Amicon-adapted enhanced FASP: an in-solution digestion-based alternative sample preparation method to FASP
Bibliographic record
Abstract
Sample preparation is a crucial step for liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics. Sodium dodecyl sulfate (SDS) is a powerful denaturing detergent that allows for long-term preservation of protein integrity. However, as it inhibits trypsin and interferes with LC-MS/MS analyses, it must be removed from samples prior to these experiments. The Filter-Aided Sample Preparation (FASP) method is actually one of the preferred and simplest methods for such purpose. Nonetheless, there exist great disparities in the quality of outcomes when comparing FASP to other protocols depending on the authors, and recent reports have pointed to concerns regarding its depth of proteome coverage. To address these issues, we propose an Amicon-adapted in-solution-based enhanced FASP (eFASP) approach that relies on current best practices in comprehensive proteomics sample preparation. Human megakaryoblastic leukaemia cancer cells’ protein extracts were treated in parallel with both Amicon-adapted eFASP and FASP, quantified for remaining SDS and then analyzed with a 1-hr gradient LC-MS/MS run. The Amicon-adapted eFASP utilizes a passivated low molecular weight cut-off Amicon filter, and incorporates a cleaning step with a high-content deoxycholate buffer and a ‘one-step-two-enzymes’ trypsin/Lys-C in-solution digestion. Amicon-adapted eFASP was found more reproducible and deepened proteome coverage, especially for membrane proteins. As compared to FASP, Amicon-adapted eFASP removed much of SDS from high-protein samples and reached a notable depth of proteome coverage with nearly 1,700 proteins identified in a 1 hr LC-MS/MS single-run analysis without prior fractionation. Amicon-adapted eFASP can therefore be regarded as a simple and reliable sample preparation approach for comprehensive proteomics.
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".