Using ddPCR to assess the DNA yield of FFPE samples
Bibliographic record
Abstract
OBJECTIVES: Detection of genomic alterations in diseases can be achieved with current molecular technologies. However, the molecules extracted from formalin-fixed, paraffin-embedded (FFPE) bio-samples are often limited possibly due to DNA fragmentation and crosslinking caused by the sample fixation and processing. The study objective was to design a droplet digital PCR (ddPCR) assay to assess the quality and quantity of DNA derived from various DNA extraction conditions on FFPE samples. METHODS: We used 10 μm-thick sections from 5 FFPE oral tumoral blocks, each consisting of 10-15 sections. The protocol variables tested included: 1) tissue staining; 2) duration and 3) temperature of post-digestion heat treatment; and 4) DNA extraction method. DNA quantity was assessed using the NanoDrop 2000 (Thermo Fisher Scientific, USA), the Qubit fluorometer (Thermo Fisher Scientific, USA), and a ddPCR-based assay. DNA quality was assessed using a ddPCR assay for the degree of fragmentation and the effectiveness of removing crosslinks with varying guanine-cytosine (GC)-content. RESULTS: Deparaffinization with xylene helped to increase the DNA yield. Tissue staining (methyl green staining, pH 6) prior to microdissection, comparing to no staining, caused additional DNA fragmentation. Compared to column-based method, DNA extracted with phenol chloroform and ethanol precipitation increased the degree of fragmentation and lowered the yield of amplifiable DNA. The cross-linking derived from GC-contents may not be the only factor impacting on the DNA quality. CONCLUSIONS: Samples undergoing different pre-treatment conditions prior to extraction can impact the yield of amplifiable DNA. Our ddPCR assay can be used to assess for both DNA quantity and quality.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".