Open-space microfluidics as a tool to study signaling dynamics
Bibliographic record
Abstract
The temporal dynamics of cell signaling are a crucial way for cells to regulate their transcriptional targets and consequently may heavily influence cell responses. Improving our understanding of signaling dynamics is important for drug treatments targeting specific signaling pathways. However, studying signaling dynamics requires multiplexed, time-sensitive experiments. Here, we use an open-space microfluidic device, the microfluidic display, which enables liquid delivery from above a surface, forming defined and stable confinement zones without enclosing samples into a chip. A device with rapid reagent switching (<7 seconds) and 6 independent confinement areas is first designed. Using this platform, we study the Notch pathway in engineered C2C12 cells to display constitutively active Notch receptors upon which we force highly controlled time-dependent modulation patterns by delivering time-varying doses of the Notch inhibitor DAPT. We replicate previous findings on Notch activation with our methodology by confirming the Notch-regulated gene Hes1 is upregulated for short Notch activation pulses, while Hey1 required sustained activation. We confirm a previously observed regime switch from Hes1 to Hey1 dominance between 2 h and 3 h of activation. Finally, by varying signal pulses while keeping dose constant in six independent experiments performed simultaneously, we further show the upregulation of the Hes1 gene for multiple short pulses, while Hey1 activation depends on duty cycle length. These results highlight microfluidic displays as a valuable tool for systems biology, enabling multiplexed, high temporal resolution stimulation of signaling pathways.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".