Microarray Technology

The increasing use of gene expression microarrays, and depositing of the resulting data into public repositories, means that more investigators are interested in using the technology either directly or through meta analysis of the publicly available data. The tools available for data analysis have generally been developed for use by experts in the field, making them difficult to use by the general research community. For those interested in entering the field, especially those without a background in statistics, it is difficult to understand why experimental results can be so variable. The purpose of this review is to go through the workflow of a typical microarray experiment, to show that decisions made at each step, from choice of platform through statistical analysis methods to biological interpretation, are all sources of this variability.

Mesoporous aerogel was produced under regular atmospheric conditions using the sol-gel polymerization of tetraethyl orthosilicate with an ionic liquid as both solvent and active agent. This was then used to build a three-dimensional structure to recognize nucleotide acids. Fourier transformation infrared spectroscopy, scanning electron microscopy, (29)Si solid-state nuclear magnetic resonance, and Brunauer-Emmett-Teller instruments were used to characterize this 3D aerogel, demonstrating that it had high porosity and large internal networking surface area that could capture nucleotide acids. The functionality of molecular recognition on nucleotide acids was demonstrated by immobilizing an oligonucleotide to probe its DNA target and confirming the tagged fluorescent signals by confocal laser scanning microscopy. The results indicated that the as-prepared 3D bioaerogel was capable of providing a very large surface area to capture and recognize human gene ATP5O.

MicroRNAs may be small, but these noncoding RNAs that regulate gene expression are creating a big stir. Finding differences in the expression of microRNAs between, say, healthy and diseased cells could potentially be used to diagnose diseases or to assess treatment effects. If researchers can understand how they work, microRNAs could provide tools for manipulating genes, not to mention help to untangle how genes are regulated.

At first glance, studying microRNAs seems more manageable than studying the menagerie of other types of RNA. Typical expression profiling experiments for protein-coding genes examine thousands of molecules; those for microRNAs examine hundreds. But researchers are still figuring out the most reliable ways to measure these important molecules.

The most common techniques for profiling microRNAs are deep sequencing, microarrays and quantitative real-time PCR (qPCR). All are supported by several commercial offerings (see Boxes 1, 2, 3). Though specific products and techniques vary, researchers generally agree on the relative strengths and weaknesses of the platforms. The best choice depends on the application, says Muneesh Tewari, who studies microRNAs at the Fred Hutchinson Cancer Research Center. “It’s a balance of cost, precision, accuracy and sample quantity,” he says. “If the purpose is to screen a bunch of samples to find a few microRNAs that change and you can tolerate a false negative, then the microarray may be the best platform. If the purpose is to detect microRNAs where the sample amount is limiting, then qPCR has better sensitivity, and if you are trying to see different isoforms or very similar microRNAs, then sequencing is going to be the best approach.”