Isolation Strategy Matters: How Tissue Processing Shapes the Composition of Placental Extracellular Vesicles
Bibliographic record
Abstract
The placenta is a vital mediator of maternal-foetal communication, and extracellular vesicles (EVs) derived from placental tissue have gained attention as promising biomarkers of pregnancy health. Accurate molecular profiling of placental EVs is critical for advancing their diagnostic and mechanistic applications. However, how different EV isolation methods influence their composition remains poorly understood. This study directly compared EVs isolated from mouse placental tissue using two common approaches, enzymatic digestion and explant culture, evaluating their structural features, size distribution and proteomic content. Both methods successfully isolated small EVs (sEVs) with canonical markers (CD63, TSG101 and HSC70) and characteristic EV morphology. The digestion method produced a higher yield of larger EVs with a broader size range. Proteomic profiling showed substantial overlap but also revealed method-specific enrichment. Explant-derived EVs were enriched in RNA-binding proteins, translation factors and proteins related to post-transcriptional regulation and stress responses. In contrast, digestion-derived EVs were enriched for extracellular matrix (ECM) proteins and ER- and mitochondrial-associated proteins. These EVs also demonstrated stronger enrichment for placental-specific proteins. Density gradient purification confirmed that canonical EV markers localized to expected fractions. However, the ER protein GRP94 was also present, indicating possible vesicle association, although its intracellular versus extracellular origin remains unclear. Together, our findings show that the tissue dissociation strategy significantly shapes placental EV composition. Enzymatic digestion may improve the recovery of matrix-embedded EVs, but it increases the likelihood of capturing intracellular components. An explant culture approach yields a more selective EV population, potentially influenced by prolonged ex vivo conditions. These results underscore the importance of aligning EV isolation methods with specific experimental objectives and highlight key considerations for placental EV biomarker discovery and translational applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".