Silicon Chip Technology Will Benefit Lupus Patients
Medical Researchers from Intel and Stanford University School of Medicine are working together to synthesize and study a grid-like array of small pieces of a disease-associated protein on the silicon chips which are commonly used in computer microprocessors. The chips, which are built utilizing a process used to create semiconductors, are being used to identify patients who have a severe form of lupus, a chronic autoimmune disease.
So far, this technology is being used in research applications, but it has the capability to vastly improve our ability to diagnose a wide number of diseases, and effectively determine which drugs work for certain patients all at a faster rate than previously possible. Some researchers believe this technology may also be able to speed up the drug development process by enabling them to better understand how the proteins interact within the body. This fares well for upcoming lupus clinical research trials.
Currently, doctors may be able to tell that a patient has arthritis when they come in to see them at the clinic. However, the doctor won't know which of the 20 or 30 types of arthritis they may have, and our existing methods may take several days or even weeks to get the answers to these types of questions. Utilizing this new technology, they can now measure thousands of protein interactions simultaneously, integrate the results in order to diagnose the disease, and even determine the severity of the patient's condition. With further application, the doctor may be able to do this all while the patient is sitting in their office.
Dr. Paul Utz was one of the senior authors for this new research, which was recently published online in Nature Medicine. The other senior authors include postdoctoral scholar Chih Long Liu, PhD, and Madoo Varma, PhD, who is the director and head of life science research operations and business strategy for Intel's Integrated Biosystems Laboratory. The first author for this research was graduate student Jordan Price. Intel Corporation has provided part of the funding for this project, and their scientists even built the silicon chips with the protein array that the Stanford researchers are examining.
Protein interactions are quite complex to say the least. If you were to relate it to interactions over a social media network, proteins will begin and subsequently exit out of physical relationships at a blaringly fast pace (this would put even your most avid Facebook user to shame). All of this craziness is what forms the framework that drives cell growth, causes immune reactions, and even creates disease. However, the truly difficult part has been figuring out why one particular protein is attracted to another.
In order to better understand these protein interactions, Intel researchers synthesized small segments of biological proteins, known as peptides, onto silicon wafers. To do this, they needed to use the same process which is used to build semiconductors. They employed a method known as photolithography (this makes use of sequential steps of light exposure and chemical reactions). The chip, termed an Intel array, has allowed Stanford researchers to analyze thousands of protein interactions simultaneously. With this technology, they can diagnose disease, assess various therapies, and even design better treatments and medicine at a faster pace than ever before.
Eventually, researchers want to embed an integrated semiconductor circuit within the silicon chip (they are microprocessor-ready). In short, this would produce a sort of minicomputer that would have the capability of taking much of the needed guesswork and decision-making out of the clinical process. Some theorize that it might even be able to spell out patient-specific diagnoses, or accurately identify which drug or therapy would be most effective.
This new technology is similar to that of DNA microarrays, which utilize a glass slide dotted with thousands of unique nucleotide sequences in a grid-like pattern in order to determine patterns of gene expression present in tissues and cells. Before working with Intel, Utz and his team had been using a similar technique with peptides. They would align them in defined patterns on glass or other substrates, and then they would wash them with solutions of blood-borne or cellular proteins. A fluorescent signal would indicate that a binding event had occurred between a protein in the solution and its slide-bound partner. These signals only appeared after researchers had conducted a series of meticulous and lengthy detection steps.
Utz and his team were then approached about four years ago by researchers from Intel. They had the idea of using the silicon as a microarray platform in order to synthesize the peptides directly on the chip, instead of separately producing the peptides and having to identify them on the array with a robot.
During this study, the researchers wanted to see whether the array had the ability to help categorize lupus patients. Lupus is a chronic autoimmune disease in which the patient's immune system has started making antibodies that seek out and attack a certain protein located in the cells called a histone (the antibodies will also attack other proteins as well).
Unfortunately, no two cases of lupus are ever a like, and some people can develop a very severe form of the disease. In fact, close to fifty percent of patients eventually require a more intensive form of therapy. Utz was interested in seeing if they could accurately identify these lupus patients using the arrays.
During the clinical study, his team was able to use the arrays to accurately determine which lupus patients had higher levels of antibodies for a particular histone referred to as 2B. Utz and his colleagues then determined that these were in fact the patients who were struggling with more severe lupus symptoms.
As medical researchers are able to better understand protein bindings at these levels of detail, it will allow them to work with drugs designed to enhance, disrupt, or mimic biological reactions within the cells in order to produce even better forms of therapy. This application should also help them better understand why some natural processes get disturbed.
Currently, Utz and his colleagues are examining new ways of using this technology to design influenza vaccines that will produce a stronger immune response. This new technology has already made a pretty big splash within the clinical research industry, and medical researchers are only looking to expand its possible range of application. With fascinating breakthroughs like this, the future certainly seems bright.
So far, this technology is being used in research applications, but it has the capability to vastly improve our ability to diagnose a wide number of diseases, and effectively determine which drugs work for certain patients all at a faster rate than previously possible. Some researchers believe this technology may also be able to speed up the drug development process by enabling them to better understand how the proteins interact within the body. This fares well for upcoming lupus clinical research trials.
Currently, doctors may be able to tell that a patient has arthritis when they come in to see them at the clinic. However, the doctor won't know which of the 20 or 30 types of arthritis they may have, and our existing methods may take several days or even weeks to get the answers to these types of questions. Utilizing this new technology, they can now measure thousands of protein interactions simultaneously, integrate the results in order to diagnose the disease, and even determine the severity of the patient's condition. With further application, the doctor may be able to do this all while the patient is sitting in their office.
Dr. Paul Utz was one of the senior authors for this new research, which was recently published online in Nature Medicine. The other senior authors include postdoctoral scholar Chih Long Liu, PhD, and Madoo Varma, PhD, who is the director and head of life science research operations and business strategy for Intel's Integrated Biosystems Laboratory. The first author for this research was graduate student Jordan Price. Intel Corporation has provided part of the funding for this project, and their scientists even built the silicon chips with the protein array that the Stanford researchers are examining.
Protein interactions are quite complex to say the least. If you were to relate it to interactions over a social media network, proteins will begin and subsequently exit out of physical relationships at a blaringly fast pace (this would put even your most avid Facebook user to shame). All of this craziness is what forms the framework that drives cell growth, causes immune reactions, and even creates disease. However, the truly difficult part has been figuring out why one particular protein is attracted to another.
In order to better understand these protein interactions, Intel researchers synthesized small segments of biological proteins, known as peptides, onto silicon wafers. To do this, they needed to use the same process which is used to build semiconductors. They employed a method known as photolithography (this makes use of sequential steps of light exposure and chemical reactions). The chip, termed an Intel array, has allowed Stanford researchers to analyze thousands of protein interactions simultaneously. With this technology, they can diagnose disease, assess various therapies, and even design better treatments and medicine at a faster pace than ever before.
Eventually, researchers want to embed an integrated semiconductor circuit within the silicon chip (they are microprocessor-ready). In short, this would produce a sort of minicomputer that would have the capability of taking much of the needed guesswork and decision-making out of the clinical process. Some theorize that it might even be able to spell out patient-specific diagnoses, or accurately identify which drug or therapy would be most effective.
This new technology is similar to that of DNA microarrays, which utilize a glass slide dotted with thousands of unique nucleotide sequences in a grid-like pattern in order to determine patterns of gene expression present in tissues and cells. Before working with Intel, Utz and his team had been using a similar technique with peptides. They would align them in defined patterns on glass or other substrates, and then they would wash them with solutions of blood-borne or cellular proteins. A fluorescent signal would indicate that a binding event had occurred between a protein in the solution and its slide-bound partner. These signals only appeared after researchers had conducted a series of meticulous and lengthy detection steps.
Utz and his team were then approached about four years ago by researchers from Intel. They had the idea of using the silicon as a microarray platform in order to synthesize the peptides directly on the chip, instead of separately producing the peptides and having to identify them on the array with a robot.
During this study, the researchers wanted to see whether the array had the ability to help categorize lupus patients. Lupus is a chronic autoimmune disease in which the patient's immune system has started making antibodies that seek out and attack a certain protein located in the cells called a histone (the antibodies will also attack other proteins as well).
Unfortunately, no two cases of lupus are ever a like, and some people can develop a very severe form of the disease. In fact, close to fifty percent of patients eventually require a more intensive form of therapy. Utz was interested in seeing if they could accurately identify these lupus patients using the arrays.
During the clinical study, his team was able to use the arrays to accurately determine which lupus patients had higher levels of antibodies for a particular histone referred to as 2B. Utz and his colleagues then determined that these were in fact the patients who were struggling with more severe lupus symptoms.
As medical researchers are able to better understand protein bindings at these levels of detail, it will allow them to work with drugs designed to enhance, disrupt, or mimic biological reactions within the cells in order to produce even better forms of therapy. This application should also help them better understand why some natural processes get disturbed.
Currently, Utz and his colleagues are examining new ways of using this technology to design influenza vaccines that will produce a stronger immune response. This new technology has already made a pretty big splash within the clinical research industry, and medical researchers are only looking to expand its possible range of application. With fascinating breakthroughs like this, the future certainly seems bright.
