生物双重显微结构三维重建图象显示及畸变补偿方法

本文介绍计算机三维重建技术的两个进展:双重显微结构三维重建图象的显示技术及三维重建图象显示畸变的补偿方法。 1.生物双重显微结构的三维显示方法 有些生物的神经核团具有双重结构,例如蛤蚧的中脑峡核,它由大细胞部(Imc)和小细胞部(Ipc)两部分组成,仅Imc与视觉有关,Ipc似乎既无视觉功能也无听觉功能。但Imc与Ipc紧挨着,且有部分结构交迭在     

录象机在生物组织连续切片的计算机三维重建系统中的应用

录象机(Video Tape Recorder)作为输入、输出设备,应用于“生物组织连续切片的计算机三维重建系统”中,其效果是令人满意的.由于研制的“同步再生”单元(Sync RecoveryUnit)有效地消除了静放噪声,从而使Cromemco微型计算机的图象输入接口SDD(Super DazzlerDigitizer)能够稳定地逐帧采集录象机输出的静止图象.录制并编辑计算机三维重建后的生物组织显微结构的空间旋转视图,使其在录象机的监视器上显示得更生动、逼真,更有体视感;还可脱离主机在任何场合演示重建结果.本文还就录象机在生物医学序列图象分析中的应用前景进行了讨论.     

利用共聚焦显微镜系统进行视觉显微结构的三维重建

本文介绍利用共聚焦激光扫描显微镜系统进行的三种动物视觉显微结构的三维重建.所重建的对象为鸽视顶盖的神经元,蜻蜒小眼的晶锥和蟾蜍两个视顶盖之间的纤维联接结构.通过对约60μm厚的样品的共聚焦激光扫描,得到了1和3μm厚的连续光学切面的图象.利用计算机对这些图象进行三维重建得到的模型富有实体感和体视感,特别是以荧光染料标记的样品其三维重建结果比预料的好.三维重建的结果首次展示了这三种视觉显微结构的三维形态,这对进一步研究视觉显微结构的定量形态学和结构功能关系有重要意义,特别是这种装置能研究活组织的三维构型.对该系统的原理和优良性能也作了介绍.     

利用共聚焦显微镜系统进行视觉显微结构的三维重建

本文介绍利用共聚焦激光扫描显微镜系统进行的三种动物视觉显微结构的三维重建.所重建的对象为鸽视顶盖的神经元,蜻蜒小眼的晶锥和蟾蜍两个视顶盖之间的纤维联接结构.通过对约60μm厚的样品的共聚焦激光扫描,得到了1和3μm厚的连续光学切面的图象.利用计算机对这些图象进行三维重建得到的模型富有实体感和体视感,特别是以荧光染料标记的样品其三维重建结果比预料的好.三维重建的结果首次展示了这三种视觉显微结构的三维形态,这对进一步研究视觉显微结构的定量形态学和结构功能关系有重要意义,特别是这种装置能研究活组织的三维构型.对该系统的原理和优良性能也作了介绍.     

蛋白质序列的矩阵图谱表达

基于经典HP模型,利用蛋白质序列的矩阵图谱表达法(MGR)及数值刻画的思想提出了一种新的蛋白质序列的比对方法,通过观察蛋白质序列的数值刻画图及计算两蛋白质序列之间的欧氏距离d,对木聚糖酶两家族的蛋白质序列进行了相似性分析.发现被划分为同一木聚糖酶家族的蛋白质序列之间的相似性更大,而且蛋白质序列的相似性程度与分子大小、结构和分子进化相关.     

利用序列保守模体和局部构象信息预测转录因子结合位点

转录因子结合位点的计算预测是研究基因转录调控的重要环节,但常用的位置特异得分矩阵方法预测特异性偏低.通过深入分析结合位点的生物特征,提出了一种综合利用序列保守模体和局部构象信息的结合位点预测方法,以极大相关得分矩阵作为保守模体的描述模型,并根据二苷参数模型计算位点序列的局部构象,将两类信息得分组合为多维特征向量,在二次判别分析的框架下进行训练和滑动预测.预测过程中还引入了位置信息量以优化似然得分和过滤备选结果.针对大肠杆菌CRP和Fis结合位点数据的留一法测试结果表明,描述模型的改进和多种信息的融合能有效地改善预测方法的性能,大幅度提高特异性.     

生物组织显微结构三维重建的灰度阴影立体图对显示技术

本文描述了一种新的显示三维重建模型的方法—灰度阴影立体图对方法(shading-stereo-pair method).该方法根据连续切片三维重建的灰度阴影法和立体图对法的显示原理,导出了生成立体图对的视差公式.在灰度阴影法三维重建过程中,根据各切片所处的深度和其本身的厚度,按视差公式计算出它们在图对上的位移量并进行水平方向的移动,经过对各切片的叠加和重组,最后生成一幅具有视差信息的灰度阴影立体图对.在体视仪下观察,即可看到重建模型的三维实体图象.最后还讨论了进一步提高图象质量和改善观察效果所必须采取的措施.     

非负矩阵算法在遗传相互作用数据中的应用

Epistatic miniarrary profile(EMAP)在多种模式生物中的研究产生了许多高通量数据和遗传相互作用网络.但是怎样探究这些数据中的有效生物学信息是现在非常关键的问题.本文我们采用非负矩阵分解的算法对遗传相互作用数据进行聚类分析,从而发现其中隐藏的功能模块,分析基因功能关系等.该方法能有效的避免传统聚类算法的诸多局限性.     

江豚耳蜗切片的计算机三维重建

在自己组装的微型机图象系统上,实现了对生物组织连续切片的三维重建。重建结果以灰度阴影方式在彩色显示器上显示,并可在打印机上以多灰度等级方式打印输出。结合一例江豚内耳的连续切片,对其耳蜗骨迷路部份进行了三维重建。并将重建结果以动态形式在显示器上转动,以便观察其各个侧面的情况。     

构建基于折叠核心的全α类蛋白取代矩阵

氨基酸残基取代矩阵是影响多序列比对效果的重要因素,现有的取代矩阵对低相似序列的比对性能较低.在已有的 BLOSUM 取代矩阵算法基础上,定义了基于蛋白质折叠核心结构的序列 结构数据块;提出一种新的基于全α类蛋白质折叠核心结构的氨基酸残基取代矩阵——TOPSSUM25,用于提高低相似度序列的比对效果.将矩阵TOPSSUM25导入多序列比对程序,对相似性小于25%的一组四螺旋束序列 结构数据块的测试结果表明,基于 TOPSSUM25的多序列比对效果明显优于BLOSUM30矩阵;基于一个BAliBASE子集的比对检验也进一步表明, TOPSSUM25在全α类蛋白质的两两序列比对上优于BLOSUM30矩阵.研究结果可为进一步的阐明低同源蛋白质序列 结构 功能关系提供帮助.     

Bacterial classification with convolutional neural networks based on different data reduction layers

Abstract

For high accuracy classification of DNA sequences through Convolutional Neural Networks (CNNs), it is essential to use an efficient sequence representation that can accelerate similarity comparison between DNA sequences. In addition, CNN networks can be improved by avoiding the dimensionality problem associated with multi-layer CNN features. This paper presents a new approach for classification of bacterial DNA sequences based on a custom layer. A CNN is used with Frequency Chaos Game Representation (FCGR) of DNA. The FCGR is adopted as a sequence representation method with a suitable choice of the frequency k-lengthen words occurrence in DNA sequences. The DNA sequence is mapped using FCGR that produces an image of a gene sequence. This sequence displays both local and global patterns. A pre-trained CNN is built for image classification. First, the image is converted to feature maps through convolutional layers. This is sometimes followed by a down-sampling operation that reduces the spatial size of the feature map and removes redundant spatial information using the pooling layers. The Random Projection (RP) with an activation function, which carries data with a decent variety with some randomness, is suggested instead of the pooling layers. The feature reduction is achieved while keeping the high accuracy for classifying bacteria into taxonomic levels. The simulation results show that the proposed CNN based on RP has a trade-off between accuracy score and processing time.     

Image analysis for understanding embryo development: a bridge from microscopy to biological insights

The digital reconstruction of the embryogenesis of model organisms from 3D+time data is revolutionizing practices in quantitative and integrative Developmental Biology. A manual and fully supervised image analysis of the massive complex data acquired with new microscopy technologies is no longer an option and automated image processing methods are required to fully exploit the potential of imaging data for biological insights. Current developments and challenges in biological image processing include algorithms for microscopy multiview fusion, cell nucleus tracking for quasi-perfect lineage reconstruction, segmentation, and validation methodologies for cell membrane shape identification, single cell gene expression quantification from in situ hybridization data, and multidimensional image registration algorithms for the construction of prototypic models. These tools will be essential to ultimately produce the multilevel in toto reconstruction that combines the cell lineage tree, cells, and tissues structural information and quantitative gene expression data in its spatio-temporal context throughout development.     

Visualization and correction of automated segmentation,tracking and lineaging from 5-D stem cell image sequences

Background

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

Results

We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

Conclusions

By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-328) contains supplementary material, which is available to authorized users.     

Real space refinement of acto-myosin structures from sectioned muscle

We have adapted a real space refinement protocol originally developed for high-resolution crystallographic analysis for use in fitting atomic models of actin filaments and myosin subfragment 1 (S1) to 3-D images of thin-sectioned, plastic-embedded whole muscle. The rationale for this effort is to obtain a refinement protocol that will optimize the fit of the model to the density obtained by electron microscopy and correct for poor geometry introduced during the manual fitting of a high-resolution atomic model into a lower resolution 3-D image. The starting atomic model consisted of a rigor acto-S1 model obtained by X-ray crystallography and helical reconstruction of electron micrographs. This model was rebuilt to fit 3-D images of rigor insect flight muscle at a resolution of 7 nm obtained by electron tomography and image averaging. Our highly constrained real space refinement resulted in modest improvements in the agreement of model and reconstruction but reduced the number of conflicting atomic contacts by 70% without loss of fit to the 3-D density. The methodology seems to be well suited to the derivation of stereochemically reasonable atomic models that are consistent with experimentally determined 3-D reconstructions computed from electron micrographs.     

A statistical approach to computer processing of cryo-electron microscope images: virion classification and 3-D reconstruction

The scattering density of the virus is represented as a truncated weighted sum of orthonormal basis functions in spherical coordinates, where the angular dependence of each basis function has icosahedral symmetry. A statistical model of the image formation process is proposed and the maximum likelihood estimation method computed by an expectation-maximization algorithm is used to estimate the weights in the sum and thereby compute a 3-D reconstruction of the virus particle. If multiple types of virus particle are represented in the boxed images then multiple 3-D reconstructions are computed simultaneously without first requiring that the type of particle shown in each boxed image be determined. Examples of the procedure are described for viruses with known structure: (1). 3-D reconstruction of Flockhouse Virus from experimental images, (2). 3-D reconstruction of the capsid of Nudaurelia Omega Capensis Virus from synthetic images, and (3). 3-D reconstruction of both the capsid and the procapsid of Nudaurelia Omega Capensis Virus from a mixture of unclassified synthetic images.     

High-resolution 3-D imaging of living cells in suspension using confocal axial tomography

Conventional flow cytometry (FC) methods report optical signals integrated from individual cells at throughput rates as high as thousands of cells per second. This is further combined with the powerful utility to subsequently sort and/or recover the cells of interest. However, these methods cannot extract spatial information. This limitation has prompted efforts by some commercial manufacturers to produce state-of-the-art commercial flow cytometry systems allowing fluorescence images to be recorded by an imaging detector. Nonetheless, there remains an immediate and growing need for technologies facilitating spatial analysis of fluorescent signals from cells maintained in flow suspension. Here, we report a novel methodological approach to this problem that combines micro-fluidic flow, and microelectrode dielectric-field control to manipulate, immobilize and image individual cells in suspension. The method also offers unique possibilities for imaging studies on cells in suspension. In particular, we report the system's immediate utility for confocal "axial tomography" using micro-rotation imaging and show that it greatly enhances 3-D optical resolution compared with conventional light reconstruction (deconvolution) image data treatment. That the method we present here is relatively rapid and lends itself to full automation suggests its eventual utility for 3-D imaging cytometry.     

C-ME: a 3D community-based, real-time collaboration tool for scientific research and training

The need for effective collaboration tools is growing as multidisciplinary proteome-wide projects and distributed research teams become more common. The resulting data is often quite disparate, stored in separate locations, and not contextually related. Collaborative Molecular Modeling Environment (C-ME) is an interactive community-based collaboration system that allows researchers to organize information, visualize data on a two-dimensional (2-D) or three-dimensional (3-D) basis, and share and manage that information with collaborators in real time. C-ME stores the information in industry-standard databases that are immediately accessible by appropriate permission within the computer network directory service or anonymously across the internet through the C-ME application or through a web browser. The system addresses two important aspects of collaboration: context and information management. C-ME allows a researcher to use a 3-D atomic structure model or a 2-D image as a contextual basis on which to attach and share annotations to specific atoms or molecules or to specific regions of a 2-D image. These annotations provide additional information about the atomic structure or image data that can then be evaluated, amended or added to by other project members.     

Three-dimensional evaluation of the spinal local neural network revealed by the high-voltage electron microscopy: a double immunohistochemical study

Three-dimensional (3-D) analysis of anatomical ultrastructures is important in biological research. However, 3-D image analysis on exact serial sets of ultra-thin sections from biological specimens is very difficult to achieve, and limited information can be obtained by 3-D reconstruction from these sections due to the small area that can be reconstructed. On the other hand, the high-penetration power of electrons by an ultra-high accelerating voltage enables thick sections of biological specimens to be examined. High-voltage electron microscopy (HVEM) is particularly useful for 3-D analysis of the central nervous system because considerably thick sections can be observed at the ultrastructure level. Here, we applied HVEM tomography assisted by light microscopy to a study of the 3-D chemical neuroanatomy of the rat lower spinal cord annotated by double-labeling immunohistochemistry. This powerful methodology is useful for studying molecular and/or chemical neuroanatomy at the 3-D ultrastructural level.