Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data

Juliane Liepe, Aaron Sim, Helen Weavers, Laura Ward, Paul Martin, Michael P.H. Stumpf

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)
295 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)102-107
Number of pages6
JournalCell Systems
Issue number1
Early online date21 Jul 2016
Publication statusPublished - 27 Jul 2016

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