While the dementia and agitation of Alzheimer's disease are painfully obvious to care-givers, the roots of the disease lie hidden and unknown deep in the brains of patients. Now a University of Rochester team has developed a technology that sheds light on the disease at its origins, in the nerve cells throughout the brain that sicken and die.
The team has taken the sharpest molecular snapshots yet of cells affected by the disease, simultaneously measuring the activity of 20 genes within those cells. Scientists believe these profiles of individual cells provide the most thorough information yet on cells from the brains of deceased Alzheimer's patients and provide a way to compare healthy and sick cells in unprecedented detail. The work is reported in the August 4 issue of the Proceedings of the National Academy of Sciences.
"Many Alzheimer's researchers are looking for a molecular change in the blood, or in the spinal fluid, but that's like looking for a needle in a haystack," says Zaven Khachaturian, director of the Alzheimer Association's Ronald and Nancy Reagan Institute and former director of Alzheimer's disease research at the National Institutes of Health. "The Rochester team is able to focus in on cells that actually show pathology from the disease and can compare those to cells that show no sign of disease. That is a significant starting point toward understanding what is happening much, much earlier in the brains of people who have this disease. It's no longer a hit-and-miss approach.
"There is good reason to be excited about this technology."
In Alzheimer's disease, nerve cells throughout the brain die -- but not all cells, not even all the cells in any given neighborhood. Healthy and sick cells are interspersed in the brains of the 4 million people in the United States who have the disease, the leading cause of dementia in the elderly.
"Most researchers take a piece of tissue -- which includes sick neurons, healthy neurons, glial cells, and all kinds of other things -- grind it up and perform their tests," says Paul Coleman, principal investigator and one of 12 researchers nationwide to have received the NIH Leadership and Excellence in Alzheimer's Disease (LEAD) award. "That seems a bit unproductive, especially since there are both healthy and sick cells. It's hard to tell the differences between cells if you analyze them together."
So Coleman set out to develop a new technique to examine brain cells individually. Through work at Cold Spring Harbor Laboratory, he began collaborating with James Eberwine, a molecular biologist at the University of Pennsylvania, and together they developed a technique to analyze several genes simultaneously.
Now Coleman's team has extended the technology to study individual human brain cells in more detail than ever before. Working with Coleman on the project were Nienwen Chow, first author of the PNAS paper and a research assistant professor; Christopher Cox, associate professor of biostatistics; Linda Callahan, instructor; technician Jill Weimer; and research fellow LiRong Guo. The work was funded by National Institutes of Health, the American Health Assistance Foundation, and the Markey Fund.
While many researchers have looked at one or two genes in Alzheimer's disease, Coleman's team studied the activity of 20 genes simultaneously in a total of 35 cells from three brains affected by Alzheimer's disease and from two brains not affected by the disease. Chow checked each cell's messenger RNA, which is present in a cell only for genes that are turned on.
Of the 20 genes tested, the expression of five genes -- cyclin D1, HSP27, GAD, alpha 1-ACT and wee1 -- differed significantly between the healthy brains and the Alzheimer's brains. Some of these are cell-cycle genes, regulating cell division and telling a cell when to die. Many are the same cell- cycle genes involved in cancer, raising the question of whether Alzheimer's disease represents a special, unique form of cancer, Coleman says.
"This is a class of genes that not a lot of people have paid attention to when it comes to Alzheimer's. A few people have looked at some of these genes individually, but no one until now has been able to look at a more complete picture that establishes that cell-cycle genes may be playing a significant role in the disease," says Coleman, a professor of neurobiology and anatomy who heads the University's Alzheimer's Disease Center, one of 27 nationally recognized Alzheimer's centers funded by NIH.
Through statistical analysis, the team was able to neatly divide up the cells into two camps: those that came from patients with the disease and those that did not. "Different cells need a different repertoire of molecules to function," says Chow. "We found a global difference in this repertoire between cells from healthy brains and cells from brains affected by Alzheimer's disease." She emphasizes, though, that these are early results from just a few dozen cells from a few brains.
Already the team has extended the technique, studying nearly 100 genes simultaneously, a project moving more quickly thanks in part to a new laser-capture micro-dissection unit purchased recently with several other research groups and with funds from the University Medical Center. Scientists hope that by studying thousands of genes, they might develop ways to distinguish between healthy and sick cells or to track the disease as it progresses. Knowing which genes are turned on in Alzheimer's disease should also provide important clues to more effective treatment.
"By the time somebody comes into the doctor's office and is diagnosed with the disease, the withering of the brain has gone on for decades," says Coleman. "We want to be able to detect the disease before there are symptoms and halt the progression. That's the ultimate dream." He believes the technology can also be used to study molecular differences between healthy and sick cells in many other diseases, including heart disease, muscle disorders, and strokes.
The above story is based on materials provided by University Of Rochester. Note: Materials may be edited for content and length.
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