Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second

Jiehua Zhou; Liye Mei; Mingjie Yu; Xiao Ma; Dan Hou; Zhuo Yin; Xun Liu; Yan Ding; Kaining Yang; Ruidong Xiao; Xiandan Yuan; Yueyun Weng; Mengping Long; Taobo Hu; Jinxuan Hou; Yu Xu; Liang Tao; Sisi Mei; Hui Shen; Yaxiaer Yalikun; Fuling Zhou; Liang Wang; Du Wang; Sheng Liu; Cheng Lei

doi:10.1038/s41377-025-01754-9

Your Location：

Home >

Browse articles >

Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second

Original Articles

- Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second
- Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second
- LSA 2025年14卷第4期页码：931-947
- Affiliations：
  
  1.The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
  2.School of Computer Science, Hubei University of Technology, Wuhan 430068, China
  3.Division of Materials Science, Nara Institute of Science and Technology, Takayama-cho 8916-5, Japan
  4.School of Science, Hubei University of Technology, Wuhan 430068, China
  5.Department of Pathology, Peking University Cancer Hospital, Beijing 100142, China
  6.Department of Breast Surgery, Peking University People’s Hospital, Beijing 100044, China
  7.Department of Thyroid and Breast Surgery, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
  8.Department of Radiation and Medical Oncology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
  9.People’s Hospital of Anshun City Guizhou Province, Anshun 561000, China
  10.Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
  11.National Engineering Laboratory for Next Generation Internet Access System, School of Optics and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
  12.Suzhou Institute of Wuhan University, Suzhou 215000, China
  13.Shenzhen Institute of Wuhan University, Shenzhen 518057, China
- Author bio：
  
  Liang Wang (hustwl@hust.edu.cn)
  Du Wang (wangdu@whu.edu.cn)
  Cheng Lei (leicheng@whu.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-01754-9
  中图分类号：
- 收稿日期：2024-07-29，
  
  修回日期：2024-12-09，
  
  录用日期：2025-01-08，
  
  网络出版日期：2025-02-10，
  
  纸质出版日期：2025-04-30
- Accepted：
Scan QR Code
Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second[J]. LSA, 2025,14(4):931-947.

Zhou, J. H. et al. Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second. Light: Science & Applications, 14, 931-947 (2025).
Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second[J]. LSA, 2025,14(4):931-947. DOI： 10.1038/s41377-025-01754-9.

Zhou, J. H. et al. Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second. Light: Science & Applications, 14, 931-947 (2025). DOI： 10.1038/s41377-025-01754-9.

摘要

Abstract

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 15m/s are clearly imaged with a spatial resolution of 780nm

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.

关键词

Keywords

references

Nitta, N. et al. Intelligent image-activated cell sorting. Cell 175 , 266–276.e13 (2018)..

George, T. C. et al. Distinguishing modes of cell death using the ImageStream ® multispectral imaging flow cytometer. Cytom. Part A 59A , 237–245 (2004)..

Litvinenko, A. L. et al. Fluorescence-free flow cytometry for measurement of shape index distribution of resting, partially activated, and fully activated platelets. Cytom. Part A 89A , 1010–1016 (2016)..

Basiji, D. A. et al. Cellular image analysis and imaging by flow cytometry. Clin. Lab. Med. 27 , 653–670 (2007)..

Song, P. M. etal. Optofluidic ptychography on a chip. Lab a Chip 21 , 4549–4556 (2021)..

Huang, K. R. et al. Deep imaging flow cytometry. Lab a Chip 22 , 876–889 (2022)..

Maryanovich, M. et al. An MTCH2 pathway repressing mitochondria metabolism regulates haematopoietic stem cell fate. Nat. Commun. 6 , 7901 (2015)..

Porichis, F. et al. High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5 , 5641 (2014)..

Sancho, D. et al. Identification of a dendritic cell receptor that couples sensing of necrosis to immunity. Nature 458 , 899–903 (2009)..

Blasi, T. et al. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7 , 10256 (2016)..

Tiwari, V., Sutton, M. A. & McNeill, S. R. Assessment of high speed imaging systems for 2D and 3D deformation measurements: methodology development and validation. Exp. Mech. 47 , 561–579 (2007)..

Nishino, N. et al. High-speed 2-D image measurement for plasma-wall interaction studies. J. Nucl. Mater. 337-339 , 1073–1076 (2005)..

Thoroddsen, S. T., Etoh, T. G. & Takehara, K. High-speed imaging of drops and bubbles. Annu. Rev. Fluid Mech. 40 , 257–285 (2008)..

Lee, D. et al. A hardware accelerated system for high throughput cellular image analysis. J. Parallel Distrib. Comput. 113 , 167–178 (2018)..

Urbanska, M. et al. A comparison of microfluidic methods for high-throughput cell deformability measurements. Nat. Methods 17 , 587–593 (2020)..

Xing, H. Z. et al. High-speed photography and dig ital optical measurement techniques for geomaterials: fundamentals and applications. Rock. Mech. Rock. Eng. 50 , 1611–1659 (2017)..

Zlatanski, M., Uhring, W. & Le Normand, J. P. Sub-500-ps temporal resolution streak-mode optical sensor. IEEE Sens. J. 15 , 6570–6583 (2015)..

Etoh, T. G. et al. The theoretical highest frame rate of silicon image sensors. Sensors 17 , 483 (2017)..

Goda, K., Tsia, K. K. & Jalali, B. Amplified dispersive Fourier-transform imaging for ultrafast displacement sensing and barcode reading. Appl. Phys. Lett. 93 , 131109 (2008)..

Goda, K., Tsia, K. K. & Jalali, B. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena. Nature 458 , 1145–1149 (2009)..

Goda, K. et al. High-throughput optical coherence tomography at 800nm. Opt. Express 20 , 19612–19617 (2012)..

Guo, Q. et al. High-speed compressive microscopy of flowing cells using sinusoidal illumination patterns. IEEE Photonics J. 9 , 3900111 (2017)..

Wu, J. L. et al. Multi-MHz laser-scanning single-cell fluorescence microscopy by spatiotemporally encoded virtual source array. Biomed. Opt. Express 8 , 4160–4171 (2017)..

Jiang, Y. Y. et al. Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy. Lab a Chip 17 , 2426–2434 (2017)..

Lei, C. et al. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nat. Protoc. 13 , 1603–1631 (2018)..

Goda, K. et al. High-throughput single-microparticle imaging flow analyzer. Proc. Natl Acad. Sci. USA 109 , 11630–11635 (2012)..

Chen, C. L. et al. Deep learning in label-free cell classification. Sci. Rep. 6 , 21471 (2016)..

Wu, Y. Z. et al. Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging. Opt. Express 28 , 519–532 (2020)..

Wu, J. L. et al. Ultrafast laser-scanning time-stretch imaging at v isible wavelengths. Light Sci. Appl. 6 , e16196 (2017)..

Lai, Q. T. K. et al. High-speed laser-scanning biological microscopy using FACED. Nat. Protoc. 16 , 4227–4264 (2021)..

Lee, K. C. M. et al. Multi-ATOM: ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution. J. Biophotonics 12 , e201800479 (2019)..

Shi, R. B. et al. A real-time coprime line scan super-resolution system for ultra-fast microscopy. IEEE Trans. Biomed. Circuits Syst. 13 , 781–792 (2019)..

Chan, A. C. S. et al. All-passive pixel super-resolution of time-stretch imaging. Sci. Rep. 7 , 44608 (2017)..

Goda, K. et al. Hybrid dispersion laser scanner. Sci. Rep. 2 , 445 (2012)..

Mahjoubfar, A. et al. Time stretch and its applications. Nat. Photonics 11 , 341–351 (2017)..

Weng, Y. Y. et al. Analysis of signal detection configurations in optical time-str etch imaging. Opt. Express 28 , 29272–29284 (2020)..

Liu, A. M., Lin, W. S. & Narwaria, M. Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21 , 1500–1512 (2012)..

Wang, Z. et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 , 600–612 (2004)..

Sheikh, H. R. & Bovik, A. C. Image information and visual quality. IEEE Trans. Image Process. 15 , 430–444 (2006)..

Pan, X. W. et al. MobileNet-light: a lightweight TCT image classification model for cervical cancer. Proceedings of the 2023 International Joint Conference on Neural Networks. Gold Coast, Australia: IEEE, 2023.

Chen, H. et al. CytoBrain: cervical cancer screening system based on deep learning technology. J. Computer Sci. Technol. 36 , 347–360 (2021)..

Cohen, P. A. et al. Cervical cancer. Lancet 393 , 169–182 (2019)..

Deng, Y. J. et al. Studying the efficacy of antiplatelet drugs on atherosclerosis by optofluidic imaging on a chip. Lab a Chip 23 , 410–420 (2023)..

Otto, O. et al. Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat. Methods 12 , 199–202 (2015)..

Fregin, B. et al. High-throughput single-cell rheology in complex samples by dynamic real-time deformability cytometry. Nat. Commun. 10 , 415 (2019)..

Stott, S. L. et al. Isolation of circulating tumor cells using a microvortex-generating herringbone-chip. Proc. Natl Acad. Sci. USA 107 , 18392–18397 (2010)..

Siegel, R. L. et al. Colorectal cancer statistics, 2023. CA: A Cancer J. Clinicians 73 , 233–254 (2023)..

Xi, Y. & Xu, P. F. Global colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 14 , 101174 (2021)..

Biller, L. H. & Schrag, D. Diagnosis and treatment of metastatic colorectal cancer: a review. JAMA 325 , 669–685 (2021)..

Weng, Y. Y. et al. Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip. Lab a Chip 23 , 1703–1712 (2023)..

Valiathan, R., Ashman, M. & Asthana, D. Effects of ageing on the immune system: infants to elderly. Scand. J. Immunol. 83 , 255–266 (2016)..

Zhou, Y. Q. et al. Intelligent platelet morphometry. Trends Biotechnol. 39 , 978–989 (2021)..

Zhou, Y. Q. et al. Intelligent classification of platelet aggregates by agonist type. eLife 9 , e52938 (2020)..

Li, Z. et al. A high-performance pixel-level fully pipelined hardware accelerator for neural networks. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/TNNLS.2024.3423664 https://doi.org/10.1109/TNNLS.2024.3423664 (2024).

Winterstein, F., Bayliss, S.&Constantinides, G. A. High-level synthesis of dynamic data structures: a case study using vivado HLS. Proceedings of the 2013 International Conference on Field-Programmable Technology. Kyoto, Japan: IEEE, 2013, 362-365.

de Fine Licht, J. et al. Transformations of high-level synthesis codes for high-performance computing. IEEE Trans. Parallel Distrib. Syst. 32 , 1014–1029 (2021)..

Han, S. et al. EIE: efficient inference engine on compressed deep neural network. Proceedings of 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture. Seoul, Korea (South): IEEE, 2016, 243-254.

Rees, P. et al. Imaging flowcytometry. Nat. Rev. Methods Prim. 2 , 86 (2022)..

Schraivogel, D. et al. High-speed fluorescence image-enabled cell sorting. Science 375 , 315–320 (2022)..

Nishikawa, M. et al. Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID- 19. Nat. Commun. 12 , 7135 (2021)..

Diebold, E. D. et al. Digitally synthesized beat frequency multiplexing for sub-millisecond fluorescence microscopy. Nat. Photonics 7 , 806–810 (2013)..

Matsumura, H. et al. Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells. Lab a Chip 23 , 1561–1575 (2023)..

Mikami, H. et al. Virtual-freezing fluorescence imaging flow cytometry. Nat. Commun. 11 , 1162 (2020)..

Han, Y. Y. & Lo, Y. H. Imaging cells in flow cytometer using spatial-temporal transformation. Sci. Rep. 5 , 13267 (2015)..

Han, Y. et al. Imaging flow cytometry using linear array spot excitation. Device 1 , 100124 (2023)..

Guo, B. S. et al. High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy. Cytom. Part A 91 , 494–502 (2017)..

Lei, C. et al. High-throughput label-free image cytometry and image-based classification of live Euglena gracilis. Biomed. Opt. Express 7 , 2703–2708 (2016)..

Guo, B. S. et al. High-throughput label-free screening of Euglena gracilis with optofluidic time-stretch quantitative phase microscopy. Proceedings of SPIE 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II. San Francisco, California, United States: SPIE, 2017.

Lei, C. et al. GHz optical time-stretch microscopy by compressive sensing. IEEE Photonics J. 9 , 3900308 (2017)..

Hou, D. et al. Optofluidic time-stretch imaging flow cytometry with a real-time storage rate beyond 5.9 GB/s. Cytom. Part A 105 , 713–721 (2024)..

Liu, X. et al. An optimized PDMS microfluidic device for ultra-fast and high-throughput imaging flow cytometry. Lab a Chip 23 , 3571–3580 (2023)..

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据