Trimodal Single-Cell Gene Regulatory Networks Reveal Principles of Stemness Loss and Cell Fate Acquisition in Human Hematopoiesis
Bibliographic record
Abstract
Hematopoiesis requires the coordinated loss of stemness and acquisition of lineage identity, yet the regulatory logic and principles underlying these transitions has remained elusive. Single-cell studies suggest that hematopoietic stem and progenitor cells progress along continuous trajectories, but this view conflicts with the existence of discrete, functionally validated populations. Here, we establish the first dynamic, enhancer-based gene regulatory networks (eGRNs) that resolve the molecular programs underlying early human hematopoietic fate decisions. Built on a high-resolution trimodal framework generated with TEA-seq, these networks integrate simultaneously measured transcription factor abundance, enhancer and promoter accessibility, and gene expression within single-cell trajectories anchored to immunophenotypically defined populations. Our framework reveals that stemness loss and lineage acquisition are temporally and mechanistically uncoupled: stemness programs decline gradually through reduced TF abundance long before chromatin closure, whereas lineage identity emerges stepwise through enhancer reconfiguration and activation of lineage-defining eGRNs. This process generates discrete regulatory states that align with immunophenotypically defined populations. Together, these findings reconcile continuous and discrete models of hematopoiesis and establish eGRNs as a powerful framework for defining cell types by their regulatory logic. In addition, we provide an interactive web-based resource to facilitate further investigation of eGRNs and trajectories during early human hematopoiesis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".