Genomics, proteomics & bioinformatics | 2021

Mapping Human Pluripotent Stem Cell-derived Erythroid Differentiation by Single-cell Transcriptome Analysis.

 
 
 
 
 
 
 

Abstract


There is an imbalance between the supply and demand of functional red blood cells (RBCs) in clinical applications. This imbalance can be addressed by regenerating RBCs using several in vitro methods. Induced pluripotent stem cells (iPSCs) can handle the low supply of cord blood and the ethical issues in embryonic stem cell research and provide a promising strategy to eliminate immune rejection. However, no complete single-cell level differentiation pathway exists for the iPSC-derived RBC differentiation system. In this study, we used iPSC line BC1 to establish a RBCs regeneration system. The 10× genomics single-cell transcriptome platform was used to map the cell lineage and differentiation trajectories on day 14 of the regeneration system. We observed that iPSCs differentiation was not synchronized during embryoid body (EB) culture. The cells (day 14) mainly consisted of mesodermal and various blood cells, similar to the yolk sac hematopoiesis. We identified six cell classifications and characterized the regulatory transcription factors (TFs) networks and cell-cell contacts underlying the system. iPSCs undergo two transformations during the differentiation trajectory, accompanied by the dynamic expression of cell adhesion molecules and estrogen-responsive genes. We identified different stages of erythroid cells, such as burst-forming unit erythroid (BFU-E) and orthochromatic erythroblasts (ortho-E), and found that the regulation of TFs (e.g., TFDP1 and FOXO3) is erythroid-stage specific. Immune erythroid cells were identified in our system. This study provides systematic theoretical guidance for optimizing the iPSCs-derived RBCs differentiation system, and this system is a useful model for simulating in vivo hematopoietic development and differentiation.

Volume None
Pages None
DOI 10.1016/j.gpb.2021.03.009
Language English
Journal Genomics, proteomics & bioinformatics

Full Text