Monolithic hydrogel nanowells-in-microwells enabling simultaneous single cell secretion and phenotype analysis
Bibliographic record
Abstract
Cytokine secretion is a form of cellular communication that regulates a wide range of biological processes. A common approach for measuring cytokine secretion from single cells is to confine individual cells in arrays of nanoliter wells (nanowells) fabricated using polydimethylsiloxane. However, this approach cannot be easily integrated in standard microwell plates in order to take advantage of high-throughput infrastructure for automated and multiplexed analysis. Here, we used laser micropatterning to fabricate monolithic hydrogel nanowells inside wells in a microwell plate (microwells) using polyethylene glycol diacrylate (PEGDA). This approach produces high-aspect ratio nanowells that retain cells and beads during reagent exchange, enabling simultaneous profiling of single cell secretion and phenotyping via immunostaining. To limit contamination between nanowells, we used methylcellulose as a media additive to reduce diffusion distance. Patterning nanowells monolithically in microwells also dramatically increases density, providing ∼1200 nanowells per microwell in a microwell plate. Using this approach, we profiled IL-8 secretion from single MDA-MB-231 cells, which showed significant heterogeneity. We further profiled the polarization of THP-1 cells into M1 and M2 macrophages, along with their associated IL-1β and CCL-22 secretion profiles. These results demonstrate the potential to use this approach for high-throughput secretion and phenotype analysis on single cells.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".