Hallmarks of Transcriptional Heterogeneity in Human Hematopoietic Stem Cells across 382 Donors
Notice bibliographique
Résumé
Our understanding of human hematopoietic stem cells (HSC) has been shaped through decades of experimental research aimed at purifying HSCs to functional homogeneity. However, recent studies have uncovered both functional and molecular heterogeneity within the human HSC pool, particularly in the context of ontogeny, aging, inflammation, and somatic mosaicism. Yet, a comprehensive understanding of human HSC variation and their relevance in health and disease is lacking. Here, we carried out a systematic analysis of human HSC heterogeneity at an unprecedented scale by applying an approach to purify human HSC and multipotent progenitors (MPPs) in-silico (Zeng et al., biorxiv 2023) to publicly available and in-house single cell RNA sequencing datasets of human hematopoiesis, excluding acute leukemia. Hence, we assembled a large database of 526,993 single cell HSC/MPP transcriptomes from over 382 donors spanning ontogeny, age, and disease status. To uncover gene expression programs that vary across HSC/MPPs, we employed consensus non-negative matrix factorization across each dataset identifying 553 programs. As most programs were recurrent across datasets, these were collapsed to form 14 consensus meta-programs constituting the transcriptional hallmarks of human HSCs. We evaluated the biological relevance of this new catalog of recurrent human HSC variation with known functional properties of HSCs. First, we uncovered classical HSC quiescence control meta-programs related to quiescence exit enriched for CTCF targets, as well as lineage associated programs related to myeloid-lymphoid or megakaryocyte-erythroid priming. Second, two distinct meta-programs corresponding to stemness, were enriched in human HSC versus progenitors (NES > 3.00, p < 1e-36) as well as serial repopulating murine HSCs defined by lineage tracing (Rodriguez-Fraticelli et al., 2020; NES > 1.50, p < 0.01). Interestingly, one of these stemness-related meta-programs was strongly enriched for genes defining quiescence including CDKN1A which encodes the cell cycle inhibitor p21. Third, we identified one meta-program underlying HSC trafficking, enriched in circulating HSCs (umbilical cord blood, peripheral blood, and mobilized peripheral blood) compared to bone marrow (BM) HSCs. Notably, pathway analysis of the top genes driving this program revealed enrichment of lymphoid priming signatures, suggesting a path to lymphopoiesis. Finally, we uncovered seven novel HSC programs; five are associated with inflammatory response and upregulated with human HSC aging (NES > 2, p < 1e-07; >40y donor HSCs vs <40y donor HSCs). Three meta-programs are also enriched in HSCs enriched in TET2 and DNMT3A clonal hematopoiesis compared to age-matched controls (NES > 1.5, padj < 1e-03). These include two meta-programs underlying our recent discovery of a human HSC inflammatory memory population (AUC > 0.90), which retains epigenetic and transcriptional memory of prior inflammation (Zeng, Nagree, Jakobsen et al., biorxiv 2023). Pathway analysis revealed convergent and divergent patterns of enrichment, with one program notably significant for TGF-B signaling and SMAD motifs. Importantly, we identified one meta-program upregulated in myelofibrosis and VEXAS syndrome (NES > 2, p < 1e-09), with key genes including CD83, the NR4A family and proinflammatory cytokines, showcasing the relevance for our approach to uncover new understanding of disease. Collectively, we have systematically uncovered transcriptional hallmarks of human HSC heterogeneity, providing a new framework for understanding how HSCs may vary and become dysregulated in hematological disease.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».