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Single cell analyses identify a highly regenerative and homogenous human CD34+ hematopoietic stem cell population

Author

Listed:
  • Fernando Anjos-Afonso

    (Haematopoietic Signalling Group, European Cancer Stem Cell Institute, School of Biosciences, Cardiff University)

  • Florian Buettner

    (German Cancer Research Center (DKFZ)
    German Cancer Consortium
    Frankfurt University)

  • Syed A. Mian

    (Haematopoietic Stem Cell Lab, The Francis Crick Institute)

  • Hefin Rhys

    (Flow Cytometry Facility, The Francis Crick Institute)

  • Jimena Perez-Lloret

    (Advance Sequencing Facility, The Francis Crick Institute)

  • Manuel Garcia-Albornoz

    (Haematopoietic Stem Cell Lab, The Francis Crick Institute)

  • Namrata Rastogi

    (Haematopoietic Signalling Group, European Cancer Stem Cell Institute, School of Biosciences, Cardiff University)

  • Linda Ariza-McNaughton

    (Haematopoietic Stem Cell Lab, The Francis Crick Institute)

  • Dominique Bonnet

    (Haematopoietic Stem Cell Lab, The Francis Crick Institute)

Abstract

The heterogeneous nature of human CD34+ hematopoietic stem cells (HSCs) has hampered our understanding of the cellular and molecular trajectories that HSCs navigate during lineage commitment. Using various platforms including single cell RNA-sequencing and extensive xenotransplantation, we have uncovered an uncharacterized human CD34+ HSC population. These CD34+EPCR+(CD38/CD45RA)− (simply as EPCR+) HSCs have a high repopulating and self-renewal abilities, reaching a stem cell frequency of ~1 in 3 cells, the highest described to date. Their unique transcriptomic wiring in which many gene modules associated with differentiated cell lineages confers their multilineage lineage output both in vivo and in vitro. At the single cell level, EPCR+ HSCs are the most transcriptomically and functionally homogenous human HSC population defined to date and can also be easily identified in post-natal tissues. Therefore, this EPCR+ population not only offers a high human HSC resolution but also a well-structured human hematopoietic hierarchical organization at the most primitive level.

Suggested Citation

  • Fernando Anjos-Afonso & Florian Buettner & Syed A. Mian & Hefin Rhys & Jimena Perez-Lloret & Manuel Garcia-Albornoz & Namrata Rastogi & Linda Ariza-McNaughton & Dominique Bonnet, 2022. "Single cell analyses identify a highly regenerative and homogenous human CD34+ hematopoietic stem cell population," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29675-w
    DOI: 10.1038/s41467-022-29675-w
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    References listed on IDEAS

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    1. Danilo Pellin & Mariana Loperfido & Cristina Baricordi & Samuel L. Wolock & Annita Montepeloso & Olga K. Weinberg & Alessandra Biffi & Allon M. Klein & Luca Biasco, 2019. "A comprehensive single cell transcriptional landscape of human hematopoietic progenitors," Nature Communications, Nature, vol. 10(1), pages 1-15, December.
    2. Jeffrey T Leek & John D Storey, 2007. "Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis," PLOS Genetics, Public Library of Science, vol. 3(9), pages 1-12, September.
    3. Serena Belluschi & Emily F. Calderbank & Valerio Ciaurro & Blanca Pijuan-Sala & Antonella Santoro & Nicole Mende & Evangelia Diamanti & Kendig Yen Chi Sham & Xiaonan Wang & Winnie W. Y. Lau & Wajid Ja, 2018. "Myelo-lymphoid lineage restriction occurs in the human haematopoietic stem cell compartment before lymphoid-primed multipotent progenitors," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
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