Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy

Spatial gene expression profiles provide a novel means of exploring the structural organization of the brain. Computational analysis of these patterns is made possible by genome-scale mapping of the C57BL/6J mouse brain in the Allen Brain Atlas. Here we describe methodology used to explore the spatial structure of gene expression patterns across a set of 3041 genes chosen on the basis of consistency across experimental observations (N = 2). The analysis was performed on smoothed, co-registered 3D expression volumes for each gene obtained by aggregating cellular resolution image data. Following dimensionality and noise reduction, voxels were clustered according to similarity of expression across the gene set. We illustrate the resulting parcellations of the mouse brain for different numbers of clusters (K) and quantitatively compare these parcellations with a classically-defined anatomical reference atlas at different levels of granularity, revealing a high degree of correspondence. These observations suggest that spatial localization of gene expression offers substantial promise in connecting knowledge at the molecular level with higher-level information about brain organization.     

BrainAligner: 3D registration atlases of Drosophila brains

Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain. We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.     

Allelic imbalance metre (Allim), a new tool for measuring allele‐specific gene expression with RNA‐seq data

Estimating differences in gene expression among alleles is of high interest for many areas in biology and medicine. Here, we present a user‐friendly software tool, Allim, to estimate allele‐specific gene expression. Because mapping bias is a major problem for reliable estimates of allele‐specific gene expression using RNA‐seq, Allim combines two different strategies to account for the mapping biases. In order to reduce the mapping bias, Allim first generates a polymorphism‐aware reference genome that accounts for the sequence variation between the alleles. Then, a sequence‐specific simulation tool estimates the residual mapping bias. Statistical tests for allelic imbalance are provided that can be used with the bias corrected RNA‐seq data.     

From spatial-data to 3D models of the developing human brain

Visualisation and interpretation of gene expression data have been crucial to advances in our understanding of mechanisms underlying early brain development. As most developmental processes involve complex changes in size, shape and structure, spatial-data can most readily provide information at multiple levels (cell type, cell location in relation to tissue organisation or body axes, etc.), that can be related to these complex changes. Although three-dimensional (3D) spatial-data are ideal, the restricted availability of suitable tissues makes it difficult to generate these for genes expressed at early human fetal stages. Mapping gene expression data to representative 3D models facilitates combinatorial analysis of multiple expression patterns but does not overcome the problems of sparsely sampled data in time and space. Here we describe software that allows 3D domains to be reconstructed by interpolating between sparse 2D gene expression patterns that have been mapped to 3D representative models of corresponding human developmental stages. A set of procedures are proposed to infer expression domains in these gaps. The procedures, which are connected in a serial way, include components clustering, components tracking, shape matching and points interpolation. Each procedure consists of a graphical user interface and a set of algorithms. Results on exemplar gene data are provided.     

Subdivision meshes for organizing spatial biomedical data

As biomedical images and volumes are being collected at an increasing speed, there is a growing demand for efficient means to organize spatial information for comparative analysis. In many scenarios, such as determining gene expression patterns by in situ hybridization, the images are collected from multiple subjects over a common anatomical region, such as the brain. A fundamental challenge in comparing spatial data from different images is how to account for the shape variations among subjects, which make direct image-to-image comparisons meaningless. In this paper, we describe subdivision meshes as a geometric means to efficiently organize 2D images and 3D volumes collected from different subjects for comparison. The key advantages of a subdivision mesh for this purpose are its light-weight geometric structure and its explicit modeling of anatomical boundaries, which enable efficient and accurate registration. The multi-resolution structure of a subdivision mesh also allows development of fast comparison algorithms among registered images and volumes.     

Mapping Accuracy of Short Reads from Massively Parallel Sequencing and the Implications for Quantitative Expression Profiling

Background

Massively parallel sequencing offers an enormous potential for expression profiling, in particular for interspecific comparisons. Currently, different platforms for massively parallel sequencing are available, which differ in read length and sequencing costs. The 454-technology offers the highest read length. The other sequencing technologies are more cost effective, on the expense of shorter reads. Reliable expression profiling by massively parallel sequencing depends crucially on the accuracy to which the reads could be mapped to the corresponding genes.

Methodology/Principal Findings

We performed an in silico analysis to evaluate whether incorrect mapping of the sequence reads results in a biased expression pattern. A comparison of six available mapping software tools indicated a considerable heterogeneity in mapping speed and accuracy. Independently of the software used to map the reads, we found that for compact genomes both short (35 bp, 50 bp) and long sequence reads (100 bp) result in an almost unbiased expression pattern. In contrast, for species with a larger genome containing more gene families and repetitive DNA, shorter reads (35–50 bp) produced a considerable bias in gene expression. In humans, about 10% of the genes had fewer than 50% of the sequence reads correctly mapped. Sequence polymorphism up to 9% had almost no effect on the mapping accuracy of 100 bp reads. For 35 bp reads up to 3% sequence divergence did not affect the mapping accuracy strongly. The effect of indels on the mapping efficiency strongly depends on the mapping software.

Conclusions/Significance

In complex genomes, expression profiling by massively parallel sequencing could introduce a considerable bias due to incorrectly mapped sequence reads if the read length is short. Nevertheless, this bias could be accounted for if the genomic sequence is known. Furthermore, sequence polymorphisms and indels also affect the mapping accuracy and may cause a biased gene expression measurement. The choice of the mapping software is highly critical and the reliability depends on the presence/absence of indels and the divergence between reads and the reference genome. Overall, we found SSAHA2 and CLC to produce the most reliable mapping results.     

More than a decade of developmental gene expression atlases: where are we now?

To unravel regulatory networks of genes functioning during embryonic development, information on in situ gene expression is required. Enormous amounts of such data are available in literature, where each paper reports on a limited number of genes and developmental stages. The best way to make these data accessible is via spatio-temporal gene expression atlases. Eleven atlases, describing developing vertebrates and covering at least 100 genes, were reviewed. This review focuses on: (i) the used anatomical framework, (ii) the handling of input data and (iii) the retrieval of information. Our aim is to provide insights into both the possibilities of the atlases, as well as to describe what more than a decade of developmental gene expression atlases can teach us about the requirements of the design of the ‘ideal atlas’. This review shows that most ingredients needed to develop the ideal atlas are already applied to some extent in at least one of the discussed atlases. A review of these atlases shows that the ideal atlas should be based on a spatial framework, i.e. a series of 3D reference models, which is anatomically annotated using an ontology with sufficient resolution, both for relations as well as for anatomical terms.     

Isolation and characterization of the mouse ortholog of the Fukuyama-type congenital muscular dystrophy gene

Fukuyama-type congenital muscular dystrophy (FCMD) is a severe autosomal-recessive muscular dystrophy accompanied by brain malformation. Previously, we identified the gene responsible for FCMD through positional cloning. Here we report the isolation of its murine ortholog, Fcmd. The predicted amino acid sequence of murine fukutin protein encoded by Fcmd is 90% identical to that of its human counterpart. Radiation hybrid mapping localized the gene to 2.02 cR telomeric to D4Mit272 on chromosome 4. Northern blot analysis revealed ubiquitous expression of Fcmd in adult mouse tissues. Through in situ hybridization, we observed a wide distribution of Fcmd expression throughout embryonic development, most predominantly in the central and peripheral nervous systems. We also detected high Fcmd expression in the ventricular zone of proliferating neurons at 13.5 days post-coitum. Brain malformation in FCMD patients is thought to result from defective neuronal migration. Our data suggest that neuronally expressed Fcmd is likely to be important in the development of normal brain structure.     

A novel 3D mouse embryo atlas based on micro-CT

The goal of the International Mouse Phenotyping Consortium (IMPC) is to phenotype targeted knockout mouse strains throughout the whole mouse genome (23,000 genes) by 2021. A significant percentage of the generated mice will be embryonic lethal; therefore, phenotyping methods tuned to the mouse embryo are needed. Methods that are robust, quantitative, automated and high-throughput are attractive owing to the numbers of mice involved. Three-dimensional (3D) imaging is a useful method for characterizing morphological phenotypes. However, tools to automatically quantify morphological information of mouse embryos from 3D imaging have not been fully developed. We present a representative mouse embryo average 3D atlas comprising micro-CT images of 35 individual C57BL/6J mouse embryos at 15.5 days post-coitum. The 35 micro-CT images were registered into a consensus average image with our automated image registration software and 48 anatomical structures were segmented manually. We report the mean and variation in volumes for each of the 48 segmented structures. Mouse organ volumes vary by 2.6-4.2% on a linear scale when normalized to whole body volume. A power analysis of the volume data reports that a 9-14% volume difference can be detected between two classes of mice with sample sizes of eight. This resource will be crucial in establishing baseline anatomical phenotypic measurements for the assessment of mutant mouse phenotypes, as any future mutant embryo image can be registered to the atlas and subsequent organ volumes calculated automatically.     

The Zebrafish Information Network (ZFIN): the zebrafish model organism database

The Zebrafish Information Network (ZFIN) is a web based community resource that serves as a centralized location for the curation and integration of zebrafish genetic, genomic and developmental data. ZFIN is publicly accessible at http://zfin.org. ZFIN provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community contact data. Recent enhancements to ZFIN include: (i) an anatomical dictionary that provides a controlled vocabulary of anatomical terms, grouped by developmental stages, that may be used to annotate and query gene expression data; (ii) gene expression data; (iii) expanded support for genome sequence; (iv) gene annotation using the standardized vocabulary of Gene Ontology (GO) terms that can be used to elucidate relationships between gene products in zebrafish and other organisms; and (v) collaborations with other databases (NCBI, Sanger Institute and SWISS-PROT) to provide standardization and interconnections based on shared curation.     

Use of X-ray computed microtomography for non-invasive determination of wood anatomical characteristics

Quantitative analysis of wood anatomical characteristics is usually performed using classical microtomy yielding optical micrographs of stained thin sections. It is time-consuming to obtain high quality cross-sections from microtomy, and sections can be damaged. This approach, therefore, is often impractical for those who need quick acquisition of quantitative data on vessel characteristics in wood. This paper reports results of a novel approach using X-ray computed microtomography (microCT) for non-invasive determination of wood anatomy. As a case study, stem wood samples of a 2-year-old beech (Fagus sylvatica L.) and a 3-year-old oak (Quercus robur L.) tree were investigated with this technique, beech being a diffuse-porous and oak a ring-porous tree species. MicroCT allowed non-invasive mapping of 2-D transverse cross-sections of both wood samples with micrometer resolution. Self-developed software 'microCTanalysis' was used for image processing of the 2-D cross-sections in order to automatically determine the inner vessel diameters, the transverse cross-sectional surface area of the vessels, the vessel density and the porosity with computer assistance. Performance of this new software was compared with manual analysis of the same micrographs. The automatically obtained results showed no significant statistical differences compared to the manual measurements. Visual inspection of the microCT slices revealed very good correspondence with the optical micrographs. Statistical analysis confirmed this observation in a more quantitative way, and it was, therefore, argued that anatomical analysis of optical micrographs can be readily substituted by automated use of microCT, and this without loss of accuracy. Furthermore, as an additional application of microCT, the 3-D renderings of the internal microstructure of the xylem vessels for both the beech and the oak sample could be reconstructed, clearly showing the complex nature of vessel networks. It can be concluded that the use of microCT in wood science offers an interesting potential for all those who need quantitative data of wood anatomical characteristics in either the 2-D or the 3-D space.