Direct Quantitative Analysis of Multiple microRNAs (DQAMmiR) with Peptide Nucleic Acid Hybridization Probes
Bibliographic record
Abstract
Direct quantitative analysis of multiple miRNAs (DQAMmiR) is a hybridization-based assay, in which the excess of the DNA hybridization probes is separated from the miRNA-probe hybrids, and the hybrids are separated from each other in gel-free capillary electrophoresis (CE) using two types of mobility shifters: single-strand DNA binding protein (SSB) added to the CE running buffer and peptide drag tags conjugated with the probes. Here we introduce the second-generation DQAMmiR, which utilizes peptide nucleic acid (PNA) rather than DNA hybridization probes and requires no SSB in the CE running buffer. PNA probes are electrically neutral, while PNA-miRNA hybrids are negatively charged, and this difference in charge can be a basis for separation of the hybrids from the probes. In this proof-of-principle work, we first experimentally confirmed that the PNA-RNA hybrid was separable from the excess of the PNA probe without SSB in the running buffer, resulting in a near 10 min time window, which would allow, theoretically, separation of up to 30 hybrids. Then, we adapted to PNA-RNA hybrids our previously developed theoretical model for predicting hybrid mobilities. The calculation performed with the modified theoretical model indicated that PNA-RNA hybrids of slightly different lengths could be separated from each other without drag tags. Accordingly, we designed a simple experimental model capable of confirming: (i) separation of tag-free hybrids of different lengths and (ii) separation of same-length hybrids due to a drag tag on the PNA probe. The experimental model included three miRNAs: 20-nt miR-147a, 20-nt miR-378g, and 22-nt miR-21. The three complementary PNA probes had lengths matching those of the corresponding target miRNAs. The probe for miR-147a had a short five-amino-acid drag tag; the other two had no drag tags. We were able to achieve baseline separation of the three hybrids from each other. The LOQ of 14 pM along with the high accuracy (recovery >90%) and precision (RSD ≈ 10%) of the assay at picomolar target concentrations suggest that PNA-facilitated DQAMmiR could potentially support practical miRNA analysis of clinical samples.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".