Belyaev Ivan, Marolda Alessandra, Praetorius Jan-Philipp, Sarkar Arjun, Medyukhina Anna, Hünniger Kerstin, Kurzai Oliver, Figge Marc Thilo
Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
Faculty of Biological Sciences, Friedrich-Schiller-University Jena, Germany.
Comput Struct Biotechnol J. 2022 May 10;20:2297-2308. doi: 10.1016/j.csbj.2022.05.007. eCollection 2022.
Rapid identification of pathogens is required for early diagnosis and treatment of life-threatening bloodstream infections in humans. This requirement is driving the current developments of molecular diagnostic tools identifying pathogens from human whole blood after successful isolation and cultivation. An alternative approach is to determine pathogen-specific signatures from human host immune cells that have been exposed to pathogens. We hypothesise that activated immune cells, such as neutrophils, may exhibit a characteristic behaviour - for instance in terms of their speed, dynamic cell morphology - that allows (i) identifying the type of pathogen indirectly and (ii) providing information on therapeutic efficacy. In this feasibility study, we propose a method for the quantitative assessment of static and morphodynamic features of neutrophils based on label-free time-lapse imaging data. We investigate neutrophil activation phenotypes after confrontation with fungal pathogens and isolation from a human whole-blood assay. In particular, we applied a machine learning supported approach to time-lapse microscopy data from different infection scenarios and were able to distinguish between and infection scenarios with test accuracies well above 75%, and to identify pathogen-free samples with accuracy reaching 100%. These results significantly exceed the test accuracies achieved using state-of-the-art deep neural networks to classify neutrophils by their morphodynamics.
快速鉴定病原体对于人类危及生命的血流感染的早期诊断和治疗至关重要。这一需求推动了当前分子诊断工具的发展，这些工具能够在成功分离和培养后从人类全血中鉴定病原体。另一种方法是从已接触病原体的人类宿主免疫细胞中确定病原体特异性特征。我们假设，活化的免疫细胞，如中性粒细胞，可能表现出一种特征性行为——例如在其速度、动态细胞形态方面——这使得（i）能够间接识别病原体类型，以及（ii）提供有关治疗效果的信息。在这项可行性研究中，我们提出了一种基于无标记延时成像数据对中性粒细胞的静态和形态动力学特征进行定量评估的方法。我们研究了中性粒细胞与真菌病原体接触并从人类全血检测中分离后的活化表型。特别是，我们将一种机器学习支持的方法应用于来自不同感染场景的延时显微镜数据，能够以远高于75%的测试准确率区分不同的感染场景，并以达到100%的准确率识别无病原体样本。这些结果显著超过了使用最先进的深度神经网络通过中性粒细胞的形态动力学对其进行分类所达到的测试准确率。
