Dataset Information

Virtual histology of transgenic mouse embryos for high-throughput phenotyping.

ABSTRACT: A bold new effort to disrupt every gene in the mouse genome necessitates systematic, interdisciplinary approaches to analyzing patterning defects in the mouse embryo. We present a novel, rapid, and inexpensive method for obtaining high-resolution virtual histology for phenotypic assessment of mouse embryos. Using osmium tetroxide to differentially stain tissues followed by volumetric X-ray computed tomography to image whole embryos, isometric resolutions of 27 mum or 8 mum were achieved with scan times of 2 h or 12 h, respectively, using mid-gestation E9.5-E12.5 embryos. The datasets generated by this method are immediately amenable to state-of-the-art computational methods of organ patterning analysis. This technique to assess embryo anatomy represents a significant improvement in resolution, time, and expense for the quantitative, three-dimensional analysis of developmental patterning defects attributed to genetically engineered mutations and chemically induced embryotoxicity.

SUBMITTER: Johnson JT

PROVIDER: S-EPMC1449902 | biostudies-literature | 2006 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Virtual histology of transgenic mouse embryos for high-throughput phenotyping.

Johnson John T JT Hansen Mark S MS Wu Isabel I Healy Lindsey J LJ Johnson Christopher R CR Jones Greg M GM Capecchi Mario R MR Keller Charles C

PLoS genetics 20060428 4

A bold new effort to disrupt every gene in the mouse genome necessitates systematic, interdisciplinary approaches to analyzing patterning defects in the mouse embryo. We present a novel, rapid, and inexpensive method for obtaining high-resolution virtual histology for phenotypic assessment of mouse embryos. Using osmium tetroxide to differentially stain tissues followed by volumetric X-ray computed tomography to image whole embryos, isometric resolutions of 27 mum or 8 mum were achieved with sca ...[more]

PMID: 16683035

Similar Datasets

Project description:BackgroundUnderstanding cellular structure and organization, which plays an important role in biological systems ranging from mechanosensation to neural organization, is a complicated multifactorial problem depending on genetics, environmental factors, and stochastic processes. Isolating these factors necessitates the measurement and sensitive quantification of many samples in a reliable, high-throughput, unbiased manner. In this manuscript we present a pipelined approach using a fully automated framework based on Synchrotron-based X-ray Tomographic Microscopy (SRXTM) for performing a full 3D characterization of millions of substructures.ResultsWe demonstrate the framework on a genetic study on the femur bones of in-bred mice. We measured 1300 femurs from a F2 cross experiment in mice without the growth hormone (which can confound many of the smaller structural differences between strains) and characterized more than 50 million osteocyte lacunae (cell-sized hollows in the bone). The results were then correlated with genetic markers in a process called quantitative trait localization (QTL). Our findings provide a mapping between regions of the genome (all 19 autosomes) and observable phenotypes which could explain between 8-40 % of the variance using between 2-10 loci for each trait. This map shows 4 areas of overlap with previous studies looking at bone strength and 3 areas not previously associated with bone.ConclusionsThe mapping of microstructural phenotypes provides a starting point for both structure-function and genetic studies on murine bone structure and the specific loci can be investigated in more detail to identify single gene candidates which can then be translated to human investigations. The flexible infrastructure offers a full spectrum of shape, distribution, and connectivity metrics for cellular networks and can be adapted to a wide variety of materials ranging from plant roots to lung tissue in studies requiring high sample counts and sensitive metrics such as the drug-gene interactions and high-throughput screening.

Project description:ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and methodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Dataset Information

Virtual histology of transgenic mouse embryos for high-throughput phenotyping.

Publications

Virtual histology of transgenic mouse embryos for high-throughput phenotyping.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets