Literature DB >> 27411635

Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios.

Philipp S Hoppe1,2, Michael Schwarzfischer3, Dirk Loeffler1,2, Konstantinos D Kokkaliaris1,2, Oliver Hilsenbeck1,2,3, Nadine Moritz2, Max Endele1,2, Adam Filipczyk2, Adriana Gambardella4, Nouraiz Ahmed1, Martin Etzrodt1, Daniel L Coutu1, Michael A Rieger2, Carsten Marr3, Michael K Strasser3, Bernhard Schauberger2, Ingo Burtscher5, Olga Ermakova6, Antje Bürger7, Heiko Lickert5,8, Claus Nerlov4,9, Fabian J Theis3,10, Timm Schroeder1,2.   

Abstract

The mechanisms underlying haematopoietic lineage decisions remain disputed. Lineage-affiliated transcription factors with the capacity for lineage reprogramming, positive auto-regulation and mutual inhibition have been described as being expressed in uncommitted cell populations. This led to the assumption that lineage choice is cell-intrinsically initiated and determined by stochastic switches of randomly fluctuating cross-antagonistic transcription factors. However, this hypothesis was developed on the basis of RNA expression data from snapshot and/or population-averaged analyses. Alternative models of lineage choice therefore cannot be excluded. Here we use novel reporter mouse lines and live imaging for continuous single-cell long-term quantification of the transcription factors GATA1 and PU.1 (also known as SPI1). We analyse individual haematopoietic stem cells throughout differentiation into megakaryocytic-erythroid and granulocytic-monocytic lineages. The observed expression dynamics are incompatible with the assumption that stochastic switching between PU.1 and GATA1 precedes and initiates megakaryocytic-erythroid versus granulocytic-monocytic lineage decision-making. Rather, our findings suggest that these transcription factors are only executing and reinforcing lineage choice once made. These results challenge the current prevailing model of early myeloid lineage choice.

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Year:  2016        PMID: 27411635     DOI: 10.1038/nature18320

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  73 in total

1.  Sequencing of RNA in single cells reveals a distinct transcriptome signature of hematopoiesis in GATA2 deficiency.

Authors:  Zhijie Wu; Shouguo Gao; Carrie Diamond; Sachiko Kajigaya; Jinguo Chen; Rongye Shi; Cindy Palmer; Amy P Hsu; Katherine R Calvo; Dennis D Hickstein; Steven M Holland; Neal S Young
Journal:  Blood Adv       Date:  2020-06-23

2.  CSF-1-induced Src signaling can instruct monocytic lineage choice.

Authors:  Max Endele; Dirk Loeffler; Konstantinos D Kokkaliaris; Oliver Hilsenbeck; Stavroula Skylaki; Philipp S Hoppe; Axel Schambach; E Richard Stanley; Timm Schroeder
Journal:  Blood       Date:  2017-02-03       Impact factor: 22.113

3.  Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges.

Authors:  Ellen V Rothenberg
Journal:  J Comput Biol       Date:  2019-05-07       Impact factor: 1.479

4.  Human haematopoietic stem cell lineage commitment is a continuous process.

Authors:  Lars Velten; Simon F Haas; Simon Raffel; Sandra Blaszkiewicz; Saiful Islam; Bianca P Hennig; Christoph Hirche; Christoph Lutz; Eike C Buss; Daniel Nowak; Tobias Boch; Wolf-Karsten Hofmann; Anthony D Ho; Wolfgang Huber; Andreas Trumpp; Marieke A G Essers; Lars M Steinmetz
Journal:  Nat Cell Biol       Date:  2017-03-20       Impact factor: 28.824

Review 5.  Impact of inflammation on early hematopoiesis and the microenvironment.

Authors:  Hitoshi Takizawa; Markus G Manz
Journal:  Int J Hematol       Date:  2017-05-30       Impact factor: 2.490

Review 6.  New "programmers" in tissue macrophage activation.

Authors:  Anna C Aschenbrenner; Joachim L Schultze
Journal:  Pflugers Arch       Date:  2017-02-09       Impact factor: 3.657

Review 7.  Hematopoietic stem cell fate through metabolic control.

Authors:  Kyoko Ito; Keisuke Ito
Journal:  Exp Hematol       Date:  2018-05-25       Impact factor: 3.084

Review 8.  Differentiation-based model of hematopoietic stem cell functions and lineage pathways.

Authors:  Thomas Höfer; Hans-Reimer Rodewald
Journal:  Blood       Date:  2018-07-24       Impact factor: 22.113

9.  A topological view of human CD34+ cell state trajectories from integrated single-cell output and proteomic data.

Authors:  David J H F Knapp; Colin A Hammond; Fangwu Wang; Nima Aghaeepour; Paul H Miller; Philip A Beer; Davide Pellacani; Michael VanInsberghe; Carl Hansen; Sean C Bendall; Garry P Nolan; Connie J Eaves
Journal:  Blood       Date:  2019-01-08       Impact factor: 22.113

10.  Single-Cell Proteomics Reveal that Quantitative Changes in Co-expressed Lineage-Specific Transcription Factors Determine Cell Fate.

Authors:  Carmen G Palii; Qian Cheng; Mark A Gillespie; Paul Shannon; Michalina Mazurczyk; Giorgio Napolitani; Nathan D Price; Jeffrey A Ranish; Edward Morrissey; Douglas R Higgs; Marjorie Brand
Journal:  Cell Stem Cell       Date:  2019-03-14       Impact factor: 24.633

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