Profiling morphological dynamics of neutrophil granulocytes to diagnose and categorize asthma

分析中性粒细胞的形态动态以诊断和分类哮喘

• 批准号: 328668586
• 负责人: Dr. Tim Becker
• 金额: --
• 依托单位: Carpenter Lab
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/328668586?language=en
• 关键词: Profiling; morphological; dynamics; neutrophil; granulocytes

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.

哮喘是最常见的慢性肺部疾病，全世界有3亿多人受到影响，患者数量也在增加(Masoli，2004年)。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)，但病因仍不清楚。基于人群的研究和临床研究表明，遗传或环境风险因素可能存在相关性，并显示哮喘应该作为一种有多种原因的综合征来治疗，而不是单一的疾病。然而，诊断仍然具有相当的挑战性，特别是对幼儿来说，他们对不同药物的反应，最关键的是，紧急药物的反应不能得到充分的测试。最近的发现揭示了一种新的方法来区分哮喘和非哮喘患者，使用一滴血(Sackmann，2014)。这种区分完全基于在体外趋化梯度中测量的中性粒细胞的速度，该梯度实施为微流控系统。我们建议开发一种新的方法，利用诱导中性粒细胞趋化迁移的高级计算动力学分析来表征哮喘和肺部疾病。我们将开发一种新的趋化迁移试验，它可以诱导中性粒细胞的定向运动，并允许详细分析它们的运动性，即形态动力学。该项目依靠先进的图像处理、数据挖掘和机器学习工具，利用观察到的中性粒细胞的迁移曲线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基于迁移的新表型来个性化诊断和治疗哮喘和肺部疾病。