Navigating Lineage Options: A Data-Driven Quest to Uncover T Cell Fate Decision Trajectories
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».