Next Generation Aqueous Two‐Phase System for Gentle, Effective, and Timely Extracellular Vesicle Isolation and Transcriptomic Analysis
Bibliographic record
Abstract
The isolation of extracellular vesicles (EVs) using currently available methods frequently compromises purity and yield to prioritize speed. Here, we present a next-generation aqueous two-phase system (next-gen ATPS) for the isolation of EVs regardless of scale and volume that is superior to conventional methods such as ultracentrifugation (UC) and commercial kits. This is made possible by the two aqueous phases, one rich in polyethylene glycol (PEG) and the other rich in dextran (DEX), whereby fully encapsulated lipid vesicles preferentially migrate to the DEX-rich phase to achieve a local energy minimum for the EVs. Isolated EVs as found in the DEX-rich phase are more amenable to biomarker analysis such as nanoscale flow cytometry (nFC) when using various pre-conjugated antibodies specific for CD9, CD63 and CD81. TRIzol RNA isolation is further enabled by the addition of dextranase, a critical component of this next-gen ATPS method. RNA yield of next-gen ATPS-isolated EVs is superior to UC and other commercial kits. This negates the use of specialized EV RNA extraction kits. The use of dextranase also enables more accurate immunoreactivity of pre-conjugated antibodies for the detection of EVs by nFC. Transcriptomic analysis of EVs isolated using the next-gen ATPS revealed a strong overlap in microRNA (miRNA), circular RNA (circRNA) and small nucleolar RNA (snoRNA) profiles with EV donor cells, as well as EVs isolated by UC and the exoRNeasy kit, while detecting a superior number of circRNAs compared to the kit in human samples. Overall, this next-gen ATPS method stands out as a rapid and highly effective approach to isolate high-quality EVs in high yield, ensuring optimal extraction and analysis of EV-encapsulated nucleic acids.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".