A Versatile Droplet Microfluidic Platform Capable of Confining Preformed Spheroids in Hydrogel Microenvironments for Downstream Growth and Analysis
Bibliographic record
Abstract
Abstract Patient-derived tumor organoids (PDTOs) are promising 3D disease models for developing personalized treatment methods. However, conventional technologies for making PDTOs have limitations such as batch-to-batch variation and low throughput. Droplet microfluidics (DM), which utilizes uniform droplets generated in microchannels, has demonstrated potential for creating organoids due to its high-throughput and controllable parameters. However, most existing DM devices require a high initial cell count, on the order of 10,6 which is difficult to acquire with biopsy samples. A novel step-stone strategy is to encapsulate preformed spheroids in hydrogel droplets, creating a microenvironment supporting their future growth into organoids or for immediate analysis. While a similar strategy has been reported, the viability and uniformity of spheroids after encapsulation, which are important for continuous growth into organoids, were not examined. We present a DM device featuring a double-cross geometry chip to encapsulate preformed spheroids into hydrogel microparticles (HMPs) with a very low initial cell count (order of 104) and ensuring high viability and uniformity of the spheroids in the recovered cross-linked HMPs. The preformed spheroids, 100–200 μm in diameter, were successfully encapsulated in well-defined HMPs. With contrasting viscosity hydrogels, a hydrodynamic focusing stream was created to leverage spheroids into their own droplets. Preformed spheroid encapsulation efficiency was affected by the width of the focusing stream and the quantity of spheroids at the inlet, with the best results reaching about 75% total encapsulation and 54% single spheroid encapsulation. Spheroid-laden HMPs were collected and cross-linked off-chip, where spheroids could continue to grow. The encapsulated spheroids maintained above 80% viability over 5 days of culture and retained uniformity with less than a 4% difference in diameter variation compared to pre-encapsulated spheroids. Ultimately, we demonstrated that preformed spheroid encapsulation using DM was a robust way to encapsulate a low sample size while maintaining viability and uniformity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".