Reverse engineering cell competition from a single-cell perspective using time-lapse microscopy and deep learning image analyses

使用延时显微镜和深度学习图像分析从单细胞角度逆向工程细胞竞争

• 批准号: 1918433
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1918433
• 关键词: Reverse; engineering; cell; competition; single

Cell competition is a widely conserved, fundamental tissue quality control mechanism. The cell competition assay of MDCK wild-type vs. MDCK mutants (Scribble-knockdown) relies on a mostly mechanical mechanism of competition [Norman, 2012] which posits that the compressive stresses of the tissue drive the outcome of the competition. According to this mechanism, proliferative wild-type cells outcompete mutant Scribble cells resulting in these apoptotic 'loser' cells being apically extruded from the epithelia. Previous studies show that there is an increased division rate of wild-type cells [Bove 2017], but what still remains a mystery is whether this is a cause or consequence of increased apoptosis in the 'loser' cell population. In my research, I aim to answer this question by using multi-modal, time-lapse microscopy to image competition assays continuously for several days. The images are then segmented into wild-type or mutant objects using a convolutional neural network (CNN) that can differentiate between the cell types, after which they are tracked throughout the time-lapse using a Bayesian multi-object tracker. Utilising a conjugate analysis of fluorescent cell-cycle indicator probes, key timepoints of cellular fate commitment can be identified that highlight which cells are driving the competition and what local conditions those cells are responding to.

细胞竞争是一种广泛保守的基本组织质量控制机制。MDCK野生型与MDCK突变体（Scribble敲除）的细胞竞争试验主要依赖于竞争的机械机制[Norman，2012]，该机制假定组织的压缩应力驱动竞争的结果。根据这种机制，增殖的野生型细胞胜过突变的Scribble细胞，导致这些凋亡的“失败者”细胞从上皮细胞顶部挤出。先前的研究表明，野生型细胞的分裂率增加[Bove 2017]，但仍然是一个谜，这是否是“失败者”细胞群中细胞凋亡增加的原因或结果。在我的研究中，我的目标是通过使用多模式，延时显微镜连续几天对竞争测定进行成像来回答这个问题。然后使用卷积神经网络（CNN）将图像分割成野生型或突变体对象，该卷积神经网络可以区分细胞类型，之后使用贝叶斯多对象跟踪器在整个时间推移中跟踪它们。利用荧光细胞周期指示剂探针的缀合物分析，可以识别细胞命运定型的关键时间点，突出哪些细胞正在驱动竞争以及这些细胞正在响应哪些局部条件。