Navigating Lineage Options: A Data-Driven Quest to Uncover T Cell Fate Decision Trajectories
Bibliographic record
Abstract
Hematopoiesis originates from hematopoietic stem and progenitor cells (HSPCs), which can self-renew and differentiate to create diverse, functional blood cells. The process through which the HSPCs give rise to differentiated cells involves intricate genetic and epigenetic decision-making. Our group previously developed an in vitro culture system in which CD34+ cells from various sources faithfully produce T-lineage cells (Shukla et al., 2017). Using this system, we sought to understand the signaling intricacies involved in HSPC's expedition to become T cells. To achieve this, we performed a lineage tracing experiment by labeling umbilical cord blood-derived CD34+ cells with lentiviral barcodes. The HSPCs were then differentiated into pro-T cells and longitudinally sampled (days 0, 3, 6, and 9) for single-cell RNAseq (scRNAseq). Our analysis revealed three distinct trajectories, one leading to the successful differentiation of T-lineage cells and the others culminating in myeloid and mast cell lineage outcomes. When we examined the lineage tracing data, we found that the divergence to the mast cell lineage occurs early in the differentiation process, with GATA1 expression driving mast cell fate. The lympho-myeloid trajectories are interwind until later, even the strongly lymphoid-biased clones showed lymphoid-myeloid bipotential, simultaneously expressing both myeloid transcription factors (SPI1, IRF8), lymphoid transcription factor (BCL11B) and multipotent factors (RUNX1 and RUNX3) until complete transition, underscoring the nuances in fate decision. We identified approximately 1600 genes that significantly drove lymphoid potential. Among the major transcription factors, GATA3 expression is the early predictor of T-lineage clonal outcome (at day 0), a key finding that significantly advances our understanding of T-cell differentiation. NOTCH responsiveness was identified as a later predictor (on day 3), with NOTCH target genes DTX1 and NRARP response predicting successful T-lymphoid outcomes. From the list of significant genes, we created a gene signature that can predict lymphoid outcomes using this data. We then collected samples at day 1 (CD34+ CD38-), day 6 (CD7+), and day 14 (CD7+ CD5+, CD7+CD1a+) of differentiation and performed a scRNA-seq and scATAC-seq on sorted cells simultaneously. We applied the lymphoid predictive gene signature to the dataset; we could see the gene signature expressing as early as day 1, even before the initiation of BCL11B, with about one-third of CD34+ CD38- HSPCs in culture expressing the gene signature. Interestingly, the expression of this gene signature progressively increased until day six and slightly reduced at day 14 after the cells acquired CD5, suggesting that the gene set may be more critical in the initial time points. Given that the gene set, when probed for the gene-disease association, had a strong correlation to T acute lymphoid leukemia (T-ALL) and the requirement of these genes at initial time points, we think a transient expression of some of these factors could help us fate engineer HSCs to produce lymphoid outcomes. We are validating the significant genes whose transient expression might lead to T-cell fate engineering without predisposing these cells to T-ALL.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".