Every now and then, there's a bit of science that's a combination of brute force and tour de force. Examples that spring to mind mostly come from the world of small, manageable experimental animals, like the mapping of every single cell division of the worm C. elegans, a feat that won John Sulston a Nobel Prize. A paper published in this week's Nature takes a method pioneered with C. elegans and extends it to the human genome: researchers have knocked down every single identified human gene, and used an automated imaging system to examine the impact on cell division. All of the 190,000 movies that resulted have been made publicly accessible.

The key to this work was the automation. Researchers have been developing some sophisticated control software that can take time-lapse images using an automated microscope that doubles as a temperature-controlled incubation chamber. The software can move the sample containers in order to image individual portions of them, allowing a plate containing dozens of samples to be imaged in a single experiment. It can also autofocus and track individual cells as they move so that it can adjust the imaging frame accordingly. These techniques can be combined with multiphoton microscopy, which allows fluorescent images to be obtained without damaging the cells via UV radiation.

The end result is a time-lapse movie that can span several days, with resolution that can range from single cells to entire embryos.

The new work combined some of this hardware with another automated system that "prints" culture plates with short interfering RNAs onto culture plates. Once cells are added, the siRNAs enter cells, where they trigger a cellular system that reduces or eliminates the production of the protein encoded by the corresponding gene. Other, similar systems have been described, so again, this is nothing especially novel.

To an extent, the novelty comes from the brute-force aspect of the work: the group prepared siRNAs for every single one of the roughly 21,000 genes we're aware of in the human genome. In fact, since the efficacy of siRNA is pretty variable, every single one of those genes was targeted at least twice.

The cells themselves were standard HeLa cells, a cancer cell line that divides rapidly in culture. The cells carry a histone protein (which normally coats its chromosomes) fused with the green fluorescent protein, to enable the chromosomes to be imaged using the automated microscope. They then trained a machine learning algorithm (support vector machine, for the curious) to identify situations where cell division has gone wrong. These include situations like a failure to separate chromosomes, a cell ending up with more than one nucleus, dying cells, etc.

There's lots of experimental noise in the process. For example, the authors validated the effectiveness of the siRNA using 1,000 genes. The average knockdown was 13 percent, but at least 3 percent of the genes that retained more than 30 percent of their normal expression. The identification algorithm was accurate in 87 percent of the cases where human categorization of the movies was available, as well. Still, over 1,000 genes came through the process, and about half of these passed some very stringent validation tests (additional siRNAs and complementation by the mouse version of the gene).

The surprise was that less than half of these genes had previously been implicated in cell division, so the work seems to be generating some significant new information. The authors were able to cluster the impacts of the siRNAs into a series of categories based on their timing and the process that was effected, such as chromosome separation or the physical process of creating two cells. This is more important than it sounds, since the ability to group genes as being involved in a single process can help researchers identify which ones might be interacting with each other or part of a single complex.

Aside from the experimental noise, there seem to be two significant limitations to the current study. For one, our decisions on what constitutes a "gene" are still in flux, given things like alternate start sites, differential splicing of transcripts, and so on. In addition, carrying the study out in an extremely aggressive cancer cell will necessarily produce an odd picture of what's involved in a normal cell division.

Still, because of the power of the technique, these issues are specific to this study, and can easily be overcome. So, for example, because of the high-throughput automation, the researchers could switch to another cell line tomorrow and have results in reasonably short order—in fact, given the pace of putting a publication together and getting it through peer review, chances are that additional studies are already complete.

But that's only a small taste of the potential for the approach. For example, it would be easy to replace the siRNA with potential drugs, and perform high-throughput screening for potential chemotherapy agents. Or label something else, and perform a similar screen looking for all the genes involved in a completely different cellular process, like protein export.

For those interested in following up on the work, the European Molecular Biology Laboratory is hosting the database of results, including all 190,000 movies, at the Mitocheck website.

Nature, 2010. DOI: 10.1038/nature08869 (About DOIs).