In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy.
In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy.
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DOI：
10.1038/nmeth.3363
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发表时间：
2015-06
期刊：
影响因子：
48
通讯作者：
Mitchison TJ
中科院分区：
文献类型：
--
作者：
Chittajallu DR;Florian S;Kohler RH;Iwamoto Y;Orth JD;Weissleder R;Danuser G;Mitchison TJ
在单细胞水平上定量细胞周期状态对于理解基本的三维生物学过程（如组织发育和癌症）至关重要。然而，3D体内图像的分析是非常具有挑战性的。当今的最佳实践，选择图像事件的手动注释，生成任意采样的数据分布，不适合可靠的机械推理。在这里，我们提出了一个综合的工作流程，定量在体内细胞周期分析。它结合了图像分析和机器学习方法，用于自动3D分割和细胞周期状态识别嵌入复杂肿瘤环境中具有广泛变化形态的单个细胞核。我们应用我们的工作流程，使用38，000个细胞的数据集，在活体小鼠的HT-1080纤维肉瘤异种移植物中量化三种抗有丝分裂癌症药物在8天内的细胞周期效应，并比较诱导的表型。与2D培养相反，观察到的有丝分裂阻滞相对较低，表明其体内抗肿瘤作用涉及其他机制。
Quantification of cell-cycle state at a single-cell level is essential to understand fundamental three-dimensional biological processes such as tissue development and cancer. Analysis of 3D in vivo images, however, is very challenging. Today’s best practice, manual annotation of select image events, generates arbitrarily sampled data distributions, unsuitable for reliable mechanistic inferences. Here, we present an integrated workflow for quantitative in vivo cell-cycle profiling. It combines image analysis and machine learning methods for automated 3D segmentation and cell-cycle state identification of individual cell-nuclei with widely varying morphologies embedded in complex tumor environments. We applied our workflow to quantify cell-cycle effects of three antimitotic cancer drugs over 8 days in HT-1080 fibrosarcoma xenografts in living mice using a dataset of 38,000 cells and compared the induced phenotypes. In contrast to 2D culture, observed mitotic arrest was relatively low, suggesting involvement of additional mechanisms in their antitumor effect in vivo.