Morphodynamical cell state description via live-cell imaging trajectory embedding.
Morphodynamical cell state description via live-cell imaging trajectory embedding.
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DOI：
10.1038/s42003-023-04837-8
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发表时间：
2023-05-04
影响因子：
5.9
通讯作者：
Zuckerman, Daniel M.
中科院分区：
文献类型：
--
作者：
Copperman, Jeremy;Gross, Sean M.;Chang, Young Hwan;Heiser, Laura M.;Zuckerman, Daniel M.
延时成像是一种深入了解细胞动态反应的有力方法，但随着时间的推移，形态学变化的定量分析仍然具有挑战性。在这里，我们利用“轨迹嵌入”的概念，利用形态特征轨迹历史来分析细胞行为，即同时使用多个时间点，而不是更常见的在单个时间点（快照）形态特征中检查形态特征时间过程的做法。我们应用这种方法来分析MCF10A乳腺上皮细胞经过微环境扰动剂处理后的活细胞图像，微环境扰动剂能强烈调节细胞运动、形态和细胞周期行为。我们的形态动力学轨迹嵌入分析构建了一个共享的细胞状态景观，揭示了配体对细胞状态转变的特异性调节，并实现了单细胞轨迹的定量和描述性模型。此外，我们表明，与基于快照的分析相比，将轨迹纳入单细胞形态分析可以(i)系统地表征细胞状态轨迹，（ii）更好地分离表型，以及（iii）更具描述性的配体诱导差异模型。这种形态动力学轨迹嵌入广泛适用于通过活细胞成像对许多生物和生物医学应用中的细胞反应进行定量分析。与基于形态学快照的分析相比，活细胞轨迹嵌入分析表征了与分子和配体诱导反应相关的形态动力学变化，并提高了亚稳态细胞状态的识别。
Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of “trajectory embedding” to analyze cellular behavior using morphological feature trajectory histories—that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications. A live-cell trajectory embedding analysis characterizes the morphodynamical changes associated with molecular and ligand-induced responses and improves identification of metastable cell states compared to morphological snapshot-based analysis.
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影响因子：
64.5
作者：
Goltsev Y;Samusik N;Kennedy-Darling J;Bhate S;Hale M;Vazquez G;Black S;Nolan GP
通讯作者：
Nolan GP
影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
2.3
作者：
Ankam, Soneela;Teo, Benjamin K. K.;Yim, Evelyn K. F.
通讯作者：
Yim, Evelyn K. F.
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
DOI：
10.1039/c5ib00283d
发表时间：
2016-01
期刊：
Integrative biology : quantitative biosciences from nano to macro
影响因子：
--
作者：
Gordonov S;Hwang MK;Wells A;Gertler FB;Lauffenburger DA;Bathe M
通讯作者：
Bathe M