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| New Method Predicts How Cells Will Divide |
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| SciMed - Horizons | ||||||
| TS-Si News Service | ||||||
| Saturday, 13 March 2010 22:00 | ||||||
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Troy, NJ, USA. Researchers at Rensselaer Polytechnic Institute discovered a new method for predicting the fate of stem cells with up to 99 percent accuracy. Using advanced computer vision technology to detect subtle cell movements that are impossible to discern with the human eye, Professor Badri Roysam and his former student Andrew Cohen can successfully forecast how a stem cell will split and key characteristics the daughter cells. By allowing the isolation of cells with specific capabilities, this discovery could one day lead to effective methods for growing stem cells on a large scale for therapeutic use. The research appears in Nature Methods. "If you have many cells in a culture, they all look the same. But our new method senses all sorts of tiny differences in the shapes and movements of the cells, and uses these cues to predict what kind of cells it will divide into," said Roysam, professor of electrical, computer, and systems engineering at Rensselaer.
Chevy Chase, MD, USA. The Endocrine Society has released a new clinical practice guideline on the diagnosis and treatment of  congenital adrenal hyperplasia (CAH). The guideline features a series of evidence-based clinical rec...
In order to achieve successful stem cell-based therapies, researchers require access to large amounts of specific cells. This has proven difficult, as there are currently no methods for controlling or manipulating the division of bulk quantities of cells. When stem cells or progenitor cells divide via mitosis, the resulting daughter cells can be self-renewing or terminal. A self-renewing cell will go on to split into two daughter cells, while a terminally differentiated cell is fated to be a specific, specialized Roysam and Cohen tracked the development of rat retinal progenitor cells cultured in their collaborator's laboratory at McGill University. The computer system they developed took images of the cells every five minutes, and employed algorithmic information theoretic prediction (AITP) to observe the behavior of the cells, analyze the behavior, and discern whether each individual cell is fated to split into self-replicating or terminal daughter cells. This process occurs in real time, so researchers know the fate of cells before they actually divide. The researchers predicted with 99 percent accuracy if the rat retinal progenitor cells would split into self-renewing or specialized cells, and predicted with 87 percent accuracy certain characteristics of the specialized cells. "Our results suggest that stem cells display subtle dynamic patterns that can be sensed computationally to predict the outcome of their next division using AITP," Roysam said. "In Roysam said prototyping and development of the system leveraged the processing power of Rensselaer's supercomputer, the Computational Center for Nanotechnology Innovations (CCNI). FundingThis project was supported in part by the U.S. National Science Foundation Center for Subsurface Sensing and Imaging Systems, the Canadian Institutes of Health Research, and the Foundation Fighting Blindness-Canada.
ParticipantsCo-authors of the paper are Michel Cayouette and Francisco Gomes of the Cellular Neurobiology Research Unit at the Institut de Recherces Cliniques de Monteal; and Roysam's former student Cohen, now an assistant professor of electrical engineering and computer science at the University of Wisconsin, Milwaukee.
CitationComputational prediction of neural progenitor cell fates. Andrew R Cohen, Francisco L A F Gomes, Badrinath Roysam and Michel Cayouette. Nature Methods 2010; 7(3): 213-s18. doi:10.1038/nmeth.1424
Abstract Understanding how stem and progenitor cells choose between alternative cell fates is a major challenge in developmental biology. Efforts to tackle this problem have been hampered by the scarcity of markers that can be used to predict cell division outcomes. Here we present a computational method, based on algorithmic information theory, to analyze dynamic features of living cells over time. Using this method, we asked whether rat retinal progenitor cells (RPCs) display characteristic phenotypes before undergoing mitosis that could foretell their fate. We predicted whether RPCs will undergo a self-renewing or terminal division with 99% accuracy, or whether they will produce two photoreceptors or another combination of offspring with 87% accuracy. Our implementation can segment, track and generate predictions for 40 cells simultaneously on a standard computer at 5 min per frame. This method could be used to isolate cell populations with specific developmental potential, enabling previously impossible investigations.
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| Last Updated on Friday, 12 March 2010 22:04 |
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