Cell populations have a high heterogeneity, even when they consist of the same type of cells. To determine various types of cells, scientists analyze the respective active transcriptome – in the form of RNA molecules – of the individual cells (single-cell analysis). Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed, thus providing an exact picture of the individual cell types. However, the observed differences between the gene expression patterns of individual cells result from numerous sources, including confounding factors, such as short-term changes in gene expression due to the cell cycle as well as biological processes of interest such as stem cell differentiation.
The scientists have now developed a statistical approach which models the sources of the observed cell-cell differences. This facilitates an accurate dissection of the observed heterogeneity into a variety of factors, which include measurement noise, confounding factors such as cell cycle effects as well as the biological processes of interest. “In our current study we show how such factors can be taken into account, thus enabling a more accurate picture of the different cell types. Through the combination of single-cell analyses with statistical methods, cell types can be identified that otherwise would remain undetected,” said first author Florian Büttner of the Institute of Computational Biology (ICB) at Helmholtz Zentrum München.
Single-cell profiles: towards a better understanding of health and disease
Using their single-cell latent variable model (scLVM), the team around Florian Büttner and Fabian Theis from Helmholtz Zentrum München as well as John Marioni and Oliver Stegle from the European Bioinformatics Institute (EMBL-EBI, Cambridge, UK) have succeeded in detecting and characterizing the maturation stages T cells undergo in their development to become T helper cells. “The analysis of single cell types is essential for medical research,” Büttner said. “Cancer cells, differentiation processes, the pathogenesis of various diseases and much more can be better explored and understood based only on known, detailed cell profiles.”
Buettner, F. et al. (2014). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-Sequencing data reveals hidden subpopulation of cells, Nature Biotechnology, doi: 10.1038/nbt.3102
As German Research Center for Environmental Health, Helmholtz Zentrum München pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes mellitus and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München has about 2,200 staff members and is headquartered in Neuherberg in the north of Munich. Helmholtz Zentrum München is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 34,000 staff members.
The Institute of Computational Biology (ICB) develops and applies methods for the model-based description of biological systems, using a data-driven approach by integrating information on multiple scales ranging from single-cell time series to large-scale omics. Given the fast technological advances in molecular biology, the aim is to provide and collaboratively apply innovative tools with experimental groups in order to jointly advance the understanding and treatment of common human diseases.
Dr. Florian Büttner, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany – Phone: +49-(0)89-3187-4217