Liquid Phase Fluorescence <i>in situ</i> RT‐PCR Analysis for Gene Expression Analysis in Woody Stems
Bibliographic record
Abstract
We explore a rapid in situ RT-PCR protocol for gene expression studies in woody stem tissues. In situ RT-PCR was performed using fluorescent dye-conjugated nucleic acid and the fluorescence signals derived from target RNAs were detected using confocal laser scanning microscopy. The signal to background ratio was greatly enhanced by performing two rounds of PCR reactions, first without the fluorescent dye and second with the dye. Using this protocol, we obtained strong gene-specific signals in secondary stem tissues. The signals were PCR-dependent, as shown by the lack of cytoplasmic signals in the tissue sections in which either DNA polymerase or primers were omitted from PCR reactions, and were RNA-dependent, as shown by great reduction of cytoplasmic signals when sections were treated with RNase before RT reactions. To verify our protocol, transcript localization of the rbcS gene was examined in secondary stems of hybrid aspen ( Populus tremula L. x tremuloides Michx.) and compared to the chlorophyll autofluorescence signal. The in situ RT-PCR signals form the rbcS gene and chlorophyll autofluorescence co-localized in the same cell types. The signal was also confirmed by Northern blot analysis of isolated RNA from the cambium and developing xylem, thus confirming the validity of the protocol. Some difficulties of in situ transcript localization and the interpretation of the signal distribution in the secondary tissues are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".