Cell ReportsCancer BiologyPeer-reviewed journal article
A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis
Asiri EdiriwickremaiD, Yusuke NakauchiiD, Amy C. FaniD, Thomas KöhnkeiD, Xiaoyi HuiD, Bogdan A. LucaiD, YeEun KimiD, Sreejith RamakrishnaniD, Margaret A. Nakamoto, Daiki KariganeiD, Miles Hamilton LindeiD, Armon AziziiD, Aaron M. NewmaniD, Andrew J. GentlesiD, Ravindra MajetiiD
Abstract
Hematopoietic multipotent progenitors (MPPs) regulate blood cell production to appropriately meet the biological demands of the human body. Human MPPs remain ill-defined whereas mouse MPPs have been well characterized with distinct immunophenotypes and lineage potencies. Using multiomic single cell analyses and complementary functional assays, we identified new human MPPs and oligopotent progenitor populations within Lin-CD34+CD38dim/lo adult bone marrow with distinct biomolecular and functional properties. These populations were prospectively isolated based on expression of CD69, CLL1, and CD2 in addition to classical markers like CD90 and CD45RA. We show that within the canonical Lin-CD34+CD38dim/loCD90CD45RA-MPP population, there is a CD69+ MPP with long-term engraftment and multilineage differentiation potential, a CLL1+ myeloid-biased MPP, and a CLL1-CD69-erythroid-biased MPP. We also show that the canonical Lin-CD34+CD38dim/loCD90-CD45RA+ LMPP population can be separated into a CD2+ LMPP with lymphoid and myeloid potential, a CD2-LMPP with high lymphoid potential, and a CLL1+ GMP with minimal lymphoid potential. We used these new HSPC profiles to study human and mouse bone marrow cells and observe limited cell type specific homology between humans and mice and cell type specific changes associated with aging. By identifying and functionally characterizing new adult MPP sub-populations, we provide an updated reference and framework for future studies in human hematopoiesis.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- approach: deep learning
- approach: experimental assay
- Citations
- 10
- FWCI
- 6.86
- Field percentile
- top 2%
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