Tracing unique cells with mathematics
27 January 2014Tweet
Scientists at the Technische Universitaet Muenchen (TUM), the Helmholtz Zentrum Muenchen and the University of Virginia (USA) have now found a way to simplify and improve the analysis of single cells by mathematical methods.
At the end of December 2013 the journal Nature Methods declared single-cell sequencing the ‘Method of the Year.’ However, analysis of individual cells is extremely complex, and the handling of the cells generates errors and inaccuracies. This is because small differences in gene regulation can be overwhelmed by the statistical noise.
Scientists led by Professor Fabian Theis, chair of mathematical modelling of biological systems at the Technische Universitaet Muenchen and director of the Institute of Computational Biology at the Helmholtz Zentrum Muenchen, have now found a way to improve single-cell analysis by applying methods from mathematical statistics.
Instead of just one cell, they took 16-80 samples with ten cells each. ‘With ten times the amount of cell material, the influences of ambient conditions can be markedly suppressed.’ said Theis However, cells with different properties are then distributed randomly on the samples. Therefore Theis's collaborator Christiane Fuchs developed statistical methods to identify the single-cell properties in the mixture of signals.
Theis and Fuchs modelled the distribution for genes that exhibit two well-defined regulatory states. Together with biologists Kevin Janes and Sameer Bajikar at the University of Virginia in Charlottesville (USA), they were able to prove experimentally that the ten cell samples deliver results of higher accuracy than analysis of the same number of single cell samples.
In many cases, several gene actions are triggered by the same factor. Even in such cases, the statistical method can be applied successfully. Fluorescent markers indicate the gene activities. The result is a mosaic, which again can be checked to spot whether different cells respond differently to the factor. The method is so sensitive that it even shows one deviation in 40 otherwise identical cells.