Profiling morphological dynamics of neutrophil granulocytes to diagnose and categorize asthma

哮喘是最常见的慢性肺部疾病，影响全球3亿多人，患者数量不断增加（Masoli 2004）。常见症状包括喘息、咳嗽、胸闷和呼吸急促。虽然哮喘的诊断是基于这些症状的特定模式，对治疗的反应和肺活量测定（肺功能测试），但原因仍然未知。基于人群的临床研究显示了遗传或环境风险因素的可能相关性，并揭示了哮喘应作为一种具有多种原因的综合征而不是单一疾病进行治疗。然而，诊断仍然非常具有挑战性，特别是对于幼儿，对不同药物的反应，最关键的是，紧急药物不能得到充分的测试。最近的研究结果揭示了一种使用单滴血区分哮喘和非哮喘患者的新方法（Sackmann 2014）。这种歧视是完全基于中性粒细胞的速度在体外趋化梯度作为一个微流控系统实现测量。我们建议开发一种新的方法来表征哮喘和肺部疾病，使用先进的计算动力学分析诱导的中性粒细胞趋化迁移。我们将开发一种新的趋化性迁移试验，诱导中性粒细胞的定向运动，并允许其运动的详细分析，也就是说，他们的形态动力学。该项目依赖于先进的图像处理，数据挖掘和机器学习工具，使用观察到的中性粒细胞的迁移特征和形态动力学来识别哮喘的不同亚型。我们认为不同亚型的中性粒细胞对不同的哮喘表型具有特异性。最终，我们的目标是通过这种新的基于迁移的表型对哮喘和肺部疾病进行个性化诊断和治疗。

Asthma is the most frequent chronic lung disease affecting more than 300 million people worldwide with increasing number of patients (Masoli 2004). Common symptoms include episodes of wheezing, coughing, chest tightness, and shortness of breath. While the diagnosis of asthma is based on specific patterns of these symptoms, response to therapy, and spirometry (pulmonary function tests), the causes still remain unknown. Population based and clinical studies show a possible correlation of genetic or environmental risk factors and reveal that asthma should be treated rather as a syndrome with many causes instead of a single disease. However, the diagnosis remains quite challenging especially for young children, where the response to different drugs and, most critically, emergency medication can not be tested sufficiently. Recent findings revealed a novel approach to discriminate between asthmatic and nonasthmatic patients using a single drop of blood (Sackmann 2014). This discrimination was solely based on the velocity of neutrophil granulocytes measured in an in vitro chemotaxis gradient implemented as a microfluidic system. We propose to develop a novel approach to the characterization of asthmatic and lung diseases using advanced computational dynamic analysis of induced chemotactic migration of neutrophil granulocytes. We will develop a novel chemotactic migration assay that induces a directed movement of neutrophils and allows for a detailed analysis of their motility, that is, their morphological dynamics. The project relies on advanced image processing, data mining and machine learning tools to identify different subtypes of asthma using the migration profile and morphological dynamics of the observed neutrophils. We believe that different subtypes of neutrophil granulocytes are specific to different phenotypes of asthma. Ultimately, we aim for the personalized diagnosis and treatment of asthmatic and lung diseases via this novel migration based phenotyping.