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Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis.

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Athanasiadis, Emmanouil I  ORCID logo
Botthof, Jan G 
Andres, Helena 
Ferreira, Lauren 
Lio, Pietro 


The success of marker-based approaches for dissecting haematopoiesis in mouse and human is reliant on the presence of well-defined cell surface markers specific for diverse progenitor populations. An inherent problem with this approach is that the presence of specific cell surface markers does not directly reflect the transcriptional state of a cell. Here, we used a marker-free approach to computationally reconstruct the blood lineage tree in zebrafish and order cells along their differentiation trajectory, based on their global transcriptional differences. Within the population of transcriptionally similar stem and progenitor cells, our analysis reveals considerable cell-to-cell differences in their probability to transition to another committed state. Once fate decision is executed, the suppression of transcription of ribosomal genes and upregulation of lineage-specific factors coordinately controls lineage differentiation. Evolutionary analysis further demonstrates that this haematopoietic programme is highly conserved between zebrafish and higher vertebrates.



Animals, Animals, Genetically Modified, Cell Lineage, Erythroid Cells, Gene Expression Profiling, Gene Ontology, Hematopoiesis, Humans, Sequence Analysis, RNA, Single-Cell Analysis, Zebrafish, Zebrafish Proteins

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Nat Commun

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Springer Science and Business Media LLC
Cancer Research Uk (None)
European Research Council (677501)
European Commission (305280)
Medical Research Council (MC_PC_12009)
The study was supported by Cancer Research UK grant number C45041/A14953 (to A.C. and E.A.), European Research Council project 677501 – ZF_Blood (to A.C.) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute. The authors would like to thank WTSI Cytometry Core Facility for their help with index cell sorting and the Core Sanger Web Team for hosting the cloud web application. The authors would also like to thank the CRUK Cambridge Institute Genomics Core Facility for their contribution in sequencing the data.
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