Ultrasensitive RNA profiling: Counting single molecules on microarrays

The ability to analyze RNA expression of a whole genome in a single microarray experiment has had widespread impact on basic research as well as drug discovery and development (Marton et al. 1998; Brown and Botstein 1999; Bentwich et al. 2005). It also holds promise as a tool to guide treatment in the clinic (Golub et al. 1999; Perou et al. 1999; Alizadeh et al. 2000; Mattie et al. 2006; Yanaihara et al. 2006).

What else lies in the future for microarray technology? Until recently, researchers have rightly limited their horizons to what the technology can do rather than what it ought to do. However, there is agreement that it ought to be able to detect RNA from small amounts of sample material, even single cells, in a way that faithfully represents RNA abundances. In addition, there would be advantages to describing abundance levels in absolute terms— numbers or molar amounts—rather than relative values, so that comparisons between genes and across many experiments can be undertaken. Furthermore, the dynamic range of microarrays should match the range of expression levels found in cells (Holland 2002). Indeed, if the sensitivity, dynamic range, and quantitative nature of measurements could be improved, the current need for cross-validation with real-time PCR would become redundant.

In order to address these issues, a change in the way we look at molecules on a microarray is needed. At present, an ensemble signal is acquired from the plurality of labeled molecules that interact with probes in a microarray spot. However, if this signal were to be resolved into its constituent parts, the individual molecules, the output would be more easily quantitated because it would be digital: An individual molecule (one bit of information) can be either present or absent, the binary 1, …