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

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.