Sub-diffraction limit localization of proteins in volumetric space using Bayesian restoration of fluorescence images from ultrathin specimens.
Sub-diffraction limit localization of proteins in volumetric space using Bayesian restoration of fluorescence images from ultrathin specimens.
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DOI：
10.1371/journal.pcbi.1002671
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发表时间：
2012
影响因子：
4.3
通讯作者：
Smith SJ
中科院分区：
文献类型：
--
作者：
Wang G;Smith SJ
光子衍射限制了传统光学显微镜在横向焦平面处的分辨率为0.61λ/NA（λ =光波长，NA =物镜的数值孔径），在轴向平面处的分辨率为1.4nλ/NA 2（n =成像介质的折射率，对于油浸为1.51），其在可见光波长和1.4NA油浸物镜的情况下在横向平面和轴向平面中分别为约220 nm和约600 nm。 这种体积分辨率对于亚细胞结构中蛋白质簇的正确定位来说太大了。在这里，我们结合联合收割机的新开发的蛋白质组成像技术，阵列断层扫描（AT），其天然的50-100 nm的轴向分辨率实现的树脂包埋组织的物理切片，和二维最大似然反卷积方法，贝叶斯规则的基础上，显着提高了蛋白质斑点的分辨率在横向平面，允许准确和快速的计算分割和分析标记的蛋白质。AT的物理切片允许组织标本在现代高NA平面消色差物镜的物理最佳条件下成像。这转化为图像具有很少的焦点光，最小的像差和波前失真。因此，AT能够提供具有真正不变的点扩散函数（PSF）的图像，这是精确反卷积的关键属性。我们表明，AT与反卷积增加了蛋白质定位的体积分析保真度显着提高调制的高空间频率，并可能超过空间频率截止的目标。此外，我们能够实现这种改进，没有明显的噪声或伪影的引入，并达到与使用超分辨率显微镜的商业实现捕获的图像体积相当的对象分割和定位精度。生物功能在其基本水平上涉及纳米尺度上的分子相互作用，正是这个原因，生物成像推动了越来越好的分辨率。光学显微镜在生物学中非常普遍，因为它结合了大视场、简单的样品制备、成本有效的使用和生物样品相对高的耐受性。光学显微镜的问题是，光的衍射将可实现的图像的分辨率限制在体积空间中的数百纳米，这对于亚细胞器或结构（例如神经元的突触）中的蛋白质的精确定位来说太低了。超分辨率光学显微镜现在是可用的，但其实施通常需要技术复杂和昂贵的成像系统。在本文中，我们展示了一种方法，结合物理薄切片的组织与贝叶斯为基础的去卷积的传统，荧光显微镜，以实现体积分辨率远低于衍射极限，并使用这种方法，我们能够大大提高计算分割和本地化的标记蛋白质在重建体积的脑组织。
Photon diffraction limits the resolution of conventional light microscopy at the lateral focal plane to 0.61λ/NA (λ = wavelength of light, NA = numerical aperture of the objective) and at the axial plane to 1.4nλ/NA2 (n = refractive index of the imaging medium, 1.51 for oil immersion), which with visible wavelengths and a 1.4NA oil immersion objective is ∼220 nm and ∼600 nm in the lateral plane and axial plane respectively. This volumetric resolution is too large for the proper localization of protein clustering in subcellular structures. Here we combine the newly developed proteomic imaging technique, Array Tomography (AT), with its native 50–100 nm axial resolution achieved by physical sectioning of resin embedded tissue, and a 2D maximum likelihood deconvolution method, based on Bayes' rule, which significantly improves the resolution of protein puncta in the lateral plane to allow accurate and fast computational segmentation and analysis of labeled proteins. The physical sectioning of AT allows tissue specimens to be imaged at the physical optimum of modern high NA plan-apochormatic objectives. This translates to images that have little out of focus light, minimal aberrations and wave-front distortions. Thus, AT is able to provide images with truly invariant point spread functions (PSF), a property critical for accurate deconvolution. We show that AT with deconvolution increases the volumetric analytical fidelity of protein localization by significantly improving the modulation of high spatial frequencies up to and potentially beyond the spatial frequency cut-off of the objective. Moreover, we are able to achieve this improvement with no noticeable introduction of noise or artifacts and arrive at object segmentation and localization accuracies on par with image volumes captured using commercial implementations of super-resolution microscopes. Biological function at its fundamental level involves molecular interactions on a nanometer scale, and it is this reason that biological imaging has pushed for increasingly better resolution. Light microscopy is highly prevalent in biology due to its combination of large field of view, simple sample preparation, cost effective usage and relatively high tolerance by biological samples. The problem with light microscopy is that diffraction of light limits the resolution of achievable images to hundreds of nanometers in volumetric space, which is much too low for the accurate localization of proteins in subcellular organelle or structures, such as the synapse of a neuron. Super-resolution light microscopy is now available, but its implementation usually requires technically complex and expensive imaging systems. In this paper, we demonstrate a method that combines physical thin sectioning of tissue with Bayesian based deconvolution of conventional, fluorescent microscopy to achieve volumetric resolution well below the diffraction limit, and that using this method we are able to greatly improve the computational segmentation and localization of labeled proteins in a reconstructed volume of brain tissue.
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影响因子：
6.5
作者：
Puschmann, KG;Kneer, F
通讯作者：
Kneer, F
影响因子：
3.7
作者：
Nanguneri S;Flottmann B;Horstmann H;Heilemann M;Kuner T
通讯作者：
Kuner T
影响因子：
16.2
作者：
Micheva, Kristina D.;Busse, Brad;Weiler, Nicholas C.;O'Rourke, Nancy;Smith, Stephen J.
通讯作者：
Smith, Stephen J.
影响因子：
5.3
作者：
LUCY, LB
通讯作者：
LUCY, LB
DOI：
10.1007/bf01702982
发表时间：
1971-01-01
期刊：
CZECHOSLOVAK JOURNAL OF PHYSICS SECTION B
影响因子：
--
作者：
PERINA, J
通讯作者：
PERINA, J