High-throughput measurement of gap junctional intercellular communication
Bibliographic record
Abstract
Gap junctional intercellular communication (GJIC) is a critical part of cellular activities and is necessary for electrical propagation among contacting cells. Disorders of gap junctions are a major cause for cardiac arrhythmias. Dye transfer through microinjection is a conventional technique for measuring GJIC. To overcome the limitations of manual microinjection and perform high-throughput GJIC measurement, here we present a new robotic microinjection system that is capable of injecting a large number of cells at a high speed. The highly automated system enables large-scale cell injection (thousands of cells vs. a few cells) without major operator training. GJIC of three cell lines of differing gap junction density, i.e., HeLa, HEK293, and HL-1, was evaluated. The effect of a GJIC inhibitor (18-α-glycyrrhetinic acid) was also quantified in the three cell lines. System operation speed, success rate, and cell viability rate were quantitatively evaluated based on robotic microinjection of over 4,000 cells. Injection speed was 22.7 cells per min, with 95% success for cell injection and >90% survival. Dye transfer cell counts and dye transfer distance correlated with the expected connexin expression of each cell type, and inhibition of dye transfer correlated with the concentration of GJIC inhibitor. Additionally, real-time monitoring of dye transfer enables the calculation of coefficients of molecular diffusion through gap junctions. This robotic microinjection dye transfer technique permits rapid assessment of gap junction function in confluent cell cultures.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".