The Preparation of Drosophila Embryos for Live-Imaging Using the Hanging Drop Protocol
Bibliographic record
Abstract
Green fluorescent protein (GFP)-based timelapse live-imaging is a powerful technique for studying the genetic regulation of dynamic processes such as tissue morphogenesis, cell-cell adhesion, or cell death. Drosophila embryos expressing GFP are readily imaged using either stereoscopic or confocal microscopy. A goal of any live-imaging protocol is to minimize detrimental effects such as dehydration and hypoxia. Previous protocols for preparing Drosophila embryos for live-imaging analysis have involved placing dechorionated embryos in halocarbon oil and sandwiching them between a halocarbon gas-permeable membrane and a coverslip. The introduction of compression through mounting embryos in this manner represents an undesirable complication for any biomechanical-based analysis of morphogenesis. Our method, which we call the hanging drop protocol, results in excellent viability of embryos during live imaging and does not require that embryos be compressed. Briefly, the hanging drop protocol involves the placement of embryos in a drop of halocarbon oil that is suspended from a coverslip, which is, in turn, fixed in position over a humid chamber. In addition to providing gas exchange and preventing dehydration, this arrangement takes advantage of the buoyancy of embryos in halocarbon oil to prevent them from drifting out of position during timelapse acquisition. This video describes in detail how to collect and prepare Drosophila embryos for live imaging using the hanging drop protocol. This protocol is suitable for imaging dechorionated embryos using stereomicroscopy or any upright compound fluorescence microscope.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.020 |
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".