the following year, boosting sales to $23.6 million in 2009, according to the latest regulatory filing. Sales are still growing with $20.9 million recorded for the first nine months of year, although the red ink is still pretty substantial, with a $13.8 million loss reported in that same period. Still, when I visited his office in September, Worthington said Fluidigm is on track to cross the break-even point and turn profitable by mid-2011. The company has grown to about 200 employees at sites in South San Francisco and Singapore.
“Despite the fact there was an enormous amount of pricing pressure in a bad economic time, the time has been good for us,” Worthington says. “It’s not been easy by any stretch of the imagination, but the company has done well.”
Biologists already have plenty of tools that can do lots of whiz-bang things, like real-time PCR machines from Life Technologies and Roche that can analyze a lot of DNA in a sample. There are microarray machines from Santa Clara, CA-based Affymetrix (NASDAQ: [[ticker:AFFX]]) that can tell the extent to which genes are dialed up or downregulated in a sample. And San Diego-based Illumina (NASDAQ: [[ticker:ILMN]]) has built an empire around tools that do a combination of gene sequencing—genotyping that spots subtle variations in genetic code, and gene expression tests that looks at how much the genes are dialed up or down.
There are many other aspiring competitors that overlap with parts of what Fluidigm does, including Caliper Life Sciences, Sequenom, NanoString Technologies, and RainDance Technologies, to name a few.
Fluidigm is hoping to find its niche through the power of looking at the genome inside individual cells. This is different than conventional approaches, in which, say, a tumor sample gets taken, ground up, and fed into a machine that spits out results representing an amalgam of what’s going on in the genome of the tumor, Worthington says. The traditional analysis can be misleading, he says, because scientists know that tumors have heterogeneous genomes—that is, one part of the tumor has a different genetic profile than another. Errors get introduced by looking at the average of what’s happening.
Looking at single cells—and doing 100 different analyses simultaneously on those single cells in a high-bandwidth instrument—enables researchers to do a few things, Worthington says. One is that they can conserve samples, which are often precious
