An accessible microfluidic perfusion platform for time-restricted control of zebrafish embryonic patterning
Bibliographic record
Abstract
Understanding the dynamic nature of developmental networks requires live imaging techniques capable of capturing real-time developmental processes in wild-type and mutant embryos as they are exposed to pharmacological perturbations. A peculiar developmental patterning process in early vertebrate embryos is the sequential segmentation of bilateral somites from the unsegmented tail tissue along their major axis. Earlier work discovered that segmentation is instructed by an oscillatory fibroblast growth factor (Fgf)/ERK signaling gradient sourced from the tailbud. Somite segmentation was recapitulated at will in the absence of the molecular oscillator, "the segmentation clock", via pulsatile drug inhibitions. Here, we present a live imaging setup for zebrafish embryos that incorporates a 3D-printed chamber and a programmed syringe pump for precise, automated, periodic drug delivery. The chamber secures the orientation of zebrafish embryos in agarose inserts and incorporates inflow and outflow ports to facilitate controlled drug perfusion. Servo motors controlled by an Arduino were integrated to automate valve switching, achieving fully automated exchange of two different fluids for alternating drug delivery and rinse cycles. Such periodic delivery of an inhibitor drug entrains the Fgf/ERK signaling gradient in the embryonic tail to oscillate in clock-deficient mutants, creating lab-reconstituted somites in otherwise defective embryos. Embryos expressing fluorescent markers can further be imaged at single-cell resolution during perturbations. Overall, this system provides a cost-effective, reproducible platform for investigating vertebrate development and interrogating cellular decision-making under controlled experimental conditions. We anticipate this setup will be broadly beneficial for the biomedical research community interested in controlled drug delivery and in vivo cellular dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".