Peptide Separations by On-Line MudPIT Compared to Isoelectric Focusing in an Off-Gel Format: Application to a Membrane-Enriched Fraction from C2C12 Mouse Skeletal Muscle Cells
Bibliographic record
Abstract
High-resolution peptide separation is pivotal for successful shotgun proteomics. The need for capable techniques propels invention and improvement of ever more sophisticated approaches. Recently, Agilent Technologies has introduced the OFFGEL fractionator, which conducts peptide separation by isoelectric focusing in an off-gel setup. This platform has been shown to accomplish high resolution of peptides for diverse sample types, yielding valuable advantages over comparable separation techniques. In this study, we deliver the first comparison of the newly emerging OFFGEL approach to the well-established on-line MudPIT platform. Samples from a membrane-enriched fraction isolated from murine C2C12 cells were subjected to replicate analysis by OFFGEL (12 fractions, pH 3-10) followed by RP-LC-MS/MS or 12-step on-line MudPIT. OFFGEL analyses yielded 1398 proteins (identified by 10,269 peptides), while 1428 proteins (11,078 peptides) were detected with the MudPIT approach. Thus, our data shows that both platforms produce highly comparable results in terms of protein/peptide identifications and reproducibility for the sample type analyzed. We achieve more accurate peptide focusing after OFFGEL fractionation with 88% of all peptides binned to a single fraction, as compared to 61% of peptides detected in only one step in MudPIT analyses. Our study suggests that both platforms are equally capable of high quality peptide separation of a sample with medium complexity, rendering them comparably valuable for comprehensive proteomic analyses.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".