Mouse embryonic phenotyping by morphometric analysis of MR images
Open Access
- 1 October 2010
- journal article
- research article
- Published by American Physiological Society in Physiological Genomics
- Vol. 42A (2) , 89-95
Abstract
A new method is described for automatic detection of subtle morphological phenotypes in mouse embryos. Based on high-resolution magnetic resonance imaging scanning and nonlinear image alignment, this method is demonstrated by comparing the morphology of two inbred strains, C57BL/6J and 129Sv/S1ImJ, at 15.5 days postconception. Mouse embryo morphology was found to be highly amenable to this kind of analysis with very low levels (on average 110 μm) of residual anatomical variation within strains after linear differences in pose and scale are removed. Mapping of local size differences showed that C57BL/6J embryos were larger than 129Sv/S1ImJ embryos, although these differences were not uniformly distributed across the anatomy. Expressed in terms of organ volumes, heart and lung were larger in C57BL/6J embryos, while brain and liver were comparable in volume between strains. The positive relationship between organ size and embryo size was consistent for the two strains but differed by organ, with the brain and liver being the least variable. Together these findings suggest the power of this technique for detecting subtle phenotypic differences arising from mutated genes.Keywords
This publication has 23 references indexed in Scilit:
- Analysis of serial magnetic resonance images of mouse brains using image registrationNeuroImage, 2009
- A Mouse for All ReasonsCell, 2007
- Neuroanatomical differences between mouse strains as shown by high-resolution 3D MRINeuroImage, 2006
- The Collaborative Cross, a community resource for the genetic analysis of complex traitsNature Genetics, 2004
- A Three-dimensional MRI Atlas of the Mouse Brain with Estimates of the Average and VariabilityCerebral Cortex, 2004
- Deformation Based Representation of Groupwise Average and VariabilityLecture Notes in Computer Science, 2004
- Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery RateNeuroImage, 2002
- A Unified Statistical Approach to Deformation-Based MorphometryNeuroImage, 2001
- Animal: Validation and Applications of Nonlinear Registration-Based SegmentationInternational Journal of Pattern Recognition and Artificial Intelligence, 1997
- An Introduction to the Bootstrap1993