Selective cell propagation <i>via</i> micropatterning of a thermally-activated hydrogel
Bibliographic record
Abstract
The ability to selectively propagate specific cells is fundamentally important to the development of clonal cell populations. Current methods rely on techniques such as limiting dilution, colony picking, and flow cytometry to transfer single cells into single wells, resulting in workflows that are low-throughput, slowed by propagation kinetics, and susceptible to contamination. Here, we developed a method, called selective laser gelation (SLG), to micropattern hydrogels in cell culture media in order to encapsulate specific cells to selectively arrest their growth. This process relies on the inverse gelation of methylcellulose, which forms a hydrogel when heated rather than cooled. Local heating using an infrared laser enables hydrogel micropatterning, while phase transition hysteresis retains the hydrogel after laser excitation. As a demonstration, we used this approach to selectively propagate transgenic CHO cells with increased antibody productivity. More generally, hydrogel micropatterning provides a simple and non-contact method for selective propagation of cells based on features identified by imaging.
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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 | 0.001 |
| 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".