Investigators have developed a new mathematical approach to analyze molecular data derived from complex mixtures of immune cells. This approach, when combined with well-established techniques, readily identifies changes in small samples of human whole blood, and has the potential to distinguish between health and disease states.
Led by Mark Davis, Ph.D., and Atul Butte, M.D., Ph.D., of Stanford University, Calif., the team of investigators received support from the National Institute of Allergy and Infectious Diseases (NIAID), as well as the National Heart, Lung, and Blood Institute and the National Cancer Institute, all part of the National Institutes of Health. Details about their work appear online at Nature Methods.
"Defining the status of the human immune system in health and disease is a major goal of human immunology research," says NIAID Director Anthony S. Fauci, M.D. "A method allowing clinicians to accurately and quickly characterize the many different immune cells in human blood would be a valuable research and diagnostic tool."
Over the past 15 years, the technology for gene expression microarrays, which allow investigators to identify and measure relative amounts of many different genes in parallel, has advanced tremendously. Today researchers can measure nearly every gene in the human genome using very small amounts of blood. However, blood contains numerous types of immune cells, such as lymphocytes, basophils and monocytes, and when microarray analysis is performed on this mixture, the interpretation of the results becomes problematic.
"Current methods that examine gene expression differences in mixtures of immune cells in blood do not take into account that, even among healthy individuals, there is a wide range of variation in the proportion of each cell type," says Dr. Davis. "This creates so-called noise that masks many differences in gene expression. Even when you do observe a difference,
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NIH/National Institute of Allergy and Infectious Diseases