Development and optimization of a protocol for automated, low-throughput RNA purification from whole blood using the PAXgene blood RNA system
Bibliographic record
Abstract
A44 Introduction: Gene expression analysis in peripheral blood is an important tool in molecular diagnostics, monitoring diseases at the molecular level, clinical research, and clinical trials of new drugs. Since the introduction of the PAXgene Blood RNA System, researchers have expressed a need for a low-throughput, automated (LTA) solution for RNA preparation from blood collected in PAXgene Blood RNA Tubes, with purification based on the proven silica-membrane spin-column technology in the PAXgene Blood RNA Kit. The aim of this research study was to develop and optimize a protocol for LTA RNA purification using PAXgene Blood RNA Tubes and the (manual) PAXgene Blood RNA Kit on a robotic platform. (Note: The combination of the PAXgene Blood RNA Kit on the robotic platform is for research use only and not for use in diagnostic procedures. It has not received clearance or approval for clinical use in the US, Canada or Europe.) Methods: Replicate blood samples were collected in PAXgene Blood RNA Tubes, and starting from the resuspended nucleic acid pellet, RNA was purified using either the manual procedure according to the PAXgene Blood RNA Kit handbook (reference protocol) or the new automated purification procedure (test protocol). To enable comparison of the test and reference protocol performance, purified RNA from both protocols was analyzed spectrophotometrically, by capillary gel electrophoresis, in real-time quantitative RT-PCR (RQ-RT-PCR), and in PCR assays to measure RNA yield, purity, integrity, RQ-RT-PCR inhibition, and traces of genomic DNA (gDNA). Results: The new automated PAXgene Blood RNA protocol was highly reliable in that there were no failures in sample processing. Furthermore, the quantity and quality of purified RNA was comparable between both protocols: Greater than 95% of samples processed on the instrument yielded RNA of >3micrograms per tube, all samples contained Conclusion: This research study demonstrates that the new automated protocol using proven silica-membrane spin-column technology and existing chemistry with the new robotic platform provides an efficient and reliable alternative LTA solution to the manual protocol. Automated processing reduces user interaction and hands-on time, resulting in a more convenient workflow compared to the manual PAXgene Blood RNA procedure.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.016 |
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".