Methodological guidelines for isolation and purification of plant extracellular vesicles
Bibliographic record
Abstract
ABSTRACT Plant extracellular vesicles (EVs) have become the focus of rising interest due to their important roles in the cross-kingdom trafficking of molecules from hosts to interacting microbes to modulate pathogen virulence. However, the isolation of pure intact EVs from plants still represents a considerable challenge. Currently, plant EVs have been isolated from apoplastic washing fluid (AWF) using a variety of methods. Here, we compare two published methods used for isolating plant EVs, and provide a detailed recommended method for AWF collection from Arabidopsis thaliana , followed by EV isolation via differential ultracentrifugation. To further separate and purify specific subclasses of EV from heterogeneous vesicles, sucrose or iodixanol density-based separation and immunoaffinity capture are then utilized. We found that immunoaffinity capture provides a significant advantage for specific EV isolation when suitable specific EV biomarkers and their corresponding antibodies are available. Overall, this study guides the selection and optimization of EV isolation methods for desired downstream applications.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.024 |
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".