Comparison of data‐acquisition methods for the identification and quantification of histone post‐translational modifications on a Q Exactive HF hybrid quadrupole Orbitrap mass spectrometer
Bibliographic record
Abstract
RATIONALE: Histone post-translational modifications (PTMs) play key roles in regulating eukaryotic gene expression. Mass spectrometry (MS) has emerged as a powerful method to characterize and quantify histone PTMs as it allows unbiased identification and quantification of multiple histone PTMs including combinations of the modifications present. METHODS: In this study we compared a range of data-acquisition methods for the identification and quantification of the histone PTMs using a Q Exactive HF Orbitrap. We compared three different data-dependent analysis (DDA) methods with MS2 resolutions of 120K, 60K, 30K. We also compared a range of data-independent analysis (DIA) methods using MS2 isolation windows of 20 m/z and DIAvw to identify and quantify histone PTMs in Chinese hamster ovary (CHO) cells. RESULTS: The increased number of MS2 scans afforded by the lower resolution methods resulted in a higher number of queries, peptide sequence matches (PSMs) and a higher number of peptide proteoforms identified with a Mascot Ion score greater than 46. No difference in the proportion of peptide proteoforms with Delta scores >17 was observed. Lower coefficients of variation (CVs) were obtained in the DIA MS1 60 K MS2 30 K 20 m/z isolation windows compared with the other data-acquisition methods. CONCLUSIONS: We observed that DIA which offers advantages in flexibility and identification of isobaric peptide proteoforms performs as well as DDA in the analysis of histone PTMs. We were able to identify 71 modified histone peptides for histone H3 and H4 and quantified 64 across each of the different acquisition methods.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".