A topological view of human CD34+ cell state trajectories from integrated single-cell output and proteomic data
Bibliographic record
Abstract
Abstract Recent advances in single-cell molecular analytical methods and clonal growth assays are enabling more refined models of human hematopoietic lineage restriction processes to be conceptualized. Here, we report the results of integrating single-cell proteome measurements with clonally determined lymphoid, neutrophilic/monocytic, and/or erythroid progeny outputs from >1000 index-sorted CD34+ human cord blood cells in short-term cultures with and without stromal cells. Surface phenotypes of functionally examined cells were individually mapped onto a molecular landscape of the entire CD34+ compartment constructed from single-cell mass cytometric measurements of 14 cell surface markers, 20 signaling/cell cycle proteins, and 6 transcription factors in ∼300 000 cells. This analysis showed that conventionally defined subsets of CD34+ cord blood cells are heterogeneous in their functional properties, transcription factor content, and signaling activities. Importantly, this molecular heterogeneity was reduced but not eliminated in phenotypes that were found to display highly restricted lineage outputs. Integration of the complete proteomic and functional data sets obtained revealed a continuous probabilistic topology of change that includes a multiplicity of lineage restriction trajectories. Each of these reflects progressive but variable changes in the levels of specific signaling intermediates and transcription factors but shared features of decreasing quiescence. Taken together, our results suggest a model in which increasingly narrowed hematopoietic output capabilities in neonatal CD34+ cord blood cells are determined by a history of external stimulation in combination with innately programmed cell state changes.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".