Role of Plasma Gelsolin Protein in the Final Stage of Erythropoiesis and in Correction of Erythroid Dysplasia In Vitro

An Optimized Human Erythroblast Differentiation System Reveals Cholesterol-Dependency of Robust Production of Cultured Red Blood Cells Ex Vivo

The generation of cultured red blood cells (cRBCs) ex vivo represents a potentially unlimited source for RBC transfusion and other cell therapies. Human cRBCs can be generated from the terminal differentiation of proliferating erythroblasts derived from hematopoietic stem/progenitor cells or erythroid precursors in peripheral blood mononuclear cells. Efficient differentiation and maturation into cRBCs highly depend on replenishing human plasma, which exhibits variable potency across donors or batches and complicates the consistent cRBC production required for clinical translation. Hence, the role of human plasma in erythroblast terminal maturation is investigated and uncovered that 1) a newly developed cell culture basal medium mimicking the metabolic profile of human plasma enhances cell growth and increases cRBC yield upon erythroblast terminal differentiation and 2) LDL-carried cholesterol, as a substitute for human plasma, is sufficient to support erythroid survival and terminal differentiation ex vivo. Consequently, a chemically-defined optimized medium (COM) is developed, enabling robust generation of cRBCs from erythroblasts of multiple origins, with improved enucleation efficiency and higher reticulocyte yield, without the need for supplementing human plasma or serum. In addition, the results reveal the crucial role of lipid metabolism during human terminal erythropoiesis.

Decoding human in vitro terminal erythropoiesis originating from umbilical cord blood mononuclear cells and pluripotent stem cells

Ex vivo red blood cell (RBC) production generates unsatisfactory erythroid cells. A deep exploration into terminally differentiated cells is required to understand the impairments for RBC generation and the underlying mechanisms. Here, we mapped an atlas of terminally differentiated cells from umbilical cord blood mononuclear cells (UCBMN) and pluripotent stem cells (PSC) and observed their dynamic regulation of erythropoiesis at single-cell resolution. Interestingly, we detected a few progenitor cells and non-erythroid cells from both origins. In PSC-derived erythropoiesis (PSCE), the expression of haemoglobin switch regulators (BCL11A and ZBTB7A) were significantly absent, which could be the restraint for its adult globin expression. We also found that PSCE were less active in stress erythropoiesis than in UCBMN-derived erythropoiesis (UCBE), and explored an agonist of stress erythropoiesis gene, TRIB3, could enhance the expression of adult globin in PSCE. Compared with UCBE, there was a lower expression of epigenetic-related proteins (e.g., CASPASE 3 and UBE2O) and transcription factors (e.g., FOXO3 and TAL1) in PSCE, which might restrict PSCE's enucleation. Moreover, we characterized a subpopulation with high proliferation capacity marked by CD99high in colony-forming unit-erythroid cells. Inhibition of CD99 reduced the proliferation of PSC-derived cells and facilitated erythroid maturation. Furthermore, CD99-CD99 mediated the interaction between macrophages and erythroid cells, illustrating a mechanism by which macrophages participate in erythropoiesis. This study provided a reference for improving ex vivo RBC generation.

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes

Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts.