Improved RNA quality and TaqMan®Pre-amplification method (PreAmp) to enhance expression analysis from formalin fixed paraffin embedded (FFPE) materials
Bibliographic record
Abstract
BACKGROUND: Archival formalin-fixed paraffin-embedded (FFPE) tissues represent an abundant source of clinical specimens; however their use is limited in applications involving analysis of gene expression due to RNA degradation and modification during fixation and processing. This study improved the quality of RNA extracted from FFPE by introducing a heating step into the selected extraction protocols. Further, it evaluated a novel pre-amplification system (PreAmp) designed to enhance expression analysis from tissue samples using assays with a range of amplicon size (62-164 bp). RESULTS: Results from the Bioanalyzer and TaqMan data showed improvement of RNA quality extracted using the modified protocols from FFPE. Incubation at 70 degrees C for 20 minutes was determined to be the best condition of those tested to disrupt cross-links while not compromising RNA integrity. TaqMan detection was influenced by master mix, amplicon size and the incorporation of a pre-amplification step. TaqMan PreAmp consistently achieved decreased CT values in both snap frozen and FFPE aliquots compared with no pre-amplification. CONCLUSION: Modification to extraction protocols has facilitated procurement of RNA that may be successfully amplified using QRT-PCR. TaqMan PreAmp system is a robust and practical solution to limited quantities of RNA from FFPE extracts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".