Schematic of the pipeline used in Fidanza et al to characterize hematopoietic cells differentiated from hPSCs. (A) The authors sequenced >40 000 cells in 2 experiments. The first scRNAseq experiment identified the transcriptome of uncommitted and lineage primed progenitors. The membrane markers associated with these cells were used to functionally validate their in silico–predicted lineage output. In the second experiment, 8 membrane markers were tagged using oligo-barcoded antibodies, and the cells were then sequenced using a CITE-seq approach. This allowed verification of the expression pattern of specific markers and associated them with a single-cell transcriptome. (B) The authors compared the single-cell transcriptome of hematopoietic cells collected from the human fetal liver with that of cells differentiated in vitro using a machine learning approach. First, they trained an artificial neural network using a vast single-cell fetal liver dataset and then used this trained network to transfer cell-type labels to the in vitro–derived cells. Finally, corresponding cell types were compared, and differentially expressed genes were listed as targets to improve the production of specific hematopoietic population in vitro from human PSCs.