Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma
Abstract
:1. Introduction
2. Results
2.1. Selectivity: Contaminant Profile
2.2. Linearity: Signal-Concentration Relationship
2.3. Linear Dynamic Range
2.4. Repeatability and Intermediate Precision
2.5. Concentration-Specific Study
3. Discussion
4. Materials and Methods
4.1. Sample Acquisition, Preparation, and Derivatization
4.2. GC-MS Analysis
4.3. Metabolite Identification and Quantification
4.4. Parameters Assessed for Method Development
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Type | Class | Metabolite | No. of Blanks (%) |
---|---|---|---|
Definite | Amino acids | Glycine | 6 (20%) |
- | Benzene derivatives | Benzoic acid | 22 (73.3%) |
- | Carbohydrates | Glucose and other aldohexoses | 20 (66.7%) |
- | - | Sucrose and similar disaccharides | 10 (33.3%) |
- | Fatty acids | Heptadecanoic acid or Octadecanol | 23 (76.7%) |
- | - | Myristic acid or Pentadecanol | 27 (90%) |
- | - | Nonanoic acid | 12 (40%) |
- | - | Oleic acid | 12 (40%) |
- | - | Palmitic acid | 27 (90%) |
- | - | Pentadecanoic acid or Hexadecanol | 14 (46.7%) |
- | - | Stearic acid | 27 (90%) |
- | Lipids | alpha-Monopalmitin | 27 (90%) |
- | - | beta-Monopalmitin | 27 (90%) |
- | - | beta-Monostearin | 27 (90%) |
- | - | Glycerol | 26 (86.7%) |
- | - | Thymol | 15 (50%) |
- | Organic acids | Pyruvic acid | 20 (66.7%) |
- | - | Succinic acid | 7 (23.3%) |
- | Other | Phosphoric acid | 23 (76.7%) |
- | - | Uridine | 27 (90%) |
Potential | Amino acids | Aspartic acid | 3 (10%) |
- | Benzene derivatives | Gentisic acid | 4 (13.3%) |
- | Phenol | 2 (6.7%) | |
- | Carbohydrates | Fructose or similar ketohexose | 1 (3.3%) |
- | Fatty acids | Arachidic acid or 1-Heneicosanol | 3 (10%) |
- | - | Decanoic acid | 1 (3.3%) |
- | - | Lauric acid | 4 (13.3%) |
- | - | Methyl palmitate | 2 (6.7%) |
- | - | Methyl stearate | 2 (6.7%) |
- | Lipids | Gamma-Tocopherol | 2 (6.7%) |
- | Organic acids | Acetoacetate or 2-Aminoisobutanoic acid | 3 (10%) |
- | - | Glycolic acid | 2 (6.7%) |
- | - | Lactic acid | 5 (16.7%) |
- | - | Urea | 2 (6.7%) |
- | Other | 1,2-Propanediol | 1 (3.3%) |
- | - | 4-Hydroxypyridine or 3-Hydroxypyridine | 3 (10%) |
- | - | Ethanolamine | 2 (6.7%) |
- | - | O-Methylphosphate | 3 (10%) |
- | - | Prunetin or similar isoflavone | 1 (3.3%) |
Summary | No. (% ) | Known | Unknown |
---|---|---|---|
R2adj greater than 0.95 | 3 | 2 (1.6%) | 1 (1.8%) |
R2adj (0.7, 0.95) | 64 | 52 (41.3%) | 12 (21.1%) |
R2adj (0.5, 0.7) | 30 | 22 (29.7%) | 8 (21.1%) |
R2adj less than 0.5 | 50 | 24 (32.5%) | 24 (63.2%) |
Plasma Extract Volume (µL) | No. Analytes (%) |
---|---|
75–100 | 8 (4.5%) |
100–150 | 100 (55.9%) |
150–200 | 62 (34.6%) |
200–300 | 6 (3.4%) |
300+ | 1 (0.6%) |
Methanolic Plasma Extract Volume (µL) | Methanol/H2O Volume 1 (µL) | Equivalent Plasma Volume Injected 2 (nL) | Equivalent Plasma Concentration 3 (v/v) |
---|---|---|---|
0 | 700 | 0 | 0 |
25 | 675 | 5.7 | 1.25 × 10−9 |
50 | 650 | 11.3 | 2.49 × 10−9 |
75 | 625 | 17.0 | 3.74 × 10−9 |
100 | 600 | 22.6 | 4.98 × 10−9 |
150 | 550 | 33.9 | 7.48 × 10−9 |
200 | 500 | 45.2 | 9.97 × 10−9 |
300 | 400 | 67.9 | 1.50 × 10−8 |
400 | 300 | 90.5 | 1.99 × 10−8 |
600 | 100 | 135.7 | 2.99 × 10−8 |
700 | 0 | 158.4 | 3.49 × 10−8 |
Experimental Design | Recommendations |
---|---|
Establish method blanks | Include 3 blank samples in the beginning, middle and end of every sequence run |
Use both blanks and manual curation for contaminant profiling | |
Establish a list of highly reproducible and potential contaminants | |
Linearity | Incorporate dilution into QC samples |
Metabolites showing linearity can be used as targets to validate the methodology and monitor changes | |
Lack of linearity may indicate contaminant effect or saturation effect | |
Repeatability and intermediate precision | Batch should be included in reporting and analysis of non-targeted GC-MS profiling |
Range | Linear dynamic range should be established through dilution studies |
Optimal concentration established through dilution studies should be used for metabolic profiling | |
Unknowns | Unknowns presenting as contaminants can be excluded from further analysis |
Highly-linear unknowns may be biologically important metabolites | |
Reproducible, highly linear and non-contaminant unknowns should be added to the library or databases for future references |
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Wang, H.; Muehlbauer, M.J.; O’Neal, S.K.; Newgard, C.B.; Hauser, E.R.; Bain, J.R.; Shah, S.H. Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma. Metabolites 2017, 7, 45. https://doi.org/10.3390/metabo7030045
Wang H, Muehlbauer MJ, O’Neal SK, Newgard CB, Hauser ER, Bain JR, Shah SH. Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma. Metabolites. 2017; 7(3):45. https://doi.org/10.3390/metabo7030045
Chicago/Turabian StyleWang, Hanghang, Michael J. Muehlbauer, Sara K. O’Neal, Christopher B. Newgard, Elizabeth R. Hauser, James R. Bain, and Svati H. Shah. 2017. "Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma" Metabolites 7, no. 3: 45. https://doi.org/10.3390/metabo7030045