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.

荧光显微镜是细胞生物学家的关键工具，因为它允许它们用荧光分子（荧光团）标记不同的蛋白质并在活细胞中观察它们。这产生了可以帮助我们理解疾病的信息，并找到新药来治疗它们。直到最近，荧光显微镜都有一个主要缺陷：它无法解析低于200nm的任何特征。尽管人类细胞的大小至少是二十倍，但细胞的许多部分小得多。在过去的十年中，已经开发了许多方法，这些方法允许荧光显微镜图像200nm以下的结构，并且这些方法现在已成为固定（死）细胞中的标准化。显微镜开发的一个主要挑战是如何在活细胞中应用这些方法，以足够可重现的方式将其用于该技术没有专家的细胞生物学实验室。在此提案中，我们使用两种方法攻击了这个问题。首先，我们将研究定位显微镜的理论限制。定位显微镜通过拍摄样品的许多图像来起作用。荧光团的行为得到控制，因此在每个图像中，只有少数荧光团发光。即使每个荧光团都会导致一个模糊的位置，我们也可以非常准确地找到该点中心的位置。然后，通过将点降低到我们在所有框架上识别的所有荧光团的位置上的位置来构建样品的图像。目前，人们将定位显微镜视为类似于其他显微镜技术。您用光照明，并获得图像，图像的质量取决于您的显微镜的效果以及光线的亮度。但是，要使本地化图像达到高分辨率，您必须找到许多荧光团的位置。不太明显，获得一定数量的荧光团所需的帧数取决于样品的结构，因为您无法对两个太近的荧光团成像。这意味着最大速度取决于样品的结构。我们将进行模拟以确定最大速度如何取决于结构，这将使细胞生物学家可以提前知道给定样品可以达到什么速度。第二，我们将开发一种可以检查实验中的原始数据并确定是否分析的方法，是否会得到样品的结构，或者是否会通过拟合形象造成的图像来表现出拟合的形象。目前，如果发生这种情况，很难解决，尤其是如果您尝试快速获取数据，这对于实时细胞实验是必需的。可以通过查看检测到的荧光团数量会随着时间而变化的方式来进行快速测试。但是，我们可能需要进行更复杂的测试。我们将使用实验中的图像，并创建一个模拟图像，在该图像中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，看看是否正确检测到了新的荧光团。通过移动荧光团圆形并在不同框架上执行测试，我们将确定图像中数据分析工作不正常的时间或位置是否存在。通过采用这两种方法，我们将为每个细胞生物学家提供定位显微镜系统，他们需要计算他们可以对其感兴趣的结构进行成像的最大速度所需的工具。这将使实时细胞定位显微镜从专业实验室和细胞生物学家的范围内。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外的结构。通过将此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而变化和发展。

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