Macrophages reside in the body cavities where they maintain serosal homeostasis and provide immune surveillance. Peritoneal macrophages are implicated in the etiology of pathologies including peritonitis, endometriosis, and metastatic cancer; thus, understanding the factors that govern their behavior 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 RNA sequencing revealed marked 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, whereas others are reliant on local microenvironment signals. 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 diseases.

The power of single-cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell typedependent 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 owing 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 Fisher's Linear Discriminant Analysislike framework 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 with batch effect, scID enhances investigator's ability to integrate and uncover development-, disease-, and perturbation-associated changes in scRNA-seq data.

IL-17A–producing γδ T cells in mice consist primarily of Vγ6+ tissue-resident cells and Vγ4+ circulating cells. How these γδ T cell subsets are regulated during homeostasis and cancer remains poorly understood. Using single-cell RNA sequencing and flow cytommetry, we show that lung Vγ4+ and Vγ6+ cells from tumor-free and tumor-bearing mice express contrasting cell surface molecules as well as distinct co-inhibitory molecules, which function to suppress their expansion. Vγ6+ cells express constitutively high levels of PD-1, whereas Vγ4+ cells upregulate TIM-3 in response to tumor-derived IL-1β and IL-23. Inhibition of either PD-1 or TIM-3 in mammary tumor–bearing mice increased Vγ6+ and Vγ4+ cell numbers, respectively. We found that genetic deletion of γδ T cells elicits responsiveness to anti–PD-1 and anti–TIM-3 immunotherapy in a mammary tumor model that is refractory to T cell checkpoint inhibitors, indicating that IL-17A–producing γδ T cells instigate resistance to immunotherapy. Together, these data demonstrate how lung IL-17A–producing γδ T cell subsets are differentially controlled by PD-1 and TIM-3 in steady-state and cancer.

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 Fisher's Linear Discriminant Analysis-like framework 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 with batch effect, scID enhances investigator's ability to integrate and uncover development, disease and perturbation associated changes in scRNA-seq data.