Neuropeptidomics: Comparison of parallel reaction monitoring and data‐independent acquisition for the analysis of neuropeptides using high‐resolution mass spectrometry
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Bibliographic record
Abstract
Abstract Targeted peptide quantitation by mass spectrometry is a rapidly emerging field. Traditionally it relied on the development and validation of multiple reaction monitoring assays that could comply with a high level of sensitivity, specificity, accuracy and reproducibility in complex biological samples. However, with the introduction of high‐resolution mass spectrometers, other acquisition modes could provide more comprehensive datasets for identification and quantification but also for in‐depth data mining. The objective of this study was to evaluate two analytical approaches, parallel‐reaction monitoring (PRM) and data‐independent analysis (DIA) using a hybrid Quadrupole–Orbitrap mass spectrometer for the quantification of neuropeptides in animal spinal cord tissues. Mouse spinal cord tissues were harvested, homogenized and neuropeptides extracted using a C 18 solid‐phase extraction protocol. Chromatography was achieved using a Thermo Biobasic C 8 100 × 1 mm (5 μm) column. The initial mobile phase conditions consisted of acetonitrile and water (both containing 0.1% of formic acid) at a ratio of 5:95. An 11 min linear gradient was applied up to a ratio of 50:50 and maintained for 3 min. The flow rate was fixed at 75 μL/min and 2 μL of sample was injected. Mass spectrometry analyses were performed using a Thermo Q Exactive Plus MS using PRM and DIA approaches. Quantitative data using an isotopic dilution and a label‐free strategy were obtained for both methods and statistically compared. Using both approaches, we were able to clearly detect endogenous neuropeptides. However, with DIA, mass spectra alone could not distinguish Leu‐Enk and Met‐Enk. We used a Bland–Altman plot (Difference plot) to analyze the agreement between both approaches and no systematic bias was observed. Further statistical analyses, including variance analysis, showed more variability in DIA compared with PRM mode. Further analyses were performed using a label‐free approach and confirmed an increase of the variance using a DIA approach.
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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 | 0.001 | 0.001 |
| 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 it