Related Experiment Video
Updated: Mar 25, 2026
A Reversible, Non-invasive Method for Airway Resistance Measurements and Bronchoalveolar Lavage Fluid Sampling in Mice
Published on: April 13, 2010
[Experimental study of metabonomics in the diagnosis of allergic rhinitis in mice]
1Department of Otorhinolaryngology, Second Hospital of Shanxi Medical University, Taiyuan 030001, China.
Objective:
To investigate the application of metabonomics in the diagnosis of allergic rhinitis.
Methods:
Eighty male Kunming mice were randomly divided into two groups, control group (30 mice) and allergic rhinitis (AR) group (50 mice). After modeling, removal behavior score more than 6 and retain 30 mice behavior score equal to 6.Collect the mice peripheral blood and preparate blood serum, using UPLC-MS chromatographic separation and detection. The data were pretreated by SPSS and Excel, after chromatographic peak matching by MZmine. Firstly , delete interference data in accordance with the 80% rule .Then, the investigate data were analyzed by PLS-DA and PCA-X.
Results:
Three-dimensional view of the control group (30 mice) and AR group (30 mice) blood serum data was drawn using PCA-X and PLS-DA method. The two groups of samples could be completely separated through views, which showed that there was a significant difference between the two groups of data. There were some differences in the blood metabolites between the control group and AR group .
Conclusion:
The study showed that it was scientific and feasible to diagnose AR using the metabonomics.
Insights
Metabonomics effectively diagnoses allergic rhinitis (AR) by analyzing blood serum metabolites. This study demonstrates the feasibility of using metabolic profiling for AR detection in mice.
Area of Science:
- Biochemistry
- Immunology
- Analytical Chemistry
Background:
- Allergic rhinitis (AR) is a common allergic disease.
- Accurate diagnosis of AR is crucial for effective treatment.
- Current diagnostic methods may have limitations.
Purpose of the Study:
- To explore the application of metabonomics for diagnosing allergic rhinitis.
- To identify potential metabolic biomarkers for AR.
Main Methods:
- Utilized Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) for blood serum analysis.
- Employed multivariate statistical methods, including Principal Component Analysis (PCA-X) and Partial Least Squares Discriminant Analysis (PLS-DA).
- Analyzed data from control and AR-induced mice models.
Main Results:
- Distinct metabolic profiles were observed between control and AR groups.
- PCA-X and PLS-DA successfully separated the two groups, indicating significant differences.
- Identified variations in blood serum metabolites associated with AR.
Conclusions:
- Metabonomics is a scientifically valid and feasible approach for diagnosing allergic rhinitis.
- Metabolic profiling holds promise for developing novel diagnostic strategies for AR.
