Schoenauer Sebag Alice, Plancade Sandra, Raulet-Tomkiewicz Céline, Barouki Robert, Vert Jean-Philippe, Walter Thomas
MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Université Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Université Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Université Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Université Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Université Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Université Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France.
MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Université Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France.
Bioinformatics. 2015 Jun 15;31(12):i320-8. doi: 10.1093/bioinformatics/btv225.
Motility is a fundamental cellular attribute, which plays a major part in processes ranging from embryonic development to metastasis. Traditionally, single cell motility is often studied by live cell imaging. Yet, such studies were so far limited to low throughput. To systematically study cell motility at a large scale, we need robust methods to quantify cell trajectories in live cell imaging data.
The primary contribution of this article is to present Motility study Integrated Workflow (MotIW), a generic workflow for the study of single cell motility in high-throughput time-lapse screening data. It is composed of cell tracking, cell trajectory mapping to an original feature space and hit detection according to a new statistical procedure. We show that this workflow is scalable and demonstrates its power by application to simulated data, as well as large-scale live cell imaging data. This application enables the identification of an ontology of cell motility patterns in a fully unsupervised manner.
Python code and examples are available online (http://cbio.ensmp.fr/∼aschoenauer/motiw.html)
运动性是细胞的一项基本属性，在从胚胎发育到转移的一系列过程中发挥着重要作用。传统上，单细胞运动性通常通过活细胞成像进行研究。然而，迄今为止，此类研究仅限于低通量。为了在大规模上系统地研究细胞运动性，我们需要可靠的方法来量化活细胞成像数据中的细胞轨迹。
本文的主要贡献是提出了运动性研究集成工作流程（MotIW），这是一种用于在高通量延时筛选数据中研究单细胞运动性的通用工作流程。它由细胞跟踪、将细胞轨迹映射到原始特征空间以及根据新的统计程序进行命中检测组成。我们表明，该工作流程具有可扩展性，并通过应用于模拟数据以及大规模活细胞成像数据展示了其强大功能。此应用能够以完全无监督的方式识别细胞运动模式的本体。
Python代码和示例可在线获取（http://cbio.ensmp.fr/∼aschoenauer/motiw.html）
Zotero 插件
