Mining the archives: a cross-platform analysis of gene expression profiles in archival formalin-fixed paraffin-embedded (FFPE) tissue
Bibliographic record
Abstract
Formalin-fixed paraffin-embedded (FFPE) tissue samples represent a potentially invaluable resource for transcriptomic-based research into the molecular basis of disease. However, use of FFPE samples in gene expression studies has been limited by technical challenges resulting from degradation of nucleic acids. Here we evaluated gene expression profiles derived from fresh-frozen (FRO) and FFPE mouse liver tissues using two DNA microarray protocols and two whole transcriptome sequencing (RNA-seq) library preparation methodologies. The ribo-depletion protocol outperformed the other three methods by having the highest correlations of differentially expressed genes (DEGs) and best overlap of pathways between FRO and FFPE groups. We next tested the effect of sample time in formalin (18 hours or 3 weeks) on gene expression profiles. Hierarchical clustering of the datasets indicated that test article treatment, and not preservation method, was the main driver of gene expression profiles. Meta- and pathway analyses indicated that biological responses were generally consistent for 18-hour and 3-week FFPE samples compared to FRO samples. However, clear erosion of signal intensity with time in formalin was evident, and DEG numbers differed by platform and preservation method. Lastly, we investigated the effect of age in FFPE block on genomic profiles. RNA-seq analysis of 8-, 19-, and 26-year-old control blocks using the ribo-depletion protocol resulted in comparable quality metrics, including expected distributions of mapped reads to exonic, UTR, intronic, and ribosomal fractions of the transcriptome. Overall, our results suggest that FFPE samples are appropriate for use in genomic studies in which frozen samples are not available, and that ribo-depletion RNA-seq is the preferred method for this type of analysis in archival and long-aged FFPE samples.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".