EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos

PLoS Comput Biol. 2018 Apr 19;14(4):e1006128. doi: 10.1371/journal.pcbi.1006128. eCollection 2018 Apr.

Abstract

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Animals, Genetically Modified
  • Cell Movement
  • Cell Tracking / statistics & numerical data*
  • Computational Biology
  • Embryonic Development
  • Embryonic Stem Cells / cytology
  • Gastrulation
  • Germ Layers / cytology
  • Imaging, Three-Dimensional
  • Microscopy, Fluorescence
  • Olfactory Mucosa / cytology
  • Olfactory Mucosa / embryology
  • Software
  • Zebrafish / embryology*

Grants and funding

We are grateful for funding by the German Research Foundation DFG (RM, JS, Grant No MI 1315/4, associated with SPP 1736 Algorithms for Big Data) and the Helmholtz Association in the programs BioInterfaces in Technology and Medicine (BS, MTr, TA, CS, AB, KL, DB, MTa, AYK, JCO, US, RM) and Science and Technology of Nanosystems (GUN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology.