If you want to measure many mRNAs in a cell, microarray technologies are by far the winner. But for more careful scrutiny of the expression of a small number of genes, quantitative RT-PCR is the way to go. qRT-PCR is viewed as more consistent & has higher throughput (for lower cost) when looking at the number of samples which can be surveyed. It doesn't hurt that one specific qRT-PCR technology was branded TaqMan, which plays on both the source of the key PCR enzyme (Thermus aquaticus aka Taq) and the key role of Taq polymerase's exonuclease activity, which munches nucleotides in a manner reminiscent of a certain video game character (though I've never heard of any reagent kits being branded 'Power Pills'!).
RT-PCR quantitation relies on watching the time course of amplification. Many variables can play with amplification efficiencies, including buffer composition, primer sequence, and temperature variations. As a result, noise is introduced and results between assays are not easily comparable.
The PNAS site has an interesting paper which uses a different paradigm for RT-PCR quantitation. Instead of trying to monitor amplification dynamics, it relies on a digital assay. The sample is diluted and then aliquoted into many amplificaiton chambers. At the dilutions used, only a fraction of the aliquots will contain a single template molecule. By counting the number of chambers positive for amplification & working back from the dilution, the number of template molecules in the original sample can be estimated.
Such digital PCR is very hot right now and lies at the heart of many next generation DNA sequencing instruments. What makes this paper particularly interesting is that the assay has been reduced to microfluidic chip format. A dozen diluted samples are loaded on the chip, which then aliquots each sample into 1200 individual chambers. Thermocycling the entire chip drives the PCR, and the number of positive wells are counted. While the estimate is best if most chambers are empty of template (because then very few started with multiple templates), the authors show good measurement at higher (but non-saturating) template concentrations.
An additional layer of neato is here as well -- each sample is derived from a single cell, separated from its mates by flow sorting. While single cell sensitivity has been achieved previously, the new paper claims greater measurement consistency. By viewing individual cells, misunderstandings created by looking at populations are avoided. For example, suppose genes A and B were mutually exclusive in their expression -- but a population contained equal quantities of A-expressors and B-expressors. For a conventional expression analysis, one would just see equal amounts of A and B. By looking at single cells, the exclusive relationship would become apparent. The data in this paper show examples of wide mRNA expression ranges for the same gene in the 'same' type of cells; a typical profile of the cell population would see only the weighted mean value.
The digital approach is very attractive since it is counting molecules. Hence, elaborate normalization schemes are largely unnecessary (though the Reverse Transcriptase step may introduce noise). Furthermore, from a modeler's perspective actual counts are gold. Rather than having fold-change information with fuzzy estimates of baseline values, this assay is actually enumerating mRNAs. Comparing the expression of two genes becomes transparent and straightforward. Ultimately, such measurements can become fodder for modeling other processes, such as estimating protein molecule per cell counts.
Cell sorters can also be built on chips (this is just one architecture; many others can be found in the Related Articles for that reference). It doesn't take much to imagine marrying the two technologies to build a compact instrument capable of going from messy clinical samples to qRT-PCR results. Such a marriage might one day put single cell qRT-PCR clinical tests into a doctor's office near you.