Evolutionarily divergent notch regulation underpins the initiation of human T-cell development
Bibliographic record
Abstract
Abstract Hematopoietic stem and progenitor cells (HSPCs) differentiate into diverse blood cell lineages through intricate genetic and epigenetic decision processes. How these decision points are regulated has not been fully elucidated. We aimed to identify the factors influencing human HSPC differentiation into T lymphocytes using a single-cell RNA sequencing-based lineage tracing approach. Our approach revealed that a bifurcation between the mast cell and lympho-myeloid lineage trajectories occurs early, with lymphoid and myeloid clones remaining interconnected until later time points, when T cell lineage commitment is established. We identified a new mechanism that primes certain HSPCs to become T cells, even though all cells express NOTCH receptors and are exposed to Notch ligands. GXYLT2, a xylosyltransferase that regulates NOTCH activation post-translationally, was differentially expressed in HSPCs with a stronger T cell clonal bias. Considering this cell-intrinsic, heritable priming state, we investigated the factors influencing lineage priming before Notch ligand exposure. We discovered that class 1 histone deacetylases (HDACs) favor lymphoid outcomes, with HDAC inhibition promoting mast cell outcomes and activation enhancing T-lymphoid differentiation. Additionally, we identified binding regions for IRF1 and GATA3, which are early predictors of human lymphoid outcomes within the GXYLT2 promoter region. Further analysis of the GXYLT2 loci revealed a retrotransposon element that acts as a regulator of the loci, appearing to be evolutionarily divergent but conserved among primates. Removing this element increased T cell bias in our cultures. Our findings revealed a previously unrecognized role for GXYLT2 and its loci in influencing early decisions toward human T cell development outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".