Single-cell Transcriptional Atlas of Human Hematopoiesis Reveals Genetic and Hierarchy-Based Determinants of Aberrant AML Differentiation
Why this work is in the frame
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Bibliographic record
Abstract
Therapeutic targeting of acute myeloid leukemia (AML) is hampered by intra- and inter-tumoral cell state heterogeneity. To develop a more precise understanding of AML cell states, we constructed a reference atlas of human hematopoiesis from 263,159 single-cell transcriptomes spanning 55 cellular states. Using this atlas, we mapped more than 1.2 million cells spanning 318 leukemia samples, revealing 12 recurrent patterns of aberrant differentiation in AML. Notably, this uncovered unexpected AML cell states resembling lymphoid and erythroid progenitors that were prognostic within the clinically heterogeneous context of normal karyotype AML, independent of genomic classifications. Systematic mapping of genotype-to-phenotype associations revealed specific differentiation landscapes associated with more than 45 genetic drivers. Importantly, distinct cellular hierarchies can arise from samples sharing the same genetic driver, potentially reflecting distinct cellular origins for disease-sustaining leukemia stem cells. Thus, precise mapping of malignant cell states provides insights into leukemogenesis and refines disease classification in acute leukemia. SIGNIFICANCE: We present a single-cell reference atlas of human hematopoiesis and a computational tool for rapid mapping and classification of healthy and leukemic cells. Applied to AML, this has enabled single-cell analysis at the scale of hundreds of patient samples, revealing the full breadth of derailment of differentiation in AML. See related commentary by Berger and Penter, p. 280.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it