Quantitative analysis of low‐abundance peptides in HeLa cell cytoplasm by targeted liquid chromatography/mass spectrometry and stable isotope dilution: emphasising the distinction between peptide detection and peptide identification
Bibliographic record
Abstract
We present the application of a targeted liquid chromatography/mass spectrometry (LC/MS) approach developed on a linear ion trap for the evaluation of the abundance of cytoplasmic proteins from a HeLa cell extract. Using a standard data-dependent approach, we identified some specific peptides from this extract which were also commercially available in their AQUA form (use for absolute quantitation). For some of the peptides, we observed a non-linear response between the intensity and the added quantity which was then fitted using a quadratic fit. All AQUA peptides spiked into a mix of 3 microg of the HeLa cell digest extract were detected down to 16 fmol. We placed an emphasis on peptide detection which, in this study, is performed using a combination of properties such as three specific Q3-like ion signatures (for a given Q1-like selection) and co-elution with the AQUA peptide counterparts. Detecting a peptide without necessarily identifying it using a search engine imposes less constraint in terms of tandem mass (MS/MS) spectra purity. An example is shown where a peptide is detected using those criteria but could not be identified by Mascot due to its lower abundance. To complement this observation, we used a cross-correlation analysis approach in order to separate two populations of MS/MS fragments based on differences in their elution patterns. Such an approach opens the door to new strategies to analyse lower intensity peptide fragments. An in silico analysis of the human trypsinosome allows the evaluation of how unique are the sets of features that we are using for peptide detection.
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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 | 0.000 | 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 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".