• DPY30 is important for the proliferation and proper differentiation of human hematopoietic progenitor cells.• dpy30 and efficient H3K4 methylation are essential for the normal hematopoiesis of zebrafish.Epigenetic mechanisms, including histone modifications, have emerged as important factors influencing cell fate determination. The functional role of H3K4 methylation, however, remains largely unclear in the maintenance and differentiation of hematopoietic stem cells (HSCs)/hematopoietic progenitor cells (HPCs). Here we show that DPY30, a shared core subunit of the SET1/MLL family methyltransferase complexes and a facilitator of their H3K4 methylation activity, is important for ex vivo proliferation and differentiation of human CD34 1 HPCs. DPY30 promotes HPC proliferation by directly regulating the expression of genes critical for cell proliferation. Interestingly, while DPY30 knockdown in HPCs impaired their differentiation into the myelomonocytic lineage, it potently promoted hemoglobin production and affected the kinetics of their differentiation into the erythroid lineage. In an in vivo model, we show that morpholino-mediated dpy30 knockdown resulted in severe defects in the development of the zebrafish hematopoietic system, which could be partially rescued by coinjection of dpy30 messenger RNA. Taken together, our results establish a critical role of DPY30 in the proliferation and appropriate differentiation of hematopoietic progenitor cells and in animal hematopoiesis. Finally, we also demonstrate a crucial role of DPY30 in the growth of several MLL1-fusion-mediated leukemia cell lines. (Blood. 2014;124(13):2025-2033 IntroductionThe maintenance, proliferation, and differentiation of stem and progenitor cells are ultimately controlled at the level of gene expression, which is closely tied to the global and local epigenetic status in the cell. A paradigm for such epigenetic control of gene expression is shown by 2 well-established antagonistic histone modifications: H3K27 methylation, catalyzed by the Polycomb group complexes, and H3K4 methylation, mainly catalyzed by the Trithorax group complexes.1 Although H3K27 methylation is generally associated with gene repression, H3K4 methylation is prevalently associated with gene activation. 2,3 Roles for Polycomb group complexes and H3K27 methylation have been extensively studied in both embryonic stem cells (ESCs) 4-6 and hematopoietic stem cells (HSCs) and hematopoietic progenitor cells (HPCs). 7-15On the other hand, the functional roles for H3K4 methylation in the maintenance and differentiation of stem and progenitor cells remain largely unclear.The SET1/MLL family complexes are the most notable H3K4 methyltransferases in mammals. They are composed of either SET1A, SET1B, MLL1, MLL2, MLL3, or MLL4 as the catalytic subunit, and WDR5, RBBP5, ASH2L, and DPY30 as integral core subunits that are required for the full methylation activity of these complexes. 2,[16][17][18][19] The functional roles of the SET1/MLL complexes are especially pertinent to the hemato...
Background The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Results Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays. Conclusions Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.
Differentiated cells can be reprogrammed to the pluripotent state by overexpression of defined factors, and this process is profoundly influenced by epigenetic mechanisms including dynamic histone modifications. Changes in H3K4 methylation have been shown to be the predominant activating response in the early stage of cellular reprogramming. Mechanisms underlying such epigenetic priming, however, are not well understood. Here we show that the expression of the reprogramming factors (Yamanaka factors, Oct4, Sox2, Klf4 and Myc), especially Myc, directly promotes the expression of certain core subunits of the Set1/Mll family of H3K4 methyltransferase complexes. A dynamic recruitment of the Set1/Mll complexes largely, though not sufficiently in its own, explains the dynamics of the H3K4 methylation during cellular reprogramming. We then demonstrate that the core subunits of the Set1/Mll complexes physically interact with mainly Sox2 and Myc among the Yamanaka factors. We further show that Sox2 directly binds the Ash2l subunit in the Set1/Mll complexes and this binding is mediated by the HMG domain of Sox2. Functionally, we show that the Set1/Mll complex core subunits are required for efficient cellular reprogramming. We also show that Dpy30, one of the core subunits in the complexes, is required for the efficient target binding of the reprogramming factors. Interestingly, such requirement is not necessarily dependent on locus-specific H3K4 methylation. Our work provides a better understanding of how the reprogramming factors physically interact and functionally coordinate with a key group of epigenetic modulators to mediate transitions of the chromatin state involved in cellular reprogramming.
The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle as both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the ubiquitous applicability of transfer learning. We show that tricycle can predict any cell’s position in the cell cycle regardless of the cell type, species of origin, and even sequencing assay. The accuracy of tricycle compares favorably to gold-standard experimental assays which generally require specialized measurements in specifically constructed in vitro systems. Unlike gold-standard assays, tricycle is easily applicable to any single-cell RNA-seq dataset. Tricycle is highly scalable, universally accurate, and eminently pertinent for atlas-level data.
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