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Toussaint, N.* ; Redhead, Y.* ; Vidal-García, M.* ; Vercio, L.L.* ; Liu, W.* ; Fisher, E.M.C.* ; Hallgrímsson, B.* ; Tybulewicz, V.L.J.* ; Schnabel, J.A.* ; Green, J.B.A.*

A landmark-free morphometrics pipeline for high-resolution phenotyping: Application to a mouse model of down syndrome.

Development 148:dev188631 (2021)

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	Open Access Gold (Paid Option)

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Characterising phenotypes often requires quantification of anatomical shape. Quantitative shape comparison (morphometrics) traditionally uses manually located landmarks and is limited by landmark number and operator accuracy. Here, we apply a landmarkfree method to characterise the craniofacial skeletal phenotype of the Dp1Tyb mouse model of Down syndrome and a population of the Diversity Outbred (DO) mouse model, comparing it with a landmarkbased approach. We identified cranial dysmorphologies in Dp1Tyb mice, especially smaller size and brachycephaly (front-back shortening), homologous to the human phenotype. Shape variation in the DO mice was partly attributable to allometry (size-dependent shape variation) and sexual dimorphism. The landmark-free method performed as well as, or better than, the landmark-based method but was less labour-intensive, required less user training and, uniquely, enabled fine mapping of local differences as planar expansion or shrinkage. Its higher resolution pinpointed reductions in interior midsnout structures and occipital bones in both the models that were not otherwise apparent. We propose that this landmark-free pipeline could make morphometrics widely accessible beyond its traditional niches in zoology and palaeontology, especially in characterising developmental mutant phenotypes.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Craniofacial ; Cranium ; Down Syndrome ; Morphometrics ; Mouse Model ; Phenotyping

ISSN (print) / ISBN 0950-1991

e-ISSN 1477-9129

Journal Development / Company of Biologists

Quellenangaben Volume: 148, Issue: 18, Article Number: dev188631

Publisher Company of Biologists

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute for Machine Learning in Biomed Imaging (IML)