Vandenbon Alexis

Affiliation and department：

Research field (1)： Systems genomics

Papers (41)：

Alexis Vandenbon, Rin Mizuno, Riyo Konishi, Masaya Onishi, Kyoko Masuda, Yuka Kobayashi, Hiroshi Kawamoto, Ayako Suzuki, Chenfeng He, Yuki Nakamura, et al. Murine breast cancers disorganize the liver transcriptome in a zonated manner. Communications biology. 2023. 6. 1. 97-97
Masanori Yoshinaga, Kyuho Han, David W Morgens, Takuro Horii, Ryosuke Kobayashi, Tatsuaki Tsuruyama, Fabian Hia, Shota Yasukura, Asako Kajiya, Ting Cai, et al. The N6-methyladenosine methyltransferase METTL16 enables erythropoiesis through safeguarding genome integrity. Nature communications. 2022. 13. 1. 6435-6435
Ai Yaku, Tadakatsu Inagaki, Ryotaro Asano, Makoto Okazawa, Hiroyoshi Mori, Ayuko Sato, Fabian Hia, Takeshi Masaki, Yusuke Manabe, Tomohiko Ishibashi, et al. Regnase-1 Prevents Pulmonary Arterial Hypertension Through mRNA Degradation of Interleukin-6 and Platelet-Derived Growth Factor in Alveolar Macrophages. Circulation. 2022. 101161CIRCULATIONAHA122059435
Masatoshi Ohgushi, Nobuko Taniyama, Alexis Vandenbon, Mototsugu Eiraku. Delamination of trophoblast-like syncytia from the amniotic ectodermal analogue in human primed embryonic stem cell-based differentiation model. Cell reports. 2022. 39. 12. 110973-110973
Ka Man Tse, Alexis Vandenbon, Xiaotong Cui, Takashi Mino, Takuya Uehata, Keiko Yasuda, Ayuko Sato, Tohru Tsujimura, Fabian Hia, Masanori Yoshinaga, et al. Enhancement of Regnase-1 expression with stem loop-targeting antisense oligonucleotides alleviates inflammatory diseases. Science translational medicine. 2022. 14. 644. eabo2137

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MISC (1)：

Michio Tomura, Ryoyo Ikebuchi, Shunsuke Teraguchi, Alexis Vandenbon, Shand H. W. Francis, Tetsuya Honda. A rare subset of skin-tropic regulatory T cells expressing Il10/Gzmb inhibits the cutaneous immune response. JOURNAL OF IMMUNOLOGY. 2017. 198. 1

Books (1)：

Epigenetics in organ specific disorders
Academic Press 2023 ISBN:9780128239315

Lectures and oral presentations (17)：

Exploratory analysis of various single-cell and spatial transcriptomics data types
(Institute for Quantitative Biosciences student seminar, the University of Tokyo 2022)
singleCellHaystack: A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
(Tsukuba Bioinformatics Assembly 2021)
singleCellHaystack: A clustering-independent method for predicting differentially expressed genes in single cell transcriptome data
(1st International Symposium of CCII -Bioinformatics and its application to cancer and other diseases 2021)
singleCellHaystack: A clustering-independent method for predicting differentially expressed genes in single cell transcriptome data
(Human Cell Atlas Asia Meeting 2020 2020)
Prediction of regulatory mechanisms of gene expression at the epigenetic, transcriptional and post-transcriptional level
(IIBMP2019 2019)

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Work history (2)：

2017/08 - 現在 Kyoto University
2009/10 - 2017/07 Osaka University

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