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1. A prenatal skin atlas reveals immune regulation of human skin morphogenesis.

Author

Gopee, Nusayhah, Winheim, Elena, Olabi, Bayanne, Admane, Chloe, Foster, April, Huang, Ni, Botting, Rachel, Torabi, Fereshteh, Sumanaweera, Dinithi, Le, Anh, Kim, Jin, Verger, Luca, Stephenson, Emily, Adão, Diana, Ganier, Clarisse, Gim, Kelly, Serdy, Sara, Deakin, CiCi, Goh, Issac, Steele, Lloyd, Annusver, Karl, Miah, Mohi-Uddin, Tun, Win, Moghimi, Pejvak, Kwakwa, Kwasi, Li, Tong, Basurto Lozada, Daniela, Rumney, Ben, Tudor, Catherine, Roberts, Kenny, Chipampe, Nana-Jane, Sidhpura, Keval, Englebert, Justin, Jardine, Laura, Reynolds, Gary, Rose, Antony, Rowe, Vicky, Pritchard, Sophie, Mulas, Ilaria, Fletcher, James, Popescu, Dorin-Mirel, Poyner, Elizabeth, Dubois, Anna, Guy, Alyson, Filby, Andrew, Lisgo, Steven, Barker, Roger, Glass, Ian, Park, Jong-Eun, Vento-Tormo, Roser, Nikolova, Marina, He, Peng, Lawrence, John, Moore, Josh, Ballereau, Stephane, Hale, Christine, Shanmugiah, Vijaya, Horsfall, David, Rajan, Neil, McGrath, John, OToole, Edel, Treutlein, Barbara, Bayraktar, Omer, Kasper, Maria, Progatzky, Fränze, Mazin, Pavel, Lee, Jiyoon, Gambardella, Laure, Koehler, Karl, Teichmann, Sarah, and Haniffa, Muzlifah

Subjects
Humans, Skin, Morphogenesis, Macrophages, Hair Follicle, Organoids, Induced Pluripotent Stem Cells, Immunity, Innate, Atlases as Topic, Single-Cell Analysis, Endothelial Cells, Female, Neovascularization, Physiologic, Transcriptome, Human Embryonic Stem Cells, Wound Healing, Fetus
Abstract

Human prenatal skin is populated by innate immune cells, including macrophages, but whether they act solely in immunity or have additional functions in morphogenesis is unclear. Here we assembled a comprehensive multi-omics reference atlas of prenatal human skin (7-17 post-conception weeks), combining single-cell and spatial transcriptomics data, to characterize the microanatomical tissue niches of the skin. This atlas revealed that crosstalk between non-immune and immune cells underpins the formation of hair follicles, is implicated in scarless wound healing and is crucial for skin angiogenesis. We systematically compared a hair-bearing skin organoid (SkO) model derived from human embryonic stem cells and induced pluripotent stem cells to prenatal and adult skin1. The SkO model closely recapitulated in vivo skin epidermal and dermal cell types during hair follicle development and expression of genes implicated in the pathogenesis of genetic hair and skin disorders. However, the SkO model lacked immune cells and had markedly reduced endothelial cell heterogeneity and quantity. Our in vivo prenatal skin cell atlas indicated that macrophages and macrophage-derived growth factors have a role in driving endothelial development. Indeed, vascular network remodelling was enhanced following transfer of autologous macrophages derived from induced pluripotent stem cells into SkO cultures. Innate immune cells are therefore key players in skin morphogenesis beyond their conventional role in immunity, a function they achieve through crosstalk with non-immune cells.

Published
2024

2. Single-cell RNA sequencing of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases

Author

Tian, Chi, Zhang, Yuntian, Tong, Yihan, Kock, Kian Hong, Sim, Donald Yuhui, Liu, Fei, Dong, Jiaqi, Jing, Zhixuan, Wang, Wenjing, Gao, Junbin, Tan, Le Min, Han, Kyung Yeon, Tomofuji, Yoshihiko, Nakano, Masahiro, Buyamin, Eliora Violain, Sonthalia, Radhika, Ando, Yoshinari, Hatano, Hiroaki, Sonehara, Kyuto, Jin, Xin, Loh, Marie, Chambers, John, Hon, Chung-Chau, Choi, Murim, Park, Jong-Eun, Ishigaki, Kazuyoshi, Okamura, Tomohisa, Fujio, Keishi, Okada, Yukinori, Park, Woong-Yang, Shin, Jay W., Roca, Xavier, Prabhakar, Shyam, and Liu, Boxiang

Published
2024
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3. A prenatal skin atlas reveals immune regulation of human skin morphogenesis

Author

Gopee, Nusayhah Hudaa, Winheim, Elena, Olabi, Bayanne, Admane, Chloe, Foster, April Rose, Huang, Ni, Botting, Rachel A., Torabi, Fereshteh, Sumanaweera, Dinithi, Le, Anh Phuong, Kim, Jin, Verger, Luca, Stephenson, Emily, Adão, Diana, Ganier, Clarisse, Gim, Kelly Y., Serdy, Sara A., Deakin, CiCi, Goh, Issac, Steele, Lloyd, Annusver, Karl, Miah, Mohi-Uddin, Tun, Win Min, Moghimi, Pejvak, Kwakwa, Kwasi Amoako, Li, Tong, Basurto Lozada, Daniela, Rumney, Ben, Tudor, Catherine L., Roberts, Kenny, Chipampe, Nana-Jane, Sidhpura, Keval, Englebert, Justin, Jardine, Laura, Reynolds, Gary, Rose, Antony, Rowe, Vicky, Pritchard, Sophie, Mulas, Ilaria, Fletcher, James, Popescu, Dorin-Mirel, Poyner, Elizabeth, Dubois, Anna, Guy, Alyson, Filby, Andrew, Lisgo, Steven, Barker, Roger A., Glass, Ian A., Park, Jong-Eun, Vento-Tormo, Roser, Nikolova, Marina Tsvetomilova, He, Peng, Lawrence, John E. G., Moore, Josh, Ballereau, Stephane, Hale, Christine B., Shanmugiah, Vijaya, Horsfall, David, Rajan, Neil, McGrath, John A., O’Toole, Edel A., Treutlein, Barbara, Bayraktar, Omer, Kasper, Maria, Progatzky, Fränze, Mazin, Pavel, Lee, Jiyoon, Gambardella, Laure, Koehler, Karl R., Teichmann, Sarah A., and Haniffa, Muzlifah

Published
2024
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5. Hippo-YAP/TAZ signalling coordinates adipose plasticity and energy balance by uncoupling leptin expression from fat mass.

Author

Choi, Sungwoo, Kang, Ju-Gyeong, Tran, Yen, Jeong, Sun-Hye, Park, Kun-Young, Shin, Hyemi, Kim, Young, Park, Myungsun, Nahmgoong, Hahn, Seol, Taejun, Jeon, Haeyon, Kim, Yeongmin, Park, Sanghee, Kim, Hee-Joo, Kim, Min-Seob, Li, Xiaoxu, Bou Sleiman, Maroun, Lee, Eries, Choi, Jinhyuk, Eisenbarth, David, Lee, Sang, Cho, Suhyeon, Auwerx, Johan, Kim, Il-Young, Kim, Jae, Park, Jong-Eun, Lim, Dae-Sik, Suh, Jae, and Moore, David

Subjects
Animals, Leptin, Signal Transduction, Energy Metabolism, Protein Serine-Threonine Kinases, Mice, Adaptor Proteins, Signal Transducing, YAP-Signaling Proteins, Adipose Tissue, Adipocytes, Hippo Signaling Pathway, Cell Cycle Proteins, Transcription Factors, Transcriptional Coactivator with PDZ-Binding Motif Proteins, Phosphoproteins, Tumor Suppressor Proteins, Trans-Activators
Abstract

Adipose tissues serve as an energy reservoir and endocrine organ, yet the mechanisms that coordinate these functions remain elusive. Here, we show that the transcriptional coregulators, YAP and TAZ, uncouple fat mass from leptin levels and regulate adipocyte plasticity to maintain metabolic homeostasis. Activating YAP/TAZ signalling in adipocytes by deletion of the upstream regulators Lats1 and Lats2 results in a profound reduction in fat mass by converting mature adipocytes into delipidated progenitor-like cells, but does not cause lipodystrophy-related metabolic dysfunction, due to a paradoxical increase in circulating leptin levels. Mechanistically, we demonstrate that YAP/TAZ-TEAD signalling upregulates leptin expression by directly binding to an upstream enhancer site of the leptin gene. We further show that YAP/TAZ activity is associated with, and functionally required for, leptin regulation during fasting and refeeding. These results suggest that adipocyte Hippo-YAP/TAZ signalling constitutes a nexus for coordinating adipose tissue lipid storage capacity and systemic energy balance through the regulation of adipocyte plasticity and leptin gene transcription.

Published
2024

12. Integrative analysis of spatial and single-cell transcriptome data from human pancreatic cancer reveals an intermediate cancer cell population associated with poor prognosis

Author

Kim, Seongryong, Leem, Galam, Choi, Junjeong, Koh, Yongjun, Lee, Suho, Nam, Sang-Hee, Kim, Jin Su, Park, Chan Hee, Hwang, Ho Kyoung, Min, Kyoung Il, Jo, Jung Hyun, Lee, Hee Seung, Chung, Moon Jae, Park, Jeong Youp, Park, Seung Woo, Song, Si Young, Shin, Eui-Cheol, Kang, Chang Moo, Bang, Seungmin, and Park, Jong-Eun

Published
2024
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13. Prognostic Accuracy of SpO2-based Respiratory Sequential Organ Failure Assessment for Predicting In-hospital Mortality

Author

Jeong, Daun, Lee, Gun Tak, Park, Jong Eun, Hwang, Sung Yeon, Kim, Taerim, Lee, Se Uk, Yoon, Hee, Cha, Won Chul, Sim, Min Seob, Jo, Ik Joon, and Shin, Tae Gun

Subjects
Sequential Organ Failure Assessment scores, Pulse oximetry, Sepsis, Respiratory failure
Abstract

Introduction: In this study we aimed to investigate the prognostic accuracy for predicting in-hospital mortality using respiratory Sequential Organ Failure Assessment (SOFA) scores by the conventional method of missing-value imputation with normal partial pressure of oxygen (PaO2)- and oxygen saturation (SpO2)-based estimation methods.Methods: This was a single-center, retrospective cohort study of patients with suspected infection in the emergency department. The primary outcome was in-hospital mortality. We compared the area under the receiver operating characteristics curve (AUROC) and calibration results of the conventional method (normal value imputation for missing PaO2) and six SpO2-based methods: using methods A, B, PaO2 is estimated by dividing SpO2 by a scale; with methods C and D, PaO2 was estimated by a mathematical model from a previous study; with methods E, F, respiratory SOFA scores was estimated by SpO2 thresholds and respiratory support use; with methods A, C, E are SpO2-based estimation for all PaO2 values, while methods B, D, F use such estimation only for missing PaO2 values.Results: Among the 15,119 patients included in the study, the in-hospital mortality rate was 4.9%. The missing PaO2was 56.0%. The calibration plots were similar among all methods. Each method yielded AUROCs that ranged from 0.735–0.772. The AUROC for the conventional method was 0.755 (95%confidence interval [CI] 0.736–0.773). The AUROC for method C (0.772; 95% CI 0.754–0.790) was higher than that of the conventional method, which was an SpO2-based estimation for all PaO2 values. The AUROC for total SOFA score from method E (0.815; 95% CI 0.800–0.831) was higher than that from the conventional method (0.806; 95% CI 0.790–0.822), in which respiratory SOFA was calculated by the predefined SpO2 cut-offs and oxygen support.Conclusion: In non-ICU settings, respiratory SOFA scores estimated by SpO2 might have acceptable prognostic accuracy for predicting in-hospital mortality. Our results suggest that SpO2-based respiratory SOFA score calculation might be an alternative for evaluating respiratory organ failure in the ED and clinical research settings.

Published
2023

20. Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues

Author

Charoensawan, Varodom, Hon, Chung-Chau, Majumder, Partha P., Matangkasombut, Ponpan, Park, Woong-Yang, Prabhakar, Shyam, Shin, Jay W., Carninci, Piero, Chambers, John C., Loh, Marie, Pithukpakorn, Manop, Suktitipat, Bhoom, Yamamoto, Kazuhiko, Rajagopalan, Deepa, Rayan, Nirmala Arul, Sankaran, Shvetha, Chantaraamporn, Juthamard, Chatterjee, Ankita, Ghosh, Supratim, Han, Kyung Yeon, Jevapatarakul, Damita, Nguantad, Sarintip, Sarkar, Sumanta, Thungsatianpun, Narita, Abe, Mai, Furukawa, Seiko, Inoue, Gyo, Myouzen, Keiko, Oh, Jin-Mi, Suzuki, Akari, Ando, Yoshinari, Kojima, Miki, Kouno, Tsukasa, Lim, Jinyeong, Maitra, Arindam, Tan, Le Min, Venkatesh, Prasanna Nori, Choi, Murim, Park, Jong-Eun, Buyamin, Eliora Violain, Kock, Kian Hong, Xuan Lin, Quy Xiao, Moody, Jonathan, Sonthalia, Radhika, Ishigaki, Kazuyoshi, Nakano, Masahiro, Okada, Yukinori, Tomofuji, Yoshihiko, Ho Namkoong, Edahiro, Ryuya, Takano, Tomomi, Nishihara, Hiroshi, Shirai, Yuya, Sonehara, Kyuto, Tanaka, Hiromu, Azekawa, Shuhei, Mikami, Yohei, Lee, Ho, Hasegawa, Takanori, Okudela, Koji, Okuzaki, Daisuke, Motooka, Daisuke, Kanai, Masahiro, Naito, Tatsuhiko, Yamamoto, Kenichi, Wang, Qingbo S., Saiki, Ryunosuke, Ishihara, Rino, Matsubara, Yuta, Hamamoto, Junko, Hayashi, Hiroyuki, Yoshimura, Yukihiro, Tachikawa, Natsuo, Yanagita, Emmy, Hyugaji, Takayoshi, Shimizu, Eigo, Katayama, Kotoe, Kato, Yasuhiro, Morita, Takayoshi, Takahashi, Kazuhisa, Harada, Norihiro, Naito, Toshio, Hiki, Makoto, Matsushita, Yasushi, Takagi, Haruhi, Aoki, Ryousuke, Nakamura, Ai, Harada, Sonoko, Sasano, Hitoshi, Kabata, Hiroki, Masaki, Katsunori, Kamata, Hirofumi, Ikemura, Shinnosuke, Chubachi, Shotaro, Okamori, Satoshi, Terai, Hideki, Morita, Atsuho, Asakura, Takanori, Sasaki, Junichi, Morisaki, Hiroshi, Uwamino, Yoshifumi, Nanki, Kosaku, Uchida, Sho, Uno, Shunsuke, Nishimura, Tomoyasu, Ishiguro, Takashi, Isono, Taisuke, Shibata, Shun, Matsui, Yuma, Hosoda, Chiaki, Takano, Kenji, Nishida, Takashi, Kobayashi, Yoichi, Takaku, Yotaro, Takayanagi, Noboru, Ueda, Soichiro, Tada, Ai, Miyawaki, Masayoshi, Yamamoto, Masaomi, Yoshida, Eriko, Hayashi, Reina, Nagasaka, Tomoki, Arai, Sawako, Kaneko, Yutaro, Sasaki, Kana, Tagaya, Etsuko, Kawana, Masatoshi, Arimura, Ken, Takahashi, Kunihiko, Anzai, Tatsuhiko, Ito, Satoshi, Endo, Akifumi, Uchimura, Yuji, Miyazaki, Yasunari, Honda, Takayuki, Tateishi, Tomoya, Tohda, Shuji, Ichimura, Naoya, Sonobe, Kazunari, Sassa, Chihiro Tani, Nakajima, Jun, Nakano, Yasushi, Nakajima, Yukiko, Anan, Ryusuke, Arai, Ryosuke, Kurihara, Yuko, Harada, Yuko, Nishio, Kazumi, Ueda, Tetsuya, Azuma, Masanori, Saito, Ryuichi, Sado, Toshikatsu, Miyazaki, Yoshimune, Sato, Ryuichi, Haruta, Yuki, Nagasaki, Tadao, Yasui, Yoshinori, Hasegawa, Yoshinori, Mutoh, Yoshikazu, Kimura, Tomoki, Sato, Tomonori, Takei, Reoto, Hagimoto, Satoshi, Noguchi, Yoichiro, Yamano, Yasuhiko, Sasano, Hajime, Ota, Sho, Nakamori, Yasushi, Yoshiya, Kazuhisa, Saito, Fukuki, Yoshihara, Tomoyuki, Wada, Daiki, Iwamura, Hiromu, Kanayama, Syuji, Maruyama, Shuhei, Yoshiyama, Takashi, Ohta, Ken, Kokuto, Hiroyuki, Ogata, Hideo, Tanaka, Yoshiaki, Arakawa, Kenichi, Shimoda, Masafumi, Osawa, Takeshi, Tateno, Hiroki, Hase, Isano, Yoshida, Shuichi, Suzuki, Shoji, Kawada, Miki, Horinouchi, Hirohisa, Saito, Fumitake, Mitamura, Keiko, Hagihara, Masao, Ochi, Junichi, Uchida, Tomoyuki, Baba, Rie, Arai, Daisuke, Ogura, Takayuki, Takahashi, Hidenori, Hagiwara, Shigehiro, Nagao, Genta, Konishi, Shunichiro, Nakachi, Ichiro, Murakami, Koji, Yamada, Mitsuhiro, Sugiura, Hisatoshi, Sano, Hirohito, Matsumoto, Shuichiro, Kimura, Nozomu, Ono, Yoshinao, Baba, Hiroaki, Suzuki, Yusuke, Nakayama, Sohei, Masuzawa, Keita, Namba, Shinichi, Suzuki, Ken, Naito, Yoko, Liu, Yu-Chen, Takuwa, Ayako, Sugihara, Fuminori, Wing, James B., Sakakibara, Shuhei, Hizawa, Nobuyuki, Shiroyama, Takayuki, Miyawaki, Satoru, Kawamura, Yusuke, Nakayama, Akiyoshi, Matsuo, Hirotaka, Yuichi, Maeda, Nii, Takuro, Noda, Yoshimi, Niitsu, Takayuki, Adachi, Yuichi, Enomoto, Takatoshi, Amiya, Saori, Hara, Reina, Yamaguchi, Yuta, Murakami, Teruaki, Kuge, Tomoki, Matsumoto, Kinnosuke, Yamamoto, Yuji, Yamamoto, Makoto, Yoneda, Midori, Kishikawa, Toshihiro, Yamada, Shuhei, Kawabata, Shuhei, Kijima, Noriyuki, Takagaki, Masatoshi, Sasa, Noah, Ueno, Yuya, Suzuki, Motoyuki, Takemoto, Norihiko, Eguchi, Hirotaka, Fukusumi, Takahito, Imai, Takao, Fukushima, Munehisa, Kishima, Haruhiko, Inohara, Hidenori, Tomono, Kazunori, Kato, Kazuto, Takahashi, Meiko, Matsuda, Fumihiko, Hirata, Haruhiko, Takeda, Yoshito, Koh, Hidefumi, Manabe, Tadashi, Funatsu, Yohei, Ito, Fumimaro, Fukui, Takahiro, Shinozuka, Keisuke, Kohashi, Sumiko, Miyazaki, Masatoshi, Shoko, Tomohisa, Kojima, Mitsuaki, Adachi, Tomohiro, Ishikawa, Motonao, Takahashi, Kenichiro, Inoue, Takashi, Hirano, Toshiyuki, Kobayashi, Keigo, Takaoka, Hatsuyo, Watanabe, Kazuyoshi, Miyazawa, Naoki, Kimura, Yasuhiro, Sado, Reiko, Sugimoto, Hideyasu, Kamiya, Akane, Kuwahara, Naota, Fujiwara, Akiko, Matsunaga, Tomohiro, Sato, Yoko, Okada, Takenori, Hirai, Yoshihiro, Kawashima, Hidetoshi, Narita, Atsuya, Niwa, Kazuki, Sekikawa, Yoshiyuki, Nishi, Koichi, Nishitsuji, Masaru, Tani, Mayuko, Suzuki, Junya, Nakatsumi, Hiroki, Ogura, Takashi, Kitamura, Hideya, Hagiwara, Eri, Murohashi, Kota, Okabayashi, Hiroko, Mochimaru, Takao, Nukaga, Shigenari, Satomi, Ryosuke, Oyamada, Yoshitaka, Mori, Nobuaki, Baba, Tomoya, Fukui, Yasutaka, Odate, Mitsuru, Mashimo, Shuko, Makino, Yasushi, Yagi, Kazuma, Hashiguchi, Mizuha, Kagyo, Junko, Shiomi, Tetsuya, Fuke, Satoshi, Saito, Hiroshi, Tsuchida, Tomoya, Fujitani, Shigeki, Takita, Mumon, Morikawa, Daiki, Yoshida, Toru, Izumo, Takehiro, Inomata, Minoru, Kuse, Naoyuki, Awano, Nobuyasu, Tone, Mari, Ito, Akihiro, Nakamura, Yoshihiko, Hoshino, Kota, Maruyama, Junichi, Ishikura, Hiroyasu, Takata, Tohru, Odani, Toshio, Amishima, Masaru, Hattori, Takeshi, Shichinohe, Yasuo, Kagaya, Takashi, Kita, Toshiyuki, Ohta, Kazuhide, Sakagami, Satoru, Koshida, Kiyoshi, Hayashi, Kentaro, Shimizu, Tetsuo, Kozu, Yutaka, Hiranuma, Hisato, Gon, Yasuhiro, Izumi, Namiki, Nagata, Kaoru, Ueda, Ken, Taki, Reiko, Hanada, Satoko, Kawamura, Kodai, Ichikado, Kazuya, Nishiyama, Kenta, Muranaka, Hiroyuki, Nakamura, Kazunori, Hashimoto, Naozumi, Wakahara, Keiko, Koji, Sakamoto, Omote, Norihito, Ando, Akira, Kodama, Nobuhiro, Kaneyama, Yasunari, Shunsuke, Maeda, Kuraki, Takashige, Matsumoto, Takemasa, Yokote, Koutaro, Nakada, Taka-Aki, Abe, Ryuzo, Oshima, Taku, Shimada, Tadanaga, Harada, Masahiro, Takahashi, Takeshi, Ono, Hiroshi, Sakurai, Toshihiro, Shibusawa, Takayuki, Kimizuka, Yoshifumi, Kawana, Akihiko, Sano, Tomoya, Watanabe, Chie, Suematsu, Ryohei, Sageshima, Hisako, Yoshifuji, Ayumi, Ito, Kazuto, Takahashi, Saeko, Ishioka, Kota, Nakamura, Morio, Masuda, Makoto, Wakabayashi, Aya, Watanabe, Hiroki, Ueda, Suguru, Nishikawa, Masanori, Chihara, Yusuke, Takeuchi, Mayumi, Onoi, Keisuke, Shinozuka, Jun, Sueyoshi, Atsushi, Nagasaki, Yoji, Okamoto, Masaki, Ishihara, Sayoko, Shimo, Masatoshi, Tokunaga, Yoshihisa, Kusaka, Yu, Ohba, Takehiko, Isogai, Susumu, Ogawa, Aki, Inoue, Takuya, Fukuyama, Satoru, Eriguchi, Yoshihiro, Yonekawa, Akiko, Kan-o, Keiko, Matsumoto, Koichiro, Kanaoka, Kensuke, Ihara, Shoichi, Komuta, Kiyoshi, Inoue, Yoshiaki, Chiba, Shigeru, Yamagata, Kunihiro, Hiramatsu, Yuji, Kai, Hirayasu, Asano, Koichiro, Oguma, Tsuyoshi, Ito, Yoko, Hashimoto, Satoru, Yamasaki, Masaki, Kasamatsu, Yu, Komase, Yuko, Hida, Naoya, Tsuburai, Takahiro, Oyama, Baku, Takada, Minoru, Kanda, Hidenori, Kitagawa, Yuichiro, Fukuta, Tetsuya, Miyake, Takahito, Yoshida, Shozo, Ogura, Shinji, Abe, Shinji, Kono, Yuta, Togashi, Yuki, Takoi, Hiroyuki, Kikuchi, Ryota, Ogawa, Shinichi, Ogata, Tomouki, Ishihara, Shoichiro, Kanehiro, Arihiko, Ozaki, Shinji, Fuchimoto, Yasuko, Wada, Sae, Fujimoto, Nobukazu, Nishiyama, Kei, Terashima, Mariko, Beppu, Satoru, Yoshida, Kosuke, Narumoto, Osamu, Nagai, Hideaki, Ooshima, Nobuharu, Motegi, Mitsuru, Umeda, Akira, Miyagawa, Kazuya, Shimada, Hisato, Endo, Mayu, Ohira, Yoshiyuki, Watanabe, Masafumi, Inoue, Sumito, Igarashi, Akira, Sato, Masamichi, Sagara, Hironori, Tanaka, Akihiko, Ohta, Shin, Kimura, Tomoyuki, Shibata, Yoko, Tanino, Yoshinori, Nikaido, Takefumi, Minemura, Hiroyuki, Sato, Yuki, Yamada, Yuichiro, Hashino, Takuya, Shinoki, Masato, Iwagoe, Hajime, Takahashi, Hiroshi, Fujii, Kazuhiko, Kishi, Hiroto, Kanai, Masayuki, Imamura, Tomonori, Yamashita, Tatsuya, Yatomi, Masakiyo, Maeno, Toshitaka, Hayashi, Shinichi, Takahashi, Mai, Kuramochi, Mizuki, Kamimaki, Isamu, Tominaga, Yoshiteru, Ishii, Tomoo, Utsugi, Mitsuyoshi, Ono, Akihiro, Tanaka, Toru, Kashiwada, Takeru, Fujita, Kazue, Saito, Yoshinobu, Seike, Masahiro, Watanabe, Hiroko, Matsuse, Hiroto, Kodaka, Norio, Nakano, Chihiro, Oshio, Takeshi, Hirouchi, Takatomo, Makino, Shohei, Egi, Moritoki, Omae, Yosuke, Nannya, Yasuhito, Ueno, Takafumi, Katayama, Kazuhiko, Ai, Masumi, Fukui, Yoshinori, Kumanogoh, Atsushi, Sato, Toshiro, Hasegawa, Naoki, Tokunaga, Katsushi, Ishii, Makoto, Koike, Ryuji, Kitagawa, Yuko, Kimura, Akinori, Imoto, Seiya, Miyano, Satoru, Ogawa, Seishi, Kanai, Takanori, Fukunaga, Koichi, Takeshima, Yusuke, Tanaka, Kentaro, Koichi Matsuda, Yamanashi, Yuji, Furukawa, Yoichi, Morisaki, Takayuki, Murakami, Yoshinori, Kamatani, Yoichiro, Muto, Kaori, Nagai, Akiko, Nakamura, Yusuke, Obara, Wataru, Yamaji, Ken, Asai, Satoshi, Takahashi, Yasuo, Higashiue, Shinichi, Kobayashi, Shuzo, Yamaguchi, Hiroki, Nagata, Yasunobu, Wakita, Satoshi, Nito, Chikako, Iwasaki, Yu-ki, Murayama, Shigeo, Yoshimori, Kozo, Miki, Yoshio, Obata, Daisuke, Higashiyama, Masahiko, Masumoto, Akihide, Koga, Yoshinobu, Koretsune, Yukihiro, Yata, Tomohiro, Ogawa, Kotaro, Namkoong, Ho, Okuno, Tatsusada, Liu, Boxiang, Matsuda, Koichi, and Mochizuki, Hideki

Published
2024
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21. Mapping the developing human immune system across organs.

Author

Suo, Chenqu, Dann, Emma, Goh, Issac, Jardine, Laura, Kleshchevnikov, Vitalii, Park, Jong-Eun, Botting, Rachel, Stephenson, Emily, Engelbert, Justin, Tuong, Zewen, Polanski, Krzysztof, Yayon, Nadav, Xu, Chuan, Suchanek, Ondrej, Elmentaite, Rasa, Domínguez Conde, Cecilia, He, Peng, Pritchard, Sophie, Miah, Mohi, Moldovan, Corina, Steemers, Alexander, Mazin, Pavel, Prete, Martin, Horsfall, Dave, Marioni, John, Clatworthy, Menna, Haniffa, Muzlifah, and Teichmann, Sarah

Subjects
Genomics, Humans, Immune System, Lymphocytes, Monocytes, Organ Specificity, RNA-Seq, Single-Cell Analysis
Abstract

Single-cell genomics studies have decoded the immune cell composition of several human prenatal organs but were limited in describing the developing immune system as a distributed network across tissues. We profiled nine prenatal tissues combining single-cell RNA sequencing, antigen-receptor sequencing, and spatial transcriptomics to reconstruct the developing human immune system. This revealed the late acquisition of immune-effector functions by myeloid and lymphoid cell subsets and the maturation of monocytes and T cells before peripheral tissue seeding. Moreover, we uncovered system-wide blood and immune cell development beyond primary hematopoietic organs, characterized human prenatal B1 cells, and shed light on the origin of unconventional T cells. Our atlas provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine, and disease understanding.

Published
2022

24. Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy

Author

Yu, Huasheng, Xiong, Jingwei, Ye, Adam Yongxin, Cranfill, Suna Li, Cannonier, Tariq, Gautam, Mayank, Zhang, Marina, Bilal, Rayan, Park, Jong-Eun, Xue, Yuji, Polam, Vidhur, Vujovic, Zora, Dai, Daniel, Ong, William, Ip, Jasper, Hsieh, Amanda, Mimouni, Nour, Lozada, Alejandra, Sosale, Medhini, Ahn, Alex, Ma, Minghong, Ding, Long, Arsuaga, Javier, and Luo, Wenqin

Subjects
Behavioral and Social Science, Mice, Animals, Deep Learning, Pruritus, Behavior, Animal, Injections, Chloroquine, Disease Models, Animal, itch, mouse behavior, scratching, deep learning, automatic quantification, Mouse, mouse, neuroscience, Biochemistry and Cell Biology
Abstract

Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and a chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening.

Published
2022

35. List of contributors

Author

Almeida, Déborah Aires, primary, Aono, Alexandre Hild, additional, Assmann, Taís Silveira, additional, Bajay, Stephanie Karenina, additional, Byadgi, Omkar, additional, Cagnin, Stefano, additional, Chaudhry, Shafqat Rasul, additional, Chemello, Francesco, additional, Chen, Shih-Chu, additional, Chimata, Anuradha Venkatakrishnan, additional, Crispim, Daisy, additional, da Silva Rodrigues, Guilherme, additional, De Jesús-Campos, Damaristelma, additional, de Oliveira, Fernanda Ancelmo, additional, de Souza, Anete P., additional, de Souza Pinhel, Marcela Augusta, additional, Deshpande, Prajakta, additional, Diaz, Rudy, additional, Domínguez-Rosas, Edmundo, additional, Fernandes-Ferreira, Rafael, additional, Ferreira, Rebecca Caroline Ulbricht, additional, Filho, Jaire F., additional, García-Coronado, Heriberto, additional, Ghosh, Subhajit, additional, Goldman, Gustavo H., additional, Gurung, Arun Bahadur, additional, Hänggi, Daniel, additional, Hayano-Kanashiro, Corina, additional, Hernández-Oñate, Miguel Ángel, additional, Horta, Maria Augusta C., additional, Islam, Khushbu, additional, Jento, Summer, additional, Joshi, Dheeraj Chandra, additional, Kattupalli, Divya, additional, Kemp, Steve, additional, Kim, Jun-Mo, additional, Kujawa, Tomasz, additional, Kumar, Ajay, additional, Kumar, Himansu, additional, Lim, Dajeong, additional, Lovio-Fragoso, José Pablo, additional, Madsen, Ole, additional, Mancini, Melina, additional, Marczyk, Michal, additional, Martínez, J. Alfredo, additional, Martínez, Octavio, additional, Mehta, Abijeet Singh, additional, Menon, Athira, additional, Milagro, Fermín, additional, Momo, John, additional, Muhammad, Sajjad, additional, Niederauer, Guilherme Francio, additional, Nonino, Carla Barbosa, additional, Noronha, Natalia Yumi, additional, Papiez, Anna, additional, Park, Jong-Eun, additional, Park, Woncheoul, additional, Patel, Surendra Singh, additional, Pillai, Beena, additional, Pimenta, Ricardo José Gonzaga, additional, Polanska, Joanna, additional, Ramchiary, Nirala, additional, Razo-Mendivil, Fernando Guadalupe, additional, Rosolen, Rafaela Rossi, additional, Sajjad, Saba, additional, Sales, Gabriele, additional, Sanchez-Martin, Victoria, additional, Shafique, Sumaira, additional, Singh, Amit, additional, Soniya, E.V., additional, Souza, Dorotéia Rossi Silva, additional, Srikanth, Krishnamoorthy, additional, Srinivasan, Asha, additional, te Pas, Marinus F.W., additional, Thakur, Subarna, additional, Townsend, Jeffrey P., additional, Trillo-Hernández, Eduardo Antonio, additional, van den Brand, Henry, additional, Vijay, Archa, additional, Wang, Pei-Chi, additional, Wang, Zheng, additional, and Watanabe, Lígia Moriguchi, additional

Published
2023
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42. Dietary Patterns Associated with Body-Composition Phenotype in a Middle-Aged and Elderly Population: A Population-Based Cross-Sectional Study.

Author

Park, Jong Eun, Yang, Narae, and Kim, Kirang

Abstract

Background/Objectives: There is currently limited understanding of the intricate dynamics between fat and muscle mass and the potential effect of dietary patterns. This study aimed to investigate the effects of overall dietary patterns on four body composition phenotypes associated with central obesity and low muscle mass in the middle-aged and elderly population of Korea. Methods: We examined data from 6120 adults aged ≥ 50 years, collected during the 4th (2008–2009) and 5th (2010–2011) Korea National Health and Nutrition Examination Survey. Participants were classified into four groups according to their combined status of central obesity and low muscle mass: healthy control, central obesity, low muscle mass, and sarcopenic obesity. Using factor analysis on the twenty-two pre-defined food groups, we identified three distinct dietary patterns: the "Healthy dietary pattern", "Convenience-based dietary pattern", and "Alcohol and side dish pattern". Multivariate analysis included potential confounders such as age, sex, place of residence, marital status, educational level, occupation, household income, and physical activity level. Results: Higher scores in the "Healthy dietary pattern" were associated with lower odds of sarcopenic obesity (OR = 0.61, 95% CI = 0.40–0.92, p for trend = 0.018). For the "Convenience-based dietary pattern", individuals in the third tertile of the dietary pattern score showed a marginal association with low muscle mass (OR = 1.18, 95% CI = 0.99–1.41, p for trend = 0.056). The "Alcohol and side dish pattern" was linked to higher odds of central obesity (OR = 1.22, 95% CI = 1.03–1.44, p for trend = 0.016) and low muscle mass (OR = 1.21, 95% CI = 1.01–1.45, p for trend = 0.043). Conclusions: The drinking practice of consuming foods high in saturated fats and salt alongside alcoholic drinks may negatively affect fat accumulation and muscle composition among older adults. Adopting a healthy eating pattern characterized by grains, vegetables, legumes, fruits, fish, and seaweed could be notably advantageous for promoting and maintaining healthy body composition and optimal skeletal muscle health. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Prevalence Estimation of the PALB2 Germline Variant in East Asians and Koreans through Population Database Analysis.

Author

Park, Jong Eun, Kang, Min-Chae, Lee, Taeheon, Cho, Eun Hye, Jang, Mi-Ae, Won, Dongju, Park, Boyoung, Ki, Chang-Seok, and Kong, Sun-Young

Subjects
*TUMOR risk factors, *BREAST tumor risk factors, *RISK assessment, *BRCA genes, *RESEARCH funding, *GENOMICS, *EAST Asians, *OVARIAN tumors, *DESCRIPTIVE statistics, *DISEASE prevalence, *GENETIC variation, *KOREANS, *WORLD health, *PANCREATIC tumors, *FINNS, *GENE expression profiling, *PSYCHOLOGY of Jews, *DISEASE susceptibility, *DATA analysis software, *PSYCHOSOCIAL factors, *DISEASE risk factors, TUMOR genetics
Abstract

Simple Summary: Pathogenic variants in the PALB2 gene significantly increase the risk of developing breast, ovarian, and pancreatic cancers. However, the prevalence of these mutations in East Asian populations, particularly Koreans, has not been well studied. This research aims to estimate the prevalence of PALB2 variants in these populations by analyzing large-scale genomic databases. Understanding the frequency of PALB2 variants can help in identifying individuals at higher risk for these cancers and guide the development of targeted screening and prevention strategies. The findings from this study provide valuable reference data that can enhance genetic counseling and improve cancer risk management in East Asian populations, ultimately contributing to better health outcomes in these communities. PALB2 is a tumor suppressor gene. Heterozygous germline pathogenic variants of PALB2 significantly increase the lifetime risk of breast cancer and moderately increase the risk of ovarian and pancreatic cancers. This study analyzed the estimated prevalence of PALB2 variants globally, focusing on East Asian and Korean populations, where limited data were previously available. We examined 125,748 exomes from the Genome Aggregation Database (gnomAD), including 9197 East Asians, and additional data from 5305 individuals in the Korean Variant Archive and 1722 in the Korean Reference Genome Database. All PALB2 variants were interpreted according to guidelines from the American College of Medical Genetics and Genomics and the Clinical Genome Resource. The global prevalence of PALB2 variants was 0.18%, with the highest prevalence in Finnish populations (0.41%) and the lowest in Ashkenazi Jewish populations (0.04%). East Asians had a prevalence of 0.09%. By combining data from Korean genome databases and gnomAD totaling 8936 individuals, the overall prevalence of PALB2 variants in the Korean population was determined to be 0.13%. This study is the first comprehensive investigation of PALB2 variant prevalence in East Asians and Koreans using gnomAD and Korean genome databases. These findings provide essential reference data for future research and highlight the importance of region-specific genetic studies that will inform genetic counseling and hereditary cancer risk management. [ABSTRACT FROM AUTHOR]

Published
2024
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