"You have no idea how many single molecules are distributed within that blurry spot, so essential features and ideas remain obscure to you," says Jennifer Lippincott-Schwartz, a Salk non-resident fellow and coauthor on the paper.
In the early 2000s, several techniques were developed to break through the Abbe Limit, launching the new field of super-resolution microscopy. Among them was a method developed by Lippincott-Schwartz and her colleagues called Photoactivated Localization Microscopy, or PALM.
PALM, and its sister techniques, work because mathematics can see what the eye cannot: within the blurry spot, there are concentrations of photons that form bright peaks, which represent single molecules. The downside to these approaches is that it can take several hours to several days to crunch all the numbers required just to produce one usable image.
"It's like taking a movie, then you go through some very complex math, so what you see is the end result of processing, which is extremely slow because there's so many parameters," Cang says. "When I first saw PALM, I was shocked by how good it was. I wanted to use it right away, but when I actually tried to use it, I found its usefulness was limited by computing speed."
Even using statistical shortcuts, processing these images was still so intense that a supercomputer was required to reduce the time to a practical level. "Calculating an area of 50 pixels can take nearly a full day on a state-of-the-art desktop computer," says Lippincott-Schwartz. "But what you'll have achieved is the difference between a guess and a definitive answer."
In their Nature Methods paper, the researchers offer other scientists the tools they need to use an easier alternative-the Amazon Elastic Compute Cloud (Amazon Elastic EC2), a service
|Contact: andy Hoang|