Minimally invasive, pressure probe based sampling allows for <i>in-situ</i> gene expression analyses in plant cells
Bibliographic record
Abstract
Abstract Background Gene expression analyses are conducted using multiple approaches and increasingly research has been focused on assessing gene expression at the level of a tissue or even single-cells. To date, methods to assess gene expression at the single-cell in plant tissues have been semi-quantitative, require tissue disruption, and/or involve laborious, possibly artifact-inducing manipulation. In this work, we used grape berries ( Vitis vinifera L. Zinfandel) as a model in order to examine the validity and reproducibility of an in-situ gene expression analysis method combining a cell pressure probe (CPP) with quantitative PCR (qPCR). Results We developed a method to directly assess gene expression levels via qPCR from cellular fluids sampled in-situ with a CPP. Cellular fluids, with volumes in the picoliter range, were collected from intact berries with a CPP at various depths across skin and mesocarp tissues. The expression of a key anthocyanin biosynthetic gene, UDP-glucose: flavonoid 3-O-glucosyltransferase ( VviUFGT ), was analyzed as a test case since its expression is restricted to cells producing anthocyanins in grape berry skins during ripening. The method identifies samples contaminated with significant levels of genomic DNA by amplifying a region of VviUFGT that spans an intron. Therefore false positives were discarded which occurred in 28% of the samples tested. Shallow probing of skin cells showed high VviUFGT expression as expected while deeper probing of mesocarp cells resulted in no VviUFGT expression. Conclusions The clear correspondence of VviUFGT expression to the targeted cell samples suggests that the in-situ gene expression analysis using a CPP is reliable and does not result in contamination as the probe moves through tissues. This method can be paired to single-cell transcriptomic analyses in the future. We conclude that this technique represents a minimally invasive method of sampling plant cells in-situ which creates an opportunity for the analysis of cellular level, spatiotemporal responses in heterogeneous plant tissues.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".