Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second Article Swipe
Jiehua Zhou
,
Liye Mei
,
Mingjie Yu
,
Xiao Ma
,
Dan Hou
,
Zongsheng Yin
,
Xun Liu
,
Yan Ding
,
Kaining Yang
,
Ruidong Xiao
,
Xiandan Yuan
,
Yueyun Weng
,
Mengping Long
,
Tianyu Hu
,
Jinxuan Hou
,
Yu Xu
,
Liang Tao
,
Shirley H. J. Mei
,
Hui Shen
,
Yaxiaer Yalikun
,
Fuling Zhou
,
Liang Wang
,
Du Wang
,
Sheng Liu
,
Cheng Lei
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41377-025-01754-9
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41377-025-01754-9
Imaging flow cytometry (IFC) combines the imaging capabilities of microscopy with the high throughput of flow cytometry, offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine, green energy, and environmental monitoring. However, due to limitations in imaging framerate and real-time data processing, the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second (eps), which is insufficient for large-scale cell analysis. In this work, we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch (OTS) imaging, microfluidic-based cell manipulation, and online image processing. Cells flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm, and images of each individual cell are captured, stored, and analyzed. The capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal samples. This work sets a new record for throughput in imaging flow cytometry, and we believe it has the potential to revolutionize cell analysis by enabling highly efficient, accurate, and intelligent measurement.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41377-025-01754-9
- OA Status
- gold
- Cited By
- 21
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407287582
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407287582Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41377-025-01754-9Digital Object Identifier
- Title
-
Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per secondWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-10Full publication date if available
- Authors
-
Jiehua Zhou, Liye Mei, Mingjie Yu, Xiao Ma, Dan Hou, Zongsheng Yin, Xun Liu, Yan Ding, Kaining Yang, Ruidong Xiao, Xiandan Yuan, Yueyun Weng, Mengping Long, Tianyu Hu, Jinxuan Hou, Yu Xu, Liang Tao, Shirley H. J. Mei, Hui Shen, Yaxiaer Yalikun, Fuling Zhou, Liang Wang, Du Wang, Sheng Liu, Cheng LeiList of authors in order
- Landing page
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https://doi.org/10.1038/s41377-025-01754-9Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1038/s41377-025-01754-9Direct OA link when available
- Concepts
-
Throughput, Computer science, Cytometry, Microfluidics, Flow cytometry, Real-time computing, Work flow, Computer hardware, Computer vision, Artificial intelligence, Nanotechnology, Materials science, Engineering, Telecommunications, Genetics, Wireless, Biology, Industrial engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 21Per-year citation counts (last 5 years)
- References (count)
-
73Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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