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About the CCDB

The Cell Centered Database or CCDB was created to house the types of high resolution 3D light and electron microscopic reconstructions produced at the National Center for Microscopy and Imaging Research. It contains structural and protein distribution information derived from confocal, multiphoton and electron microscopy, including correlated microscopy. Many of the data sets are derived from electron tomography. Electron tomography is similar in concept to medical imaging techniques like CAT scans and MRI in that it derives a 3D volume from a series of 2D projections through a structure. In this case, the structures are contained in sections prepared for electron microscopy which are tilted through a limited angular range. Many of the data sets in the CCDB come from studies of the nervous system, although the CCDB is not restricted to neuronal information. Additional information on some of the tomography projects underway at NCMIR can be found here. The CCDB is built on an object-relational framework using Oracle 9i. The current CCDB has over 80 tables containing a large amount of descriptive data. The simplified schema may be viewed here. The CCDB is built around 3D reconstructions performed at the light and electron microscopic levels, including correlated datasets. It models the entire process of reconstruction, from specimen preparation to segmentation and analysis. A volume reconstruction is stored along with pointers to all of the raw images and the processing details required to reconstruct the volume from the raw data. Each object that is segmented from the 3D volume is stored as a separate object indexed to the parent reconstruction. Four types of segmented objects are currently modeled in the CCDB:

  1. surface objects : polygonal surface meshes representing 3D objects in the reconstruction, extracted using either isosurfacing methods or manual contouring

  2. contour objects : a series of contours that have not been fitted with a surface

  3. volume objects : subvolumes containing an object of interest

  4. tree objects: skeletons of branching objects like dendrites and axons, derived from Neurolucida (Microbrightfield, Inc., VT)

Each object is stored along with any measurements like surface area, volume, length, number and labeling intensity. Whenever possible, parsers are written for the output of analysis programs so that results can be uploaded directly into the CCDB. For example, measurement summaries for tree objects are uploaded directly from the output of NeuroExplorer, an analysis program for Neurolucida derived data.

Rationale

The rationale behind creating the CCDB as a publically accessible data base is to provide a resource to the structural biology and neuroscience communities. First, we wanted a venue for disseminated the very rich and unique datasets acquired by electron tomography. Because of the superior resolution of tomographic datasets, they often contain much more data than is analyzed by a single researcher. A single electron tomographic study generally relies on a very small sample size because of the labor involved in acquiring and analyzing the specimens. Thus, having a repository where these data sets can be accumulated and reanalyzed will help researchers gain a better picture of variation across structures. Because of the labor intensive nature of the process, tomographers are constantly looking at new algorithms for segmenting and visualizing data. Thus, the CCDB can serve as a resource for those involved in these pursuits. Finally, the CCDB serves a resource for researchers interested in computational modeling of cells and the cellular microenvironment. The CCDB contains many individual neurons that have been skeletonized in a form usable by modeling programs such as Genesis and Neuron. Tomographic reconstructions are serving as the basis for sophisticated modeling studies on molecular dynamics in the cellular microenvironment.

Simplified Schema

Entity-Relationship (ER) Diagram

The ER diagram (approx 1.5 MB) represents the schema as of September 24th, 2003.

Data Entry

For a description of how files are managed in the CCDB and how to prepare files for entry, see this page.

Future Development Plans

  1. In future versions of the CCDB, users will have access to more advanced query forms which take advantage of the rich data model of the CCDB. An example of such a query can be found here.

  2. Future plans also include the implementation of an atlas-based interface and implementation of a knowledge-based query system. More information on this can be found at the "Federation of Brain Data" and BIRN sites. A demonstration of the "Smart Atlas" is given in this Powerpoint presentation.

Project Team Members

  • Leaders:
    • Maryann Martone
    • Amarnath Gupta
    • Mark Ellisman

  • Database design and implementation:
    • Yujun Wang
    • Julia Sun
    • Xufei Qian

  • Web design:
    • Mona Wong-Barnum

  • Contributors:
    • Diana Price
    • Masako Terada
    • Andrea Thor

Publications

  • Shenglan Zhang, Xufei Qian, Amarnath Gupta, Maryann E. Martone. A Practical Approach for Microscopy Imaging Data Management (MIDM) in Neuroscience. 15th International Conference on Scientific and Statistical Database Management (SSDBM 2003) July 9-11 Cambridge, Massachusetts, 2003.

  • Shenglan Zhang, Diana Price, Xufei Qian, Amarnath Gupta, Mark H. Ellisman, Maryann E. Martone. A Cell Centered Database (CCDB) for Multi-Scale Microscopy Data Management. Microscopy and Microanalysis 2003 (M&M 2003) August 3-7 San Antonio, Texas, 2003.

  • Martone, M. E., Gupta, A., Wong, M., Qian, X., Sosinsky, G., Ludaescher, B., and Ellisman, M. H. A cell centered database for electron tomographic data. J. Struct. Biology 138: 145-155, 2002.

  • Martone, M. E., Gupta, A. Qian, X., Wong, M., Zhang, S., Ludaescher, B., Zaslavsky, I. , D. Martinez-Price and Ellisman, M. H. The cell-centered database: an online resource for high resolution cell level data, Soc. for Neurosci. Abstr., 2002. [PDF : 423KB, 1.57MB, 4.1MB]

  • Martone, M. E., Gupta, A., Ludascher, Zaslavsky, I. and Ellisman, M. H. Federation of brain data through knowledge-guided mediation. In Kotter, R. Neuroscience Databases: a Practical Guide, Boston: Kluwer Academic Press, 275-292, 2002.

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Last updated on february 25th, 2004
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