. Giles C, et al. Contemporary lipidomic analytics: opportunities and pitfalls. Prog Lipid Res. 2018 Jul; 71: 86-100.
  2. Wang R, et al. Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression. J Genet Genomics. 2020 Feb 20; 47(2): 69-83.
* For Research Use Only. Not for use in diagnostic procedures.
Refresh to display the verification code
Online Inquiry