Multiplex isolation and profiling of extracellular vesicles using a microfluidic DICE device
Bibliographic record
Abstract
Profiling of extracellular vesicles (EVs) is an emerging area in the field of liquid biopsies because of their innate significance in diseases and abundant information reflecting disease status. However, unbiased enrichment of EVs and thorough profiling of EVs is challenging. In this paper, we present a simple strategy to immobilize and analyze EVs for multiple markers on a single microfluidic device and perform differentiated immunostaining-based characterization of extracellular vesicles (DICE). This device, composed of four quadrants with a single inlet, captures biotinylated EVs efficiently and facilitates multiplexed immunostaining to profile their extracellular proteins, allowing for a multiplexed approach for non-invasive cancer diagnostics in the future. From controlled sample experiments using cancer cell line derived EVs and specific fluorescence staining with lipophilic dyes, we identified that the DICE device is capable of isolating biotinylated EVs with 84.4% immobilization efficiency. We extended our study to profile EVs of 9 clinical samples from non-small cell lung cancer (NSCLC) patients and healthy donors and found that the DICE device successfully facilitates immunofluorescent staining for both the NSCLC patients and the healthy control. This versatile and simple method to profile EVs could be extended to EVs of any biological origin, promoting discoveries of the role of EVs in disease diagnostics and monitoring.
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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 | 0.000 | 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) | 0.000 | 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".