Neuropeptidomics: Comparison of parallel reaction monitoring and data‐independent acquisition for the analysis of neuropeptides using high‐resolution mass spectrometry
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.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".