RNA has always presented challenges to molecular biologists, and it is clear that large-scale projects involving isolation and characterization of thousands of gene products will require drastic improvements in RNA-handling technology. The search for drug targets has adopted microarray technology based on gene expression. However the history of the last decade of genomics and proteomics analysis is not encouraging for the data-without-a-model concept of doing science.
For years, pharmas and biotechnology companies and research labs cranked out results from genomic and proteomic screening, in which they searched for viable targets for new therapies. After spending millions of dollars and countless years of effort they came up with essentially nothing. The work was not well controlled, it was not thoroughly researched, the experimental designs were poor, and there was no hypothesis or model, just an endless search for targets.
There were many papers in the peer-reviewed literature as well as countless reports at meetings in which hundreds or thousands of new potential targets were described. But these all vanished when they could not be confirmed or when drugs that attacked these targets were found to have serious side effects.
The new technologies now available provide a much-needed solution to the problem. They are simple, robust, and repeatable, and the validation process removes unnecessary steps.
Until recently, lab workers would question the cost of a kit since they had all those reagents on the shelf. Yet the consistency, simplicity of use, and lack of error in their assembly makes them well worth the additional cost.
These products will go a long way toward bringing order and repeatability into the field of gene expression, and should speed the process of translational medicine through good, reliable data.