Melania Barile1,2, Ivan Imaz-Rosshandler1,2, Isabella Inzani3, Shila Ghazanfar4, Jennifer Nichols2,5, John C Marioni4,6,7, Carolina Guibentif8,9,10, Berthold Göttgens11,12. 1. Department of Haematology, University of Cambridge, Cambridge, CB2 0AW, UK. 2. Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, CB2 0AW, UK. 3. University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Cambridge, CB2 0QQ, UK. 4. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK. 5. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, CB2 3DY, UK. 6. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SA, UK. 7. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK. 8. Department of Haematology, University of Cambridge, Cambridge, CB2 0AW, UK. carolina.guibentif@gu.se. 9. Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, CB2 0AW, UK. carolina.guibentif@gu.se. 10. Sahlgrenska Center for Cancer Research, Department of Microbiology and Immunology, University of Gothenburg, 413 90, Gothenburg, Sweden. carolina.guibentif@gu.se. 11. Department of Haematology, University of Cambridge, Cambridge, CB2 0AW, UK. bg200@cam.ac.uk. 12. Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, CB2 0AW, UK. bg200@cam.ac.uk.
Abstract
BACKGROUND: Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. RESULTS: Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. CONCLUSIONS: By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.
BACKGROUND: Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. RESULTS: Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. CONCLUSIONS: By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.
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