Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, Wisconsin, USA.
Nat Methods. 2010 Mar;7(3):213-8. doi: 10.1038/nmeth.1424. Epub 2010 Feb 7.
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.
了解干细胞和祖细胞如何在不同的细胞命运之间做出选择，是发育生物学中的一个主要挑战。解决这个问题的努力受到可用作预测细胞分裂结果的标记物稀缺的阻碍。在这里，我们提出了一种基于算法信息论的计算方法，用于随时间分析活细胞的动态特征。使用这种方法，我们询问大鼠视网膜祖细胞 (RPC) 在经历有丝分裂之前是否表现出可以预测其命运的特征表型。我们以 99%的准确率预测 RPC 是进行自我更新还是终端分裂，或者以 87%的准确率预测它们将产生两个光感受器还是另一种后代组合。我们的实现可以在标准计算机上以 5 分钟/帧的速度同时对 40 个细胞进行分割、跟踪和生成预测。该方法可用于分离具有特定发育潜力的细胞群体，从而实现以前不可能进行的研究。
