Customizable live-cell imaging chambers for multimodal and multiplex fluorescence microscopy
Bibliographic record
Abstract
Using multiple imaging modalities while performing independent experiments in parallel can greatly enhance the throughput of microscopy-based research, but requires the provision of appropriate experimental conditions in a format that meets the optical requirements of the microscope. Although customized imaging chambers can meet these challenges, the difficulty of manufacturing custom chambers and the relatively high cost and design inflexibility of commercial chambers has limited the adoption of this approach. Herein, we demonstrate the use of 3D printing to produce inexpensive, customized, live-cell imaging chambers that are compatible with a range of imaging modalities, including super-resolution microscopy. In this approach, biocompatible plastics are used to print imaging chambers designed to meet the specific needs of an experiment, followed by adhesion of the printed chamber to a glass coverslip, producing a chamber that is impermeant to liquids and that supports the growth and imaging of cells over multiple days. This approach can also be used to produce moulds for casting microfluidic devices made of polydimethylsiloxane. The utility of these chambers is demonstrated using designs for multiplex microscopy, imaging under shear, chemotaxis, and general cellular imaging. Together, this approach represents an inexpensive yet highly customizable approach for producing imaging chambers that are compatible with modern microscopy techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".