Biology subjects

Boufea, K.

Publications and source records attributed to Boufea, K..

3 recordsLinked to original sources

bioRxiv2021

Single-cell analysis uncovers differential regulation of lung γδ T cell subsets by the co-inhibitory molecules, PD-1 and TIM-3

IL-17A-producing {gamma}{delta} T cells within the lung consist of both V{gamma}6+ tissue-resident cells and V{gamma}4+ circulating cells that play important roles in homeostasis, inflammation, infection, tumor progression and metastasis. How these {gamma}{delta} T cell subsets are regulated in the lung environment during homeostasis and cancer remains poorly understood. Using single-cell RNA sequencing and flow cytometry, we show that lung V{gamma}6+ cells express a repertoire of cell surface molecules distinctive from V{gamma}4+ cells, including PD-1 and ICOS. We found that PD-1 functions as a co-inhibitory molecule on V{gamma}6+ cells to reduce IL-17A production, whereas manipulation of ICOS signaling fails to affect IL-17A in V{gamma}6+ cells. In a mammary tumor model, ICOS and PD-1 expression on lung V{gamma}6+ cells remained stable. However, V{gamma}6+ and V{gamma}4+ cells within the lung pre-metastatic niche increased expression of IL-17A, IL-17F, amphiregulin (AREG) and TIM-3 in response to tumor-derived IL-1{beta} and IL-23, where the upregulation of TIM-3 was specific to V{gamma}4+ cells. Inhibition of either PD-1 or TIM-3 in mammary tumor-bearing mice further increased IL-17A by V{gamma}6+ and V{gamma}4+ cells, indicating that both PD-1 and TIM-3 function as negative regulators of IL-17A-producing {gamma}{delta} T cell subsets. Together, these data demonstrate how lung {gamma}{delta} T cell subsets are differentially controlled by co-inhibitory molecules in steady-state and cancer.

immunology↗

bioRxiv2019

Origin and microenvironment contribute to the sexually dimorphic phenotype and function of peritoneal macrophages.

Macrophages reside in the body cavities where they maintain serosal homeostasis and provide immune surveillance. Peritoneal macrophages are implicated in the aetiology of pathologies including peritonitis, endometriosis and metastatic cancer thus understanding the factors that govern their behaviour is vital. Using a combination of fate mapping techniques, we have investigated the impact of sex and age on murine peritoneal macrophage differentiation, turnover and function. We demonstrate that the sexually dimorphic replenishment of peritoneal macrophages from the bone marrow, which is high in males and very low in females, is driven by changes in the local microenvironment that arise upon sexual maturation. Population and single cell RNAseq revealed striking dimorphisms in gene expression between male and female peritoneal macrophages that was in part explained by differences in composition of these populations. By estimating the time of residency of different subsets within the cavity and assessing development of dimorphisms with age and in monocytopenic Ccr2-/- mice, we demonstrate that key sex-dependent features of peritoneal macrophages are a function of the differential rate of replenishment from the bone marrow while others are reliant on local microenvironment signals. Importantly, we demonstrate that the dimorphic turnover of peritoneal macrophages contributes to differences in the ability to protect against pneumococcal peritonitis between the sexes. These data highlight the importance of considering both sex and age in susceptibility to inflammatory and infectious disease.

immunology↗

bioRxiv2018

Mapping transcriptionally equivalent populations across single cell RNA-seq datasets

The power of single cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell type-dependent phenotypes, which rests on the accuracy of cell type identification. However, resolving cell types within and, thus, comparison of scRNA-seq data across conditions is challenging due to technical factors such as sparsity, low number of cells and batch effect. To address these challenges we developed scID (Single Cell IDentification), which uses the framework of Fishers Linear Discriminant Analysis to identify transcriptionally related cell types between scRNA-seq datasets. We demonstrate the accuracy and performance of scID relative to existing methods on several published datasets. By increasing power to identify transcriptionally similar cell types across datasets, scID enhances investigators ability to extract biological insights from scRNA-seq data.

bioinformatics↗