VIEWS: 0 PAGES: 5 POSTED ON: 9/22/2012
ATLAS INTEGRATION FOR MOUSE BIRN: VERSION 1.0 OVERALL VISION The Mouse BIRN aims to bring together the various atlas projects that are underway at the current project sites to provide an integrated set of tools for: 1) registering brain imaging data at multiple scales to a common spatial reference system; 2) visualizing data sets registered to the atlas along with delineations of known anatomical structures; 3) querying the data registered to the atlas, including protein localization and gene expression data, brought together from distributed sources. The Mouse BIRN will accomplish this through creating a set of interoperable brain atlases and tools and providing a common user interface. For the purposes of mouse BIRN, we will define an atlas as a set of 2D images or a 3D volume in a specified coordinate system with a set of spatially registered annotations. In the current Mouse BIRN partner sites, we have several atlases that provide delineations of anatomical structures in the mouse brain: the LONI Mouse Brain Atlas, Drexel’s Neuroterrain and the Paxinos and Franklin stereotaxic mouse brain atlas, which is currently used by the Smart Atlas. The purpose of these atlases in the most traditional sense is to aid a user in defining anatomical regions. Because the atlases are electronic and 3D (with the exception of the Paxinos and Franklin atlas), users can utilize various tools to help them with this process, e.g., resectioning the brain to more closely match the orientation of a user-supplied image, spatially warping and scaling an image to match the reference brain, bringing up multiple parcellation schemes for a given area, attaching the annotations of a given area to additional knowledge sources, e.g., ontologies, literature references. The BIRN project also is using the brain atlas as a query interface for information contained within spatially registered data, e.g., to find out what genes are expressed in a given location. WORKFLOWS A user coming to the Mouse BIRN may want to do one of several things: 1) access a free brain atlas to learn about brain anatomy; 2) compare different methods of visualizing brain structure, e.g., Nissl, MRI, DTI imaging; 3) view and manipulate individual 2D images or 3D volumes, e.g., reslice; 4) spatially register their data to the brain atlas; 5) compare their own data against that of the atlas; 6) use the brain atlas interface to retrieve data indexed to a particular location, e.g., EM data sets; 7) use the brain atlas interface to examine patterns of gene expression; 8) Add their data to the BIRN data resources. Any integrated interface to the Mouse BIRN atlas needs to consider what tasks the user would like to accomplish and present them with a set of instructions to accomplish that task. USER SCENARIOS 1) Registration of brain data: All atlases assume that the data will be input into a common reference system. Current reference systems include the stereotaxic coordinate system of the mouse brain created by Paxinos and Franklin (2000) and other similar coordinate systems. The reference system can also be image based, that is, a reference image or volume can be selected and all data can be registered with respect to the image coordinates. The Smart Atlas and Neuroterrain use the stereotaxic coordinate system of Paxinos and Franklin; the LONI atlas uses an image-based coordinate system. As long as the two sets of coordinates can be interchanged, it technically doesn’t matter which coordinate system is utilized. An advantage of having the stererotaxic coordinate system superimposed on the image-based system is that other types of data that rely on stereotaxic coordinates, e.g., physiological data, can be references to the atlas. a. Tools for Registration of brain data: The Mouse BIRN currently has several tools that have been developed or are being developed for spatial normalization of mouse brain data. The tools are summarized in Table 1. The tools range in functionality, from fully automatic to semi-automatic to fully manual. Tool Source Language Platform Auto 2D 3D API LONI UCLA Neuroterrain Drexel Jibber UCSD Java Manual Y N Jargon Table 1: Spatial registration tools currently utilized by Mouse BIRN partner sites b. Scenario for registration of brain data: A user who wishes to spatially normalize their data to the BIRN atlas would come to the Mouse BIRN Integrated Atlas and select “Register Data”. The user will determine the correct tool based on whether their data was 2D or 3D and how many fiducial cues are present. Users should be able to select the appropriate template against which to match their data. Two types of users may be envisioned: i. Users may wish to compare their data against the BIRN data resources but not make it generally available. ii. Users may wish to add their data to the BIRN data resources. In this case, provision must be made to upload their data to the BIRN data grid and to collect sufficient metadata so that the data can be interpreted. If the data involve spatially distributed gene or protein signals, then the users must access the Spatial Histogram generation pipeline. c. Visualization of Atlas Data: Users will have the need to visualize both 2D slices and 3D data, in some cases overlaid with anatomical delineations. Each of the different atlas tools has overlapping and complementary viewing capabilities. Ideally, any viewing tool should be able to view all data, although image file and annotation formats may cause difficulties. Annotations are currently 1D (points), 2D (polygons) and 3D (volumes) and may be vectors, rasters or pixel paints (?). d. Annotations: The current atlases available for the Mouse BIRN have varying degrees of annotation (by this we mean delineation of anatomical brain structures). Apologies in advance if the following doesn’t capture the current state of the atlas. Please correct as necessary: Atlas Positives Negatives Paxinos and Franklin Detailed delineations; Polygons not defined (Smart Atlas interface) well defined coordinate for all brain regions; system; vector-based copyrighted product delineations may put restrictions of use; 2.5 D Mouse Brain Atlas Nissl and block face 2D, coarse (UCLA) images available for segmentation each slice; all slices compared to Paxinos delineated Shiva 3D, 3D volume Very coarse segmentations; delineations; atlas painting tools available very low resolution; no clear coordinate system Neurterrain 3D isotropic resolution Only 3 brain regions of Nissl stain; thoroughly segmented extremely well aligned; very detailed 3D delineations; registered to stereotaxic coordinate system e. Atlas Integration Scenarios for Gene Expression and Protein Expression Data Background: The Mouse BIRN brain atlases will bring together images containing signals for different types of cell and tissue components: e.g., proteins, cells or cell parts and nucleic acids. Some of these images will be of brain tissue and some may be of microarrays taken from a particular location. Each of these components can be localized using one or more techniques, e.g., immunohistochemistry for proteins, radioactive and non-radioactive in situ hybridization for nucleic acids, histochemistry for cells and tissues. Each technique may have a different sensitivity and resolution, depending on how it was performed. Different types of signals may be found in the same cell, but because the targeted component is found in different locations in the cell, the resulting images may be quite different. For example, localization of the mRNA for a peptide like enkephalin will give rise to intense signals in the caudatoputamen while localization of the enkephalin peptide will result in intense signals in the globus pallidus and substantia nigra. mRNA’s are, with few exceptions, confined to the cell soma while proteins may targeted to very specific subcompartments. In the case of enkephalin, it is found in dense core vesicles and the cytoplasm in the axon terminals. Cell bodies in the caudoputamen project to the globus pallidus/substantia nigra; thus, the labeling patterns obtained in this case are consistent as long as one knows the connectivity. Different techniques may yield inconsistent results, even when they are targeting the same gene or gene product. It is therefore critical that any system that is developed by the Mouse BIRN supply comparisons between the associated descriptive data supplied with each data set, e.g., age of animal, type of signal, technique used to create signal. In the example currently in the Smart Atlas (slice 119), the transcription factor Lhx5 gene is targeted in 4 different ways: radioactive in situ hybridization (mRNA), non-radioactive in situ hybridization (mRNA), immunohistochemistry (protein) and a gene specific cell fill. The gene specific cell fill (GSCF) is what I am calling the signals contained in the GENSAT images: a gene-specific promoter is used to drive expression of GFP. The GFP diffuses freely throughout the cell, filling all processes. Although expression is limited to those cells that carry the promoter for that particular gene, the GFP itself isn’t targeted in the same manner as the protein. All 4 methods show intense labeling of the cerebellar cortex. Of these 4 methods, 3 of them (non-radioactive and radioactive in situ hybridization and immunocytochemistry) show localization in the Purkinje cells of the cerebellar cortex. The GSCF, however, shows weak-to-no labeling in the Purkinje cell but strong to moderate signal in the basket cells and the stellate cells. The protein localization signal, while consistent with the in situ hybridization signal, is peculiar in that it is found in dendrites while we usually think of transcription factors as being localized to the nucleus. Whenever this type of inconsistency arises, additional information must be obtained to see if they can be reconciled, e.g., different techniques or subject characteristics. In some cases, even when the same technique is used, the probes employed may target different portions of the molecule which may lead to different localizations. In many cases, the results cannot be reconciled and additional experiments must be performed. Use Case 1: A researcher selects a region in the Smart Atlas and wants to know: “What genes are expressed in this location and what cells express each?” The first source to be consulted would be microarrays because they would have the largest numbers of possible candidates. However, depending upon the location covered by the microarray, it may not have the spatial resolution required to be certain that it is only targeting the desired location. The results from the microarray can be compared against in situ hybridization tissues to find out what mRNAs and DNA’s are found in this area and the cells that express them. Non-radioactive in situ hybridization has better spatial resolution but isn’t as sensitive as radioactive in situ hybridization. Depending upon the additional information that may be present in a section, e.g., Nissl stain, other counterstains, we may or may not be able to determine the cellular identity of labeled cells. We would then compare the list of genes against the proteins found in the area. Protein maps may yield additional candidates although just because a protein is found there, doesn’t mean that the gene is found there. Comparison of results with a connectivity map might resolve some of the discrepancies. A similar caveat applies to the GCSF: one would have to determine whether the signal is found in cell bodies, dendrites or axon terminals to determine whether or not we would expect the signal to be consistent with the microarray data. Variations on the above query include: “What genes are expressed at high levels in location X and Y?” What genes are expressed in high levels in location X and low levels in location Y?” Use Case 2: A researcher interested in the Parkinson’s disease model uses the Smart Atlas to investigate patterns of gene expression for genes determined through GeneNetwork to be affected in Parkinson’s disease. These patterns are used to focus investigations of anatomical alterations in the MRM or whole brain histology data available through the LONI or Neuroterrain atlases or retrieved through the Mouse BIRN data federation. Alternatively, changes observed from the MRM data can be used to issue a query in the Smart Atlas for gene expression patterns (or protein expression patterns) that correlate with the observed changes.
Pages to are hidden for
"Atlas Integration Scenarios"Please download to view full document