Nontargeted Metabolome Analysis by Use of Fourier Transform Ion Cyclotron Mass Spectrometry
Bibliographic record
Abstract
Advanced functional genomic tools now allow the parallel and high-throughput analyses of gene and protein expression. Although this information is crucial to our understanding of gene function, it offers insufficient insight into phenotypic changes associated with metabolism. Here we introduce a high-capacity Fourier Transform Ion Cyclotron Mass Spectrometry (FTMS)-based method, capable of nontargeted metabolic analysis and suitable for rapid screening of similarities and dissimilarities in large collections of biological samples (e.g., plant mutant populations). Separation of the metabolites was achieved solely by ultra-high mass resolution; Identification of the putative metabolite or class of metabolites to which it belongs was achieved by determining the elemental composition of the metabolite based upon the accurate mass determination; and relative quantitation was achieved by comparing the absolute intensities of each mass using internal calibration. Crude plant extracts were introduced via direct (continuous flow) injection and ionized by either electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI) in both positive or negative ionization modes. We first analyzed four consecutive stages of strawberry fruit development and identified changes in the levels of a large range of masses corresponding to known fruit metabolites. The data also revealed novel information on the metabolic transition from immature to ripe fruit. In another set of experiments, the method was used to track changes in metabolic profiles of tobacco flowers overexpressing a strawberry MYB transcription factor and altered in petal color. Only nine masses appeared different between transgenic and control plants, among which was the mass corresponding to cyanidin-3-rhamnoglucoside, the main flower pigment. The results demonstrate the feasibility and utility of the FTMS approach for a nontargeted and rapid metabolic "fingerprinting," which will greatly speed up current efforts to study the metabolome and derive gene function in any biological system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".