OrgDyn: Feature-and model-based characterization of spatial and temporal organoid dynamics

Zaki Hasnain, Andrew K. Fraser, Dan Georgess, Alex Choi, Paul MacKlin, Joel S. Bader, Shelly R. Peyton, Andrew J. Ewald, Paul K. Newton

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Organoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature-and model-based approaches on time series of 2D organoid contour images. Our pipeline consists of (i) geometrical and signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical paths, (iii) time series clustering to identify coherent groups of organoids and (iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease.

Original languageEnglish (US)
Pages (from-to)3292-3294
Number of pages3
JournalBioinformatics
Volume36
Issue number10
DOIs
StatePublished - May 1 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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