Bayesian analysis of images to provide fluorescence ultramicroscopy

显微镜是现代细胞生物学中非常强大和重要的技术。例如，电子显微镜允许在死细胞中以几纳米（nm）的分辨率对细胞进行成像，并且荧光显微镜允许在活细胞中观察特定蛋白质的位置。在荧光显微镜下，我们感兴趣的细胞中的蛋白质被一种可以发光的化学物质（荧光团）染色，为我们提供了关于细胞如何运作、分裂和死亡的重要信息。荧光显微镜的分辨率受到基本物理光的限制。然而，最近已经找到了绕过这一限制的方法。例如，想象只有染料分子在发光。即使它的图像被模糊成一个斑点，你也可以非常准确地找到斑点中心的位置。但这只有在点是单独的时候才有效，而且需要成千上万的模糊点的图像来组合一张图片。为了将荧光团的发射分散在数千张图像上，我们使用荧光团，它们在发光（开）和不发光（关）的状态之间切换。这种技术，定位显微镜，可以实现几十纳米的分辨率。目前，当从一系列图像重建定位显微图像时，关于荧光团何时发光的所有信息都被丢弃。我们建议创建一种新的数据分析技术，该技术将允许我们提取每个荧光团在开和关状态之间切换的频率的信息。为了提取荧光团随时间推移的行为的信息，我们必须创建一个复杂的数据模型。要做到这一点，我们将使用贝叶斯统计，其中我们将所有关于系统的信息构建成一系列模型，并找到最有可能的模型。例如，您可以比较一个模型，其中荧光团根本没有关闭和打开，而另一个模型则快速关闭和打开。你可以逐渐改变模型，找出哪一个最有可能是正确的。贝叶斯统计学的特别之处在于，即使你不知道荧光团是什么样子，你也可以计算出它，这些信息将使细胞生物学家能够进行三种新的实验：1）对于某些荧光团，它在开和关之间切换的速率会随着荧光团周围化学环境的变化而变化。这将使我们能够形成细胞化学环境如何变化的图像，分辨率为数十nm。2）某些荧光团在非常接近第二种荧光团时会改变其强度和发光时间。这种效应已经用于通过寻找强度变化来监测两种不同蛋白质的距离何时接近10纳米。但是，如果其中一种类型的荧光团自发地在开和关之间切换，我们将能够形成细胞的定位图像，分辨率为数十nm，并且还可以从荧光团在开和关之间切换的强度和时间中获得关于第二种蛋白质与第一种蛋白质的接近程度的信息。当两个相同类型的荧光团靠近时，某些荧光团也会改变它们发光的时间。这将为我们提供关于荧光团来自两个不同来源的信息：定位信息和分子距离的信息。通过使用这两种类型的信息的荧光团建模，我们将创建一个新的超分辨率荧光显微镜技术，分辨率为5 nm，类似于电子显微镜实现的分辨率。但与电子显微镜不同的是，它将有可能在活细胞中进行实验，使我们能够比以往任何时候都更清晰地观察生命。

Microscopy is a very powerful and important technique in modern cell biology. For example, electron microscopy allows cells to be imaged with a resolution of a few nanometres (nm) in dead cells and fluorescence microscopy allows the positions of specific proteins to be observed in live cells. In fluorescence microscopy the protein in the cell which interests us is dyed with a chemical which can glow (a fluorophore), giving us vital information about how cells function, divide and die. Fluorescence microscopy is limited in resolution by the fundamental physics light. However, recently ways have been found to bypass this limit. For example, imagine that only molecule of dye is glowing. Even though an image of it is blurred into a blob, you can find the position of the centre of the blob very accurately. But this only works if the dot is alone, and so many thousands of images of blurred dots are needed to assemble a picture. In order to spread out the emission of fluorophores over thousands of images we use fluorophores which switch between a state where they are emitting light (on) and a state where they don't (off). This technique, localisation microscopy, can achieve resolutions of a few tens of nm. Currently when a localisation microscopy image is reconstructed from a series of images all the information about when the fluorophores are emitting light is all discarded. We are proposing to create a new data analysis technique which will allow us to extract information about how often each fluorophore switches between the on and off states.In order to extract information about how the fluorophore behaves over time, we will have to create a sophisticated model of the data. To do this we will use Bayesian statistics, in which we build all the information we have about a system into a series of models, and find which is most likely. For example, you could compare one model in which a fluorophore did not switch off and on at all and another in which it switched off and on rapidly. You can gradually change the model to work out which one is the most likely to be correct. The particular power of Bayesian statistics is that you can calculate this even if you do not know what the fluorophore looks like.This information will allow cell biologists to carry out three new types of experiments:1) For some fluorophores, the rate at which it switches between on and off changes as the chemical environment round the fluorophore changes. This would allow us to form an image of how the chemical environment of the cell changes, with a resolution of tens of nm.2) Certain fluorophores change their intensity and the times at which they emit light when they are very close to a second type of fluorophore. This effect is already used to monitor when two different proteins are closer than 10 nm by looking for intensity changes. But if one of the types of fluorophore spontaneously switches between on and off, we will be able to form a localisation image of the cell, with a resolution of tens of nm, and also get information at each point about how close the second type of protein is to the first from the intensity and the times at which the fluorophores switch between on and off.3) Certain fluorophores also change the times at which they emit light when two fluorophores of the same type are close together. This will give us information about where fluorophores are from two different sources: the localisation information and the information about how far apart molecules are. By modelling the fluorophores using both types of information we will create a new ultraresolution fluorescence microscopy technique with a resolution of 5 nm, similar to the resolution achieved by electron microscopy. But unlike electron microscopy, it will be possible to do experiments in live cells, allowing us to look at life in sharper focus than ever before.