Definitive haematopoietic stem and progenitor cells (HSPCs) generate erythroid, lymphoid and myeloid lineages. HSPCs are produced in the embryo via transdifferentiation of haemogenic endothelial cells in the aorta–gonad–mesonephros (AGM). HSPCs in the AGM are heterogeneous in differentiation and proliferative output, but how these intrinsic differences are acquired remains unanswered. Here we discovered that loss of microRNA (miR)-128 in zebrafish leads to an expansion of HSPCs in the AGM with different cell cycle states and a skew towards erythroid and lymphoid progenitors. Manipulating miR-128 in differentiating haemogenic endothelial cells, before their transition to HSPCs, recapitulated the lineage skewing in both zebrafish and human pluripotent stem cells. miR-128 promotes Wnt and Notch signalling in the AGM via post-transcriptional repression of the Wnt inhibitor csnk1a1 and the Notch ligand jag1b. De-repression of cskn1a1 resulted in replicative and erythroid-biased HSPCs, whereas de-repression of jag1b resulted in G2/M and lymphoid-biased HSPCs with long-term consequence on the respective blood lineages. We propose that HSPC heterogeneity arises in the AGM endothelium and is programmed in part by Wnt and Notch signalling.

Multipotent hematopoietic stem/progenitor cells (HSPCs) generate all mature blood cells in the erythroid, lymphoid, and myeloid lineages. HSPCs are initially produced in the embryo, via transdifferentiation of hemogenic endothelial cells (hemECs) in the aorta-gonad mesonephros (AGM). HSPCs in the AGM are functionally heterogenous in differentiation and proliferative output, but how these intrinsic differences are acquired remains unanswered. This knowledge could inform approaches to overcome the dysregulation of HSPC heterogeneity associated with poor outcomes of autologous transplants. Here we discovered that loss of microRNA (miR)-128 (miR-128Δ/Δ) in zebrafish leads to an expansion of hemECs forming replicative HSPCs in the AGM, and a skew towards the erythroid and lymphoid lineages in larval and adult stages. Furthermore, we found that inhibiting miR-128 during the differentiation of human pluripotent stem cells into hemECs, but not during the endothelial-to-hematopoietic transition, recapitulated the lineage skewing. In vivo, expression of wild-type miR-128 in endothelium restored the blood lineage distribution in miR-128Δ/Δ zebrafish. We found that miR-128 represses the expression of the Wnt inhibitor csnk1a1 and the Notch ligand jag1b, and thus promotes Wnt and Notch signaling in hemECs. De-repression of cskn1a1 resulted in hemECs generating replicative and erythroid-biased HSPCs, whereas de-repression of jag1b resulted in hemECs forming lymphoid-biased HSPCs in the AGM and relative mature blood cells in adult. We propose that HSPC heterogeneity is established in hemogenic endothelium prior to transdifferentiation and is programmed in part by Wnt and Notch signaling modulation.

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Alterations in three–dimensional (3D) genome structures are associated with cancer. However, how genome folding evolves and diversifies during subclonal cancer progression in the native tissue environment remains unknown. Here, we leveraged a genome–wide chromatin tracing technology to directly visualize 3D genome folding in situ in a faithful Kras–driven mouse model of lung adenocarcinoma (LUAD), generating the first single–cell 3D genome atlas of any cancer. We discovered stereotypical 3D genome alterations during cancer development, including a striking structural bottleneck in preinvasive adenomas prior to progression to LUAD, indicating a stringent selection on the 3D genome early in cancer progression. We further showed that the 3D genome precisely encodes cancer states in single cells, despite considerable cell–to–cell heterogeneity. Finally, evolutionary changes in 3D genome compartmentalization — partially regulated by polycomb group protein Rnf2 through its ubiquitin ligase–independent activity — reveal novel genetic drivers and suppressors of LUAD progression. Our results demonstrate the importance of mapping the single–cell cancer 3D genome and the potential to identify new diagnostic and therapeutic biomarkers from 3D genomic architectures.