Biology subjects

Itoga, H.

Publications and source records attributed to Itoga, H..

5 recordsLinked to original sources

bioRxiv2023

Volumetric trans-scale imaging of massive quantity of heterogeneous cell populations in centimeter-wide tissue and embryo

We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of 2x and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 {micro}m across an FOV of approximately 1.5 x 1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hours, visualizing the movement of over 4.0 x 105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.

developmental biology↗

bioRxiv2023

An atlas and database of neuropeptide gene expression in the adult zebrafish forebrain

Zebrafish is a useful model organism in neuroscience; however, its gene expression atlas in the adult brain is not well developed. In the present study, we examined the expression of 38 neuropeptides, and glutamatergic neuron marker gene mix (slc17a6a, slc17a6b, slc17a7a, and slc17a7b) in the adult zebrafish brain using in situ hybridization. The results are summarized as an expression atlas in 19 coronal planes of the forebrain. Furthermore, the scan data of all sections were made publicly available as a database. Based on these data, we performed detailed neuroanatomical analyses of the hypothalamus. By scrutinizing and comparing the expression patterns of neuropeptides, we found that several regions described as one nucleus in the reference zebrafish brain atlas contain two or more subregions with significantly different neuropeptide/neurotransmitter expression profiles, and we proposed them as novel subnuclei. Subsequently, the expression data obtained in this study were compared with those in mice, and a cluster analysis was performed to examine the similarities. As a result, several nuclei in zebrafish and mice were clustered in close vicinity: zebrafish ventral part of the anterior part of the parvocellular preoptic nucleus (PPav)/magnocellular preoptic nucleus (PM) and mouse paraventricular hypothalamic nucleus (Pa), zebrafish posterior part of the parvocellular preoptic nucleus (PPp) and mouse medial preoptic area (MPA), zebrafish dorsal part of the ventral zone of periventricular hypothalamus (Hvd)/anterior tuberal nucleus (ATN) and mouse ventromedial hypothalamic nucleus (VMN). The present expression atlas, database, and anatomical findings will contribute to future neuroscientific research using zebrafish. Key pointsO_LIThe expression of 38 neuropeptides and GABAergic/glutamatergic neuronal marker genes in adult zebrafish forebrain was examined and compiled as an atlas. C_LIO_LIAll scanned brain section data were published as a database. C_LIO_LIBased on the expression data obtained, multiple subnuclei in the zebrafish hypothalamus were proposed, and comparisons with the mouse hypothalamus were conducted. C_LI

neuroscience↗

bioRxiv2022

PHi-C2: interpreting Hi-C data as the dynamic 3D genome state

Hi-C is a widely used assay for studying three-dimensional (3D) genome organization across the whole genome. Here, we present PHi-C2, a Python package supported by mathematical and biophysical polymer modeling, that converts an input Hi-C matrix data into the polymer models dynamics, structural conformations, and rheological features. The updated optimization algorithm to regenerate a highly similar Hi-C matrix provides a fast and accurate optimal solution compared to the previous version by eliminating a computational bottleneck in the iterative optimization process. Besides, we newly set up the availability on Google Colab workflow to run, easily change parameters and check the results in the notebook. Overall, PHi-C2 can be a valuable tool to mine the dynamic 3D genome state embedded in Hi-C data. Availability and ImplementationPHi-C2 as the phic Python package is freely available under the GPL license and can be installed from the Python package index. The source code is available from GitHub at https://github.com/soyashinkai/PHi-C2. Without preparing a Python environment, PHi-C2 can run on Google Colab (https://bit.ly/3rlptGI). Contactsoya.shinkai@riken.jp or sonami@riken.jp

bioinformatics↗

bioRxiv2020

Deep Collection of Quantitative Nuclear Division Dynamics Data in RNAi-treated Caenorhabditis elegans Embryos

Recent advances in bioimage informatics techniques have yielded quantitative data on multicellular dynamics from microscopy images of animal development. Several such data collections have been created for Caenorhabditis elegans embryos under various gene silencing conditions. However, because of the limited depth of the datasets, it is impractical to apply standard statistical methods to these collections. Here, we created a deep collection of quantitative data on nuclear division dynamics during the first three rounds of cell division in C. elegans embryos, in which 263 essential embryonic genes were silenced individually by RNA-mediated interference. The collection consists of datasets from 33 wild-type and 1142 RNAi-treated embryos, including five or more datasets for 189 genes. Application of a two-sample t-test identified 8660 reproducible RNAi-induced phenotypes for 421 phenotypic characters. Clustering analysis suggested 24 functional processes essential for early embryogenesis. Our collection is a rich resource for understanding animal development mechanisms. In BriefKyoda et al. used bioimage informatics techniques to create a deep collection of quantitative data on nuclear division dynamics in RNAi-treated C. elegans embryos for 263 essential embryonic genes. Statistical analysis identified 8660 reproducible RNAi phenotypes for 421 phenotypic characters. The collection is a rich resource for understanding animal development. HighlightsO_LIBioimage informatics quantified nuclear division dynamics in C. elegans embryos C_LIO_LIFrom RNAi-silenced embryos we collected 1142 data sets on 263 essential genes C_LIO_LIStatistical analysis identified 8660 reproducible RNAi phenotypes C_LIO_LIClustering analysis suggested 24 functional processes in C. elegans embryogenesis C_LI

developmental biology↗

bioRxiv2020

BD5: an open HDF5-based data format to represent quantitative biological dynamics data

BD5 is a new binary data format based on HDF5 (hierarchical data format version 5). It can be used for representing quantitative biological dynamics data obtained from bioimage informatics techniques and mechanobiological simulations. Biological Dynamics Markup Language (BDML) is an XML(Extensible Markup Language)-based open format that is also used to represent such data; however, it becomes difficult to access quantitative data in BDML files when the file size is large because parsing XML- based files requires large computational resources to first read the whole file sequentially into computer memory. BD5 enables fast random (i.e., direct) access to quantitative data on disk without parsing the entire file. Therefore, it allows practical reuse of data for understanding biological mechanisms underlying the dynamics.

bioinformatics↗