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1.
J Exp Med ; 220(2)2023 02 06.
Artículo en Inglés | MEDLINE | ID: mdl-36480166

RESUMEN

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-1ß 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.


Asunto(s)
Receptor 2 Celular del Virus de la Hepatitis A , Interleucina-17 , Neoplasias , Receptor de Muerte Celular Programada 1 , Subgrupos de Linfocitos T , Animales , Ratones , Receptor de Muerte Celular Programada 1/metabolismo , Receptor 2 Celular del Virus de la Hepatitis A/metabolismo
2.
Life Sci Alliance ; 4(1)2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33268347

RESUMEN

The association of increased levels of tumour-infiltrating gamma-delta (γδ) T cells with favorable prognosis across many cancer types and their ability to recognize stress antigens in an MHC unrestricted manner has led to an increased interest in exploiting them for cancer immunotherapy. We performed single-cell RNA sequencing (scRNA-seq) of peripheral blood γδ T cells from healthy adult donors and from fresh tumour biopsies of breast cancer patients. We identified five γδ T cells subtypes in blood and three subtypes of γδ T cells in breast tumour. These subtypes differed in the expression of genes contributing to effector functions such as antigen presentation, cytotoxicity, and IL17A and IFNγ production. Compared with the blood γδ T cells, the breast tumour-infiltrating γδ T cells were more activated, expressed higher levels of cytotoxic genes, yet were immunosuppressed. One subtype in the breast tumour that was IFNγ-positive had no obvious similarity to any of the subtypes observed in the blood γδ T cell and was the only subtype associated with improved overall survival of breast cancer patients. Taken together, our study has identified markers of subtypes of human blood γδ T cells and uncovered a tumour-infiltrating γδ T cells subtype associated improved overall cancer survival.


Asunto(s)
Neoplasias de la Mama/genética , Neoplasias de la Mama/inmunología , Linfocitos Intraepiteliales/inmunología , Linfocitos Infiltrantes de Tumor/inmunología , RNA-Seq/métodos , Receptores de Antígenos de Linfocitos T gamma-delta/genética , Análisis de la Célula Individual/métodos , Adulto , Secuencia de Bases , Donantes de Sangre , Neoplasias de la Mama/mortalidad , Neoplasias de la Mama/patología , Estudios de Casos y Controles , Células Cultivadas , Femenino , Regulación Neoplásica de la Expresión Génica , Humanos , Estimación de Kaplan-Meier , Pronóstico , Microambiente Tumoral/genética , Microambiente Tumoral/inmunología
3.
iScience ; 23(3): 100914, 2020 Mar 27.
Artículo en Inglés | MEDLINE | ID: mdl-32151972

RESUMEN

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 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 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.

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