A simple, cost-effective microfluidic device using a 3D cross-flow T-junction for producing decellularized extracellular matrix-derived microcarriers
Bibliographic record
Abstract
ABSTRACT Cell therapies using human mesenchymal stromal cells (MSCs) are promising for a wide variety of clinical applications. However, broad-scale clinical translation is limited by conventional culture methods for MSC expansion within 2D tissue-culture flasks. MSC expansion on ECM-derived microcarriers within stirred bioreactor systems offers a promising approach to support MSC growth. Previously, our team established methods for fabricating ECM-derived microcarriers from a variety of decellularized tissue sources using electrospraying techniques. However, these microcarriers are relatively large and have a broad size distribution, which may limit their utility. Smaller and more uniform microcarriers may be favorable for MSC growth within bioreactors and have greater potential to serve as a minimally invasive injectable cell delivery platform. To address these limitations, the current project focused on the development of a new microfluidic-based approach enabling both uniform and small microcarrier production. Using a novel, modified 3D T-junction design, we successfully generated microcarriers using human decellularized adipose tissue (DAT) as the ECM source. Our new cost-effective device produced microbeads that were small and monodisperse, at a range of flow rate combinations and with high production rates. Photo-crosslinking using rose bengal allowed for the generation of microcarriers that were stable following rehydration, with a mean diameter of 196 ± 47 µm. Following methods optimization and microcarrier characterization, in vitro studies confirmed that the new microcarriers supported human adipose-derived stromal cell (hASC) attachment and growth, as well as ECM production, across 14 days within spinner flask bioreactors. Overall, this study demonstrates the feasibility of using our novel, cost-effective, and reusable microfluidics device to generate cell-supportive microcarriers comprised exclusively of ECM in a size range that could be injected via a small gauge needle and were stable in long-term culture.
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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) | 0.000 | 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 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".