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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".