Cells are the basic unit of a living organism. The human body consists of a vast array of highly specialized cells, such as blood cells, skin cells and neurons. In total more than 250 different cell types exist. How are the different types related to each other? Which factors are unique for each cell type? And what in the end determines the development of a certain cell?
To answer these questions, the research team designed a computer-based method that uses already existing biological data from research groups all over the world and analyses them in an entirely new way. This led to the identifications of unique factors for 166 different human cell types. These factor, or master regulators, determine the development and distinguish different cell types from each other. With this information they could map the relationship between the cell types in a family tree. These outcomes may serve as basis for the development of cell replacement therapies.
“Many diseases, such as Parkinson’s disease and diabetes, or extensive burns result in the loss or altered functionality of cells,” explains the first author of the paper, Dr Merja Heinäniemi.
“Ideally one would like to replace those sick or lost cells again by healthy ones to cure the patients. This study forms an important step towards the development of such therapies.”
Dr Heinäniemi has previously worked at the Life Sciences Research Unit and the Luxembourg Centre of Systems Biomedicine (LCSB) at the University of Luxembourg. She now continues her research at the Unit of Biomedicine and the Unit of Biotechnology and Molecular Medicine at the University of Eastern Finland.
“The next goal is to better understand the differentiation of cells into other cell types with the help of master regulators on a genome-wide basis in order to find ways to enhance cell differentiation for medical applications.”
“This study illustrates the importance of computational biology for medicine. Such large amounts of biological data can only be analyzed with computer-based methods,” says Professor Matti Nykter of the University of Tampere. He worked at Tampere University of Technology at the time of the study.
The use of previously published research data allows for comprehensive, large-scale studies without massive investments in data collection. “Indeed, we were able to use data from nearly 3000 earlier gene chip analyses which would have been too expensive to carry out within a single study.”
The study was financed by the Academy of Finland, Tekes FiDiPro Programme, the University of Luxembourg, the U.S. National Health institute and Alberta Innovates the Future Programme.
For further information, please contact:
Merja Heinäniemi, tel. +358 403553049, merja.heinaniemi (a) uef.fi
UEF Postdoctoral Researcher, Unit of Biotechnology and Molecular Medicine
University Lecturer, Unit of Biomedicine
University of Eastern Finland
Matti Nykter, tel. +358 50 318 6869, matti.nykter (a) uta.fi
Professor, Institute of Biomedical Technology, University of Tampere