Bayesian analysis of images to provide fluorescence ultramicroscopy
对图像进行贝叶斯分析以提供荧光超显微术
基本信息
- 批准号:BB/K01563X/1
- 负责人:
- 金额:$ 15.35万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2013
- 资助国家:英国
- 起止时间:2013 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
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。这种效应已经被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超过10 nm。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。
项目成果
期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
RhoC and ROCKs regulate cancer cell interactions with endothelial cells.
- DOI:10.1016/j.molonc.2015.01.004
- 发表时间:2015-06
- 期刊:
- 影响因子:6.6
- 作者:Reymond N;Im JH;Garg R;Cox S;Soyer M;Riou P;Colomba A;Muschel RJ;Ridley AJ
- 通讯作者:Ridley AJ
Super-resolution imaging in live cells.
- DOI:10.1016/j.ydbio.2014.11.025
- 发表时间:2015-05-01
- 期刊:
- 影响因子:2.7
- 作者:Cox, Susan
- 通讯作者:Cox, Susan
Fixed pattern noise in localization microscopy.
定位显微镜中的固定图案噪声。
- DOI:10.1002/cphc.201300756
- 发表时间:2014
- 期刊:
- 影响因子:0
- 作者:Fox-Roberts P
- 通讯作者:Fox-Roberts P
Investigation of podosome ring protein arrangement using localization microscopy images.
使用定位显微镜图像研究足体环蛋白质排列。
- DOI:10.1016/j.ymeth.2016.11.005
- 发表时间:2017
- 期刊:
- 影响因子:0
- 作者:Staszowska AD
- 通讯作者:Staszowska AD
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:
- 影响因子:0
- 作者:Staszowska AD;Fox-Roberts P;Hirvonen LM;Peddie CJ;Collinson LM;Jones GE;Cox S
- 通讯作者:Cox S
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Susan Cox其他文献
Force-transducing molecular ensembles at growing microtubule tips control mitotic spindle size
生长中的微管尖端的力转导分子集合控制有丝分裂纺锤体大小
- DOI:
10.1038/s41467-024-54123-2 - 发表时间:
2024-11-14 - 期刊:
- 影响因子:15.700
- 作者:
Lee-Ya Chu;Daniel Stedman;Julian Gannon;Susan Cox;Georgii Pobegalov;Maxim I. Molodtsov - 通讯作者:
Maxim I. Molodtsov
Assessing the Knowledge of Fourth-Year Medical Students in Milestones Level 1
- DOI:
10.1007/s40670-016-0292-1 - 发表时间:
2016-07-02 - 期刊:
- 影响因子:1.800
- 作者:
David Marzano;Emily Kobernik;Susan Cox;John L. Dalrymple;Lorraine Dugoff;Maya Hammoud - 通讯作者:
Maya Hammoud
“Tis Better to Give Than to Receive?” Health-related Benefits of Delivering Peer Support in Type 2 Diabetes: An Explanatory Sequential Mixed-methods Study
- DOI:
10.1016/j.jcjd.2022.02.006 - 发表时间:
2022-07-01 - 期刊:
- 影响因子:
- 作者:
Rowshanak Afshar;Rawel Sidhu;Amir S. Askari;Diana Sherifali;Pat G. Camp;Susan Cox;Tricia S. Tang - 通讯作者:
Tricia S. Tang
Synergistic inhibition of human immunodeficiency virus replication in vitro by combinations of 3'-azido-3'-deoxythymidine and 3'-fluoro-3'-deoxythymidine.
3-叠氮基-3-脱氧胸苷和3-氟-3-脱氧胸苷的组合在体外协同抑制人类免疫缺陷病毒复制。
- DOI:
10.1089/aid.1990.6.1197 - 发表时间:
1990 - 期刊:
- 影响因子:1.5
- 作者:
Johan Harmenberg;A. Åkesson;L. Vrang;Susan Cox - 通讯作者:
Susan Cox
Recent high-magnetic-field experiments on the “High <math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si79.gif" overflow="scroll" class="math"><msub><mrow><mi>T</mi></mrow><mrow><mi mathvariant="normal">c</mi></mrow></msub></math>” cuprates; Fermi-surface instabilities as a driver for superconductivity
- DOI:
10.1016/j.physb.2008.11.013 - 发表时间:
2009-03-01 - 期刊:
- 影响因子:
- 作者:
John Singleton;Ross D. McDonald;Susan Cox - 通讯作者:
Susan Cox
Susan Cox的其他文献
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{{ truncateString('Susan Cox', 18)}}的其他基金
Enabling Reliable Testing Of SMLM Datasets
实现 SMLM 数据集的可靠测试
- 批准号:
BB/X01858X/1 - 财政年份:2024
- 资助金额:
$ 15.35万 - 项目类别:
Research Grant
Mesoscale structural biology using deep learning
使用深度学习的介观结构生物学
- 批准号:
BB/T011823/1 - 财政年份:2021
- 资助金额:
$ 15.35万 - 项目类别:
Research Grant
A Bessel beam light sheet microscope
贝塞尔光束光片显微镜
- 批准号:
BB/S019065/1 - 财政年份:2019
- 资助金额:
$ 15.35万 - 项目类别:
Research Grant
Molecular relativity: tracking single molecule movement relative to cell structures
分子相对论:跟踪相对于细胞结构的单分子运动
- 批准号:
BB/R021767/1 - 财政年份:2018
- 资助金额:
$ 15.35万 - 项目类别:
Research Grant
Optimising acquisition speed in localisation microscopy
优化定位显微镜的采集速度
- 批准号:
BB/N022696/1 - 财政年份:2016
- 资助金额:
$ 15.35万 - 项目类别:
Research Grant
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