Rapid electrotransfer probing for improved detection sensitivity in in-gel immunoassays
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".