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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)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Craniofacial ; Cranium ; Down Syndrome ; Morphometrics ; Mouse Model ; Phenotyping
ISSN (print) / ISBN 0950-1991
e-ISSN 1477-9129
Zeitschrift Development / Company of Biologists
Quellenangaben Band: 148, Heft: 18, Seiten: , Artikelnummer: dev188631 Supplement: ,
Verlag Company of Biologists
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)