Universal Drag Tag for Direct Quantitative Analysis of Multiple MicroRNAs
Bibliographic record
Abstract
Using microRNA (miRNA) as molecular markers of diseases requires a method for accurate measurement of multiple miRNAs in biological samples. Direct quantitative analysis of multiple miRNAs (DQAMmiR) has been recently developed based on a classical hybridization approach. In DQAMmiR, miRNAs are hybridized with excess fluorescently labeled complementary DNA probes. Capillary electrophoresis (CE) is used to separate the unreacted probes from the hybrids and the hybrids from each other. The challenging separation is achieved by using two types of mobility modifiers. Single-strand DNA binding protein (SSB) is added to the running buffer to bind and shift the single-stranded unreacted probes from the double-stranded hybrids. Different drag tags are built into the probes to introduce significant differential mobility between their respective hybrids. For the method to be practical it requires a universal extendable drag tag. Polymers are a logical choice for making extendable drag tags. Our recent theoretical work suggested that short peptides could provide a sufficient mobility shift to facilitate DQAMmiR. Here, we experimentally confirm this prediction in the analysis of five miRNAs: mir10b, mir21, mir125b, mir145, and mir155. We conjugated four fluorescently labeled DNA molecules with peptides of 5, 10, 15, or 20 neutral amino acids in length; the fifth probe was peptide-free. The peptide tags showed no interference with SSB binding to the probes and facilitated separation of the five hybrids. The mobilities of the five hybrids were used to refine the previously suggested theory. The analysis was performed in both a pure buffer and in cell lysate. Our analysis of the experimental data suggests that using DNA-peptide probes can readily facilitate simultaneous analysis of more than 10 miRNAs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".