Making measurements in single cells.

Cells, even genetically identical cells in an in vitro population, are not homogenous entities. There are by now several instan-ces in which studies of enzyme activity, gene expression or response to signaling, among other readouts, indicate that there is substantial variability from cell to cell. This may be particularly true for cell types such as stem or progenitor cells, for which changes in state are an inherent part of their biology, but it is also true for other mammalian primary cell types, for cell lines in culture and for prokaryotic cells and yeast as well.

In order to more finely dissect cellular biology, therefore, methods that can be used on single cells are needed. Just about any 'omic' technique, from genomic approaches such as RNA-Seq to proteomic or metabolomic profiling, is likely to be usefully applied to single cells, as it will give a large-scale picture of how cells differ from each other and how this may contribute to cellular function. But humbler approaches, too—RT-PCR, for instance, or amplification-based methods for protein detection—can give insight into cellular variation if applied at the single-cell level. Some of these technologies, such as single-cell RNA-Seq, have been shown to be possible, whereas others, such as single-cell proteomics, are still over a distant horizon. As these methods develop, an added perk is that they will be generally useful for analyses where the amounts of starting material are very limited.

It may be argued that imaging is the ultimate single-cell method, and indeed, in this case the challenge is principally in the development of image acquisition and analysis methods that allow the resulting high-content data to be collected at sufficiently large scale and to be properly interpreted so that meaningful conclusions can be drawn. As many existing methods converge onto the single cell both in vitro and in vivo, and as bioinformatic and modeling approaches are applied to understand how the measured heterogeneity contributes to cellular function, a more dynamic and nuanced picture of biology is likely to emerge.