Comparison of sample preparation techniques for large‐scale proteomics
Bibliographic record
Abstract
Numerous workflows exist for large-scale bottom-up proteomics, many of which achieve exceptional proteome depth. Herein, we evaluated the performance of several commonly used sample preparation techniques for proteomic characterization of HeLa lysates [unfractionated in-solution digests, SDS-PAGE coupled with in-gel digestion, gel-eluted liquid fraction entrapment electrophoresis (GELFrEE) technology, SCX StageTips and high-/low-pH reversed phase fractionation (HpH)]. HpH fractionation was found to be superior in terms of proteome depth (>8400 proteins detected) and fractionation efficiency compared to other techniques. SCX StageTip fractionation required minimal sample handling and was also a substantial improvement over SDS-PAGE separation and GELFrEE technology. Sequence coverage of the HeLa proteome increased to 38% when combining all workflows, however, total proteins detected improved only slightly to 8710. In summary, HpH fractionation and SCX StageTips are robust techniques and highly suited for complex proteome analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".