Biology subjects

Kojima, Y.

Publications and source records attributed to Kojima, Y..

23 records · Page 2Linked to original sources

bioRxiv2021

P-sort: an open-source software for cerebellar neurophysiology

Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents challenges for spike detection. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. Here, we analyzed data from over 300 P-cells in marmosets, macaques, and mice, using an open-source, semi-automated software called P-sort that addresses the spike identification and attribution problems. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge seemingly disparate clusters, or split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns.

neuroscience↗

bioRxiv2020

Capturing Human Trophoblast Development with Naive Pluripotent Stem Cells In Vitro

Trophoblast are extra-embryonic cells that are essential to maintain pregnancy. Human trophoblasts arise from the morula as trophectoderm (TE), which, after implantation, differentiates into cytotrophoblast (CT), syncytiotrophoblast (ST), and extravillous trophoblast (EVT) composing the placenta. Here we show that naive, but not primed, human pluripotent stem cells (PSCs) recapitulate trophoblast development. Naive PSC-derived TE and CT (nCT) recreated the human and monkey TE-to-CT transition. nCT self-renewed as CT stem cells and had the characteristics of proliferating villous CT and CT in the cell column of the first trimester. Notably, although primed PSCs differentiated into trophoblast-like cells (pBAP), pBAP were distinct from nCT and human placenta-derived CT stem cells, exhibiting properties consistent of the amnion. Our findings establish an authentic paradigm for human trophoblast development, demonstrating the invaluable properties of naive human PSCs. Our system will provide a platform to study the molecular mechanisms underlying trophoblast development and related diseases.

developmental biology↗

bioRxiv2020

Interleukin-11 is a Marker for Both Cancer- and Inflammation-Associated Fibroblasts that Contribute to Colorectal Cancer Progression

Interleukin (IL)-11 is a member of the IL-6 family of cytokines and involved in multiple cellular responses, including tumor development. However, the origin and functions of IL-11-producing (IL-11+) cells are not fully understood. To characterize IL-11+ cells in vivo, we generated Il11 reporter mice. IL-11+ cells appeared in the colon of three murine tumor models, and a murine acute colitis model. Il11ra1 or Il11 deletion attenuated the development of colitis-associated colorectal cancer. IL-11+ cells expressed fibroblast markers, and genes associated with cell proliferation and tissue repair. IL-11 induced STAT3 phosphorylation in colonic fibroblasts, suggesting the activation of IL-11+ fibroblasts. Analysis using the human cancer database revealed that genes enriched in IL-11+ fibroblasts were elevated in human colorectal cancer, and correlated with reduced disease-free survival. Together, our results suggested that tumor cells induced IL-11+ fibroblasts, and that a feed-forward loop between IL-11 and IL-11+ fibroblasts might contribute to tumor development.

cancer biology↗

bioRxiv2018

Comparative evaluation of the GeeniusTM HIV 1/2 Confirmatory Assay and the HIV-1 and HIV-2 Western blots in the Japanese population

Accurate diagnosis of earlier HIV infection is essential for treatment and prevention. Currently, confirmation tests of HIV infection in Japan are performed using Western blot (WB), but WB has several limitations including low sensitivity and cross-reactivity between HIV-1 and HIV-2 antibodies. To address these problems, a new HIV testing algorithm and a more reliable confirmation and HIV-1/2 differentiation assay are required. The Bio-Rad Geenius HIV-1/2 Confirmatory Assay (Geenius) has recently been approved and recommended for use in the revised guidelines for diagnosis of HIV infection by the Center for Disease Control and Prevention (USA). We made comprehensive comparison of the performance of Geenius and the Bio-Rad NEW LAV BLOT 1 and 2 (NLB 1 and 2) which are WB kits for HIV-1 and HIV-2, respectively, to examine if Geenius is a suitable alternative to these WB assays which are now being used in HIV testing in Japan. A total of 166 HIV-1 positive samples (146 from patients with established HIV-1 infection and 20 from patients with acute infection), five HIV-1 seroconversion panels containing 21 samples and 30 HIV-2 positive samples were used. In addition, a total of 140 HIV negative samples containing 10 false-positives on screening tests were examined. The sensitivity of Geenius and NLB 1 for HIV-1 positive samples was 99.3% and 98.6%, respectively. Geenius provided more positive results in the samples from acute infections and detected positivity 0 to 32 days earlier in seroconversion panels than NLB 1. NLB 2 gave positive results in 12.3% of HIV-1 positive samples. The sensitivity of both Geenius and NLB 2 for HIV-2 positive samples was 100%. The specificity of Geenius, NLB 1 and NLB 2 was 98.5%, 81.5% and 90.0%, respectively. Geenius is an attractive alternative to WB for confirmation and differentiation of HIV-1 and HIV-2 infections. The adaptation of Geenius to the HIV testing algorithm may be advantageous for rapid diagnosis and the reduction of testing costs.

molecular biology↗

bioRxiv2017

An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis

The spatial patterns of gene expression in early Drosophila melanogaster embryogenesis have been studied experimentally and theoretically to reveal the molecular basis of morphogenesis. In particular, the gene regulatory network (GRN) of gap genes has been investigated through mathematical modeling and simulation. Although these simulation-based approaches are useful for describing complex dynamics and have revealed several important regulations in spatial patterning, they are computationally intensive because they optimize GRN with iterative simulation. Recently, the advance of experimental technologies is enabling the acquisition of comprehensive spatial expression data, and an efficient algorithm will be necessary to analyze such large-scale data. In this research, we developed an efficient algorithm to infer the GRN based on a linear reaction-diffusion model. First, we qualitatively analyzed the GRNs of gap genes and pair-rule genes based on our algorithm and showed that two mutual repressions are fundamental regulations. Then, we inferred the GRN from gap gene data, and identified asymmetric regulations in addition to the two mutual repressions. We analyzed the effect of these asymmetric regulations on spatial patterns, and showed that they have the potential to adjust peak position. Our algorithm runs in sub-second time, which is significantly smaller than the runtime of simulation-based approaches (between 8 and 160 h, for exmaple). Neverthe-less, our inferred GRN was highly correlated with the simulation-based GRNs. We also analyzed the gap gene network of Clogmia albipunctata and showed that different mutual repression regulations might be important in comparison with those of Drosophila melanogaster. As our algorithm can infer GRNs efficiently and can be applied to several different network analysis, it will be a valuable approach for analyzing large-scale data.

bioinformatics↗