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Abstract
The analysis of massive microscopy datasets using deep neural networks provides an alternative to molecular labeling to characterize cellular states.
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Affiliation(s)
- Antony Orth
- ARC Centre of Excellence for Nanoscale BioPhotonics, RMIT University, Melbourne, VIC 3001, Australia.
| | - Diane Schaak
- The Rowland Institute at Harvard, Cambridge, MA 02141, USA
| | - Ethan Schonbrun
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
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Ota S, Horisaki R, Kawamura Y, Ugawa M, Sato I, Hashimoto K, Kamesawa R, Setoyama K, Yamaguchi S, Fujiu K, Waki K, Noji H. Ghost cytometry. Science 2018; 360:1246-1251. [PMID: 29903975 DOI: 10.1126/science.aan0096] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 03/10/2018] [Accepted: 05/14/2018] [Indexed: 12/13/2022]
Abstract
Ghost imaging is a technique used to produce an object's image without using a spatially resolving detector. Here we develop a technique we term "ghost cytometry," an image-free ultrafast fluorescence "imaging" cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry. Despite a compact and inexpensive instrumentation, image-free ghost cytometry achieves accurate and high-throughput cell classification and selective sorting on the basis of cell morphology without a specific biomarker, both of which have been challenging to accomplish using conventional flow cytometers.
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Affiliation(s)
- Sadao Ota
- Thinkcyte Inc., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan. .,University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.,Japan Science and Technology Agency, PRESTO, 4-1-8 Honcho, Kawaguchi-shi, Saitama 332-0012, Japan
| | - Ryoichi Horisaki
- Japan Science and Technology Agency, PRESTO, 4-1-8 Honcho, Kawaguchi-shi, Saitama 332-0012, Japan.,Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yoko Kawamura
- Thinkcyte Inc., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.,University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Masashi Ugawa
- Thinkcyte Inc., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Issei Sato
- Thinkcyte Inc., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.,University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.,Japan Science and Technology Agency, PRESTO, 4-1-8 Honcho, Kawaguchi-shi, Saitama 332-0012, Japan.,RIKEN AIP, Nihonbashi 1-chome Mitsui Building, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Kazuki Hashimoto
- University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.,Japan Aerospace Exploration Agency, 6-13-1 Osawa, Mitaka-shi, Tokyo 181-0015, Japan
| | - Ryosuke Kamesawa
- Thinkcyte Inc., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.,University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Kotaro Setoyama
- Thinkcyte Inc., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Satoko Yamaguchi
- University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Katsuhito Fujiu
- University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Kayo Waki
- University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Hiroyuki Noji
- University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.,ImPACT Program, Cabinet Office, Government of Japan, Chiyoda-ku Tokyo 100-8914, Japan
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Orth A, Wilson ER, Thompson JG, Gibson BC. A dual-mode mobile phone microscope using the onboard camera flash and ambient light. Sci Rep 2018; 8:3298. [PMID: 29459650 PMCID: PMC5818495 DOI: 10.1038/s41598-018-21543-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 02/06/2018] [Indexed: 12/16/2022] Open
Abstract
Mobile phone microscopes are a natural platform for point-of-care imaging, but current solutions require an externally powered illumination source, thereby adding bulk and cost. We present a mobile phone microscope that uses the internal flash or sunlight as the illumination source, thereby reducing complexity whilst maintaining functionality and performance. The microscope is capable of both brightfield and darkfield imaging modes, enabling microscopic visualisation of samples ranging from plant to mammalian cells. We describe the microscope design principles, assembly process, and demonstrate its imaging capabilities through the visualisation of unlabelled cell nuclei to observing the motility of cattle sperm and zooplankton.
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Affiliation(s)
- A Orth
- ARC Centre of Excellence for Nanoscale BioPhotonics, School of Science, RMIT University, Melbourne, Australia.
| | - E R Wilson
- ARC Centre of Excellence for Nanoscale BioPhotonics, School of Science, RMIT University, Melbourne, Australia
| | - J G Thompson
- ARC Centre of Excellence for Nanoscale BioPhotonics, Robinson Research Institute, School of Medicine, The University of Adelaide, Adelaide, Australia
| | - B C Gibson
- ARC Centre of Excellence for Nanoscale BioPhotonics, School of Science, RMIT University, Melbourne, Australia
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