All cells are not equal
10 Nov 2011
Transcriptomics, i.e. studying gene expression levels in an organism, has become a routine activity in molecular biology. RNA is isolated from millions of cells and analysed using standard microarray technology. The resulting gene expression levels represent an average calculated from all those cells, which is very suitable to compare different individuals or populations, but obscures differences between individual cells.
However, it is also known that cellular heterogeneity is a widespread phenomenon. To gain insight into the extent of this heterogeneity in the filamentous fungus Aspergillus niger (a workhorse of the fermentation industry), Charissa de Bekker and Han Wösten (Utrecht University) performed the first-ever single cell transcriptomics measurements on microbial cells. They isolated RNA from five individual hyphae (the 'arms' of the fungus) of an A. niger colony, but their standard analysis methods were insufficient to deal with these absolute tiny amounts of material. "We basically did not have enough material to ensure proper hybridisation. Furthermore, as we were interested in individual cells, we had five unique datasets to deal with, without the duplicates you normally include. Clearly, we needed help", Wösten explains.
Through the Kluyver Centre, he became aware of the support offered by NBIC and contacted the group of Timo Breit (University of Amsterdam). "They really helped us out by applying advanced statistics to our datasets, so that we could generate reliable results. Without their contribution, we would never have been able to analyse the data." This would have been a shame, as their findings pave the way to a whole new area to be explored. Wösten: "We have shown that the general assumption that cells in a microbe are simply copies of each other is not true. Each cell is unique."
Charissa de Bekker, Oskar Bruning, Martijs J Jonker, Timo M Breit and Han AB Wösten
Single cell transcriptomics of neighbouring hyphae of Aspergillus niger
Genome Biology 2011, 12:R71
Bioinformatics Research Support Team