Transcriptome Tomography for Brain Analysis in the Web-Accessible Anatomical Space

Abstract

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.

Broad Integration of Expression Maps and Co-Expression Networks Compassing Novel Gene Functions in the Brain

“…We set the FDR value at < 0.05 to choose genes with variable expression10. These genes were then employed in searches for the associated SETs.…”

Parallelized analysis of spatial gene expression patterns by database integration

“…ViBrism DB (http://vibrism.neuroinf.jp/) datasets are comprehensive gene expression 3D maps of the mouse brain (C57BL/6J, males, from a variety of developmental stages), produced with the microtomy-based method of Transcriptome Tomography: TT (Okamura-Oho et al 2012). Expression densities in the adult brain (8 weeks) are detected with 36,558 microarray probes for genes/transcripts; the density data are each reconstructed computationally in voxels to create 3D expression maps located within the standard brain coordinate system, WHS.…”

“…Indeed, overestimation occurs in gene expression areas contained within the ViBrism DB datasets, as a result of the TT method (described in a previous paper) ( Okamura-Oho et al 2012). Comparison of the two results could help to identify falsely positive expression areas within the 3D maps.…”

“…Then, we compared the calculated intensities to expression intensity data of 3D maps within the coordinate space. In this study, we used three types of image data: (1) Waxholm space (WHS) datasets, which consist of mouse brain MR images along with a probabilistic atlas (Johnson et al 2010) as standard coordinates for transformation; (2) 2,810 ISH images of the mouse brain, within the BrainTx DB (http://www.cdtdb.neuroinf.jp) (Furuichi et al 2011;Sato et al 2008), as 2D ISH images that are transformed into the WHS; and (3) volume data of ViBrism DB gene expression 3D maps that are located within the WHS MR image space (http://vibrism.neuroinf.jp) (Okamura-Oho et al 2012. These ISH images exhibit high-resolution information in the X-Y plane, but few images are available for each gene.…”

Spatially resolved transcriptomics and beyond

“…When identical samples are available (such as in the case of genetically identical model organisms), they can be sectioned separately along each Cartesian coordinate in order to obtain transcriptional maps in one, two or three dimensions. Two and three identical samples are required to generate 2D and 3D maps, respectively 80,81 . By applying tomo-seq to genetically identical zebrafish (Danio rerio) embryos collected at different developmental stages, and using cluster analysis to identify similar spatial patterns of gene expression, an atlas of developmental transcriptional dynamics in two and three dimensions was generated, which revealed previously unknown spatial patterns for many genes 80 .…”