Biology subjects

Hayashi, T.

Publications and source records attributed to Hayashi, T..

5 recordsLinked to original sources

bioRxiv2018

Does the Diffusion Tensor Model Predict the Neurite Distribution of Cerebral Cortical Gray Matter? - Cortical DTI-NODDI

Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient. We analyzed a combination of high, intermediate and low b-value data of multi-shell diffusion-weighted MRI (dMRI) in healthy 456 subjects of the Human Connectome Project using NODDI, DTI and a mathematical conversion from DTI to NODDI. Cortical distributions of DTI and DTI-derived NODDI metrics were remarkably associated with those in NODDI, particularly when applied highly diffusion-weighted data (b-value =3000 sec/mm2). This was supported by simulation analysis, which revealed that DTI-derived parameters with lower b-value datasets suffered from errors due to heterogeneity of cerebrospinal fluid fraction and partial volume. These findings suggest that high b-value DTI redundantly parallels with NODDI-based cortical neurite measures, but the conventional low b-value DTI does not reasonably characterize cortical microarchitecture.

neuroscience↗

bioRxiv2018

A comprehensive reference transcriptome resource for the Iberian ribbed newt Pleurodeles waltl, an emerging model for developmental and regeneration biology

Urodele amphibian newts have unique biological properties, notably including prominent regeneration ability. Iberian ribbed newt, Pleurodeles waltl, is a promising model newt along with the successful development of the easy breeding system and efficient transgenic and genome editing methods. However, genetic information of P. waltl was limited. In the present study, we conducted an intensive transcriptome analysis of P. waltl using RNA-sequencing to build gene models and annotate them. We generated 1.2 billion Illumina reads from a wide variety of samples across 11 different tissues and 9 time points during embryogenesis. They were assembled into 202,788 non-redundant contigs that appear to cover nearly complete (~98%) P. waltl protein-coding genes. Using the gene set as a reference, our gene network analysis identified regeneration-, developmental-stage-, and tissue-specific co-expressed gene modules. Ortholog analyses with other vertebrates revealed the gene repertoire evolution of amphibians which includes urodele-specific loss of bmp4 and duplications of wnt11b. Our transcriptome resource will enhance future research employing this emerging model animal for regeneration research as well as other areas such as developmental biology, stem cell biology, cancer research, ethology and toxico-genomics. These data are available via our portal website, iNewt (http://www.nibb.ac.jp/imori/main/).

genomics↗

bioRxiv2017

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts (at a rate of 30%-50%) and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes in total from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

genomics↗

bioRxiv2017

A randomized comparison of different hormone replacement protocols for thawed blastocyst transfer

BackgroundThere are no randomized controlled trials evaluating the pregnancy rates after thawed blastocyst transfers in patients treated with various hormone replacement regimens.\n\nMethodsA prospective randomized controlled trial was conducted to evaluate the outcomes in three different hormone replacement protocols for thawed blastocyst transfer. A total of 330 women (median age 38.2 years) who were undergoing IVF at our clinic were enrolled.\n\nResultsSerum estradiol (E2) levels were 267.71 pg/ml in Premarin group, 391.22 pg/ml in Estrogel group and 495.12 pg/ml in Estrana tape group. Therefore, serum E2 levels in Estrana tape group were higher than those of the other two groups (P<0.01). The pregnancy rate in the Estrogel group was higher than that in the Premarin group (30.0% versus 17.3%, P=0.026, odds ratio 2.05, 95% confidence interval: 1.09-3.87). Furthermore, the pregnancy rate in the Estrana tape group was higher than that in the Estrogel group (43.6% versus 30.0%, P=0.036, odds ratio 1.81, 95% confidence interval: 1.04-3.14).\n\nConclusionThe serum E2 levels contributed to differences observed in the pregnancy rate among the three different protocols. Thus, Estrana tape has an advantage as a hormone replacement protocol for thawed blastocyst transfer.

clinical trials↗

bioRxiv2016

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.\n\nIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.\n\nThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

bioinformatics↗