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      Single-Cell Analyses Reveal Megakaryocyte-Biased Hematopoiesis in Myelofibrosis and Identify Mutant Clone-Specific Targets

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          Abstract

          Summary Myelofibrosis is a severe myeloproliferative neoplasm characterized by increased numbers of abnormal bone marrow megakaryocytes that induce fibrosis, destroying the hematopoietic microenvironment. To determine the cellular and molecular basis for aberrant megakaryopoiesis in myelofibrosis, we performed single-cell transcriptome profiling of 135,929 CD34+ lineage− hematopoietic stem and progenitor cells (HSPCs), single-cell proteomics, genomics, and functional assays. We identified a bias toward megakaryocyte differentiation apparent from early multipotent stem cells in myelofibrosis and associated aberrant molecular signatures. A sub-fraction of myelofibrosis megakaryocyte progenitors (MkPs) are transcriptionally similar to healthy-donor MkPs, but the majority are disease specific, with distinct populations expressing fibrosis- and proliferation-associated genes. Mutant-clone HSPCs have increased expression of megakaryocyte-associated genes compared to wild-type HSPCs, and we provide early validation of G6B as a potential immunotherapy target. Our study paves the way for selective targeting of the myelofibrosis clone and illustrates the power of single-cell multi-omics to discover tumor-specific therapeutic targets and mediators of tissue fibrosis.

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          Most cited references55

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          Accounting for technical noise in single-cell RNA-seq experiments.

          Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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            Identification of clonogenic common lymphoid progenitors in mouse bone marrow.

            The existence of a common lymphoid progenitor that can only give rise to T cells, B cells, and natural killer (NK) cells remains controversial and constitutes an important gap in the hematopoietic lineage maps. Here, we report that the Lin(-)IL-7R(+)Thy-1(-)Sca-1loc-Kit(lo) population from adult mouse bone marrow possessed a rapid lymphoid-restricted (T, B, and NK) reconstitution capacity in vivo but completely lacked myeloid differentiation potential either in vivo or in vitro. A single Lin(-)IL-7R(+)Thy-1(-)Sca-1loc-Kit(lo) cell could generate at least both T and B cells. These data provide direct evidence for the existence of common lymphoid progenitors in sites of early hematopoiesis.
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              From haematopoietic stem cells to complex differentiation landscapes.

              The development of mature blood cells from haematopoietic stem cells has long served as a model for stem-cell research, with the haematopoietic differentiation tree being widely used as a model for the maintenance of hierarchically organized tissues. Recent results and new technologies have challenged the demarcations between stem and progenitor cell populations, the timing of cell-fate choices and the contribution of stem and multipotent progenitor cells to the maintenance of steady-state blood production. These evolving views of haematopoiesis have broad implications for our understanding of the functions of adult stem cells, as well as the development of new therapies for malignant and non-malignant haematopoietic diseases.
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                Author and article information

                Journal
                Molecular Cell
                Molecular Cell
                Elsevier BV
                10972765
                May 2020
                May 2020
                : 78
                : 3
                : 477-492.e8
                Article
                10.1016/j.molcel.2020.04.008
                14f34700-bc46-4e4e-a66f-60de58b460c9
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by/4.0/

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