Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data.
Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data.
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DOI：
10.1016/j.cels.2016.06.002
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发表时间：
2016-07
期刊：
影响因子：
9.3
通讯作者：
Stumpf MP
中科院分区：
文献类型：
--
作者：
Liepe J;Sim A;Weavers H;Ward L;Martin P;Stumpf MP
空间结构通常会限制体内细胞或颗粒的3D运动，但当使用标准方法分析显微镜数据时，这些信息会变得模糊。在这里，我们提出的方法，称为展开和黎曼流形学习，映射粒子跟踪数据沿沿着看不见的和不规则的曲面到适当的二维表示。这在概念上类似于从传统墨卡托地图重建精确地理的问题，但我们的方法不需要事先了解环境的物理结构。展开和黎曼流形学习可以从3D成像数据准确地恢复底层的2D几何形状，而无需基准标记。它们优于标准的x-y投影，并且与标准的降维技术不同，它们还成功地检测了细胞迁移模式中的偏差和持久性。我们在模拟数据和斑马鱼和果蝇体内免疫细胞轨迹数据集上展示了这些功能。提供了实现展开和黎曼流形学习的软件包。细胞移动通常受到限制，例如，细胞结构的表面我们开发的方法来检测这种约束，从体内活成像数据考虑这些结构是必要的正确分析的细胞轨道Liepe等。提出了一套工具，使我们能够占的空间约束作用于细胞和粒子的运动在现场成像研究，并显示这些如何使我们能够准确地解释这些数据。
Spatial structures often constrain the 3D movement of cells or particles in vivo, yet this information is obscured when microscopy data are analyzed using standard approaches. Here, we present methods, called unwrapping and Riemannian manifold learning, for mapping particle-tracking data along unseen and irregularly curved surfaces onto appropriate 2D representations. This is conceptually similar to the problem of reconstructing accurate geography from conventional Mercator maps, but our methods do not require prior knowledge of the environments’ physical structure. Unwrapping and Riemannian manifold learning accurately recover the underlying 2D geometry from 3D imaging data without the need for fiducial marks. They outperform standard x-y projections, and unlike standard dimensionality reduction techniques, they also successfully detect both bias and persistence in cell migration modes. We demonstrate these features on simulated data and zebrafish and Drosophila in vivo immune cell trajectory datasets. Software packages that implement unwrapping and Riemannian manifold learning are provided. Cell movement is often constrained, e.g., to surfaces of cellular structures We develop approaches to detect such constraints from in vivo live imaging data Accounting for these structures is necessary for correct analysis of cell tracks Liepe et al. present a set of tools that enable us to account for the spatial constraints acting on the motion of cells and particles in live imaging studies, and show how these allow us to accurately interpret these data.