代谢组

代谢组

Introduction

If genomics and proteomics in systems biology are compared to the science of exploring the causes of biological phenomena, metabolomics can be understood as the science of exploring the results of biological phenomena, that is, the science of directly studying the change law and function of all metabolites regulated by genes and proteins. On the one hand, metabonomics research can explore some new molecular markers. On the other hand, through linkage analysis with proteomics and genomics data, it can analyze the internal changes of organisms from both causes and results, so as to push the research of system biology to a higher level.

1 technical characteristics  

1.1 GC-MS (gas chromatography) 

GC-MS is a classic technology in metabolomics research. It has the characteristics of mature and stable technology and high sensitivity. At the same time, due to its relatively perfect database, the qualitative analysis is more accurate and reliable. The disadvantages are mainly that the sample processing is relatively complex and it is difficult to determine the qualitative and quantitative analysis of substances that are not easy to be derived.

1.2 LC MS (liquid chromatography) 

The advantages mainly lie in simple sample preparation and pretreatment, good experimental repeatability, high resolution, wide separation and analysis range, while the disadvantages mainly lie in insufficient database perfection and relatively difficult qualitative analysis.

1.3 NMR (nuclear magnetic resonance) 

The biggest advantage is that it is non-destructive to the sample and non biased in the determination, that is, it is suitable for liquid samples such as blood, urine and body fluids, as well as solid samples such as tissues and organs. It has fast determination speed and can realize the dynamic monitoring of sample metabolome. The main disadvantage is low resolution.

2 Data analysis characteristics 

2.1 PCA (principal component) statistical analysis 

Principal component analysis is one of the commonly used analysis methods for metabolomic data. Through principal component analysis, we can understand the weight of different metabolites in distinguishing different samples, which is of great significance and value for the screening of different metabolites, especially important molecular markers.

2.2 PLSA Da (partial least squares discriminant analysis) statistical analysis 

Partial least squares discriminant analysis (PLS DA) is an important method of pattern recognition. This method can extract the simplified information from complex data to the greatest extent for analysis.

2.3 Metabolite function analysis 

Based on the screening of differential metabolites, the ultimate goal of metabolomics is to analyze the biological significance of metabolites. We can have a deeper understanding of the function and role of metabolites in organisms through statistical analysis such as PCA and clustering combined with pathway backtracking, bioinformatics analysis such as metabolites and protein / gene interaction.