Internal standard application strategies in mass spectrometry imaging by desorption electrospray ionization mass spectrometry
Bibliographic record
Abstract
RATIONALE: We developed a model case study to evaluate three internal standard (IS) application strategies (methods I-III) using the psycholeptic phenobarbital (PB) and the isotopically labelled IS phenobarbital-D5 (PB-D5) from in vitro dosed tissues of the golden apple snail (Pomacea diffusa) by desorption electrospray ionization mass spectrometry imaging (DESI-MSI). METHODS: In method I, the IS was deposited as microspots on top of 10 μm thick snail tissues; in method II, a thin IS film was applied; and in method III, the IS was spiked into the DESI solvent spray. DESI-MSI analyses were performed using a Thermo LTQ mass spectrometer equipped with a custom-built DESI source and two-dimensional moving stage. PB (m/z 231) and PB-D5 (m/z 236) were monitored in selected ion monitoring mode between m/z 227 and 239. RESULTS: The analytical performance of two IS strategies (methods I and II) in DESI-MSI was evaluated based on an intra- and inter-day precision assay, an accuracy assessment, and statistical analysis. In the inter-day DESI-MSI assay, method I exhibited better precision (6.5%-7.4%) than method II (10.7%-17.6%) between 10 and 100 ng/μL. In the accuracy assessment, PB quality controls of 75 ng/μL were back-calculated as 71 ± 4 and 83 ± 9 ng/μL, resulting in relative errors of -5% and 11% for methods I and II, respectively. Method III did not work under the experimental design and was not evaluated. CONCLUSIONS: Three IS application strategies were investigated and compared for a routine quantitative DESI-MSI approach. Methods I and II were not statistically significantly different as shown by a Bland-Altman plot, suggesting that these two methods can be used interchangeably. However, method III requires further research for future quantitative DESI-MSI analyses.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".