Microfluidic device for high-throughput affinity-based isolation of extracellular vesicles
Bibliographic record
Abstract
Immunoaffinity based EV isolation technologies use antibodies targeting surface markers on EVs to provide higher isolation specificity and purity compared to existing approaches. One standing challenge for researchers is how to release captured EVs from the substrate to increase downstream and biological studies. The strong binding between the antibody and antigen or the antibody and substrate is commonly unbreakable without operating at conditions outside of the critical physiological range, making the release of EVs problematic. Additionally, immuno-affinity approaches are usually low-throughput due to their low flow velocity to ensure adequate time for antibody-antigen binding. To overcome these limitations, we modified the OncoBean chip, a previously reported circulating tumor cell isolation microfluidic device. The OncoBean chip is a radial flow microfluidic device with bean-shape microposts functionalized with biotin-conjugated EPCAM antibody through biotin-avidin link chemistry. It was demonstrated that the high surface area and varying shear rate provided by the bean-shaped posts and the radial flow design in the chip, enabled efficient capture of CTCs at high flow rate. We replace the anti-EPCAM with antibodies that recognize common EV surface markers to achieve high-throughput EV isolation. Moreover, by incorporating desthiobiotin-conjugated antibodies, EVs can be released from the device after capture, which offers a significant improvement over the existing isolation. The released EVs were found to be functional by confirming their uptake by cells using flow cytometry and fluorescent microscopy. We believe the proposed technology can facilitate both the study of EVs as cell-to-cell communicators and the further identification of EV markers.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".