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用于红细胞生物力学的自动运动跟踪与数据提取

Automated Motion Tracking and Data Extraction for Red Blood Cell Biomechanics.

作者信息

Kumar Arun, Schmidt Brendan R, Sanchez Zyrina Alura C, Yazar Feyza, Davis Ronald W, Ramasubramanian Anand K, Saha Amit K

机构信息

Department of Biomedical Engineering, San José State University, San José, California.

Department of Chemical and Materials Engineering, San José State University, San José, California.

出版信息

Curr Protoc Cytom. 2020 Jun;93(1):e75. doi: 10.1002/cpcy.75.

Abstract

Red blood cell biomechanics can provide us with a deeper understanding of macroscopic physiology and have the potential of being used for diagnostic purposes. In diseases like sickle cell anemia and malaria, reduced red blood cell deformability can be used as a biomarker, leading to further assays and diagnoses. A microfluidic system is useful for studying these biomechanical properties. We can observe detailed red blood cell mechanical behavior as they flow through microcapillaries using high-speed imaging and microscopy. Microfluidic devices are advantageous over traditional methods because they can serve as high-throughput tests. However, to rapidly analyze thousands of cells, there is a need for powerful image processing tools and software automation. We describe a workflow process using Image-Pro to identify and track red blood cells in a video, take measurements, and export the data for use in statistical analysis tools. The information in this protocol can be applied to large-scale blood studies where entire cell populations need to be analyzed from many cohorts of donors. © 2020 The Authors. Basic Protocol 1: Enhancing raw video for motion tracking Basic Protocol 2: Extracting motion tracking data from enhanced video.

摘要

红细胞生物力学能够让我们更深入地理解宏观生理学,并且具有用于诊断目的的潜力。在镰状细胞贫血和疟疾等疾病中,红细胞变形性降低可作为一种生物标志物,从而进行进一步的检测和诊断。微流控系统对于研究这些生物力学特性很有用。通过高速成像和显微镜技术,我们可以观察红细胞在流经微毛细管时的详细力学行为。微流控设备相较于传统方法具有优势,因为它们可用于高通量检测。然而,为了快速分析数千个细胞,需要强大的图像处理工具和软件自动化。我们描述了一个使用Image-Pro在视频中识别和跟踪红细胞、进行测量并导出数据以供统计分析工具使用的工作流程。本方案中的信息可应用于大规模血液研究,这类研究需要分析来自许多供体队列的整个细胞群体。© 2020作者。基本方案1:增强原始视频以进行运动跟踪 基本方案2:从增强后的视频中提取运动跟踪数据。

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