Written by Lia Chappell
I’m studying for my PhD at the Sanger Institute and my interest is in understanding how the parasites responsible for malaria are able to adapt to live in both people and mosquitoes. I’m always looking for more effective, accurate and cost-efficient ways for us to see what is happening within the parasites’ cells.
Malaria parasites have a complicated life cycle, moving through different parts of the mosquito and human host, changing the shape of their single cell drastically in the process. This is impressive for an organism with about the same number of genes as a yeast cell that just floats around in it environment! To understand what happens when they change from one form to the next we can use a technology called RNA-seq. We can use RNA-seq to detect and count the RNA molecules present in a parasite (these are encoded in the genome, are made when genes are switched on, and control the amount of proteins being made). From this we can work out which genes and biological pathways are responsible for the parasite’s adaptability, which might help to identify targets for drug treatment.
RNA-seq is very useful at looking at the how much a gene is switched on in many types of living things, but the unusual nature the malaria parasite’s genome means it’s more challenging than most.
Recently I wrote a review called ‘Looking for a needle in a haystack’ in Nature Reviews Microbiology. The study I reviewed was particularly helpful because the researchers had taken the time to look carefully at a technological challenge. This problem can prevent many researchers from answering their questions or can make them blow their entire research budget on looking at molecules that aren’t of interest. They conducted a methodical comparison between technologies and manufacturers that many small laboratories would find too expensive to be able to carry out for themselves. I wanted to bring their work to the attention of a wider audience (who might not read as many method papers as me!) to highlight how important these details can be.
The authors of the study found that there are significant differences between processes and manufacturers in their ability to remove unwanted RNA molecules and increase the proportion of useful data produced. For example, one technology (Ribo-Zero) enriched RNA transcripts by up to 40-fold and increased useful data by as much as 98 per cent of the information sequenced. In addition, this particular technology also matched the relative abundances of molecules as those in the untreated controls. Others were less effective or, even worse, distorted the counts of different molecules (something you want to avoid when you are trying to compare the differing levels of gene expression).
I hope that my review encourages scientists to think carefully about the protocols that they use when using this technology to explore how genes work. It’s often hard to know which details you should focus on and spend your time and budget optimising, if the review helps my colleagues to spot potential biases and informs their choice of approach, then I will be very happy.