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)
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.
Impact Factor
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Times Cited
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Typ der Hochschulschrift
Herausgeber
Schlagwörter
Craniofacial ; Cranium ; Down Syndrome ; Morphometrics ; Mouse Model ; Phenotyping
Keywords plus
Sprache
englisch
Veröffentlichungsjahr
2021
Prepublished im Jahr
HGF-Berichtsjahr
2021
ISSN (print) / ISBN
0950-1991
e-ISSN
1477-9129
ISBN
Bandtitel
Konferenztitel
Konferzenzdatum
Konferenzort
Konferenzband
Quellenangaben
Band: 148,
Heft: 18,
Seiten: ,
Artikelnummer: dev188631
Supplement: ,
Reihe
Verlag
Company of Biologists
Verlagsort
Tag d. mündl. Prüfung
0000-00-00
Betreuer
Gutachter
Prüfer
Topic
Hochschule
Hochschulort
Fakultät
Veröffentlichungsdatum
0000-00-00
Anmeldedatum
0000-00-00
Anmelder/Inhaber
weitere Inhaber
Anmeldeland
Priorität
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-507100-001
Förderungen
Copyright
Erfassungsdatum
2022-09-07