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Digital cell quantification identifies global immune cell dynamics during influenza infection

机译:数字细胞定量可识别流感感染期间的全球免疫细胞动态

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摘要

Hundreds of immune cell types work in coordination to maintain tissue homeostasis. Upon infection, dramatic changes occur with the localization, migration, and proliferation of the immune cells to first alert the body of the danger, confine it to limit spreading, and finally extinguish the threat and bring the tissue back to homeostasis. Since current technologies can follow the dynamics of only a limited number of cell types, we have yet to grasp the full complexity of global in vivo cell dynamics in normal developmental processes and disease. Here, we devise a computational method, digital cell quantification (DCQ), which combines genome‐wide gene expression data with an immune cell compendium to infer in vivo changes in the quantities of 213 immune cell subpopulations. DCQ was applied to study global immune cell dynamics in mice lungs at ten time points during 7 days of flu infection. We find dramatic changes in quantities of 70 immune cell types, including various innate, adaptive, and progenitor immune cells. We focus on the previously unreported dynamics of four immune dendritic cell subtypes and suggest a specific role for CD103+ CD11b DCs in early stages of disease and CD8+ pDC in late stages of flu infection.
机译:数百种免疫细胞类型协同工作以维持组织稳态。感染后,免疫细胞的定位,迁移和增殖会发生巨大变化,从而首先警告机体危险,将其限制在传播范围之内,最后消除威胁并将组织恢复至稳态。由于当前的技术只能遵循有限数量的细胞类型的动态变化,因此我们尚未掌握正常发育过程和疾病中全球体内细胞动态变化的全部复杂性。在这里,我们设计了一种计算方法,即数字细胞量化(DCQ),它将全基因组范围内的基因表达数据与免疫细胞纲要相结合,以推断213个免疫细胞亚群的体内变化。 DCQ被用于研究流感感染7天中十个时间点的小鼠肺部的整体免疫细胞动力学。我们发现70种免疫细胞类型(包括各种先天性,适应性和祖先免疫细胞)的数量发生了巨大变化。我们关注以前未报道的四种免疫树突状细胞亚型的动态,并提出CD103 + CD11b - DC在疾病早期和CD8 + < / sup>流感感染后期的pDC。

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