Predictive microfluidic control of regulatory ligand trajectories in individual pluripotent cells
Bibliographic record
Abstract
Local (cell-level) signaling environments, regulated by autocrine and paracrine signaling, and modulated by cell organization, are hypothesized to be fundamental stem cell fate control mechanisms used during development. It has, however, been challenging to demonstrate the impact of cell-level organization on stem cell fate control and to relate stem cell fate outcomes to autocrine and paracrine signaling. We address this fundamental problem using a combined in silico and experimental approach in which we directly manipulate, using laminar fluid flow, the local impact of endogenously secreted gp130-activating ligands and their activation of signal transducer and activator of transcription3 (STAT3) signaling in mouse embryonic stem cells (mESC). Our model analysis predicted that flow-dependent changes in autocrine and paracrine ligand binding would impact heterogeneity in cell- and colony-level STAT3 signaling activation and cause a gradient of cell fate determination along the direction of flow. Interestingly, analysis also predicted that local cell density would be inversely proportional to the degree to which endogenous secretion contributed to cell fate determination. Experimental validation using functional activation of STAT3 by secreted factors under microfluidic perfusion culture demonstrated that STAT3 activation and consequently mESC fate were manipulable by flow rate, position in the flow field, and local cell organization. As a unique demonstration of how quantitative control of autocrine and paracrine signaling can be integrated with spatial organization to elicit higher order cell fate effects, this work provides a general template to investigate organizing principles due to secreted factors.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".