Automated pipeline for anatomical phenotyping of mouse embryos using micro-CT

Author:

Wong Michael D.1,Maezawa Yoshiro2,Lerch Jason P.13,Henkelman R. Mark13

Affiliation:

1. Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario M5T 3H7, Canada

2. The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada

3. Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada

Abstract

The International Mouse Phenotyping Consortium (IMPC) plans to phenotype 20,000 single-gene knockout mice to gain an insight into gene function. Approximately 30% of these knockout mouse lines will be embryonic or perinatal lethal. The IMPC has selected three-dimensional (3D) imaging to phenotype these mouse lines at relevant stages of embryonic development in an attempt to discover the cause of lethality using detailed anatomical information. Rate of throughput is paramount as IMPC production centers have been given the ambitious task of completing this phenotyping project by 2021. Sifting through the wealth of data within high-resolution 3D mouse embryo data sets by trained human experts is infeasible at this scale. Here, we present a phenotyping pipeline that identifies statistically significant anatomical differences in the knockout, in comparison with the wild type, through a computer-automated image registration algorithm. This phenotyping pipeline consists of three analyses (intensity, deformation, and atlas based) that can detect missing anatomical structures and differences in volume of whole organs as well as on the voxel level. This phenotyping pipeline was applied to micro-CT images of two perinatal lethal mouse lines: a hypomorphic mutation of the Tcf21 gene (Tcf21-hypo) and a knockout of the Satb2 gene. With the proposed pipeline we were able to identify the majority of morphological phenotypes previously published for both the Tcf21-hypo and Satb2 mutant mouse embryos in addition to novel phenotypes. This phenotyping pipeline is an unbiased, automated method that highlights only those structural abnormalities that survive statistical scrutiny and illustrates them in a straightforward fashion.

Publisher

The Company of Biologists

Subject

Developmental Biology,Molecular Biology

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