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      A comprehensive single cell transcriptional landscape of human hematopoietic progenitors

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          Abstract

          Hematopoietic Stem/Progenitor cells (HSPCs) are endowed with the role of maintaining a diverse pool of blood cells throughout the human life. Despite recent efforts, the nature of the early cell fate decisions remains contentious. Using single-cell RNA-Seq, we show that existing approaches to stratify bone marrow CD34+ cells reveal a hierarchically-structured transcriptional landscape of hematopoietic differentiation. Still, this landscape misses important early fate decisions. We here provide a broader transcriptional profiling of bone marrow lineage negative hematopoietic progenitors that recovers a key missing branchpoint into basophils and expands our understanding of the underlying structure of early adult human haematopoiesis. We also show that this map has strong similarities in topology and gene expression to that found in mouse. Finally, we identify the sialomucin CD164, as a reliable marker for the earliest branches of HSPCs specification and we showed how its use can foster the design of alternative transplantation cell products.

          Abstract

          Human Hematopoietic stem and progenitor cells (HSPCs) are commonly defined by CD34 expression. Here, the authors map single-cell RNA states both inside and outside the CD34 compartment, uncovering previously unappreciated branchpoints and validating CD164 as an efficient marker for early HSPCs.

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          Most cited references30

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          Distinct routes of lineage development reshape the human blood hierarchy across ontogeny.

          In a classical view of hematopoiesis, the various blood cell lineages arise via a hierarchical scheme starting with multipotent stem cells that become increasingly restricted in their differentiation potential through oligopotent and then unipotent progenitors. We developed a cell-sorting scheme to resolve myeloid (My), erythroid (Er), and megakaryocytic (Mk) fates from single CD34(+) cells and then mapped the progenitor hierarchy across human development. Fetal liver contained large numbers of distinct oligopotent progenitors with intermingled My, Er, and Mk fates. However, few oligopotent progenitor intermediates were present in the adult bone marrow. Instead, only two progenitor classes predominate, multipotent and unipotent, with Er-Mk lineages emerging from multipotent cells. The developmental shift to an adult "two-tier" hierarchy challenges current dogma and provides a revised framework to understand normal and disease states of human hematopoiesis.
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            Diffusion maps for high-dimensional single-cell analysis of differentiation data.

            Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.
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              Single-cell barcoding and sequencing using droplet microfluidics.

              Single-cell RNA sequencing has recently emerged as a powerful tool for mapping cellular heterogeneity in diseased and healthy tissues, yet high-throughput methods are needed for capturing the unbiased diversity of cells. Droplet microfluidics is among the most promising candidates for capturing and processing thousands of individual cells for whole-transcriptome or genomic analysis in a massively parallel manner with minimal reagent use. We recently established a method called inDrops, which has the capability to index >15,000 cells in an hour. A suspension of cells is first encapsulated into nanoliter droplets with hydrogel beads (HBs) bearing barcoding DNA primers. Cells are then lysed and mRNA is barcoded (indexed) by a reverse transcription (RT) reaction. Here we provide details for (i) establishing an inDrops platform (1 d); (ii) performing hydrogel bead synthesis (4 d); (iii) encapsulating and barcoding cells (1 d); and (iv) RNA-seq library preparation (2 d). inDrops is a robust and scalable platform, and it is unique in its ability to capture and profile >75% of cells in even very small samples, on a scale of thousands or tens of thousands of cells.
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                Author and article information

                Contributors
                Allon_Klein@hms.harvard.edu
                l.biasco@ucl.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 June 2019
                3 June 2019
                2019
                : 10
                : 2395
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Gene Therapy Program, Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, , Harvard Medical School, ; Boston, MA 02115 USA
                [2 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Systems Biology, , Harvard Medical School, ; Boston, MA 02115 USA
                [3 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Department of Pathology, , Boston Children’s Hospital and Harvard Medical School, ; Boston, MA 02115 USA
                [4 ]University College of London (UCL), Great Ormond Street Institute of Child Health Faculty of Population Health Sciences, London, WC1N 1EH UK
                Author information
                http://orcid.org/0000-0001-9365-5388
                http://orcid.org/0000-0002-2120-4788
                http://orcid.org/0000-0001-8913-7879
                Article
                10291
                10.1038/s41467-019-10291-0
                6546699
                31160568
                f7e57590-50df-4988-be5c-86217ed84494
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 January 2019
                : 3 May 2019
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

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                computational biology and bioinformatics,haematopoietic stem cells,stem-cell differentiation

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