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Intelligent Cell Search Engine

Authors :
Atsuhiro Nakagawa
Yoshitaka Shirasaki
Kotaro Hiramatsu
Yasuhiro Fujiwaki
Yusuke Kasai
Hideya Fukuzawa
Hiroshi Karakawa
Taichiro Endo
Daichi Murakami
Yusuke Oguchi
Ming Li
Takanori Maeno
Dino Di Carlo
Kiyotaka Shiba
Satoshi Matsusaka
Yu Hoshino
Nao Nitta
Fumihito Arai
Sangwook Lee
Chihana Toyokawa
Yaxiaer Yalikun
Kenichi Koizumi
Akihiro Isozaki
Hideharu Mikami
Takuro Ito
Hiroshi Tezuka
Yuta Suzuki
Shinya Sakuma
Takanori Iino
Keisuke Goda
Takeaki Sugimura
Hirofumi Shintaku
Mary Inaba
Sotaro Uemura
Kei Hiraki
Cheng Lei
Tadataka Ota
Masayuki Yazawa
Yo Tanaka
Minoru Oikawa
Misa Hase
Takashi Yamano
Yoichiroh Hosokawa
Makoto Yamada
Yasuyuki Ozeki
Mai Yamagishi
Nobutake Suzuki
Yutaka Yatomi
Kanako Suga
Takeshi Hayakawa
Atsushi Yasumoto
Source :
SSRN Electronic Journal.
Publication Year :
2018
Publisher :
Elsevier BV, 2018.

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 real-time machine intelligence technology based on a radically new architecture that realizes real-time image-based intelligent cell search and sorting at an unprecedented rate. The technology integrates high-throughput cell imaging, cell focusing, and cell sorting on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision making, and actuation. Specifically, 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. Our technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.

Details

ISSN :
15565068
Database :
OpenAIRE
Journal :
SSRN Electronic Journal
Accession number :
edsair.doi...........a91bab379ff9703b4bb44709b6ce87c1