| Literature DB >> 30166209 |
Nao Nitta1, Takeaki Sugimura1, Akihiro Isozaki2, Hideharu Mikami2, Kei Hiraki2, Shinya Sakuma3, Takanori Iino4, Fumihito Arai3, Taichiro Endo5, Yasuhiro Fujiwaki4, Hideya Fukuzawa6, Misa Hase2, Takeshi Hayakawa7, Kotaro Hiramatsu2, Yu Hoshino8, Mary Inaba9, Takuro Ito1, Hiroshi Karakawa2, Yusuke Kasai3, Kenichi Koizumi9, SangWook Lee2, Cheng Lei2, Ming Li10, Takanori Maeno11, Satoshi Matsusaka12, Daichi Murakami9, Atsuhiro Nakagawa13, Yusuke Oguchi14, Minoru Oikawa15, Tadataka Ota2, Kiyotaka Shiba16, Hirofumi Shintaku17, Yoshitaka Shirasaki14, Kanako Suga16, Yuta Suzuki4, Nobutake Suzuki14, Yo Tanaka18, Hiroshi Tezuka9, Chihana Toyokawa6, Yaxiaer Yalikun18, Makoto Yamada19, Mai Yamagishi14, Takashi Yamano6, Atsushi Yasumoto20, Yutaka Yatomi20, Masayuki Yazawa21, Dino Di Carlo22, Yoichiroh Hosokawa23, Sotaro Uemura14, Yasuyuki Ozeki4, Keisuke Goda24.
Abstract
A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.Keywords: cellular heterogeneity; cellular morphology; convolutional neural network; deep learning; high-throughput microscopy; high-throughput screening; image-activated cell sorting; machine intelligence
Mesh:
Year: 2018 PMID: 30166209 DOI: 10.1016/j.cell.2018.08.028
Source DB: PubMed Journal: Cell ISSN: 0092-8674 Impact factor: 41.582