Rapid electrotransfer probing for improved detection sensitivity in in-gel immunoassays
Why this work is in the frame
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Bibliographic record
Abstract
Protein electrotransfer in conventional western blotting facilitates detection of size-separated proteins by diffusive immunoprobing, as analytes are transferred from a small-pore sizing gel to a blotting membrane for detection. This additional transfer step can, however, impair detection sensitivity through protein losses and confound protein localization. To overcome challenges associated with protein transfer, in-gel immunoassays immobilize target proteins to the hydrogel matrix for subsequent in-gel immunoprobing. Yet, detection sensitivity in diffusive immunoprobing of hydrogels is determined by the gel pore size relative to the probe size, and in-gel immunoprobing results in (i) reduced in-gel probe concentration compared to surrounding free-solution, and (ii) slow in-gel probe transfer compared to immunocomplex dissociation. Here, we demonstrate electrotransfer probing for effective and rapid immunoprobing of in-gel immunoassays. Critically, probe (rather than target protein) is electrotransferred from an inert, large-pore 'loading gel' to a small-pore protein sizing gel. Electric field is used as a tuneable parameter for electromigration velocity, providing electrotransfer probing with a fundamental advantage over diffusive probing. Using electrotransfer probing, we observe 6.5 ± 0.1× greater probe concentration loaded in-gel in ∼82× time reduction, and 2.7 ± 0.4× less probe concentration remaining in-gel after unloading in ∼180× time reduction (compared to diffusive probing). We then apply electrotransfer probing to detect OVA immobilized in-gel and achieve 4.1 ± 3.4× greater signal-to-noise ratio and 30× reduction in total immunoprobing duration compared to diffusive probing. We demonstrate electrotransfer probing as a substantially faster immunoprobing method for improved detection sensitivity of protein sizing in-gel immunoassays.
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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 | 0.000 | 0.001 |
| 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 it