Automated compartmental analysis for high-throughput skeletal phenotyping in femora of genetic mouse models

Mouse models are widely used in genomic studies for quantitative trait loci (QTL) analyses. In the field of skeletal micro-structure, mu CT has proven to be an invaluable imaging tool for the characterization of structural bone traits. However, the definition of analysis compartments requires a lot of user interaction, and therefore is not applicable as a standard way to analyze genetic linkage studies with several hundreds of animals. Here, we developed an automated three-dimensional based algorithm for a high-throughput regional analysis of three compartments in murine femora, including whole bone, cortical bone in the diaphysis and trabecular bone in the metaphysis. The algorithm relies on basic image processing concepts using morphological operators as well as a new approach of separating cortical from trabecular bone. Reproducibility of the automatic approach was investigated with respect to precision errors (PE%CV) of micro-structural indices analyzed in these automatically defined compartments. The developed algorithm was then used to perform a high-throughput analysis of over 2000 femora in a genetic linkage study for further examination of stability and performance. Precision errors were 3.5% or less for all micro-structural indices. The analysis of one femur (mask generation and parameter evaluation) took 7 min on an AlphaServer DS25. The algorithm showed a very high reliability and worked successfully for 99.64% of all femora. Investigations of correlations amongst the assessed micro-structural indices together with heritability and polygene estimates revealed apparent volume density (AVD), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp) and cortical thickness (Ct.Th) as candidates for a successful QTL analysis. The presented automatic analysis allows for standardized high-throughput phenotypic screening in mice femora for large genetic linkage studies with very high reliability and good precision. (C) 2007 Elsevier Inc. All rights reserved.