PUBLICATION

An image-based data-driven analysis of cellular architecture in a developing tissue

Authors
Hartmann, J., Wong, M., Gallo, E., Gilmour, D.
ID
ZDB-PUB-200606-27
Date
2020
Source
eLIFE   9: (Journal)
Registered Authors
Gilmour, Darren
Keywords
cellular morphometry, computational biology, context-guided visualization, data integration, developmental biology, image analysis, lateral line primordium, morphogenesis, systems biology, zebrafish
MeSH Terms
  • Algorithms
  • Animals
  • Cytological Techniques/methods*
  • Embryo, Nonmammalian/cytology
  • Imaging, Three-Dimensional/methods*
  • Microscopy/methods*
  • Organogenesis/physiology*
  • Zebrafish
PubMed
32501214 Full text @ Elife
Abstract
Quantitative microscopy is becoming increasingly crucial in efforts to disentangle the complexity of organogenesis, yet adoption of the potent new toolbox provided by modern data science has been slow, primarily because it is often not directly applicable to developmental imaging data. We tackle this issue with a newly developed algorithm that uses point cloud-based morphometry to unpack the rich information encoded in 3D image data into a straightforward numerical representation. This enabled us to employ data science tools, including machine learning, to analyze and integrate cell morphology, intracellular organization, gene expression and annotated contextual knowledge. We apply these techniques to construct and explore a quantitative atlas of cellular architecture for the zebrafish posterior lateral line primordium, an experimentally tractable model of complex self-organized organogenesis. In doing so, we are able to retrieve both previously established and novel biologically relevant patterns, demonstrating the potential of our data-driven approach.
Genes / Markers
Figures
Show all Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping