Using artificial intelligence to identify spatio-temporal mechanisms of cell competition

胚胎干细胞（Embryonic stem cells，ESCs）是一种存在于哺乳动物早期胚胎中的细胞类型，具有产生形成胎儿的所有细胞类型和组织的潜力。这些细胞开始形成更特化细胞的过程称为分化，并且非常容易出错。因此，有严格的质量控制措施，以防止异常胚胎干细胞的出现。其中一种控制是细胞竞争，它通过比较干细胞群体中细胞的适应性并消除那些比它们的邻居适应性差的细胞来起作用。通过细胞竞争消除异常细胞，确保只有最适合的细胞继续形成后期胚胎。重要的是，细胞竞争也被证明可以调节从癌症到衰老等各种其他情况下的细胞适应性。然而，尽管细胞竞争的重要性，我们仍然不了解决定细胞适应性的参数，因此确定哪些细胞是这种适应性竞争的赢家，哪些是输家。主要困难在于组织中的细胞重排和细胞竞争消除之间的反馈;需要确定细胞增殖、细胞运动、细胞凋亡和细胞信号传导在竞争过程中的作用。为了解决这个问题，我们将开发一个细胞竞争的计算模型，该模型将利用人工智能的力量，以无偏见的方式从活细胞成像中识别不同竞争机制的贡献。人工智能推理将确定参数，然后我们将通过实验验证这些参数，以确定决定细胞竞争结果的最重要因素。这些因素的识别将首次深入了解多能干细胞竞争行为的规则，未来的工作将旨在研究哪些其他细胞类型遵守这些规则，以及这些规则的普遍性。

Embryonic stem cells, also called pluripotent stem cells, are a cell type that exists in the early mammalian embryo and that have the potential to give rise to all the cell types and tissues that will form the foetus. The process by which these cells start to form more specialised cells is called differentiation and is very error prone. For this reason there are strict quality controls in place that prevent the emergence of abnormal embryonic stem cells. One such control is cells competition, that acts by comparing the fitness of cells within the stem cell population and eliminating those cells that are less fit than their neighbours. The elimination of abnormal cells by cell competition ensures that only the fittest cells go on to form the later embryo. Importantly, cell competition has also been shown to regulate cell fitness a variety of other contexts, from cancer to ageing. However, despite the importance of cell competition, we still do not understand the parameters that determine cell fitness, and therefore that establish which cells are the winners and which are losers of this fitness competition. The main difficulty lies in the feedback between cell rearrangements in the tissue and cell competition elimination; requiring to identify the role that cell proliferation, cell motility, apoptosis and cell signalling have during the competition process. To tackle this problem, we will develop a computational model of cell competition that will use the power of artificial intelligence to identify the contribution of different mechanisms of competition from live cell imaging in an unbiassed way. The artificial intelligence inference will identify the parameters that then we will verify experimentally to establish the most important factors that determine the outcome of cell competition. The identification of these factors will provide for the first-time insight into the rules that govern the competitive behaviour of pluripotent stem cells, and future work will be aimed at studying what other cell types abide by these rules, and therefore how universal these rules are.