PUBLICATION
Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data
- Authors
- Liepe, J., Sim, A., Weavers, H., Ward, L., Martin, P., Stumpf, M.P.
- ID
- ZDB-PUB-160728-23
- Date
- 2016
- Source
- Cell systems 3(1): 102-7 (Journal)
- Registered Authors
- Martin, Paul
- Keywords
- none
- MeSH Terms
-
- Algorithms
- Imaging, Three-Dimensional*
- Magnetic Resonance Imaging
- Microscopy
- Phantoms, Imaging
- Reproducibility of Results
- PubMed
- 27453447 Full text @ Cell Syst
Citation
Liepe, J., Sim, A., Weavers, H., Ward, L., Martin, P., Stumpf, M.P. (2016) Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data. Cell systems. 3(1):102-7.
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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping