Unraveling subcellular heterogeneity of molecular coordination by machine learning

项目总结/摘要 荧光显微镜的最新进展使研究人员能够获得前所未有的活细胞数量。 高空间和时间分辨率的细胞图像数据。然而，这些图像构成了一个重大挑战 因为大量的亚细胞异质性。虽然传统的计算机视觉算法 已经促进了自动图像分析，传统的亚细胞异质性的整体平均可以 导致关键的机械细节的丢失。鉴于目前来自新的细胞生物学数据的快速增长， 技术的发展，如果我们仅仅依靠 人工智能用于算法开发和数据分析。最近，机器学习（ML）正在使 这是一个巨大的进步，并表明计算机可以在复杂的高性能分析中超越人类。 多维数据集。然而，细胞生物学中的常规ML应用通常限于固定的细胞或细胞外基质。 低空间分辨率设置（单细胞分辨率），这在分析动态亚细胞时受到限制 信息.为了填补这一空白，我们一直在开发一个用于荧光活细胞图像的ML框架 亚细胞水平的分析。在我们以前的研究中，我们建立了反卷积的方法， 肝细胞成像中板状伪足突起的亚细胞异质性， 具有不同药物敏感性的突出表型。因此，我们的目标是推进这个ML框架， 解决活细胞分析中的技术和细胞生物学挑战。我们研究的总体目标是两个- 折叠：i）推进细胞生物学研究（技术开发）的新ML框架，ii）应用 我们的ML框架将机械生物学和代谢整合到细胞突起（靶向细胞生物学）中， 研究）。首先，我们将提出我们的ML框架，用于对突起的亚细胞异质性进行去卷积。 以及活细胞中的分子协调。该方法将时间序列建模与ML相结合， 亚细胞分子协调其次，我们将开发基于深度学习的高通量荧光 活细胞成像这将包括显微镜自动化，分辨率增强和数据合成， 将为机器学习建立大量的数据集。第三，我们将应用我们的ML框架来研究机械敏感性， 亚细胞生物能量状态的一个重要特征。我们将评估AMPK对机械力的反应 并控制肌动蛋白装配和线粒体的亚细胞组织，以促进能量需求 突出表型我们的ML框架将为细胞生物学带来前所未有的分析能力， 以高空间分辨率分析大量的单个细胞，并自动提取 大量的亚细胞表型。该框架可以应用于细胞生物学的各个领域，例如 细胞骨架、膜重塑和膜结合细胞器。

PROJECT SUMMARY/ABSTRACT Recent advances in fluorescence microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatial and temporal resolutions. However, these images pose a significant challenge for data analyses due to massive subcellular heterogeneity. Although conventional computer vision algorithms have facilitated automatic image analysis, traditional ensemble-averaging of subcellular heterogeneity could lead to the loss of critical mechanistic details. Given the current rapid growth of cell biological data from new technological development, it is nearly impossible to keep up with the data generation if we solely rely on human intelligence for algorithm development and data analysis. Recently, machine learning (ML) is making tremendous progress and has shown that computers can outperform humans in the analysis of complex high dimensional datasets. Conventional ML application in cell biology, however, is usually limited to fixed cells or low spatial resolution setting (single cell resolution), which is limited in analyzing dynamic subcellular information. To fill this voids, we have been developing an ML framework for fluorescence live cell image analyses at the subcellular level. In our previous study, we established the method to deconvolve the subcellular heterogeneity of lamellipodial protrusion from live cell imaging, which identified distinct subcellular protrusion phenotypes with differential drug susceptibility. Thus, our goal is to advance this ML framework and address technical and cell biological challenges in the live cell analysis. The overall goal of our research is two- fold: i) advancing a new ML framework for cell biological research (technological development) and ii) applying our ML framework to integrate mechanobiology and metabolism in cell protrusion (targeted cell biological study). First, we will advance our ML framework for the deconvolution of subcellular heterogeneity of protrusion and molecular coordination in live cells. This method will integrate time-series modeling and ML to deconvolve subcellular molecular coordination. Second, we will develop deep learning based high-throughput fluorescence live cell imaging. This will include microscope automation, resolution enhancement, and data synthesis, which will build up the massive dataset for ML. Third, we will apply our ML framework to study the mechanosensitivity of subcellular bioenergetic status in cell protrusion. We will evaluate how AMPK reacts to mechanical forces and controls the subcellular organization of actin assembly and mitochondria to promote energy-demanding protrusion phenotypes. Our ML framework will bring unprecedented analytical power to cell biology by analyzing a large numbers of individual cells at the high spatial resolution and automatically extracting a multitude of subcellular phenotypes. This framework can be applied to various areas of cell biology such as cytoskeleton, membrane remodeling, and membrane-bound organelles.