Identification of DYRK1b as a novel regulator of small extracellular vesicle release using a high throughput nanoscale flow cytometry screening platform
Bibliographic record
Abstract
Extracellular vesicles (EVs) are important mediators of intercellular communication and have various roles in physiological and pathological processes. Discovery of regulators of EV biogenesis and release has led to significant improvements in our understanding of EV biology and has highlighted disease-specific pathways. Large scale discovery studies of EV regulators are limited by conventional methods of EV analysis with limited throughput and sensitivity. To address this, this study presents a high-throughput flow cytometry-based platform for the quantification of EVs released from cells. Here, a system was developed using the MDA-MB-231 cell line stably expressing ZsGreen, which passively loads ZsGreen proteins into EVs, and nanoscale flow cytometry. EV detection and quantitation was optimized and validated for a 96-well format. The high-throughput flow cytometry screening platform quantified the effect of 156 kinase inhibitors on EV number and identified AZ191 - a DYRK1b inhibitor - as a potent EV inhibitor. DYRK1b inhibition and knockdown confirmed a significant reduction in total EV number, with small EVs demonstrating the largest reduction. DYRK1b knockdown altered the intracellular distribution of EV marker CD63, suggesting a role for DYRK1b in EV trafficking. In conclusion, our study establishes a platform for high-throughput analysis of EV dynamics and introduces DYRK1b kinase as a novel EV-regulator.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".