%0 Journal Article %1 Cohen2010Computational %A Cohen, Andrew R. %A Gomes, Francisco L. A. F. %A Roysam, Badrinath %A Cayouette, Michel %D 2010 %I Nature Publishing Group %J Nature Methods %K cell-fate modelling %N 3 %P 213--218 %R 10.1038/nmeth.1424 %T Computational prediction of neural progenitor cell fates %U http://dx.doi.org/10.1038/nmeth.1424 %V 7 %X 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.

@article{Cohen2010Computational, 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.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Cohen, Andrew R. and Gomes, Francisco L. A. F. and Roysam, Badrinath and Cayouette, Michel}, biburl = {https://www.bibsonomy.org/bibtex/2080e91df42db15beed2cf6f8ec1881ea/karthikraman}, citeulike-article-id = {6743241}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nmeth.1424}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nmeth.1424}, day = 07, doi = {10.1038/nmeth.1424}, interhash = {a705206ea9f60b957fc89c91d898ea00}, intrahash = {080e91df42db15beed2cf6f8ec1881ea}, issn = {1548-7091}, journal = {Nature Methods}, keywords = {cell-fate modelling}, month = feb, number = 3, pages = {213--218}, posted-at = {2010-03-04 11:43:35}, priority = {2}, publisher = {Nature Publishing Group}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Computational prediction of neural progenitor cell fates}, url = {http://dx.doi.org/10.1038/nmeth.1424}, volume = 7, year = 2010 }