A High-Sensitivity, Medium-Density, and Target Amplification–Free Planar Waveguide Microarray System for Gene Expression Analysis of Formalin-Fixed and Paraffin-Embedded Tissue
Bibliographic record
Abstract
BACKGROUND: Many microarray platforms and their associated assay chemistries do not work properly with RNA extracted from formalin-fixed, paraffin-embedded (FFPE) tissue samples, a feature that severely hampers the use of microarrays in oncology applications, for which FFPE tissue is the routine specimen. Furthermore, the limited sensitivity of most microarray platforms requires time-consuming and costly amplification reactions of the target RNA, which negatively affects clinical laboratory work flow. METHODS: We developed an approach for sensitively and reliably measuring mRNA abundances in FFPE tissue samples. This approach involves automated RNA extractions, direct hybridization of extracted RNA to immobilized capture probes, antibody-mediated labeling, and readout with an instrument applying the principle of planar waveguides (PWG). A 14-gene multiplex assay conducted with RNA isolated from 20 FFPE blocks was correlated to an analysis of the same with reverse-transcription quantitative real-time PCR (RT-qPCR). RESULTS: The assay sensitivity for gene expression analysis obtained for the PWG microarray platform was <10 fmol/L, eliminating the need for target preamplification. We observed a correlation coefficient of 0.87 to state-of-the-art RT-qPCR technology with RNA isolated from FFPE tissue, despite a compressed dynamic range for the PWG system (a 2.9-log dynamic range for PWG in our test system vs 5.0 logs for RT-qPCR). The precision of the PWG platform was comparable to RT-qPCR (Pearson correlation coefficient of 0.9851 for PWG vs 0.9896 for RT-qPCR) for technical replicates. CONCLUSIONS: The presented PWG platform demonstrated excellent sensitivity and precision and is especially well suited for any application for which fast, simple, and robust multiplex assays of RNA in FFPE tissue are required.
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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 | 0.001 | 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".