The Isolation of Nucleic Acids from Fixed, Paraffin-Embedded Tissues–Which Methods Are Useful When?
Bibliographic record
Abstract
Museums and pathology collections around the world represent an archive of genetic material to study populations and diseases. For preservation purposes, a large portion of these collections has been fixed in formalin-containing solutions, a treatment that results in cross-linking of biomolecules. Cross-linking not only complicates isolation of nucleic acid but also introduces polymerase "blocks" during PCR. A wide variety of methods exists for the recovery of DNA and RNA from archival tissues, and although a number of previous studies have qualitatively compared the relative merits of the different techniques, very few have undertaken wide scale quantitative comparisons. To help address this issue, we have undertaken a study that investigates the quality of nucleic acids recovered from a test panel of fixed specimens that have been manipulated following a number of the published protocols. These include methods of pre-treating the samples prior to extraction, extraction and nucleic acid purification methods themselves, and a post-extraction enzymatic repair technique. We find that although many of the published methods have distinct positive effects on some characteristics of the nucleic acids, the benefits often come at a cost. In addition, a number of the previously published techniques appear to have no effect at all. Our findings recommend that the extraction methodology adopted should be chosen carefully. Here we provide a quick reference table that can be used to determine appropriate protocols for particular aims.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".