Satoko Takemoto

Local Nucleosome Dynamics Facilitate Chromatin Accessibility in Living Mammalian Cells

Genome information, which is three-dimensionally organized within cells as chromatin, is searched and read by various proteins for diverse cell functions. Although how the protein factors find their targets remains unclear, the dynamic and flexible nature of chromatin is likely crucial. Using a combined approach of fluorescence correlation spectroscopy, single-nucleosome imaging, and Monte Carlo computer simulations, we demonstrate local chromatin dynamics in living mammalian cells. We show that similar to interphase chromatin, dense mitotic chromosomes also have considerable chromatin accessibility. For both interphase and mitotic chromatin, we observed local fluctuation of individual nucleosomes (~50 nm movement/30 ms), which is caused by confined Brownian motion. Inhibition of these local dynamics by crosslinking impaired accessibility in the dense chromatin regions. Our findings show that local nucleosome dynamics drive chromatin accessibility. We propose that this local nucleosome fluctuation is the basis for scanning genome information.

Algorithm Selection for Intracellular Image Segmentation Based on Region Similarity

This paper deals with the problem of intracellular image segmentation. Our goal is to propose an algorithm selection framework that has the potential to be general enough to be used for a variety of intracellular image segmentation tasks. With this framework, an optimal algorithm suited to each segmentation task can be selected automatically by our proposed evaluation criteria derived from region similarity of image features and boundary shape. Furthermore, using our framework, we can rank different algorithms, as well as define each algorithms parameters. We tested our prototype framework on confocal microscope images and showed that application of these criteria gave highly accurate segmentation results without missing any biologically important image characteristics.

Transcriptome Tomography for Brain Analysis in the Web-Accessible Anatomical Space

Increased information on the encoded mammalian genome is expected to facilitate an integrated understanding of complex anatomical structure and function based on the knowledge of gene products. Determination of gene expression-anatomy associations is crucial for this understanding. To elicit the association in the three-dimensional (3D) space, we introduce a novel technique for comprehensive mapping of endogenous gene expression into a web-accessible standard space: Transcriptome Tomography. The technique is based on conjugation of sequential tissue-block sectioning, all fractions of which are used for molecular measurements of gene expression densities, and the block- face imaging, which are used for 3D reconstruction of the fractions. To generate a 3D map, tissues are serially sectioned in each of three orthogonal planes and the expression density data are mapped using a tomographic technique. This rapid and unbiased mapping technique using a relatively small number of original data points allows researchers to create their own expression maps in the broad anatomical context of the space. In the first instance we generated a dataset of 36,000 maps, reconstructed from data of 61 fractions measured with microarray, covering the whole mouse brain (ViBrism: http://vibrism.riken.jp/3dviewer/ex/index.html) in one month. After computational estimation of the mapping accuracy we validated the dataset against existing data with respect to the expression location and density. To demonstrate the relevance of the framework, we showed disease related expression of Huntington’s disease gene and Bdnf. Our tomographic approach is applicable to analysis of any biological molecules derived from frozen tissues, organs and whole embryos, and the maps are spatially isotropic and well suited to the analysis in the standard space (e.g. Waxholm Space for brain-atlas databases). This will facilitate research creating and using open-standards for a molecular-based understanding of complex structures; and will contribute to new insights into a broad range of biological and medical questions.

Three‐dimensional Reconstruction of the Equine Ovary

The equine ovary has a very unique structure in terms of its extreme large size, the presence of the ovulation fossa and the inverted location of its cortex and medulla. In the previous study, it was recognized that the application of three‐dimensional internal structure microscopy (3D‐ISM) to observe the mare ovary is very effective. Three‐dimensional reconstruction of serially sliced images made by 3D‐ISM was successful in this study with the aid of the sophisticated image processing technique. The rotation of the reconstructed ovary has been carried out with and without the application of the transparency technique in the ovarian stromal region. The spatial localization of follicles and corpus luteum was clearly visualized by rotating the reconstructed image of the ovary. The extraction of the images of follicles and corpus luteum was also available and gave a quantifiable understanding of their structure.

Analysis of the equine ovarian structure during the first twelve months of life by three-dimensional internal structure microscopy.

A three-dimensional internal structure microscopy (3D-ISM) can clarify the anatomical arrangement of internal structures of equine ovaries. In this study, morphological changes of the equine ovary over the first 12 months of life were investigated by 3D-ISM in 59 fillies and by histological analysis in 2 fillies. The weight and volume of the paired ovaries initially decreased from 0 to 1 months to 2 to 3 months of age and then significantly increased at 8 to 12 months of age. The ovulation fossa was first observed around the 3rd month and became evident after the 6th month. The number of follicles with a diameter of ≥10 mm and the diameter of the largest follicle increased gradually after 6 months of age. On a volume basis, the medulla accounted for nearly 90% of the whole ovary at 0 to 1 months of age, but significantly decreased from 2 to 3 months of age. The volume of the cortex increased progressively after birth and reached approximately 60% of the total volume at 8 to 12 months of age. This significant development of the cortex coincided with the increased number and size of large follicles observed from 6 months of age. These results suggest that the development of the cortex plays a role in the maturation of the follicles and the equine ovary undergoes substantial morphological changes postnatally until puberty.

Algorithm Selection Based on a Region Similarity Metric for Intracellular Image Segmentation

Live-cell imaging using fluorescence microscopy has become popular in modern biology to analyze complex cellular events such as the dynamics of substances inside cells (Eils & Athale, 2003; Bhaskar & Singh, 2007). The next step in furthering this type of analysis is accumulating useful information from the observed images to quantify the dynamics (Cong & Parvin, 2000; Goldman & Spector, 2004; Harder et al., 2008, Waltera et al., 2010). However, quantification of intracellular images is a difficult process because microscopic images with ultra-high sensitivity have a low signal-to-noise ratio. In addition, the amount of data required for quantification has gradually increased as microscopy has developed. These obstacles make it more difficult for cell biologists to identify regions of interest and accumulate various types of quantitative information, such as the volume, shape, and dynamics of intracellular substances. Hence, it is important to develop computational methods for identifying objective targets, such as organelles labeled with, for example, a fluorescent protein. Image segmentation, the process by which an image is divided into multiple regions corresponding to the components pictured in the image, plays a key role as one of the first steps in the quantification of objective targets from observed images. The use of segmented regions allows us to distinguish substances of interest from irrelevant regions, including background and noise. Numerous segmentation algorithms have been proposed (e.g., Haralick & Shapiro, 1985; Pal & Pal, 1993), but most approaches have been developed for a specific task and cannot be generalized for other segmentation tasks. As a result, researchers have had to face the difficult duty of choosing the most suitable algorithm for a given task while facing increasing numbers of images needing quantification. Moreover, recent notable improvements in live-cell imaging require that segmentation algorithms be flexible enough to accommodate time-variable changes in targets. No single algorithm performed with a fixed-parameter setting is considered to be sufficient for analyzing all time-lapse images, and optimizing algorithms for a variety of images is a tedious task for researchers. Solutions to these problems have been proposed based on the idea of algorithm selection (e.g., Cardoso & Corte-Real, 2005; Zhang, 2006; Polak et al., 2009). An appropriate algorithm with an optimized parameter setting for each task is automatically selected according to unique evaluation metrics of algorithm performance. Evaluation can be roughly divided into two types: unsupervised evaluation and supervised evaluation. The former type can

Population of follicles and luteal structures during the oestrous cycle of mares detected by three-dimensional internal structure microscopy.

The structure of the equine ovary is different from that of other mammals in its extremely large size, the presence of ovarian fossa and the inverted location of its cortex and medulla. A three‐dimensional internal structure microscopy (3D‐ISM), which consists of a computer‐controlled slicer, a CCD camera, a laser disc recorder and a PC, is very useful for the observation of the internal structures in equine ovaries. In addition, the three‐dimensional images of follicles and corpus luteum (CL) reconstructed by the segmentation technique can clarify the spatial arrangement in the equine ovary. In this study, to understand the changes in the ovarian internal structures of the mare during the oestrous cycle, the size and numbers of follicles and luteal structures were analysed by 3D‐ISM in addition to the concentrations of progesterone (P4) and oestradiol‐17β. As a result, many small follicles (<10 mm in diameter) were detected. It was recognized that the luteal structures were distinguished into three types, such as the corpus haemorragicum (CH), which is formed by blood elements at the cavity after ovulation, CL and corpus albican (CA). There were some CHs and CL in the group, which had the concentration of P4 > 1 ng/ml. CHs were also observed in the group, which had low level of P4 (P4 < 1 ng/ml). CAs were found regardless of the P4 level. In conclusion, 3D‐ISM enabled the internal observation of the ovarian structures in detail, and estimation of the stage of the ovarian cycle with complementary physiological information. The findings by 3D‐ISM provide basic information for clinical applications.

INTERACTIVE REGISTRATION OF INTRACELLULAR VOLUMES WITH RADIAL BASIS FUNCTIONS

We propose a novel approach to 3D image registration of intracellular volumes. The approach extends a standard image registration framework to the curved cell geometry. An intracellular volume is mapped onto another intracellular domain by using two pairs of point set surfaces approximating their nuclear and plasma membranes. The mapping function consists of the affine transformation, tetrahedral barycentric interpolation, and least-squares formulation of radial basis functions for extracted cell geometry features. An interactive volume registration system is also developed based on our approach. We demonstrate that our approach is capable of creating cell models containing multiple organelles from observed data of living cells.

Semi-automated color segmentation from a biological cross-sectional image series: follicle segmentation from the equine ovary

This paper proposes a semi-automatic segmentation method for the 3D-ISM system, which enables the capture of a high-resolution full-color cross sectional image series of a biological sample. Our approach is based on region-based segmentation and an adaptive classification technique by using the Otsu method, so it can be applied to an object like biological tissue, which has different colors by location. As a result, we have achieved to develop the method to decrease the degree of manual operation required. This paper also shows experimental results of applying our method to visualize the internal structure of the equine ovary. We have confirmed the spatial arrangement inside the ovary, which had not been revealed so far.

Broad Integration of Expression Maps and Co-Expression Networks Compassing Novel Gene Functions in the Brain

Using a recently invented technique for gene expression mapping in the whole-anatomy context, termed transcriptome tomography, we have generated a dataset of 36,000 maps of overall gene expression in the adult-mouse brain. Here, using an informatics approach, we identified a broad co-expression network that follows an inverse power law and is rich in functional interaction and gene-ontology terms. Our framework for the integrated analysis of expression maps and graphs of co-expression networks revealed that groups of combinatorially expressed genes, which regulate cell differentiation during development, were present in the adult brain and each of these groups was associated with a discrete cell types. These groups included non-coding genes of unknown function. We found that these genes specifically linked developmentally conserved groups in the network. A previously unrecognized robust expression pattern covering the whole brain was related to the molecular anatomy of key biological processes occurring in particular areas.