High-resolution voxelation mapping of human and rodent brain gene expression

Transcriptome tomography for brain analysis in the web-accessible anatomical space

Increased information on the encoded mammalian genome is expected to facilitate an integrated understanding of complex anatomical structure and function based on the knowledge of gene products. Determination of gene expression-anatomy associations is crucial for this understanding. To elicit the association in the three-dimensional (3D) space, we introduce a novel technique for comprehensive mapping of endogenous gene expression into a web-accessible standard space: Transcriptome Tomography. The technique is based on conjugation of sequential tissue-block sectioning, all fractions of which are used for molecular measurements of gene expression densities, and the block- face imaging, which are used for 3D reconstruction of the fractions. To generate a 3D map, tissues are serially sectioned in each of three orthogonal planes and the expression density data are mapped using a tomographic technique. This rapid and unbiased mapping technique using a relatively small number of original data points allows researchers to create their own expression maps in the broad anatomical context of the space. In the first instance we generated a dataset of 36,000 maps, reconstructed from data of 61 fractions measured with microarray, covering the whole mouse brain (ViBrism: http://vibrism.riken.jp/3dviewer/ex/index.html) in one month. After computational estimation of the mapping accuracy we validated the dataset against existing data with respect to the expression location and density. To demonstrate the relevance of the framework, we showed disease related expression of Huntington's disease gene and Bdnf. Our tomographic approach is applicable to analysis of any biological molecules derived from frozen tissues, organs and whole embryos, and the maps are spatially isotropic and well suited to the analysis in the standard space (e.g. Waxholm Space for brain-atlas databases). This will facilitate research creating and using open-standards for a molecular-based understanding of complex structures; and will contribute to new insights into a broad range of biological and medical questions.

Learning pair-wise gene functional similarity by multiplex gene expression maps

Background

The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute.

Results

Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction.

Conclusions

By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.

Quantifying mRNA levels across tissue sections with 2D-RT-qPCR

Measurement of mRNA levels across tissue samples facilitates an understanding of how genes function and what their roles are in disease. Quantifying low-abundance mRNA requires a workflow that preserves spatial information, isolates RNA, and performs reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR). This is complex because these steps are typically performed in three separate platforms. In the present study, we describe two-dimensional RT-qPCR (2D-RT-qPCR), a method that quantifies RNA across tissues sections in a single integrated platform. The method uses the grid format of a multi-well plate to macrodissect tissue sections and preserve the spatial location of the RNA; this also eliminates the need for physical homogenization of the tissue. A new lysis and nucleic acid purification protocol is performed in the same multi-well plate, followed by RT-qPCR. The feasibility 2D-RT-qPCR was demonstrated on a variety of tissue types. Potential applications of the technology as a high-throughput tissue analysis platform are discussed.

Analysis of multiplex gene expression maps obtained by voxelation

BACKGROUND

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.

RESULTS

To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum.

CONCLUSION

The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.

Brain spatial normalization

Neuroanatomical informatics, a subspecialty of neuroinformatics, focuses on technological solutions to neuroimage database access. Its current main goal is an image-based query system that is able to retrieve imagery based on anatomical location. Here, we describe a set of tools that collectively form such a solution for sectional material and that are available to investigators to use on their own data sets. The system accepts slide images as input and yields a matrix of transformation parameters that map each point on the input image to a standardized 3D brain atlas. In essence, this spatial normalization makes the atlas a spatial indexer from which queries can be issued simply by specifying a location on the reference atlas. Our objective here is to familiarize potential users of the system with the steps required of them as well as steps that take place behind the scene. We detail the capabilities and the limitations of the current implementation and briefly describe the enhancements planned for the near future.

Quantitative methods for genome-scale analysis of in situ hybridization and correlation with microarray data

This study introduces a novel method for standardized relative quantification of colorimetric in situ hybridization signal that enables a large-scale cross-platform expression level comparison of in situ hybridization with two publicly available microarray brain data sources.

With the emergence of genome-wide colorimetric in situ hybridization (ISH) data sets such as the Allen Brain Atlas, it is important to understand the relationship between this gene expression modality and those derived from more quantitative based technologies. This study introduces a novel method for standardized relative quantification of colorimetric ISH signal that enables a large-scale cross-platform expression level comparison of ISH with two publicly available microarray brain data sources.

A genome-scale map of expression for a mouse brain section obtained using voxelation

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation, and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genes with unexpected patterns were identified, and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.

NeuroTerrain--a client-server system for browsing 3D biomedical image data sets

Background

Three dimensional biomedical image sets are becoming ubiquitous, along with the canonical atlases providing the necessary spatial context for analysis. To make full use of these 3D image sets, one must be able to present views for 2D display, either surface renderings or 2D cross-sections through the data. Typical display software is limited to presentations along one of the three orthogonal anatomical axes (coronal, horizontal, or sagittal). However, data sets precisely oriented along the major axes are rare. To make fullest use of these datasets, one must reasonably match the atlas' orientation; this involves resampling the atlas in planes matched to the data set. Traditionally, this requires the atlas and browser reside on the user's desktop; unfortunately, in addition to being monolithic programs, these tools often require substantial local resources. In this article, we describe a network-capable, client-server framework to slice and visualize 3D atlases at off-axis angles, along with an open client architecture and development kit to support integration into complex data analysis environments.

Results

Here we describe the basic architecture of a client-server 3D visualization system, consisting of a thin Java client built on a development kit, and a computationally robust, high-performance server written in ANSI C++. The Java client components (NetOStat) support arbitrary-angle viewing and run on readily available desktop computers running Mac OS X, Windows XP, or Linux as a downloadable Java Application. Using the NeuroTerrain Software Development Kit (NT-SDK), sophisticated atlas browsing can be added to any Java-compatible application requiring as little as 50 lines of Java glue code, thus making it eminently re-useable and much more accessible to programmers building more complex, biomedical data analysis tools. The NT-SDK separates the interactive GUI components from the server control and monitoring, so as to support development of non-interactive applications. The server implementation takes full advantage of data center's high-performance hardware, where it can be co-localized with centrally-located, 3D dataset repositories, extending access to the researcher community throughout the Internet.

Conclusion

The combination of an optimized server and modular, platform-independent client provides an ideal environment for viewing complex 3D biomedical datasets, taking full advantage of high-performance servers to prepare images and subsets of associated meta-data for viewing, as well as the graphical capabilities in Java to actually display the data.

Visualization, reconstruction, and integration of neuronal structures in digital brain atlases

Brain atlases are used in neuroanatomy to define the spatial layout of neuronal structures. Their digital variant can serve as a database and common reference frame for integrating data from different biological experiments. This article presents an overview of methods for three-dimensional visualization of neuroanatomical image data, reconstructing neuronal structures from image data, creating digital brain atlases, and registering data in an atlas. This enables analysis of spatial relations between individual structures imaged in different experiments as well as between these structures and the atlas.

Development and evaluation of a micro- and nanoscale proteomic sample preparation method

Challenges associated with the efficient and effective preparation of micro- and nanoscale (micro- and nanogram) clinical specimens for proteomic applications include the unmitigated sample losses that occur during the processing steps. Herein, we describe a simple "single-tube" preparation protocol appropriate for small proteomic samples using the organic cosolvent, trifluoroethanol (TFE) that circumvents the loss of sample by facilitating both protein extraction and protein denaturation without requiring a separate cleanup step. The performance of the TFE-based method was initially evaluated by comparisons to traditional detergent-based methods on relatively large scale sample processing using human breast cancer cells and mouse brain tissue. The results demonstrated that the TFE-based protocol provided comparable results to the traditional detergent-based protocols for larger, conventionally sized proteomic samples (>100 microg protein content), based on both sample recovery and numbers of peptide/protein identifications. The effectiveness of this protocol for micro- and nanoscale sample processing was then evaluated for the extraction of proteins/peptides and shown effective for small mouse brain tissue samples (approximately 30 microg total protein content) and also for samples of approximately 5000 MCF-7 human breast cancer cells (approximately 500 ng total protein content), where the detergent-based methods were ineffective due to losses during cleanup and transfer steps.

Investigation of different transcript quantitation tools for high-throughput mapping of brain gene expression using voxelation

Voxelation is a new approach for genome scale acquisition of brain gene expression patterns. The method employs high-throughput analysis of spatially registered voxels (cubes) to create multiple volumetric images of brain gene expression, similar to those obtained from biomedical imaging systems. The spatial resolution of voxelation depends on voxel size, with smaller voxels giving higher resolution. An important question is the applicability of different transcript profiling tools for the various levels of resolution that can be employed. Here, we describe the use of three methods to analyze voxel transcript abundance: real-time PCR, microarray analysis and linear amplification coupled with microarrays. We show statistically significant concordance between real-time PCR and microarray analysis for the myelin basic protein gene in human brain specimens at differing levels of spatial resolution. In addition, we also demonstrate the feasibility of using linear amplification coupled with microarray analysis to create voxelation maps from the mouse brain at high resolution, 1 microl. These data indicate the suitability of a number of transcript profiling tools for various levels of spatial resolution in voxelation.

Anatomical methods for voxelation of the mammalian brain

Voxelation allows high-throughput acquisition of three-dimensional gene expression patterns in the brain through analysis of spatially registered voxels (cubes). The method results in multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging techniques. An important issue for voxelation is the development of approaches to anchor correctly harvested voxels to the underlying anatomy. Here, we describe experiments to identify fixation and cryopreservation protocols for improved registration of harvested voxels with neuroanatomical structures. Paraformaldehyde fixation greatly reduced RNA recovery as judged by ribosomal RNA abundance. However, gene expression signals from paraformaldehyde-fixed samples were not appreciably diminished as judged by average signal-noise ratios from microarrays, highlighting the difficulties of accurate quantitation of cross-linked RNA. Additional use of cryoprotection helped to improve further RNA recovery and signal from fixed tissue. It appears that the best protocol to provide the necessary resolution of neuroanatomical information in voxelation entails a controlled dose of fixation and thorough cryoprotection, complemented by histological staining.

Neurogenomics: at the intersection of neurobiology and genome sciences

Neurogenomics is the study of how the genome as a whole contributes to the evolution, development, structure and function of the nervous system. It includes investigations of how genome products (transcriptomes and proteomes) vary in time and space. Neurogenomics differs markedly from the application of genome sciences to other systems, particularly in the spatial category, because anatomy and connectivity are paramount to our understanding of function in the nervous system. We focus here on some of the influences of genomics and its associated technologies on neuroscience. We discuss comparative genomics, gene expression atlases of the brain, network genetics and applications to behavioral phenotypes, and consider the culture, organization and funding of genome-scale projects.

Voxelation and gene expression tomography for the acquisition of 3-D gene expression maps in the brain

Voxelation and gene expression tomography or GET are novel methods for the high-throughput acquisition of gene expression patterns in the mammalian brain. Voxelation employs analysis of spatially registered voxels (cubes), while GET employs analysis of sets of parallel slices rotated about multiple independent axes of rotation. Both methods employ reconstruction of the data to result in multiple volumetric maps of gene expression analogous to those obtained from biomedical imaging techniques. Here, we describe the methodologies underlying voxelation and GET and briefly outline the insights that can be obtained from these approaches.

Genome scale mapping of brain gene expression

Two new approaches, voxelation and gene expression tomography (GET), permit multiplex acquisition of gene expression patterns in the brain. Both methods result in volumetric images of gene expression analogous to those produced in biomedical imaging systems. Voxelation employs analysis of spatially registered cubes from the brain, whereas GET entails analysis of parallel slices obtained by rotation about multiple axes. These methods have been used to investigate neurologic diseases and their models in both humans and mice. The results of these studies are discussed, as is the future of high-throughput gene expression mapping in the brain.