LRRN4 and UPK3B Are Markers of Primary Mesothelial Cells

Mesothelioma is a highly malignant tumor that is primarily caused by occupational or environmental exposure to asbestos fibers. Despite worldwide restrictions on asbestos usage, further cases are expected as diagnosis is typically 20–40 years after exposure. Once diagnosed there is a very poor prognosis with a median survival rate of 9 months. Considering this the development of early pre clinical diagnostic markers may help improve clinical outcomes.Microarray expression arrays on mesothelium and other tissues dissected from mice were used to identify candidate mesothelial lineage markers. Candidates were further tested by qRTPCR and in-situ hybridization across a mouse tissue panel. Two candidate biomarkers with the potential for secretion, uroplakin 3B (UPK3B), and leucine rich repeat neuronal 4 (LRRN4) and one commercialized mesothelioma marker, mesothelin (MSLN) were then chosen for validation across a panel of normal human primary cells, 16 established mesothelioma cell lines, 10 lung cancer lines, and a further set of 8 unrelated cancer cell lines.Within the primary cell panel, LRRN4 was only detected in primary mesothelial cells, but MSLN and UPK3B were also detected in other cell types. MSLN was detected in bronchial epithelial cells and alveolar epithelial cells and UPK3B was detected in retinal pigment epithelial cells and urothelial cells. Testing the cell line panel, MSLN was detected in 15 of the 16 mesothelioma cells lines, whereas LRRN4 was only detected in 8 and UPK3B in 6. Interestingly MSLN levels appear to be upregulated in the mesothelioma lines compared to the primary mesothelial cells, while LRRN4 and UPK3B, are either lost or down-regulated. Despite the higher fraction of mesothelioma lines positive for MSLN, it was also detected at high levels in 2 lung cancer lines and 3 other unrelated cancer lines derived from papillotubular adenocarcinoma, signet ring carcinoma and transitional cell carcinoma

Large-scale clustering of CAGE tag expression data

Background: Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed. Results: We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70-100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups. Conclusion: Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data

Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates

BackgroundAssignment of anatomical reference is a key step in integration of the rapidly expanding collection of rodent brain data. Landmark-based registration facilitates spatial anchoring of diverse types of data not suitable for automated methods operating on voxel-based image information.New toolHere we propose a standardized set of anatomical landmarks for registration of whole brain imaging datasets from the mouse and rat brain, and in particular for integration of experimental image data in Waxholm Space (WHS).ResultsSixteen internal landmarks of the C57BL/6J mouse brain have been reliably identified: by different individuals, independent of their experience in anatomy; across different MRI contrasts (T1, T2, View the MathML sourceT2*) and other modalities (Nissl histology and block-face anatomy); in different specimens; in different slice acquisition angles; and in different image resolutions. We present a registration example between T1-weighted MRI and the mouse WHS template using these landmarks and reaching fairly high accuracy. Landmark positions identified in the mouse WHS template are shared through the Scalable Brain Atlas, accompanied by graphical and textual guidelines for locating each landmark. We identified 14 of the 16 landmarks in the WHS template for the Sprague Dawley rat.Comparison with existing methodsThis landmark set can withstand substantial differences in acquisition angle, imaging modality, and is less vulnerable to subjectivity.ConclusionsThis facilitates registration of multimodal 3D brain data to standard coordinate spaces for mouse and rat brain taking a step toward the creation of a common rodent reference system; raising data sharing to a qualitatively higher level

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

<div><p>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: <a href="http://vibrism.riken.jp/3dviewer/ex/index.html" target="_blank">http://vibrism.riken.jp/3dviewer/ex/index.html</a>) 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 <i>Bdnf</i>. 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.</p></div