Biology subjects

Westerberg, H.

Publications and source records attributed to Westerberg, H..

5 recordsLinked to original sources

bioRxiv2021

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N=10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

genetics↗

bioRxiv2021

A convolutional neural network for the quality control of MRI defacing

Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Mo-tivated by a large clinical trials dataset of Multiple Sclerosis with over 235,000 MRI scans, we consider the challenge of defacing - anonymisation to remove identifying facial features. The defacing process must undergo quality control (QC) checks to ensure that the facial features have been adequately anonymised and that the brain tissue is left completely intact. Visual QC checks - particularly on a project of this scale - are time-consuming and can cause delays in preparing data. We have developed a convolutional neural network (CNN) that can assist with the QC of the application of MRI defacing; our CNN is able to distinguish between scans that are correctly defaced, and can also classify defacing failures into three sub-types to facilitate parameter tuning during remedial re-defacing. Since integrating the CNN into our anonymisation pipeline, over 75,000 scans have been processed. Strict thresholds have been applied so that ambiguous classifications are referred for visual QC checks, however all scans still undergo a time-efficient verification check before being marked as passed. After applying the thresholds, our network is 92% accurate and is able to classify nearly half of the scans without the need for protracted manual checks. Even with the introduction of the verification checks, incorporation of the CNN has reduced the time spent undertaking QC checks by 42% during initial defacing, and by 35% overall. With the help of the CNN, we have been able to successfully deface 96% of the scans in the project whilst maintaining high QC standards. In a similarly sized new project of this scale, we would expect the model to reduce the time spent on manual QC checks by 125 hours. Our approach is applicable to other projects with the potential to greatly improve the efficiency of imaging anonymisation pipelines.

neuroscience↗

bioRxiv2021

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

White adipose tissue (WAT) plays a central role in metabolism, with multiple diseases and genetic mutations causing its remodeling. Quantitative analysis of white adipocyte size is of great interest to understand physiology and disease, but previous studies of H&E histology have been limited to a subsample of whole depot cross-sections. In this paper, we present the deep learning pipeline DeepCytometer, that can segment mouse and human whole slides ([~=]40,000 cells per mouse slide on average) using an adaptive tiling method, correct for cell overlap and reject non-white adipocytes from the segmentation. Using quantile colour maps we show intra- and inter-depot cell size heterogeneity with local correlation; quantile estimates also suggest significant differences in population estimates from 75 whole slides compared to smaller data sets. We propose three linked levels (body weight BW, depot weight DW and cell area quartiles) for exploratory analysis of mouse Klf14 phenotypes in gonadal and subcutaneous depots. We find a rich set of phenotypes when stratifying by sex, depot and three genotype strata: (1) WTs/Hets with a Het father (Controls), (2) WTs with a Het mother, and (3) Hets with a Het mother (functional KOs or FKOs). Namely, at BW level, mean difference testing suggests that female FKOs are similar to Controls, but WTs with a Het mother are significantly larger. At DW and cell levels, linear models with interaction terms and BW or DW covariates, respectively, reveal phenotypes not shown by difference of means tests. For example, at DW level, gonadal and subcutaneous models are similar, and female FKOs have lower fat percentage than Controls due to both an offset and the DW/BW slope in the linear model. Meanwhile, female WTs with a Het mother have on average similar fat percentage to Controls, but as their slopes are close to zero, their DWs are uncorrelated to BW, suggesting that larger female WTs with a Het mother have lower fat percentage than smaller ones. In contrast to depot level, at cell level female gonadal phenotypes diverge from subcutaneous ones. Furthermore, male Controls and FKOs have similar average area values in subcutaneous depots, but area~DW slope flattening in FKOs suggests that larger DWs could be caused by cell size increase in Controls and by cell count increase in FKOs. Thus, DeepCytometer and associated exploratory analysis reveal new insights into adipocyte heterogeneity and phenotyping.

bioinformatics↗

bioRxiv2020

LAMA: Automated image analysis for developmentalphenotyping of mouse embryos

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.

bioinformatics↗

bioRxiv2019

Human and mouse essentiality screens as a resource for disease gene discovery

Although genomic sequencing has been transformative in the study of rare genetic diseases, identifying causal variants remains a considerable challenge that can be addressed in part by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from the comprehensive viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and from human cell line essentiality screens. We propose a novel, cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing characteristics in the biological processes they regulate, tissue expression levels and human mutation rates. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented in the developmental lethal category, representing genes not essential for cell survival but required for organism development. Exploiting this finding, we have screened developmental disorder cases from three independent disease sequencing consortia and identified potentially pathogenic, de novo variants shared in different patients for several developmental lethal genes that have not previously been associated with rare disease. We therefore propose FUSIL as an efficient resource for disease gene discovery.

genomics↗