Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs

Abstract

Abstract Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. 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 explored in the future to improve the in vitro production of HSCs.