Accurate MicroRNA Analysis in Crude Cell Lysate by Capillary Electrophoresis-Based Hybridization Assay in Comparison with Quantitative Reverse Transcription-Polymerase Chain Reaction
Bibliographic record
Abstract
Accurate quantitation of microRNA (miRNA) in tissue samples is required for validation and clinical use of miRNA-based disease biomarkers. Since sample processing, such as RNA extraction, introduces undesirable biases, it is advantageous to measure miRNA in a crude cell lysate. Here, we report on accurate miRNA quantitation in crude cell lysate by a CE-based hybridization assay termed direct quantitative analysis of multiple miRNAs (DQAMmiR). Accuracy and precision of miRNA quantitation were determined for miRNA samples in a crude cell lysate, RNA extract from the lysate, and a pure buffer. The results showed that the measurements were matrix-independent with inaccuracies of below 13% from true values and relative standard deviations of below 11% from the mean values in a miRNA concentration range of 2 orders of magnitude. We compared DQAMmiR-derived results with those obtained by a benchmark miRNA-quantitation method-quantitative reverse transcription-polymerase chain reaction (qRT-PCR). qRT-PCR-based measurements revealed multifold inaccuracies and relative standard deviations of up to 70% in crude cell lysate. Robustness of DQAMmiR to changes in sample matrix makes it a perfect candidate for validation and clinical use of miRNA-based disease biomarkers.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".