Yuki Tsujimura

In Vivo Imaging of Hierarchical Spatiotemporal Activation of Caspase-8 during Apoptosis

Background Activation of caspases is crucial for the execution of apoptosis. Although the caspase cascade associated with activation of the initiator caspase-8 (CASP8) has been investigated in molecular and biochemical detail, the dynamics of CASP8 activation are not fully understood. Methodology/Principal Findings We have established a biosensor based on fluorescence resonance energy transfer (FRET) for visualizing apoptotic signals associated with CASP8 activation at the single-cell level. Our dual FRET (dual-FRET) system, comprising a triple fusion fluorescent protein, enabled us to simultaneously monitor the activation of CASP8 and its downstream effector, caspase-3 (CASP3) in single live cells. With the dual-FRET-based biosensor, we detected distinct activation patterns of CASP8 and CASP3 in response to various apoptotic stimuli in mammalian cells, resulting in the positive feedback amplification of CASP8 activation. We reproduced these observations by in vitro reconstitution of the cascade, with a recombinant protein mixture that included procaspases. Furthermore, using a plasma membrane-bound FRET-based biosensor, we captured the spatiotemporal dynamics of CASP8 activation by the diffusion process, suggesting the focal activation of CASP8 is sufficient to propagate apoptotic signals through death receptors. Conclusions Our new FRET-based system visualized the activation process of both initiator and effector caspases in a single apoptotic cell and also elucidated the necessity of an amplification loop for full activation of CASP8.

The molecular mechanism of apoptosis upon caspase-8 activation: Quantitative experimental validation of a mathematical model

Caspase-8 (CASP8) is a cysteine protease that plays a pivotal role in the extrinsic apoptotic signaling pathway via death receptors. The kinetics, dynamics, and selectivity with which the pathway transmits apoptotic signals to downstream molecules upon CASP8 activation are not fully understood. We have developed a system for using high-sensitivity FRET-based biosensors to monitor the protease activity of CASP8 and its downstream effector, caspase-3, in living single cells. Using this system, we systematically investigated the caspase cascade by regulating the magnitude of extrinsic signals received by the cell. Furthermore, we determined the molar concentration of five caspases and Bid required for hierarchical transmission of apoptotic signals in a HeLa cell. Based on these quantitative experimental data, we validated a mathematical model suitable for estimation of the kinetics and dynamics of caspases, which predicts the minimal concentration of CASP8 required to act as an initiator. Consequently, we found that less than 1% of the total CASP8 proteins are sufficient to set the apoptotic program in motion if activated. Taken together, our findings demonstrate the precise cascade of CASP8-mediated apoptotic signals through the extrinsic pathway.

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.

Estimation of loci involved in non-shattering of seeds in early rice domestication

Rice (Oryza sativa L.) is widely cultivated around the world and is known to be domesticated from its wild form, O. rufipogon. A loss of seed shattering is one of the most obvious phenotypic changes selected for during rice domestication. Previously, three seed-shattering loci, qSH1, sh4, and qSH3 were reported to be involved in non-shattering of seeds of Japonica-type cultivated rice, O. sativa cv. Nipponbare. In this study, we focused on non-shattering characteristics of O. sativa Indica cv. IR36 having functional allele at qSH1. We produced backcross recombinant inbred lines having chromosomal segments from IR36 in the genetic background of wild rice, O. rufipogon W630. Histological and quantitative trait loci analyses of abscission layer formation were conducted. In the analysis of quantitative trait loci, a strong peak was observed close to sh4. We, nevertheless, found that some lines showed complete abscission layer formation despite carrying the IR36 allele at sh4, implying that non-shattering of seeds of IR36 could be regulated by the combination of mutations at sh4 and other seed-shattering loci. We also genotyped qSH3, a recently identified seed-shattering locus. Lines that have the IR36 alleles at sh4 and qSH3 showed inhibition of abscission layer formation but the degree of seed shattering was different from that of IR36. On the basis of these results, we estimated that non-shattering of seeds in early rice domestication involved mutations in at least three loci, and these genetic materials produced in this study may help to identify novel seed-shattering loci.

Biomedical Image Communication Platform

Recent progress in the field of biomedical imaging has led to the demand for large-scale three-dimensional (3D) image analysis in modern biology. Existing image-processing systems lack either cloud computing services or advanced 3D processing ability. This paper proposes a novel cloud-based communication platform for biomedical images. The system provides visualization, data management, and image-processing functions through a standard web browser. Therefore, the sharing of limited software and hardware resources is achieved, allowing for effective collaboration between researchers. We demonstrate the applicability and functionality of the system by examining typical case studies on biomedical images.

Detection of quantitative trait loci controlling grain zinc concentration using Australian wild rice, Oryza meridionalis, a potential genetic resource for biofortification of rice

Zinc (Zn) is one of the essential mineral elements for both plants and humans. Zn deficiency in human is one of the major causes of hidden hunger, a serious health problem observed in many developing countries. Therefore, increasing Zn concentration in edible part is an important issue for improving human Zn nutrition. Here, we found that an Australian wild rice O. meridionalis showed higher grain Zn concentrations compared with cultivated and other wild rice species. The quantitative trait loci (QTL) analysis was then performed to identify the genomic regions controlling grain Zn levels using backcross recombinant inbred lines derived from O. sativa ‘Nipponbare’ and O. meridionalis W1627. Four QTLs responsible for high grain Zn were detected on chromosomes 2, 9, and 10. The QTL on the chromosome 9 (named qGZn9), which showed the largest effect on grain Zn concentration was confirmed with the introgression line, which had a W1627 chromosomal segment covering the qGZn9 region in the genetic background of O. sativa ‘Nipponbare’. Fine mapping of this QTL resulted in identification of two tightly linked loci, qGZn9a and qGZn9b. The candidate regions of qGZn9a and qGZn9b were estimated to be 190 and 950 kb, respectively. Furthermore, we also found that plants having a wild chromosomal segment covering qGZn9a, but not qGZn9b, is associated with fertility reduction. qGZn9b, therefore, provides a valuable allele for breeding rice with high Zn in the grains.

A Novel Performance Evaluation System for Fluorescent Cell Image Segmentation

Image segmentation is crucial to modern cell biology which depends on quantitative analysis of fluorescent microscopy images. Segmented regions are useful to estimate localization and dynamics of cells and sub-cellular objects. Although many segmentation algorithms have been proposed in image processing and pattern recognition fields, most approaches are designed for some specific tasks and may not work well for other tasks. This makes it difficult to find an algorithm suitable for a given task for biologists who do not have knowledge about segmentation algorithms. Automatic selection of an appropriate segmentation algorithm including its parameters efficiently has become a very important duty, since recent advances in cell and sub-cellular imaging technology significantly increases a number of observed images for biologists. In this paper, we propose a novel segmentation system which has the function of performance evaluation. The set of candidate algorithms including parameter variation are automatically generated by using combinations of the prescribed image processing methods implemented in our system, and the system is designed in order to add and replace these algorithms easily. Their segmentation qualities are evaluated by comparing each result with the ground-truth provided by biologists based on either a single or multiple similarity metrics. Finally, our system predicts which algorithm will provide the best performance on a set of images similar to the original image with ground-truth reference. We examine our system using typical segmentation algorithms under several evaluation metrics and find it useful especially for detection of fluorescent labeled targets with granular shapes on real sub-cellular images, as well as simulated images with small sub-cellular objects.