Single Cell Analysis Elucidates the Maturation of Human Stem and Progenitor Cell Function from Fetal through Adult Hematopoiesis
Notice bibliographique
Résumé
Abstract Hematopoiesis continually replenishes the supply of circulating blood cells from embryonic development through the entirety of human lifespan. Although all hematopoietic lineages are produced throughout life, biases in lineage output occur at various stages, including lymphoid bias in childhood and myeloid bias in adulthood. Furthermore, many blood disorders demonstrate marked biases in age of onset, such as bone marrow failure disorders, clonal hematopoiesis of indeterminate potential, and numerous hematologic malignancies. A lack of insight into the normal physiologic changes occurring in the hematopoietic stem and progenitor cell (HSPC) compartments during development and maturation fundamentally limits our understanding of how age biased blood disorders arise. Two major unresolved questions are: (1) what changes in the molecular regulation of hematopoietic lineage commitment occur over the course of human life? And (2) are certain HSPC states present only during specific ages of life, and if so, do age-specific HSPCs have distinct biology? To address these questions we performed single cell RNA sequencing (scRNAseq) on human HSPCs from first and second trimester fetal liver hematopoiesis, and bone marrow hematopoiesis spanning childhood into mature adulthood. In total, HSPC samples were obtained from 14 distinct human donors. Dimensionality reduction and marker gene analysis identified uncommitted hematopoietic stem cells (HSCs) and the developmental trajectories of each lineage emanating from multipotent HSCs. We then identified the genes activated upon commitment to each hematopoietic lineage during fetal, childhood, and mature adult hematopoiesis using the Population Balance Analysis and Stationary Optimal Transport algorithms, followed by Elastic Net gene regression. For each lineage we determined the putative transcription factor network that is consistently active in driving commitment to that lineage throughout life, but surprisingly also found the existence of adjunctive transcription factor networks that only drove lineage commitment at specific ages. We next used unbiased clustering of scRNAseq data to identify 21 distinct subtypes in the HSPC compartment across human life. Using marker gene analysis and singleCellNet algorithm comparisons to an existing human adult bone marrow scRNAseq data set, we hierarchically ordered and annotated these HSPC subtypes ranging from uncommitted HSCs to lineage committed progenitor cells. We found that cellular distribution within the HSPC compartment amongst these subtypes varied markedly throughout human lifetime, with higher representation of HSCs in fetal life, predominance of lymphoid progenitors in childhood, and higher representation of myeloid progenitors in adulthood. Focusing on the distribution of cells among HSC subtypes over human life, we identified an HSC subtype exclusive to mid-gestation that was not present in early fetal or postnatal timepoints. This HSC subtype had a characteristic immunophenotype and was enriched for expression of early response transcription factors and mRNA decay factors. We functionally validated that this mid-gestation-specific HSC subtype was phenotypically unique using colony formation assays and xenotransplantation. Mid-gestation-specific HSCs were more clonogenic with a greater number of multi-lineage outcomes, and also demonstrated increased multilineage engraftment capacity compared to other HSC subtypes Our findings reveal that the intrinsic biology of hematopoietic lineage commitment fundamentally changes over the course of the human lifetime, and define and validate age-specific HSPC subtypes. In particular, the biology of the mid-gestation-specific HSC we identified has potential applications for improving engraftment and multi-lineage reconstitution in hematopoietic cell transplantation. Disclosures Regev: Genentech: Current Employment; Celsius Therapeutics: Current equity holder in publicly-traded company, Other: Co-founder; Immunitas: Current equity holder in publicly-traded company; ThermoFisher Scientific: Membership on an entity's Board of Directors or advisory committees; Syros Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Neogene Therapeutics: Membership on an entity's Board of Directors or advisory committees; Asimov: Membership on an entity's Board of Directors or advisory committees.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».