Asthma is a chronic inflammatory disease of the airways that afflicts over 30 million individuals in the United States and over 300 million individuals worldwide. The inflammatory response in the airways is often characterized by the analysis of sputum, which contains multiple types of cells including neutrophils, macrophages, lymphocytes, and rare bronchial epithelial cells. Subtyping patients using microscopy of the sputum has identified both neutrophilic and eosinophilic infiltrates in airway inflammation. However, with the extensive heterogeneity among these cell types, a higher resolution understanding of the inflammatory cell types present in the sputum is needed to dissect the heterogeneity of disease. Improved recognition of the distinct phenotypes and sources of inflammation in asthmatic granulocytes may identify relevant pathways for clinical management or investigation of novel therapeutic mediators. Here, we employed mass cytometry or cytometry by time‐of‐flight to quantify frequency and define functional status of sputum derived airway cells in asthmatic patients and healthy controls. This in‐depth single cell analysis method identified multiple distinct subtypes of airway immune cells, especially in neutrophils. Significance was discovered by statistical analysis as well as a data‐driven unbiased clustering approach. Our multidimensional assessment method identifies differences in cellular function and supports identification of cellular status that may contribute to diverse clinical responses. This technical advance is relevant for studies of pathogenesis and may provide meaningful insights to advance our knowledge of asthmatic inflammation.

中文翻译：

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用单细胞多参数 CyTOF 分析哮喘中的细胞异质性。

哮喘是一种慢性气道炎性疾病，在美国折磨着超过 3000 万人，在全世界折磨着超过 3 亿人。气道中的炎症反应通常以痰的分析为特征，痰中含有多种类型的细胞，包括中性粒细胞、巨噬细胞、淋巴细胞和罕见的支气管上皮细胞。使用痰显微镜检查对患者进行亚型分型已在气道炎症中发现中性粒细胞和嗜酸性粒细胞浸润。然而，由于这些细胞类型之间存在广泛的异质性，需要对痰中存在的炎症细胞类型进行更高分辨率的了解，以剖析疾病的异质性。改进对哮喘粒细胞中不同表型和炎症来源的识别可以确定临床管理或研究新型治疗介质的相关途径。在这里，我们采用质谱流式细胞术或飞行时间流式细胞术来量化频率并定义哮喘患者和健康对照痰源气道细胞的功能状态。这种深入的单细胞分析方法鉴定了多种不同的气道免疫细胞亚型，尤其是中性粒细胞。通过统计分析以及数据驱动的无偏聚类方法发现了显着性。我们的多维评估方法可识别细胞功能的差异，并支持识别可能导致不同临床反应的细胞状态。