Pollen DNA Isolation Methods: Literature Review and Benefit of Adding Chloroform:Isoamyl Alcohol to Commercial DNA Kits
Bibliographic record
Abstract
Successful isolation of pollen DNA is required for multiple disciplines. However, pollen DNA yields are typically low, perhaps because of physical-chemical challenges associated with lysing and exine removal. Here, an initial systematic literature review showed that efforts to improve pollen DNA isolation have focused primarily on optimizing lysis bead type and duration, and identifying effective commercial kits and/or in-house methods. Studies have not apparently focused on preventing clogging of kit-based DNA purification columns caused by post-lysis pollen debris including the exine. Here, chloroform:isoamyl alcohol 24:1 (C:I) was added as a post-lysis cleaning treatment to a commercial DNA isolation kit-based protocol to test its effect on pollen DNA yield of maize (Zea mays L., corn) as a model. C:I was tested in conjunction with different lysis durations and pollen quantities, along with lysis bead type, to determine optimal combinations. C:I improved final DNA yields ≤31% regardless of lysis duration and pollen quantity, compared to non-C:I controls. The optimal lysis duration depended on pollen quantity and vice versa, although bead type demonstrated a greater impact when coupled with C:I. The resulting DNA was of sufficient quality for microbiome analysis. This optimized protocol may minimize the pollen amount required for robust DNA isolation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".