Edinburgh Research Explorer

Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis

Research output: Contribution to journalArticle

  • Victoria Moignard
  • Iain C Macaulay
  • Gemma Swiers
  • Florian Buettner
  • Judith Schütte
  • Fernando J Calero-Nieto
  • Sarah Kinston
  • Anagha Joshi
  • Rebecca Hannah
  • Fabian J Theis
  • Sten Eirik Jacobsen
  • Marella F de Bruijn
  • Berthold Göttgens

Related Edinburgh Organisations

Open Access permissions



  • Download as Adobe PDF

    Rights statement: Published in final edited form as: Nat Cell Biol. 2013 April; 15(4): 363–372. Published online 2013 March 24. doi: 10.1038/ncb2709

    Accepted author manuscript, 2.56 MB, PDF document

Original languageEnglish
Pages (from-to)363-72
Number of pages10
JournalNature Cell Biology
Issue number4
Publication statusPublished - Apr 2013


Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single-cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.

Download statistics

No data available

ID: 7542480