Machine learning for extracting spatio-temporal biological patterns on evolving domains

我们正在进入一个新时代，荧光活细胞显微镜的进步使得在几秒钟的时间尺度上获得详细的细胞3D扫描成为可能，揭示了以前无法成像的快速生物过程。然而，新的3D数据的复杂性及其体积对其定量分析构成了重大挑战。为了应对这些挑战，我们将开发新的机器学习工具，可以自动检测3D细胞图像中的生物结构，并量化它们随时间的变化。我们将专注于与不断变化的表面相关的更易处理和更普遍的问题。进化表面是生物系统跨尺度的一个特征，从迁移或分裂细胞中细胞膜的变形，到发育过程中形成的胚胎表面。在数学上，这样的表面可以表示为图形，连接节点的网格，其中表面上或接近表面的测量可以是每个节点的特征，例如已经用荧光标记物标记的特定分子的丰度。引入图的概念是变革性的，因为最近高效的图神经网络（GNN）推动了图上特征的自动分类。 我们将建立在最近的工作，我们开发了一种高度准确的概念验证方法，用于检测3D图像中的生物表面。为了在高吞吐量环境中使用，这种方法需要在计算上变得更高效，这将通过采用快速发展的强大的Tensorflow框架来实现高度并行的GPU编程。为了在真实的实验数据上验证这些方法，我们将研究与细胞饮水相关的细胞表面变形，以及斑马鱼肌肉段形成过程中细胞伸长和融合的更复杂的多细胞问题。 下一个主要步骤将是生成随时间变化的生物表面的图形表示。这里的挑战是在后续时间点匹配图上的对应节点，注意尽管时间分辨率很高，但一个时间点和下一个时间点之间的变形可能很大。初步结果表明，谱图匹配的方法，通常与大的表面变形的斗争，可以通过将额外的功能，如表面的曲率得到改善。在这里，我们还建议将荧光蛋白的空间分布或接近细胞膜，以提高表面匹配的准确性。除了前面的生物表面的例子，我们将包括早期斑马鱼胚胎表面，这是近球形，并研究细胞形状的变化在前神经板，神经系统的前体的形成中的作用。 为了分析表面上的时间相关特征，我们将引入新的方法来随着时间的推移进化图结构，保持节点数量不变，这是执行分类和特征提取任务时所需的。特征的监督学习需要手动注释，这对于动态特征是难以获得的。我们将开发新的图形界面，并使用虚拟现实技术，使研究人员能够以直观的方式与数据进行交互和注释。 我们将开发的自动特征检测工具将帮助生物学家获得更详细的高质量数据，这些数据可用于通过比较健康细胞和患病细胞来研究生物机制，或研究药物的作用。这些工具的开发将与实验者和更广泛的生物成像用户社区密切合作，与三个主要的成像设施，克里克研究所和MRC LMS在伦敦，并在利物浦大学的伙伴关系。

We are arriving in a new era where advances in fluorescence live cell microscopy make it possible to acquire detailed 3D scans of cells on a timescale of seconds, revealing fast biological processes that could not be imaged before. The complexity of the new 3D data and their volume, however, pose significant challenges for their quantitative analysis. To address these challenges, we will develop novel machine learning tools that can automatically detect biological structures within the 3D cell images and quantify how they change over time. We will focus on more tractable and generalisable problems that are associated with evolving surfaces. Evolving surfaces are a feature of biological systems across scales, from deformations of the cell membrane in migrating or dividing cells, to the surface of an embryo that takes on shape during development. Mathematically, such surfaces can be represented as a graph, a mesh of connected nodes, where measurements on or close to the surface can be a feature of each node, for example the abundance of a particular molecule that has been tagged with a fluorescent marker. Introducing the concept of graphs is transformative, because automated classification of features on graphs has recently been propelled by highly efficient graph neural networks (GNNs). We will build on recent work where we developed a highly accurate proof-of-concept method for detecting biological surfaces in 3D images. To be used in a high-throughput environment this method needs to become computationally more efficient, which will be achieved by employing the rapidly evolving, powerful Tensorflow framework for highly parallel GPU-programming. To validate these methods on real experimental data, we will investigate cell surfaces deformation associated with cell drinking, and the more complex multicellular problem of cell elongation and fusion during zebrafish muscle segment formation. The next major step will be to generate graph representations of biological surfaces that change over time. The challenge here is to match corresponding nodes on the graph at subsequent timepoints, noting that despite a high temporal resolution, deformations between one timepoint and the next can be large. Preliminary results show that spectral methods for graph matching, which normally struggle with large surface deformations, can be improved by incorporating additional features such as curvature of the surface. Here we propose to also incorporate spatial distributions of fluorescent proteins in or close to the cell membrane to improve the accuracy of surface matching. In addition to the previous examples of biological surfaces, we will include that of the early zebrafish embryo surface which is near spherical, and investigate the role of cell shape changes in the formation of the anterior neural plate, a precursor of the nervous system. For analysing time dependent features on surfaces, we will be introducing novel methods to evolve the graph structure over time, keeping the number of nodes constant, as is required when performing classification and feature extraction tasks. Supervised learning of features requires manual annotations, which for dynamic features are difficult to obtain. We will develop new graphical interfaces and use virtual reality technology to allow researchers to interact with and annotate the data in an intuitive manner. The tools for automated feature detection that we are going to develop will help biologists to obtain more detailed high-quality data, which can be used to investigate biological mechanisms by comparing healthy with diseased cells, or to study the effects of drugs. The development of these tools will be undertaken in close collaboration with experimentalists and the wider community of bioimaging users, in partnership with three major imaging facilities, the Crick Institute and MRC LMS in London, and at Liverpool University.