Mission

CellFinder aims at mapping stem cell information to lift the cellome to an even level with the other –omics fields. It will provide a basis for global understanding of cells, increase comparability of data and relate cells to more complex systems. CellFinder can be positioned as an interface between large biomedical research infrastructures, stem cell registries, research consortia and individual research groups.

We will develop a virtual environment and data repository for research and therapy that will gather, integrate and analyze the available data on stem cells and their derivatives. Established cell biology-oriented databases and tools will be exploited to assimilate the diverse dimensions of data spanning molecular, functional, anatomical and cyto-histological, as well as auxiliary levels. The repository will aid standardization and comparability of complex datasets for each cell and organize these in a cellome environment by ontological description and technical implementation.

Operated under the Open Source/Access model, community and scientific networking applications will allow users to store and retrieve their data and to explore cells and their interactions on singular and complex resolution levels. The involvement of stem cell registries and banks will allow direct access to selected cells.

The set up the stem cell data repository will involve three lines of action:

• the acquisition of scientific data and contents
• the standardized description of this data, its organization with the help of ontologies and technical implementation
• the integration of existing sources/logistics and to ensure sustainable long-term operation

Background

Cellular therapies are increasingly relevant for personalized medicine, requiring resources informing about the complex characteristics of cells. Large-scale research initiatives worldwide resulted in major technological developments and data output in stem cell analysis. Even though some data have been gathered so far, much still remains to be done in order to streamline the research performed at many laboratories with diverse technologies and to maximize the potential of stem cell therapies. Although large amounts of research data on stem cells are already available, these are scattered, derived by diverse technologies, not standardized and are not available at the necessary integration level for cellome modelling. Consequently, the selection of cells, e.g. for therapeutic applications, is based on rather incomplete information.