Back

Inflammatory Cytokines Regulate T-cell Development from Blood Progenitor Cells in a Stage and Dose-Specific Manner

Edgar, J. M.; Zandstra, P. W.

2021-01-19 immunology
10.1101/2021.01.18.427186 bioRxiv
Show abstract

T-cell development from hematopoietic stem and progenitor cells (HSPCs) is tightly regulated through Notch pathway activation by the Notch ligands Delta-like (DL) 1 and 4 and Jagged-2. Other molecules, such as stem cell factor (SCF), FMS-like tyrosine kinase 3 ligand (Flt3L) and interleukin (IL)-7, play a supportive role in regulating the survival, differentiation, and proliferation of developing progenitor (pro)T-cells. Numerous other signaling molecules are known to instruct T-lineage development in vivo, but little work has been done to optimize their use for T-cell production in vitro. Using a defined T-lineage differentiation assay consisting of plates coated with the Notch ligand DL4 and adhesion molecule VCAM-1, we performed a cytokine screen that identified IL-3 and tumor necrosis factor (TNF) as enhancers of proT-cell differentiation and expansion. Mechanistically, we found that TNF induced T-lineage differentiation through the positive regulation of T-lineage genes GATA3, TCF7, and BCL11b. TNF also synergized with IL-3 to induce proliferation by upregulating the expression of the IL-3 receptor on CD34+ HSPCs, yielding 753.2 (532.4-1026.9; 5-95 percentile)-fold expansion of total cells after 14 days compared to 8.9 (4.3-21.5)-fold expansion in conditions without IL-3 and TNF. We then optimized cytokine concentrations for T-cell maturation. Focusing on T-cell maturation, we used quantitative models to optimize dynamically changing cytokine requirements and used these to construct a three-stage assay for generating CD3+CD4+CD8+ and CD3+CD4-CD8+ T-cells. Our work provides new insight into T-cell development and a robust in vitro assay for generating T-cells to enable clinical therapies for treating cancer and immune disorders.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.