RT Journal Article
SR Electronic
T1 LAMA: Automated image analysis for developmental phenotyping of mouse embryos
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.04.075853
DO 10.1101/2020.05.04.075853
A1 Neil R. Horner
A1 Shanmugasundaram Venkataraman
A1 Ramón Casero
A1 James M. Brown
A1 Sara Johnson
A1 Lydia Teboul
A1 Sara Wells
A1 Steve Brown
A1 Henrik Westerberg
A1 Ann-Marie Mallon
YR 2020
UL http://biorxiv.org/content/early/2020/05/04/2020.05.04.075853.abstract
AB Advanced 3D imaging modalities such as micro computed tomography (micro-CT), high resolution episcopic microscopy (HREM), and optical projection tomography (OPT) have been readily incorporated into high-throughput phenotyping pipelines, such as the International Mouse Phenotyping Consortium (IMPC). Such modalities generate large volumes of raw data that cannot be immediately harnessed without significant resources of manpower and expertise. Thus, rapid automated analysis and annotation is critical to ensure that 3D imaging data is able to be integrated with other multi-dimensional phenotyping data. To this end, we present an automated computational mouse phenotyping pipeline called LAMA, based on image registration, which requires minimal technical expertise and human input to use. Designed predominantly for developmental biologists, our software performs image pre-processing, registration, statistical and gene function annotation, and segmentation of 3D micro-CT data. We address several limitations of current methods and create an easy to use, fast solution application for mouse embryo phenotyping. We also present a highly granular, novel anatomical E14.5 (14.5 days post coitus) atlas of a population average that integrates with our pipeline to allow a range of dysmorphologies to be automatically annotated as well as results from the validation of the pipeline.Competing Interest StatementThe authors have declared no competing interest.