Membrane‐Based SDS Depletion Ahead of Peptide and Protein Analysis by Mass Spectrometry
Bibliographic record
Abstract
SDS interferes with both bottom-up and top-down MS analysis, requiring removal prior to detection. Filter-aided sample preparation (FASP) is favored for bottom-up proteomics (BUP) while acetone precipitation is popular for top-down proteomics (TDP). We recently demonstrated acetone precipitation in a membrane filter cartridge. Alternatively, our automated electrophoretic device, termed transmembrane electrophoresis (TME), depletes SDS for both TDP and BUP studies. Here TME is compared to these two alternative methods of SDS depletion in both BUP and TDP workflows. To do so, a modified FASP method is described applicable to the SDS purification and recovery of intact proteins, suitable for LC/MS. All three methods reliably deplete >99.8% SDS. TME provide higher sample yields (average 90%) than FASP (55%) or acetone precipitation (57%), translating into higher total protein identifications (973 vs 877 FASP or 890 acetone) and higher spectral matches (2.5 times) per protein. In a top down workflow, each SDS-depletion method yields high-quality MS spectra for intact proteins. These results show each of these membrane-based strategies is capable of depleting SDS with high sample recovery and high spectra quality for both BUP and TDP studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
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".