Ghost cytometry
Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H.
American Association for the Advancement of Science (AAAS)
Published 2018
American Association for the Advancement of Science (AAAS)
Published 2018
| Publication Date: |
2018-06-15
|
|---|---|
| Publisher: |
American Association for the Advancement of Science (AAAS)
|
| Print ISSN: |
0036-8075
|
| Electronic ISSN: |
1095-9203
|
| Topics: |
Biology
Chemistry and Pharmacology
Geosciences
Computer Science
Medicine
Natural Sciences in General
Physics
|
| Keywords: |
Biochemistry, Cell Biology
|
| Published by: |
| _version_ | 1836398973555834880 |
|---|---|
| autor | Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. |
| beschreibung | 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. |
| citation_standardnr | 6284040 |
| datenlieferant | ipn_articles |
| feed_id | 25 |
| feed_publisher | American Association for the Advancement of Science (AAAS) |
| feed_publisher_url | http://www.aaas.org/ |
| insertion_date | 2018-06-15 |
| journaleissn | 1095-9203 |
| journalissn | 0036-8075 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | American Association for the Advancement of Science (AAAS) |
| quelle | Science |
| relation | http://science.sciencemag.org/cgi/content/short/360/6394/1246?rss=1 |
| schlagwort | Biochemistry, Cell Biology |
| search_space | articles |
| shingle_author_1 | Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. |
| shingle_author_2 | Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. |
| shingle_author_3 | Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. |
| shingle_author_4 | Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. |
| shingle_catch_all_1 | Ghost cytometry Biochemistry, Cell Biology 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. Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. American Association for the Advancement of Science (AAAS) 0036-8075 00368075 1095-9203 10959203 |
| shingle_catch_all_2 | Ghost cytometry Biochemistry, Cell Biology 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. Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. American Association for the Advancement of Science (AAAS) 0036-8075 00368075 1095-9203 10959203 |
| shingle_catch_all_3 | Ghost cytometry Biochemistry, Cell Biology 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. Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. American Association for the Advancement of Science (AAAS) 0036-8075 00368075 1095-9203 10959203 |
| shingle_catch_all_4 | Ghost cytometry Biochemistry, Cell Biology 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. Ota, S., Horisaki, R., Kawamura, Y., Ugawa, M., Sato, I., Hashimoto, K., Kamesawa, R., Setoyama, K., Yamaguchi, S., Fujiu, K., Waki, K., Noji, H. American Association for the Advancement of Science (AAAS) 0036-8075 00368075 1095-9203 10959203 |
| shingle_title_1 | Ghost cytometry |
| shingle_title_2 | Ghost cytometry |
| shingle_title_3 | Ghost cytometry |
| shingle_title_4 | Ghost cytometry |
| timestamp | 2025-06-30T23:35:34.480Z |
| titel | Ghost cytometry |
| titel_suche | Ghost cytometry |
| topic | W V TE-TZ SQ-SU WW-YZ TA-TD U |
| uid | ipn_articles_6284040 |