Coomassie staining provides routine (sub)femtomole in‐gel detection of intact proteoforms: Expanding opportunities for genuine Top‐down Proteomics
Bibliographic record
Abstract
Modified colloidal Coomassie Brilliant Blue (cCBB) staining utilising a novel destain protocol and near-infrared fluorescence detection (nIRFD) rivals the in-gel protein detection sensitivity (DS) of SYPRO Ruby. However, established DS estimates are likely inaccurate in terms of 2DE-resolved proteoform 'spots' since DS is routinely measured from comparatively diffuse protein 'bands' following wide-well 1DE. Here, cCBB DS for 2DE-based proteomics was more accurately determined using narrow-well 1DE. As precise estimates of protein standard monomer concentrations are essential for accurate quantitation, coupling UV absorbance with gel-based purity assessments is described. Further, as cCBB is compatible with both nIRFD and densitometry, the impacts of imaging method (and image resolution) on DS were assessed. Narrow-well 1DE enabled more accurate quantitation of cCBB DS for 2DE, achieving (sub)femtomole DS with either nIRFD or densitometry. While densitometry offers comparative simplicity and affordability, nIRFD has the unique potential for enhanced DS with Deep Imaging. Higher-resolution nIRFD also improved analysis of a 2DE-resolved proteome, surpassing the DS of standard nIRFD and densitometry, with nIRFD Deep Imaging further maximising proteome coverage. cCBB DS for intact proteins rivals that of mass spectrometry (MS) for peptides in complex mixtures, reaffirming that 2DE-MS currently provides the most routine, broadly applicable, robust, and information-rich Top-down approach to Discovery Proteomics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".