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  Accurate reconstruction of cell and particle tracks from 3D live imaging data.

Liepe, J., Sim, A., Weavers, H., Ward, L., Martin, P., & Stump, M. P. H. (2016). Accurate reconstruction of cell and particle tracks from 3D live imaging data. Cell Systems, 3(1), 102-107. doi:10.1016/j.cels.2016.06.002.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-CFB1-5 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-CFB6-C
Genre: Journal Article

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 Creators:
Liepe, J.1, Author              
Sim, A., Author
Weavers, H., Author
Ward, L., Author
Martin, P., Author
Stump, M. P. H., Author
Affiliations:
1Research Group of Quantitative and System Biology, MPI for Biophysical Chemistry, Max Planck Society, ou_2466694              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2016-07-212016-07-27
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.cels.2016.06.002
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Title: Cell Systems
Source Genre: Journal
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Pages: - Volume / Issue: 3 (1) Sequence Number: - Start / End Page: 102 - 107 Identifier: -