Optimising acquisition speed in localisation microscopy

优化定位显微镜的采集速度

• 批准号: BB/N022696/1
• 负责人: Susan Cox
• 金额: $ 17.09万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN022696%2F1
• 关键词: Optimising; acquisition; speed; localisation; microscopy

Fluorescence microscopy is a crucial tool for cell biologists because it allows them to label different proteins with fluorescent molecules (fluorophores) and observe them in live cells. This yields information which can help us to understand diseases, and find new drugs to treat them. Until recently fluorescence microscopy had a major flaw: it could not resolve any features below 200nm. While human cells are at least twenty times this size, there are many parts of a cell which are much smaller. Over the last ten years a number of methods have been developed which allow fluorescence microscopes to image structures below 200nm, and these methods are now becoming standard in fixed (dead) cells. A major challenge in microscopy development is how to apply these methods in live cells, in a way that is reproducible enough that it can be used in cell biology laboratories where there are no experts in the technique. In this proposal we attack this problem with two approaches.Firstly, we will investigate the theoretical limits of localisation microscopy. Localisation microscopy works by taking many images of the sample. The behaviour of the fluorophores is controlled so that in each image only a few of the fluorophores are emitting light. Even though each fluorophore results in a blurred spot, we can find the position of the centre of the spot very accurately. The image of the sample is then built up by putting a point down at the position of all the fluorophores we identify across all the frames. At the moment, people think about localisation microscopy as being similar to other microscopy techniques; you illuminate with light and you get an image, with the quality of the image depending on how good your microscope is and how bright your light is. However, for a localisation image to achieve a high resolution, you have to find the position of lots of fluorophores. Less obviously, the number of frames it takes to get a certain number of fluorophores depends on the structure of your sample, since you cannot image two fluorophores which are too close together. This means that the maximum speed depends on the structure of your sample. We will carry out simulations to work out how the maximum speed depends on the structure, which will allow cell biologists to know in advance what speed can be achieved for a given sample.Secondly, we will develop a method which can examine the raw data from an experiment and determine whether, if you analyse it, you will get an image which reflects the structure of the sample, or if you will get an image with features caused by fitting the positions of fluorophores inaccurately. Currently, it is very hard to work out if this has happened, particularly if you try to get data quickly, which is necessary for live cell experiments. It may be possible to perform a quick test by looking at how the number of fluorophores which is detected changes over time. However, we are likely to need a more sophisticated test. We will use the images from an experiment and create a simulated image where we add a single fluorophore at a known position. We can then run the data analysis and see if the new fluorophore is correctly detected. By moving the fluorophore round, and performing the test on different frames, we will determine if there are particular times or places in the images where the data analysis is not working well. By taking these two approaches, we will give every cell biologist with a localisation microscopy system the tools they need to calculate the maximum speed at which they can image the structure they are interested in. This will bring live cell localisation microscopy out of specialist labs and into the reach of cell biologists. Fixed cell localisation microscopy has already shown us many new and unexpected structures in the cell; by extending this technique into live cells, we will be able to see how these structures change and evolve over time.

荧光显微镜对细胞生物学家来说是一个至关重要的工具，因为它允许他们用荧光分子（荧光团）标记不同的蛋白质，并在活细胞中观察它们。由此产生的信息可以帮助我们了解疾病，并找到治疗疾病的新药。直到最近，荧光显微镜还存在一个主要缺陷：它无法分辨200nm以下的任何特征。虽然人类细胞的大小至少是它的20倍，但细胞的许多部分都要小得多。在过去的十年中，已经开发了许多方法，使荧光显微镜能够对200nm以下的结构进行成像，这些方法现在正在成为固定（死亡）细胞的标准方法。显微镜发展的一个主要挑战是如何将这些方法应用于活细胞，以一种足够可重复的方式，使其可以在没有技术专家的细胞生物学实验室中使用。在本建议中，我们用两种方法来解决这个问题。首先，我们将研究定位显微镜的理论限制。定位显微镜的工作原理是拍摄许多样品的图像。控制荧光团的行为，以便在每个图像中只有少数荧光团发光。尽管每个荧光团产生一个模糊的斑点，但我们可以非常准确地找到斑点中心的位置。然后通过在所有帧中识别的所有荧光团的位置上放置一个点来构建样品的图像。目前，人们认为定位显微镜与其他显微镜技术类似；你用光照射就能得到图像，图像的质量取决于你的显微镜有多好，光线有多亮。然而，为了获得高分辨率的定位图像，您必须找到许多荧光团的位置。不太明显的是，获得一定数量的荧光团所需的帧数取决于样品的结构，因为你不能对两个靠得太近的荧光团成像。这意味着最大速度取决于样品的结构。我们将进行模拟，计算出最大速度如何取决于结构，这将使细胞生物学家提前知道给定样品可以达到的速度。其次，我们将开发一种方法，可以检查从实验中得到的原始数据，并确定，如果你分析它，你会得到一个图像，反映样品的结构，或者你会得到一个图像，由于不准确地拟合荧光团的位置导致的特征。目前，很难确定这种情况是否发生过，特别是如果你试图快速获得数据，这对活细胞实验是必要的。也许可以通过观察检测到的荧光团的数量如何随时间变化来进行快速测试。然而，我们可能需要一个更复杂的测试。我们将使用来自实验的图像，并创建一个模拟图像，我们在已知位置添加单个荧光团。然后我们可以运行数据分析，看看新的荧光团是否被正确检测到。通过移动荧光团，并在不同的帧上执行测试，我们将确定图像中是否有特定的时间或位置数据分析工作不佳。通过采用这两种方法，我们将为每个拥有定位显微镜系统的细胞生物学家提供他们所需的工具，以计算他们可以对他们感兴趣的结构成像的最大速度。这将使活细胞定位显微镜走出专业实验室，进入细胞生物学家的范围。固定细胞定位显微镜已经向我们展示了细胞中许多新的和意想不到的结构；通过将这项技术扩展到活细胞中，我们将能够看到这些结构如何随着时间的推移而变化和进化。

项目成果

The Rényi divergence enables accurate and precise cluster analysis for localization microscopy.

• DOI: 10.1093/bioinformatics/bty403
• 发表时间: 2018-12-01
• 期刊: Bioinformatics (Oxford, England)
• 作者: Staszowska AD;Fox-Roberts P;Hirvonen LM;Peddie CJ;Collinson LM;Jones GE;Cox S
• 通讯作者: Cox S