GSE115765
Deciphering the regulatory landscape of fetal and adult gd T-cell development at single-cell resolution
Gamma delta T (gdT) cells with distinct properties develop in the embryonic and adult thymus and have been identified as critical players in a broad range of infections, antitumor surveillance, autoimmune diseases and tissue homeostasis. Despite their potential value for immunotherapy, differentiation of gdT cells in the thymus is incompletely understood. Here, we establish a high-resolution map of gdT cell differentiation from the fetal and adult thymus using single-cell RNA-sequencing. We reveal novel sub-types of immature and mature gdT cells and identify an unpolarized thymic population which is expanded in the blood and lymph nodes. Our detailed comparative analysis reveals remarkable similarities between the gene networks active during fetal and adult gdT cell differentiation. By performing a combined single-cell analysis of Sox13, Maf and Rorc knockout mice, we demonstrate sequential activation of these factors during IL-17-producing gdT cell (gdT17) differentiation. These findings substantially expand our understanding of gdT cell ontogeny in fetal and adult life. Our experimental and computational strategy provides a blueprint for comparing immune cell differentiation across developmental stages. Overall design: Single cell RNA sequencing was performed on T cell progenitors - DN1, DN2, DN3 as well as cells encompassing various stages of gdT cell differentiation from fetal and adult murine thymus. Furthermore, single cells were also profiled from the Sox13, Maf and Rorc deficient mice in order to gain molecular insights into the regulation of IL-17-producing gdT cell differentiation in the thymus. Single-cell RNA sequencing was performed using the mCEL-Seq2 protocol, an automated and miniaturized version of CEL-Seq2 (Hashimshony et al., 2016; Herman et al., 2018). Note that we had 192 polyT primers containing unique cell barcodes, therefore each count file is demultiplexed into 192 cells. Although only 96 cells were pooled per library so each fastq paired-end file and the corresponding .csv count file contains data from 96 cells and NOT from 192 cells. The remaining 96 columns in the .csv count files are either empty or have very low counts due to spillover or index hopping.