Single cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs
File(s)202006 Fidanza et ACCEPTED_FINAL.pdf (353.31 KB)
Accepted version
Author(s)
Type
Journal Article
Abstract
Haematopoietic stem and progenitor cells (HSPCs) develop through distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) is able to recapitulate some of these processes, however, it has proven difficult to generate functional haematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we exploited single cell RNA sequencing (scRNAseq) in combination with single cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors as well as erythroid, megakaryocyte and leukocyte-committed progenitors and we identified CD44, CD326, ICAM2/CD9 and CD18 as markers of these progenitors, respectively. Using an artificial neural network (ANN), that we trained on a scRNAseq derived from human fetal liver, we were able to identify a wide range of hPSCs-derived HPSC phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded and was completely missing in the dataset that had been generated using cells selected on the basis of CD43expression. By comparing the single cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver we identified transcription factors and molecular pathways that can be exploited in the future to improve the in vitro production of HSCs.
Date Issued
2020-07-02
Date Acceptance
2020-06-20
Citation
Blood, 2020, 136 (25), pp.2893-2904
ISSN
0006-4971
Publisher
American Society of Hematology
Start Page
2893
End Page
2904
Journal / Book Title
Blood
Volume
136
Issue
25
Copyright Statement
© 2020 American Society of Hematology
Sponsor
Biotechnology and Biological Sciences Research Council (BBSRC)
Identifier
https://ashpublications.org/blood/article/doi/10.1182/blood.2020006229/461263/Single-cell-analyses-and-machine-learning-define
Grant Number
BB/N011597/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Hematology
PLURIPOTENT STEM-CELLS
DEFINITIVE HEMATOPOIESIS
LINEAGE COMMITMENT
BLOOD-CELLS
YOLK-SAC
DIFFERENTIATION
MEGAKARYOCYTE
IDENTIFICATION
PRECURSORS
EMERGENCE
Immunology
1102 Cardiorespiratory Medicine and Haematology
1103 Clinical Sciences
1114 Paediatrics and Reproductive Medicine
Publication Status
Published online
Date Publish Online
2020-07-02