Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.
Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.
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DOI：
10.1093/bioinformatics/btac833
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发表时间：
2023-01-01
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在这项工作中，我们提出了一种用于量化肿瘤细胞群体的单细胞形态和细胞网络拓扑结构的分析方法，并利用它来预测三维细胞行为。
我们利用一种有监督的深度学习方法，对各种细胞密度的无标记活细胞图像进行实例分割。我们测量了136个源自YUMM1.7和YUMMER1.7小鼠黑色素瘤细胞系的单细胞克隆的细胞形状特性，并对其网络拓扑结构进行了表征。利用一种无监督聚类算法，我们确定了六个不同的形态亚类。我们还在体外三维球体模型中观察到了不同亚类之间肿瘤生长和侵袭动态的差异。与现有的量化二维或三维表型的方法相比，我们的分析方法耗时更短，不需要专门的设备，并且通量更高，这使其非常适合高通量药物筛选和临床诊断等应用。
https://github.com/trevor - chan/Melanoma_NetworkMorphology
补充数据可在Bioinformatics在线获取。
In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior. We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quantifying 2D or 3D phenotype, our analytical method requires less time, needs no specialized equipment and is capable of much higher throughput, making it ideal for applications such as high-throughput drug screening and clinical diagnosis. https://github.com/trevor-chan/Melanoma_NetworkMorphology. Supplementary data are available at Bioinformatics online.
DOI：
10.1083/jcb.201201003
发表时间：
2012-06-11
期刊：
The Journal of cell biology
影响因子：
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作者：
Meyer AS;Hughes-Alford SK;Kay JE;Castillo A;Wells A;Gertler FB;Lauffenburger DA
通讯作者：
Lauffenburger DA