Three-dimensional imaging on a chip using optofluidics light-sheet fluorescence microscopy
Bibliographic record
Abstract
Volumetric, sub-micron to micron level resolution imaging is necessary to assay phenotypes or characteristics at the sub-cellular/organelle scale. However, three-dimensional fluorescence imaging of cells is typically low throughput or compromises on the achievable resolution in space and time. Here, we capitalise on the flow control capabilities of microfluidics and combine it with microoptics to integrate light-sheet based imaging directly into a microfluidic chip. Our optofluidic system flows suspended cells through a sub-micrometer thick light-sheet formed using micro-optical components that are cast directly in polydimethylsiloxane (PDMS). This design ensures accurate alignment, drift-free operation, and easy integration with conventional microfluidics, while providing sufficient spatial resolution, optical sectioning and volumetric data acquisition. We demonstrate imaging rates of 120 ms per cell at sub-μm resolution, that allow extraction of complex cellular phenotypes, exemplified by imaging of cell clusters, receptor distribution, and the analysis of endosomal size changes.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".