Sol–Gel-Derived Materials for Production of Pin-Printed Reporter Gene Living-Cell Microarrays
Bibliographic record
Abstract
We report the fabrication of three-dimensional living-cell microarrays via pin-printing of soft sol-gel-derived silica materials containing bacterial cells. Bacterial cells entrapped in the silica-glycerol microarray spots can express reporter genes and produce strong fluorescence signals. The signals responded to the presence and concentration of inducers or repressors as expected, indicating that the entrapped cells remained metabolically active. Microscopic imaging of individual microarray spots at different culture times suggests that the entrapped cells can grow and divide, phenomena further confirmed by experiments in bulk sol-gel materials that demonstrated the increases of entrapped cell density and fluorescence during incubation in culture media. The cell microarrays can also be printed into 96-well glass bottom microtiter plates in a multiplexed manner, and the fluorescence signals generated were able to quantitatively and selectively respond to the concentration of inducers, thus demonstrating the potential for multitarget biosensing and high-throughput/high-content cell-based screening. The signal levels of bacterial cells in silica were significantly higher than those in alginate arrays, presumably due to viability of the entrapped cells in silica sol-gels. Microarray stability assays proved that the entrapped cells retained their physiological activity after storage for four weeks. Given that a large number of fluorescent and luminescent protein-based cell assays have been developed, the reporter gene living-cell microarrays demonstrated in this paper are expected to be applicable to a wide variety of research areas ranging from bioanalysis and chemical biology to drug discovery and probing of cell-material interactions.
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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 | 0.001 |
| 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 | 0.001 | 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".