Isolation of plant nuclei for estimation of nuclear DNA content: Overview and best practices
Bibliographic record
Abstract
A critical aspect for obtaining accurate, reliable, and high-resolution estimates of nuclear DNA content is the release of nuclei from the cytoplasm in sufficient amounts, while maintaining their integrity throughout the analysis, protecting their DNA from degradation by endonucleases, and enabling stoichiometric DNA staining. In embryophytes, the most common method consists of chopping the plant material with a sharp razor blade to release nuclei into an isolation buffer, filtering the homogenate, and staining the nuclei in buffered suspension with a fluorochrome of choice. Despite the recent description of alternative approaches to isolate nuclei, the chopping procedure remains the most widely adopted method, due to its simplicity, rapidity, and effectiveness. In this review article, we discuss the specifics of nuclei isolation buffers and the distorting effects that secondary metabolites may have in nuclear suspensions and how to test them. We also present alternatives to the chopping procedure, options for filtering and fluorochromes, and discuss the applications of these varied approaches. A summary of the best practices regarding the isolation of plant nuclei for the estimation of nuclear DNA content is also provided.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".