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Journal article
Computational prediction of neural progenitor cell fates
Nature methods, v 7(3), U75
01 Mar 2010
PMID: 20139969
Featured in Collection : UN Sustainable Development Goals @ Drexel
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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Details
- Title
- Computational prediction of neural progenitor cell fates
- Creators
- Andrew R. Cohen - Rensselaer Polytechnic InstituteFrancisco L. A. F. Gomes - State University of CearáBadrinath Roysam - Rensselaer Polytechnic InstituteMichel Cayouette - University of Montreal
- Publication Details
- Nature methods, v 7(3), U75
- Publisher
- Springer Nature
- Number of pages
- 9
- Grant note
- Foundation Fighting Blindness, Canada Canadian Institutes of Health Research New Investigator program; Canadian Institutes of Health Research (CIHR) Rensselaer Polytechnic Institute EEC-9986821 / Center for Subsurface Sensing and Imaging Systems Foundation Fighting Blindness-Canada University of Wisconsin-Milwaukee Canadian Institutes of Health Research; Canadian Institutes of Health Research (CIHR)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000275058200023
- Scopus ID
- 2-s2.0-77649263847
- Other Identifier
- 991019296801004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods