High-throughput combinatorial cell co-culture using microfluidics
Bibliographic record
Abstract
Co-culture strategies are foundational in cell biology. These systems, which serve as mimics of in vivo tissue niches, are typically poorly defined in terms of cell ratios, local cues and supportive cell-cell interactions. In the stem cell niche, the ability to screen cell-cell interactions and identify local supportive microenvironments has a broad range of applications in transplantation, tissue engineering and wound healing. We present a microfluidic platform for the high-throughput generation of hydrogel microbeads for cell co-culture. Encapsulation of different cell populations in microgels was achieved by introducing in a microfluidic device two streams of distinct cell suspensions, emulsifying the mixed suspension, and gelling the precursor droplets. The cellular composition in the microgels was controlled by varying the volumetric flow rates of the corresponding streams. We demonstrate one of the applications of the microfluidic method by co-encapsulating factor-dependent and responsive blood progenitor cell lines (MBA2 and M07e cells, respectively) at varying ratios, and show that in-bead paracrine secretion can modulate the viability of the factor dependent cells. Furthermore, we show the application of the method as a tool to screen the impact of specific growth factors on a primary human heterogeneous cell population. Co-encapsulation of IL-3 secreting MBA2 cells with umbilical cord blood cells revealed differential sub-population responsiveness to paracrine signals (CD14+ cells were particularly responsive to locally delivered IL-3). This microfluidic co-culture platform should enable high throughput screening of cell co-culture conditions, leading to new strategies to manipulate cell fate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".