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Intel inside Ivy
STANFORD, Calif.—"An app on a smart phone for allergy testing is realistic," says Stanford's Dr. Paul ("P.J.") Utz, as the eventual result of the collaboration between researchers at the Stanford University School of Medicine and Intel Corp.
To date, the team has synthesized and studied a grid-like array of short pieces of a disease-associated protein on silicon chips normally used in computer microprocessors. They used this chip, which was created through a process used to make semiconductors, to identify patients with a particularly severe form of the autoimmune disease lupus linked to individual peptide features.
"When I see patients in the clinic right now, I may know they have arthritis, but I don't know which of the 20 or 30 types of the disease they have," notes Utz, an associate professor of medicine in Stanford's division of immunology and rheumatology. He adds that existing methods can take days or even weeks to answer such questions. "Now we can measure thousands of protein interactions at a time, integrate this information to diagnose the disease and even determine how severe it may be. We may soon be able to do this routinely while the patient is still in the physician's office."
Using the new silicon chips, the Stanford team was able to identify patients with lupus who expressed high levels of antibodies against a particular histone called 2B. They then confirmed that these patients were precisely the ones struggling with a more severe form of the disease.
"Lupus is highly variable, and in some cases is quite severe," says Utz. "Companies developing therapies are now accepting patients with lupus for clinical trials without knowing which subset of disease they are in. This method could potentially be used to identify only those patients likely to benefit, and aid in the identification of effective drugs."
To better understand these interactions, researchers at Intel synthesized short segments of peptides on silicon wafers using photolithography, the same process used to make semiconductors.
"With the Intel chip, the number of dots could be up to a million," Utz says, "but right now, 50,000 peptides is our next goal. Then we need to build a circuit that can integrate the array."
The researchers hope to eventually embed an integrated semiconductor circuit within the microprocessor- ready silicon chip to create a sort of minicomputer that could take the guesswork and decision-making out of many clinical processes (and perhaps lead to that smartphone app for allergy testing). It might also spell out patient-specific diagnoses or identify which potential treatments are most likely to be effective.
Initially, the Stanford/Intel team built a microarray using the last 21 amino acids of histone 2B. In making the array, they synthesized every possible overlapping sequence of every length from the short string of amino acids: 1-21 (the full-length sequence) to 17-20 (four amino acids) to 2-20 (19 amino acids) and all other possible variations creating 9,000 unique peptide dots on the array. They then washed the chip with solutions of antibodies known to bind the sequence.
The pattern of binding showed that one antibody could recognize and bind to a sequence composed of only two amino acids of the original 21. Another required at least four amino acids, one of them modified, for binding. Analyzing the binding of solutions of other antibodies in each case delineated specific binding regions, or epitopes, within the original short sequence.
When Intel approached Utz and his colleagues with the idea of using silicon as a microarray platform to synthesize the peptides directly on the chip the Stanford group, he was skeptical.
"Intel didn't know anything about biology and, honestly, we thought it wouldn't work," says Utz.
But it did. Silicon also allows the researchers to arrange the individual peptides more closely together, using the space much more efficiently. Finally, unlike glass, silicon alone does not fluoresce, making signal detection easier.
"If we couple these Intel arrays with an electronic detection method, we could have real-time sensing over a period of minutes," says Utz.
Utz notes that the four-year effort has cost "about $500,000." Silicon wound up being "one of the best surfaces we've ever worked with," he states.
Although the new technology is focused on research applications, it has the potential to eventually improve diagnoses of a multitude of diseases, as well as to determine more quickly what drugs may be most effective for a particular patient. It may also speed drug development by enabling researchers to better understand how proteins interact in the body.
The researchers are now exploring the use of the technique to help design influenza vaccines that elicit a strong immune response, as well as ways to incorporate the three-dimensional folding involved in most protein interactions.