The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death
The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death
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DOI：
10.1038/s41598-020-58474-w
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发表时间：
2020-01-31
影响因子：
4.6
通讯作者：
Balvan, Jan
中科院分区：
文献类型：
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作者：
Vicar, Tomas;Raudenska, Martina;Balvan, Jan
细胞活性和细胞毒性检测在抗肿瘤或其他治疗药物的药物筛选和细胞毒性测试中非常重要。尽管基于生化的检测非常有助于获得初步的预览，但它们的结果应该通过基于直接细胞死亡评估的方法来确认。在这项研究中，确定了细胞死亡过程中定量时相参数随时间的变化，并介绍了可用于快速和无标记的直接细胞死亡评估的方法。我们研究的目的是根据形态特征区分细胞凋亡和原发的溶细胞死亡。我们根据细胞死亡的终点特征来区分裂解型和非裂解型细胞死亡(死亡之舞是典型的细胞死亡，而肿胀和膜破裂是各种坏死的典型特征，常见的是坏死性下垂、下垂、铁下垂和细胞意外死亡)。我们的方法利用定量相位成像(QPI)，能够随时间推移观察细胞质量分布的细微变化。根据我们的结果，从QPI显微图像中提取的形态和动力学特征适合于细胞死亡检测(与人工标注相比，准确率为76%)。此外，仅基于QPI数据和机器学习，我们能够对caspase 3，7依赖和非依赖细胞死亡过程中细胞形态的典型动态变化进行分类。用于这些细胞死亡模式的无标记检测的主要参数是细胞密度(pg/像素)和细胞像素的平均强度变化，进一步称为细胞动态评分(CDS)。据我们所知，这是首次将CDS和细胞密度作为单个细胞死亡子程序的典型参数，对caspase 3，7依赖和非独立细胞死亡的预测准确率为75.4%。
Cell viability and cytotoxicity assays are highly important for drug screening and cytotoxicity tests of antineoplastic or other therapeutic drugs. Even though biochemical-based tests are very helpful to obtain preliminary preview, their results should be confirmed by methods based on direct cell death assessment. In this study, time-dependent changes in quantitative phase-based parameters during cell death were determined and methodology useable for rapid and label-free assessment of direct cell death was introduced. The goal of our study was distinction between apoptosis and primary lytic cell death based on morphologic features. We have distinguished the lytic and non-lytic type of cell death according to their end-point features (Dance of Death typical for apoptosis versus swelling and membrane rupture typical for all kinds of necrosis common for necroptosis, pyroptosis, ferroptosis and accidental cell death). Our method utilizes Quantitative Phase Imaging (QPI) which enables the timelapse observation of subtle changes in cell mass distribution. According to our results, morphological and dynamical features extracted from QPI micrographs are suitable for cell death detection (76% accuracy in comparison with manual annotation). Furthermore, based on QPI data alone and machine learning, we were able to classify typical dynamical changes of cell morphology during both caspase 3,7-dependent and -independent cell death subroutines. The main parameters used for label-free detection of these cell death modalities were cell density (pg/pixel) and average intensity change of cell pixels further designated as Cell Dynamic Score (CDS). To the best of our knowledge, this is the first study introducing CDS and cell density as a parameter typical for individual cell death subroutines with prediction accuracy 75.4% for caspase 3,7-dependent and -independent cell death.