Analyzing Extracellular Vesicle‐associated DNA Using Transmission Electron Microscopy at the Single EV‐level
Bibliographic record
Abstract
Extracellular vesicles (EVs) play an important role in cell-cell communication, carrying bioactive molecules including DNA. EV-associated DNA (EV-DNA) has created enormous interest in the field of biomarkers, particularly related to liquid biopsy. However, its analysis is challenging due to the nanoscale structure of EVs, the low abundance of EV-DNA, and surrounding biogenetic debate. Therefore, novel protocols to enhance the accurate detection of EV-DNA are essential to study its role in normal physiology and disease states. Here, we provide two protocols for confirming the presence of EV-DNA from biological samples. In the first protocol, ultrathin sectioning of EVs is combined with immunogold labeling to detect the presence of double-stranded (ds) DNA within the EV lumen using transmission electron microscopy (TEM). In the second protocol, whole-mount EV immunogold labeling allows detailed morphological analysis of EVs and their surface-associated DNA. Using TEM imaging, we have demonstrated that cancer-cell-derived individual EVs exhibit simultaneous positivity for dsDNA and the EV surface protein tetraspanin 9. We believe that this method can be used to label any proteins of interest inside as well as on the surface of EVs. This can aid in the characterization of single EVs and in the identification and verification of EV-associated biomarkers. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: EV isolation from cell-culture-conditioned medium, EV embedding, ultrathin sectioning, labeling, and imaging Basic Protocol 2: Whole-mount immunolabeling of EV-DNA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".