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Record W4405040379 · doi:10.1182/blood-2024-208184

Navigating Lineage Options: A Data-Driven Quest to Uncover T Cell Fate Decision Trajectories

2024· article· en· W4405040379 on OpenAlexaff
Vinothkumar Rajan, Christina R. Lee, Juan Carlos Zúñiga‐Pflücker

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsBiologyHaematopoiesisMyeloidProgenitor cellIRF8Stem cellCell biologyLymphopoiesisCellular differentiationCD33ImmunologyCD34Cell fate determinationTranscription factorLineage markersGeneticsGene

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.021
GPT teacher head0.288
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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