Characterizing multiple exogenous and endogenous small RNA populations in parallel with subfemtomolar sensitivity using a streptavidin gel-shift assay
Bibliographic record
Abstract
Here we present a simple and inexpensive gel-shift assay for the detection and quantification of small RNAs. The assay is at least 5-10 times more sensitive than a conventional Northern, and is highly scalable. Total RNA is first size purified to enrich the desired size range, phosphatase treated, and then radiolabeled to high specific activity using polynucleotide kinase. The resulting RNA stock is then hybridized to an excess of biotinylated DNA probe oligonucleotide, prior to mixing with streptavidin and loading on a native gel. The amount of supershifted material was proportional to the amount of labeled target RNA in the sample. We applied this method to verify sequencing data originally obtained from a four-point comparison study on the effect of endogenous expression of HC-Pro on Y-satellite/cucumber mosaic virus infection in tobacco plants. The results of the streptavidin gel-shift assay were consistent with the concentrations of small RNA infected plants inferred by our original cloning data, and rapidly provided information about the relative concentration of a number of viral and endogenous small RNAs. Further straightforward improvements to this simple methodology might be expected to improve the methods sensitivity by as much as another 10-fold.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.002 |
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".