Developmental and Stem Cell Biology PhD Program, University of California, San Francisco, San Francisco, CA, USA; Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA, USA.
Boston University Bioinformatics Program, Boston, MA, USA.
Cell Syst. 2019 Nov 27;9(5):483-495.e10. doi: 10.1016/j.cels.2019.10.008. Epub 2019 Nov 20.
Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.
人类多能干细胞（hPSCs）具有内在的自我组织能力，能够形成复杂的多细胞类器官，再现组织发育的许多方面。然而，要稳健地指导 hPSC 衍生类器官的形态发生，需要新的方法来准确控制自我导向的模式形成。在这里，我们将遗传工程与计算建模、机器学习和数学模式优化相结合，创建了一种数据驱动的方法，通过敲低先前显示影响干细胞集落组织的基因 CDH1 和 ROCK1 来控制 hPSC 的自我组织。使用扩展的细胞 Potts 模型对体外系统进行计算复制，使机器学习能够驱动参数优化，从而产生所需模式的出现。此外，在体外，预测的实验参数定量再现了计算机模拟的模式。这些结果表明，通过机器学习对 hPSC 行为进行模型驱动探索，可以准确预测形态发生动力学，从而实现对多细胞模式的空间控制，以工程化人类类器官和组织。本文的透明同行评审过程记录包含在补充信息中。
