Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification
Abstract
:1. Introduction
2. Results
2.1. Mixed Mode Chromatography for Central Carbon Metabolite Detection
2.2. Method Validation Using Purified Metabolites
2.3. Structural Diversity Assessment
2.4. Mathematical Model for Structure Digitization and Prediction
2.4.1. Model Training
2.4.2. Model Performance Predicting RT
2.4.3. Identification of Important Functional Groups
2.5. Application to GLS2 KO Mouse Primary Hepatocytes
3. Discussion
4. Materials and Methods
4.1. Chemicals
4.2. Standard Solutions and Sample Preparations
4.3. LCMS
4.4. Hepatocyte and Tissue Isolation and Treatment
4.5. Cell and Tissue Extraction
4.6. Message Passing Neural Network (MPNN)
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Metabolite | LOD (pmole) | CV (n = 4) | Linear Range (pmole) | Linear Coefficient (R2) | Mass Error (ppm) |
---|---|---|---|---|---|
2-phosphoglycerate | 0.0254 | 10.8 | 0.229–1000 | 0.98729 | 0.54 |
6-phosphogluconate | 0.076 | 10.7 | 0.229–500 | 0.9993 | 0 |
a-ketoglutarate | 0.685 | 15.8 | 2.06–1500 | 0.99622 | 0.69 |
L-alanine | 6.15 | 20.9 | 6.15–1500 | 0.99199 | 2.27 |
L-aspartate | 0.076 | 9.15 | 0.229–1500 | 0.99862 | 0 |
cis-aconitate | 0.0254 | 39.3 | 0.0760–166.5 | 0.99015 | 0.58 |
DHAP | 18.5 | 33.2 | 18.5–1500 | 0.99815 | 0.59 |
sedoheptulose 7P | 0.076 | 12.8 | 0.229–500 | 0.9981 | 0.69 |
fructose-13C6 | 0.0254 | 12 | 0.0254–166.5 | 0.99749 | 0.54 |
fructose-1,6-BP | 0.0254 | 17.6 | 0.0254–1500 | 0.99259 | 0.59 |
fructose-1P | 0.0254 | 9.62 | 0.0760–1500 | 0.99408 | 0 |
fructose-6P | 0.0254 | 12.8 | 0.0760–1500 | 0.99078 | 0 |
fumarate | 0.685 | 11.3 | 2.06–1500 | 0.98993 | 0 |
glucose | 0.685 | 13.8 | 2.06–1500 | 0.98751 | 0.56 |
glucose-1P | 0.0254 | 12.3 | 0.0760–500 | 0.9955 | 0 |
glucose-6P | 0.0254 | 11.4 | 0.0760–500 | 0.99585 | 0 |
glutamate | 0.0254 | 8.95 | 0.229–3000 | 0.99572 | −0.68 |
glutamine | 0.076 | 6.85 | 0.229–1500 | 0.99001 | −0.69 |
glyceraldehyde-3P | 55.5 | 5.45 | 55.5–1500 | 0.98939 | 0.59 |
glycine | 18.5 | 4.71 | 18.5–1500 | 0.99478 | 0 |
glyecrol-3P | 0.076 | 7.95 | 0.229–500 | 0.99723 | 0.58 |
isocitrate | 6.15 | 31.6 | 6.15–500 | 0.96498 | 0.52 |
lactate | 0.229 | z12.4 | 0.229–55.5 | 0.98791 | 0 |
malate | 0.076 | 39.2 | 0.229–166.5 | 0.99544 | 0.75 |
myo-inositol-d6 | 6.15 | 15.1 | 6.15–1500 | 0.9838 | 0.54 |
phosphoenolpyruvate | 0.076 | 14.3 | 0.229–1500 | 0.99564 | 0.6 |
pyruvate | 2.06 | 18.9 | 2.06–1000 | 0.96721 | 0 |
ribulose-5P | 0.685 | 9.84 | 0.685–1000 | 0.98069 | −0.44 |
serine | 0.229 | 9.88 | 0.229–1500 | 0.99155 | 0.96 |
sorbitol | 0.076 | 11.2 | 0.229–500 | 0.99293 | 0 |
succinate | 0.229 | 12.2 | 2.06–1000 | 0.96498 | 0 |
UDP-glucose | 0.229 | 12.1 | 0.229–1500 | 0.99135 | 1.77 |
xylitol-13C5 | 0.229 | 9.35 | 2.06–1000 | 0.98754 | 0.64 |
Training | Test | |||
---|---|---|---|---|
Subset | Dataset Size | # Points with Error <2 min | Dataset Size | # Points with Error <2 min |
Sugars | 55 | 55 | 11 | 10 |
Sugar-P | 39 | 39 | 6 | 3 |
Carboxylic acids | 148 | 145 | 35 | 27 |
Isomers | 37 | 34 | 11 | 8 |
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Xing, G.; Sresht, V.; Sun, Z.; Shi, Y.; Clasquin, M.F. Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification. Metabolites 2021, 11, 772. https://doi.org/10.3390/metabo11110772
Xing G, Sresht V, Sun Z, Shi Y, Clasquin MF. Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification. Metabolites. 2021; 11(11):772. https://doi.org/10.3390/metabo11110772
Chicago/Turabian StyleXing, Gang, Vishnu Sresht, Zhongyuan Sun, Yuji Shi, and Michelle F. Clasquin. 2021. "Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification" Metabolites 11, no. 11: 772. https://doi.org/10.3390/metabo11110772