Metabolomics
Features
● Non-targeted Metabolomics (Discovery Metabolomics)
Based on liquid chromatography-mass spectrometry (LC-MS) technology, non-targeted metabolomics enables unbiased detection of as many small-molecule metabolites as possible in biological samples such as cells, tissues, organs, or body fluids. It conducts comparative analysis between experimental and control groups, screens differential metabolites through statistical analysis.
● Non-targeted Metabolomics for Lipids
Leveraging liquid chromatography-mass spectrometry (LC-MS) technology, non-targeted Metabolomics for Lipids achieves unbiased detection of as many lipid molecules as possible in biological samples including cells, tissues, organs, or body fluids.
● Wide-Targeted Metabolomics
This service perfectly combines the high-resolution and wide-coverage advantages of untargeted technology with the high sensitivity and accurate quantification capabilities of targeted MRM (Multiple Reaction Monitoring) technology. The workflow is as follows: First, high-resolution mass spectrometry is used to perform ultra-high-coverage MS² scanning on samples to obtain MS² spectra of "all" metabolites. Then, accurate metabolite identification is conducted by combining with a high-resolution MS² library (covering over 25,000 metabolites, with multiple standard high-resolution MS² spectra for each metabolite). Next, MRM ion pair information is extracted from the mass spectra to establish a specific library for samples. Finally, triple quadrupole mass spectrometry with MRM technology is applied for accurate quantification.
● Targeted Metabolomics
Targeted metabolomics focuses on several target compounds or all/partial metabolites involved in a specific pathway. Using standard substances, it establishes a detection method with strong specificity, high sensitivity, and good repeatability for the quantification and analysis of target compounds. It adopts external standard combined with internal standard for absolute quantification, with the linearity of the standard curve reaching over 0.99 and the sensitivity up to ng/mL level.
Advantages
● Advanced Detection Platform
-Equipped with top-tier high-resolution mass spectrometers and triple quadrupole mass spectrometers, enabling simultaneous detection and analysis of thousands of metabolites.
● Comprehensive Databases
- Public Databases: Cover METLIN, KEGG, HMDB, NP Atlas, and Lipidmaps, encompassing over 500,000 metabolites.
- In-house Plant-specific Database: Contains over 25,000 metabolites, including primary and secondary metabolites.
- In-house Animal/Medical-specific Database: Includes over 12,000 metabolites.
● Strict Quality Control System
-Stringent quality control covering instrument stability, substance residue, QC sample PCA and correlation analysis to ensure reliable data quality.
● High Metabolite Detection Capacity
- Non-targeted Metabolomics: Detects an average of over 4,200 metabolites per run, ideal for exploring unknown metabolites in samples and identifying metabolites in complex samples.
- Wide-Targeted Metabolomics (Plant): Detects an average of over 2,410 metabolites per run, including primary and secondary metabolites.
- Wide-Targeted Metabolomics (Animal): Detects an average of over 1,250 metabolites per run.
- Targeted Metabolomics: Panels for multiple metabolite categories.
● Comprehensive Analysis
-Offers more than 10 analysis items and over 20 visualization charts.
● Thoughtful After-sales Service
-Provides after-sales consultation and final report interpretation support.
Service Specifications
| Solution | Platform | Recommended Biological replicates |
|---|---|---|
| Non-Targeted Metabolomics | UHPLC-TOF-MS (Waters Xevo G2-XS QTof) | Plant and microbial sample: ≥ 6
Animal sample: ≥ 10 Clinical sample: ≥ 30 All biological replicate samples analyzed independently. |
| Wide-Targeted Metabolomics | Water Xevo G2-XS QTOF + AB Sciex QTRAP 6500+ | Plant sample: ≥ 3 |
| Targeted Metabolomics | UHPLC-QQQ-MS (AB Sciex QTRAP 6500+)
GC-MS (Agilent 7890-5977, Agilent 7820-5977) |
Plant sample: ≥ 3
Animal sample: ≥ 6 |
Sample Requirements
Are you wondering whether your samples meet our criteria? Click here to get our latest sample requirements .
Service Work Flow
Sample Collection
Metabolite Extraction
Data Acquisition
Data Analysis
Data Delivery
1. Background noise and low-quality data processing of raw data
2. Data Quality Assessment
2.1 Principal component analysis
2.2 Reproducibility assessment
3. Metabolite Annotation
3.1 KEGG database annotation
3.2 HMDB database annotation
3.3 Lipidmaps database annotation
4. Sample Grouping Data Analysis (biological replicates ≥ 3)
4.1 Group principal component analysis
4.2 Orthogonal partial least squares discriminant analysis (OPLS-DA)
4.3 Group differential metabolite analysis
5. Differential Metabolite Selection (biological replicates ≥ 3)
5.1 Differential fold change analysis
5.2 Differential metabolite volcano plot
5.3 Differential metabolite clustering heatmap
5.4 Differential metabolite correlation plot
5.5 Differential metabolite Z-score plot
5.6 Differential metabolite radar plot analysis
5.7 Differential metabolite violin plot
5.8 Differential metabolite box plot
5.9 Differential metabolite KEGG functional annotation and enrichment analysis
5.10 ROC curve analysis
5.11 Differential metabolite k-means clustering
5.12 Differential metabolite Venn diagram
● Data Quality Assessment
↑Correlation analysis (Test sample/QC sample)
↑Principal component analysis
↑Metabolite clustering analysis
3. Metabolite Annotation
↑KEGG database annotation
↑HMDB database annotation
↑Lipidmaps database annotation
4. Sample Grouping Data Analysis (biological replicates ≥ 3)
↑Group principal component analysis (2D/3D)
↑Orthogonal partial least squares discriminant analysis (OPLS-DA)
5. Differential Metabolite Selection (biological replicates ≥ 3)
↑Differential fold change analysis
↑Differential metabolite volcano plot
↑Differential metabolite clustering heatmap
↑Differential metabolite correlation plot
↑Differential metabolite Z-score plot
↑Differential metabolite radar plot analysis
↑Differential metabolite violin plot
↑Differential metabolite box plot
↑Differential metabolite KEGG functional annotation and enrichment analysis
↑ROC curve analysis
↑Differential metabolite k-means clustering
↑Differential metabolite Venn diagram
|
2025 |
BEL1-like homeodomain transcription factor SAWTOOTH1 (MdSAW1) in Malus domestica enhances the tolerance of transgenic apple and Arabidopsis to zinc excess stress |
International Journal of Biological Macromolecules |
Non-Targeted Metabolomics |
|
2025 |
Interactive effect of Moringa oleifera mediated green nanoparticles and arbuscular mycorrhizal fungi on growth, root system architecture, and nutrient uptake in maize (Zea mays L.) |
Plant Physiology and Biochemistry |
Wide-Targeted Metabolomics |
|
2025 |
Jasmonate activates a SlJAZ2/3‐SlMYC3‐like module regulating K+ uptake in tomato response to low K+ stress |
Journal of Integrative Plant Biology |
Targeted Metabolomics |
