Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.
Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.
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DOI：
10.1039/c5ib00283d
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Bathe M
中科院分区：
文献类型：
--
作者：
Gordonov S;Hwang MK;Wells A;Gertler FB;Lauffenburger DA;Bathe M
活细胞成像可以用来捕捉固定细胞成像无法获得的细胞反应的时空方面。随着活细胞成像的使用不断增加，需要新的计算程序来表征和分类单个细胞的时间动力学。为此，这里我们提出了通用的实验-计算框架SAPHIRE(表型个体细胞响应的随机注释)来表征来自时间序列成像数据集的表型细胞响应。隐马尔可夫模型(HMM)被用来从图像的细胞形状测量中推断和注释形态状态和状态切换特性。时间序列建模是在每个细胞上单独执行的，使得该方法对于分析异步细胞群体广泛有用。同时表达肌动蛋白和核报告蛋白的双色荧光细胞使我们能够描述药物抑制细胞骨架调节信号通路后细胞形状的时间变化。结果与传统应用于固定细胞成像数据集的现有方法进行了比较，表明时间序列建模捕获了不同种类的动态细胞响应，可以改进药物分类，并为药物作用机制提供额外的重要见解。
Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling (HMM) is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton–regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action.
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用于定量细胞迁移，突起和荧光强度的开源软件。
DOI：
10.1083/jcb.201501081
发表时间：
2015-04-13
期刊：
The Journal of cell biology
影响因子：
--
作者：
Barry DJ;Durkin CH;Abella JV;Way M
通讯作者：
Way M
影响因子：
48
作者：
Loo, Lit-Hsin;Wu, Lani F.;Altschuler, Steven J.
通讯作者：
Altschuler, Steven J.
影响因子：
3.2
作者：
Bosgraaf, Leonard;Van Haastert, Peter J. M.
通讯作者：
Van Haastert, Peter J. M.
影响因子：
64.8
作者：
Keren, Kinneret;Pincus, Zachary;Theriot, Julie A.
通讯作者：
Theriot, Julie A.
影响因子：
64.8
作者：
Garnett, Mathew J.;Edelman, Elena J.;Heidorn, Sonja J.;Greenman, Chris D.;Dastur, Anahita;Lau, King Wai;Greninger, Patricia;Thompson, I. Richard;Luo, Xi;Soares, Jorge;Liu, Qingsong;Iorio, Francesco;Surdez, Didier;Chen, Li;Milano, Randy J.;Bignell, Graham R.;Tam, Ah T.;Davies, Helen;Stevenson, Jesse A.;Barthorpe, Syd;Lutz, Stephen R.;Kogera, Fiona;Lawrence, Karl;McLaren-Douglas, Anne;Mitropoulos, Xeni;Mironenko, Tatiana;Thi, Helen;Richardson, Laura;Zhou, Wenjun;Jewitt, Frances;Zhang, Tinghu;O'Brien, Patrick;Boisvert, Jessica L.;Price, Stacey;Hur, Wooyoung;Yang, Wanjuan;Deng, Xianming;Butler, Adam;Choi, Hwan Geun;Chang, JaeWon;Baselga, Jose;Stamenkovic, Ivan;Engelman, Jeffrey A.;Sharma, Sreenath V.;Delattre, Olivier;Saez-Rodriguez, Julio;Gray, Nathanael S.;Settleman, Jeffrey;Futreal, P. Andrew;Haber, Daniel A.;Stratton, Michael R.;Ramaswamy, Sridhar;McDermott, Ultan;Benes, Cyril H.
通讯作者：
Benes, Cyril H.