Capturing Plant Metabolome with Direct-Immersion in Vivo Solid Phase Microextraction of Plant Tissues
Bibliographic record
Abstract
For the first time, an in vivo sampling mode of direct immersion-solid phase microextraction (DI-SPME) was employed to capture the metabolome of living plant specimens, using apple (Malus × domestica Borkh.) as a model system. Metabolites were extracted from apple tissues and introduced by thermal desorption into a comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry instrument. The feasibility of this sampling approach, based on exploitation of microextraction principles, including negligible depletion of free analyte concentrations, solventless sampling and sample preparation, and on-site compatibility, was determined in global metabolite analysis. Rather than adopting an approach of traditional sample preparation, requiring metabolism quenching and laborious sample preparation, the objective of the study was to capture the metabolome in vivo, evaluate the feasibility of the approach to provide unbiased extraction coverage, and compare analytical precision when different SPME sampling modes are employed. The potential of in vivo DI-SPME in quantitative plant metabolomics was assessed by evaluating changes in metabolic fingerprints in response to fruit maturation. The in vivo SPME sampling approach has been demonstrated as capable of sampling living systems with high reproducibility, considering that nearly 50% of hundreds of evaluated compounds included in the determination of analytical performance met the 15% RSD FDA criterion. Esters were extracted with high repeatability (% RSD for hexyl butanoate and butyl butanoate of 16.5 and 5.9, respectively, from 9 determinations in 3 apples) and found to be upregulated in response to apple fruit maturation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".