A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data
A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data
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DOI：
10.1371/journal.pone.0197208
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发表时间：
2018-05-16
期刊：
影响因子：
3.7
通讯作者：
Flassig, Robert J.
中科院分区：
文献类型：
--
作者：
Fischer, Dennis;Buchbinder, Joern H.;Flassig, Robert J.
成像流式细胞术是一种强大的实验技术，结合了显微镜和流式细胞术的强度，可以在详细的显微镜尺度上对细胞群进行高通量表征。这种方法对于区分不同的细胞表型（如增殖、细胞分裂和细胞死亡）具有越来越重要的意义。在经历这些不同途径的过程中，每个细胞都具有大量的特性。这使得很难过滤出最相关的细胞状态判别信息。传统的细胞状态判别方法依赖于基于染料的二维门控策略，忽略了隐藏在高维属性空间中的信息。为了利用传统方法忽略的信息，我们提出了一种基于机器学习技术的简单有效的方法来区分细胞群内的生物状态。我们展示了与不同分类方案相结合的滤波技术的优缺点。这些技术说明了凋亡检测在HeLa细胞的两个案例研究。因此，我们强调(i)成像流式细胞术在自动化，无标记细胞状态识别方面的能力和（ii）经常遇到的陷阱。此外，还提供了一个MATLAB脚本，它可以进一步了解本研究中提出的计算工作。
Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular phenotypes such as proliferation, cell division and cell death. In the course of undergoing these different pathways, each cell is characterized by a high amount of properties. This makes it hard to filter the most relevant information for cell state discrimination. The traditional methods for cell state discrimination rely on dye based two-dimensional gating strategies ignoring information that is hidden in the high-dimensional property space. In order to make use of the information ignored by the traditional methods, we present a simple and efficient approach to distinguish biological states within a cell population based on machine learning techniques. We demonstrate the advantages and drawbacks of filter techniques combined with different classification schemes. These techniques are illustrated with two case studies of apoptosis detection in HeLa cells. Thereby we highlight (i) the aptitude of imaging flow cytometry regarding automated, label-free cell state discrimination and (ii) pitfalls that are frequently encountered. Additionally a MATLAB script is provided, which gives further insight regarding the computational work presented in this study.