The 12 prototypes look like prefabricated children’s forts–boxes the size of freezers, faced with bright red plastic and grouped in twos and threes on a concrete floor at Pacific Biosciences, a startup in Menlo Park, CA. But the simple exterior of the machines belies the complexity within. Each box houses a small chip packed with thousands of strands of DNA from bacteria or viruses, each strand in a nano-sized well. An enzyme stuck to the bottom of each well speedily builds a corresponding strand, stringing together the bases, or chemical subunits of DNA, that pair properly with those of the original. Each of the four types of bases, represented by the letters A, T, C, and G, is labeled with a different fluorescent marker, which is activated by the reaction that attaches a new base to the strand. Because the machine tracks the reactions as they happen, it can churn out reams of raw data on the sequences of the DNA samples as fast as a built-in camera can record them.
A computer monitor installed next to each machine displays a snapshot of the action taking place. A series of lights scatter across the screen, bursting and fading in quick succession. Each flash lasts just tens of milliseconds, but its color indicates which of the four bases has just been added to a strand of DNA, and its position indicates where. The video must be slowed for viewing: the flashes come too fast for the human eye to process. Computer algorithms convert the pattern of flashes into DNA sequences hundreds to thousands of bases long. Additional algorithms then compare millions of these stretches of DNA, identify sequences that overlap at their ends, and fit the pieces together to capture a complete genome.
When it comes to sequencing DNA, time is money, and Pacific Biosciences’ commercial machines, due out in 2010, could prove to be the fastest ever made. It took the Human Genome Project roughly $300 million and 13 years to work out the sequence of the three billion DNA base pairs in a composite human genome, a task completed in 2003. By October 2008, researchers using a variety of new types of machines were saying that they could sequence an individual genome for less than $100,000; one company promises a $5,000 genome by next spring. And Pacific Biosciences predicts that by 2013, its machines will be able to sequence a person’s genome in 15 minutes, for less than $1,000.
Up to now, scientists have sequenced the genomes of a handful of people, and that’s given them a general sense of human variability. But fast, cheap sequencing technology could make it practical to read the genomes of thousands, perhaps millions, of people. By combing through those myriad genomes and linking specific DNA sequences to different characteristics–handedness, height, blood pressure, and susceptibility to anxiety, to name a few–scientists should be able to unravel the complex interplay of genetic variants that makes each individual unique. Most important, that kind of sequencing capacity might finally reveal the inherited basis of common diseases–a riddle that has been taunting geneticists for decades.
The actual impact on medicine, however, is far less certain and may be much less positive. For almost two decades, researchers have promised that advances in sequencing technology will enable doctors to practice personalized medicine, targeting treatments to patients on the basis of their genetic profiles. The assumption was that a limited number of common genetic variants would turn out to underlie a particular disease, and physicians would be able to prescribe drugs according to which variants their patients carried. But the latest data suggest that even the most common heritable illnesses, such as diabetes and heart disease, are linked to many different variants, each of them relatively rare. If that’s true, then practicing personalized medicine could become very complicated–and very expensive. “It would not be good to have a $5,000 genome and a $500,000 analysis,” says Francis Collins, the former director of the National Human Genome Research Institute and a leader of the Human Genome Project.