Cell fate-decision as high-dimensional critical state transition
Bibliographic record
Abstract
Abstract Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. However, how progenitor cells overcome the stability of their robust gene expression configuration (attractor) and exit their state remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or the myeloid lineage is preceded by the destabilization of their high-dimensional attractor state and that cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space: decrease of correlation between cells with concomitant increase of correlation between genes as cells approach a tipping point. The detection of “rebellious cells” which enter the fate opposite to the one intended corroborates the model of preceding destabilization of the progenitor state. Thus, “early-warning signals” associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal tool for analyzing single-cell’s molecular profiles that goes beyond computational pattern recognition but is based on dynamical systems theory and can predict impending major shifts in cell populations in development and disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".