Biology subjects

Kobayashi, T. J.

Publications and source records attributed to Kobayashi, T. J..

3 recordsLinked to original sources

bioRxiv2018

Transfer RNA Genes Affect Chromosome Structure and Function via Local Effects

The genome is packaged and organized in an ordered, non-random manner and specific chromatin segments contact nuclear substructures to mediate this organization. While transfer RNA genes (tDNAs) are essential for the generation of tRNAs, these loci are also binding sites for transcription factors and architectural proteins and are thought to play an important role in the organization of the genome. In this study, we investigate the role of tDNAs in genomic organization and chromosome function by editing a chromosome so that it lacks any tDNAs. Surprisingly our analyses of this tDNA-less chromosome show that loss of tDNAs does not grossly affect chromosome folding or chromosome tethering. However, loss of tDNAs affects local nucleosome positioning and the binding of SMC proteins at these loci. The absence of tDNAs also leads to changes in centromere clustering and a reduction in the frequency of long range HML-HMR heterochromatin clustering. We propose that the tDNAs primarily affect local chromatin structure that result in effects on long-range chromosome architecture.

molecular biology↗

bioRxiv2018

Development of Convolutional Neural Network Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Mouse Development

In embryology, analytical approaches such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current machine learning techniques outperform traditional image processing algorithms. However segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing algorithms can perform only semantic segmentation, which distin-guishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially out-performed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.

systems biology↗

bioRxiv2017

Quantification of inter-sample differences in T cell receptor sequences

Inter-sample comparisons of the T cell receptor (TCR) repertoire are crucial for gaining a better understanding into the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. As a theoretical approach for the quantitative comparison, previous studies utilized the Poisson abundance models and the conventional methods in ecology, which focus on the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have the contributions to the observed inter-sample differences. In this paper, we propose a new comparative approach based on TCR sequence information, which can estimate the low-dimensional structure by projecting the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences are then quantified according to information-theoretic measures among the distributions of data estimated in the embedded space. Using an actual dataset of TCR sequences in transgenic mice that have strong restrictions on somatic recombination, we demonstrate that our proposed method can accurately identify the inter-sample hierarchical structure, which is consistent with that estimated by previous methods based on abundance or count information. Moreover, we identified the key sequences that contribute to the pairwise sample differences. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.

immunology↗