Computational image analysis of colony and nuclear morphology to evaluate human induced pluripotent stem cells.
Computational image analysis of colony and nuclear morphology to evaluate human induced pluripotent stem cells.
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DOI：
10.1038/srep06996
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发表时间：
2014-11-11
影响因子：
4.6
通讯作者：
Nakao M
中科院分区：
文献类型：
--
作者：
Tokunaga K;Saitoh N;Goldberg IG;Sakamoto C;Yasuda Y;Yoshida Y;Yamanaka S;Nakao M
需要通过先进的图像分析对细胞重编程进行非侵入性评估，以保持用于再生医学的细胞的质量。在这里，我们构建了各种人类诱导多能干细胞（iPSC）系的活的和未标记的集落图像库，用于监督机器学习模式识别，以准确区分真正的iPSC和不正确的重编程细胞。此外，我们发现，图像特征，有效的歧视驻留在细胞成分。事实上，对核形态的广泛分析揭示了动态和特征性特征，包括iPSC中早幼粒细胞白血病（PML）定义的结构的线性形式，其在分化时逆转为规则的球体。我们的数据显示，iPSCs具有明显不同的整体核结构，这可能有助于基于细胞重编程状态进行高度准确的区分。
Non-invasive evaluation of cell reprogramming by advanced image analysis is required to maintain the quality of cells intended for regenerative medicine. Here, we constructed living and unlabelled colony image libraries of various human induced pluripotent stem cell (iPSC) lines for supervised machine learning pattern recognition to accurately distinguish bona fide iPSCs from improperly reprogrammed cells. Furthermore, we found that image features for efficient discrimination reside in cellular components. In fact, extensive analysis of nuclear morphologies revealed dynamic and characteristic signatures, including the linear form of the promyelocytic leukaemia (PML)-defined structure in iPSCs, which was reversed to a regular sphere upon differentiation. Our data revealed that iPSCs have a markedly different overall nuclear architecture that may contribute to highly accurate discrimination based on the cell reprogramming status.
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影响因子：
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作者：
Norton, John T;Huang, Sui
通讯作者：
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影响因子：
3.2
作者：
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作者：
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