Recent Developments (After 2020) in Flow Cytometry Worldwide and Within China
Open accessAbstract
This article reviews recent developments in flow cytometry that have a significant impact on both scientific research and clinical applications in the field of single-cell analysis, from the perspective of instrumentation and technical advances. As a starting point, this article investigates the latest state-of-the-art instruments of flow cytometry including different types in spectral, mass, imaging, nano, and label-free flow cytometry. A comparative analysis of the parameters and features of instruments from different companies elucidates the development trends in flow cytometry instrumentation. Following this, this article delves into cutting-edge technical advancements in flow cytometry. It summarizes the current research status of flow cytometry not only globally but also within China, highlighting emerging trends and innovations in the field. Finally, this article outlines future directions for the development of flow cytometry, indicating that each type of flow cytometry will follow its own trajectory toward achieving enhanced performance and broader applications in diverse fields.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Other online flow cytometry monitoring systems, such as the Cytosense flow cytometer, are designed for monitoring low to very low cell densities in marine or freshwater ecosystems.
“Other online flow cytometry monitoring systems, such as the Cytosense flow cytometer [240], are designed for monitoring low to very low cell densities in marine or freshwater ecosystems.”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [240]
Section 2.3 (page not available) lists CytoSense XR as an imaging flow cytometry instrument. It does not describe the instrument as an online monitoring system or say it is designed for low cell densities in marine or freshwater ecosystems. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
High-speed fluorescence image–enabled cell sorting
Schraivogel, D., Kuhn, T. M., Rauscher, B., Rodríguez‐Martínez, M., Paulsen, M. S., Owsley, K., Middlebrook, A. J., Tischer, C., Ramasz, B., Ordoñez‐Rueda, D., et al. (2022) · Science · doi:10.1126/science.abj3013 · Cited by 289 · see citing works
Raman image-activated cell sorting
Nitta, N., Iino, T., Isozaki, A., Yamagishi, M., Kitahama, Y., Sakuma, S., Suzuki, Y., Tezuka, H., Oikawa, M., Arai, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17285-3 · Cited by 205 · see citing works
Two-color immunofluorescence using a fluorescence-activated cell sorter.
Loken, M. R., Parks, D. R., & Herzenberg, L. A. (1977) · Journal of Histochemistry & Cytochemistry · doi:10.1177/25.7.330738 · Cited by 196 · see citing works
Fluorescence Spectral Overlap Compensation for Any Number of Flow Cytometry Parameters
Bagwell, C. B., & Adams, E. G. (1993) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1993.tb38775.x · Cited by 155 · see citing works
Virtual-freezing fluorescence imaging flow cytometry
Mikami, H., Kawaguchi, M., Huang, C., Matsumura, H., Sugimura, T., Huang, K., Lei, C., Ueno, S., Miura, T., Ito, T., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14929-2 · Cited by 148 · see citing works
Levine, L. S., Hiam-Galvez, K. J., Marquez, D. M., Tenvooren, I., Madden, M. Z., Contreras, D. C., Dahunsi, D. O., Irish, J. M., Oluwole, O. O., Rathmell, J. C., et al. (2020) · Immunity · doi:10.1016/j.immuni.2021.02.018 · Cited by 128 · see citing works
Holzner, G., Mateescu, B., van Leeuwen, D., Cereghetti, G., Dechant, R. C., Stavrakis, S., & deMello, A. J. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108824 · Cited by 87 · see citing works
Kay, D. B., Cambier, J. L., & Wheeless, L. L. (1979) · Journal of Histochemistry & Cytochemistry · doi:10.1177/27.1.374597 · Cited by 61 · see citing works
Impedance‐Based Multimodal Electrical‐Mechanical Intrinsic Flow Cytometry
Feng, Y., Zhu, J., Chai, H., He, W., Huang, L. J., & Wang, W. (2023) · Small · doi:10.1002/smll.202303416 · Cited by 53 · see citing works
Hua, X., Han, K., Mandracchia, B., Radmand, A., Liu, W., Kim, H., Zhou, Y., Ehrlich, S. M., Li, K., Zheng, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46250-7 · Cited by 51 · see citing works
COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning
Salek, M., Li, N., Chou, H.-P., Saini, K., Jovic, A., Jacobs, K. B., Johnson, C., Lu, V., Lee, E. J., Chang, C. C., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05325-9 · Cited by 47 · see citing works
Tang, D., Chen, M., Yu, H., Xiang, N., & Ni, Z. (2021) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2021.129719 · Cited by 46 · see citing works
High-dimensional multi-pass flow cytometry via spectrally encoded cellular barcoding
Kwok, S. J. J., Forward, S., Fahlberg, M. D., Assita, E. R., Cosgriff, S., Lee, S. H., Abbott, G. R., Zhu, H., Minasian, N. H., Vote, A. S., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01144-9 · Cited by 40 · see citing works
Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry
Lun, X., Sheng, K., Yu, X., Lam, C. Y., Gowri, G., Serrata, M. P., Zhai, Y., Su, H., Luan, J., Kim, Y., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02316-x · Cited by 39 · see citing works
Quantitative Assessment of the Physical Virus Titer and Purity by Ultrasensitive Flow Virometry
Niu, Q., Ma, L., Zhu, S., Li, L., Zheng, Q., Hou, J., Lian, H., Wu, L., & Yan, X. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202100872 · Cited by 37 · see citing works
High-throughput fluorescence lifetime imaging flow cytometry
Kanno, H., Hiramatsu, K., Mikami, H., Nakayashiki, A., Yamashita, S., Nagai, A., Okabe, K., Li, F., Yin, F., Tominaga, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51125-y · Cited by 37 · see citing works
Floating-Electrode-Enabled Impedance Cytometry for Single-Cell 3D Localization
Fang, Q., Feng, Y., Zhu, J., Huang, L. J., & Wang, W. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c05822 · Cited by 36 · see citing works
Doan, M., Barnes, C. M., McQuin, C., Caicedo, J. C., Goodman, A., Carpenter, A. E., & Rees, P. C. T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00549-7 · Cited by 35 · see citing works
Zhang, Y., Liang, H., Tan, H., Chen, D., Wang, Y., Xu, Y., Wang, J., & Chen, J. (2020) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.128231 · Cited by 29 · see citing works
Self-citationPerformance-enhanced clogging-free viscous sheath constriction impedance flow cytometry
Zhu, J., Feng, Y., Chai, H., Liang, F., Cheng, Z., & Wang, W. (2023) · Lab on a Chip · doi:10.1039/d3lc00178d · Cited by 28 · see citing works
Wang, M., Zhang, J., Chen, X., Li, Y., Huang, X., Wang, J., Li, Y., Huo, X., & Chen, J. (2024) · Microsystems & Nanoengineering · doi:10.1038/s41378-024-00833-y · Cited by 25 · see citing works
Self-citationA high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer
Zhang, Z., Tang, R. H., Chen, X., Waller, L., Kau, A., Fung, A. A., Gutierrez, B., An, C., Cho, S. H., Shi, L., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2118068119 · Cited by 22 · see citing works
Self-citationLi, L., Wang, S., Xue, J., Lin, Y., Su, L., Xue, C., Mao, C., Cai, N., Tian, Y., Zhu, S., et al. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c05159 · Cited by 19 · see citing works
Luan, X., Liu, P., Huang, D., Zhao, H., Li, Y., Sun, S., Zhang, W., Zhang, L., Li, M., Zhi, T., et al. (2023) · Microsystems & Nanoengineering · doi:10.1038/s41378-023-00545-9 · Cited by 16 · see citing works
Tang, D., Jiang, L., Tang, W., Xiang, N., & Ni, Z. (2022) · Talanta · doi:10.1016/j.talanta.2022.123274 · Cited by 14 · see citing works
Yao, C., Jiang, L., Zhang, X., Ni, Z., & Xiang, N. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c03792 · Cited by 14 · see citing works
Label-free scattering image-spectro-microscopy for single cell and extracellular vesicle analysis
Eltigani, F. M., Zhang, X., Liu, Q., Liu, M., Peng, J., & Su, X. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.134572 · Cited by 13 · see citing works
Imaging flow cytometry using linear array spot excitation
Han, Y., Zhao, J., Chao, Z., Liang, K., Zhang, C., Jiang, L., Jiao, Z., Bai, F., Tárnok, A., & You, Z. (2023) · Device · doi:10.1016/j.device.2023.100124 · Cited by 13 · see citing works
Nanomaterial Assisted Exosome Analysis Using Mass Spectrometry
Zhang, Y., Zhou, B., Li, Q., Jin, M., & Bai, Y. (2024) · Chemical Research in Chinese Universities · doi:10.1007/s40242-024-4004-x · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Dębowski, M., Kazimierowicz, J., & Zieliński, M. (2025) · Applied Sciences · doi:10.3390/app15137324 · Cited by 30 · see citing works
Advances in single extracellular vesicle characterization and multiplexed profiling
Park, J., Kim, H., Roh, Y. H., & Ko, J. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118588 · Cited by 18 · see citing works
Flow Cytometry: Advances, Challenges and Trends
Robinson, J. P., Gmyrek, G. B., & Rajwa, B. P. (2025) · BioEssays · doi:10.1002/bies.70091 · Cited by 16 · see citing works
Imaging Flow Cytometry as a Molecular Biology Tool: From Cell Morphology to Molecular Mechanisms
Matsuoka, Y. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199261 · Cited by 2 · see citing works
Sun, M., Wang, Z., Gao, C., Huo, X., Wang, J., Chen, X., Zhang, Y., & Chen, J. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c05822 · Cited by 2 · see citing works
Self-citationRecent Advances in Structural and Functional Characterization of Biological Macromolecules
Fatima, R., Nehra, B., Debnath, B., Chatterjee, M., Nath, R., Ashique, S., Hussain, M. S., Yasmin, S., Akhtar, M. S., & Ansari, M. Y. (2026) · Critical Reviews in Analytical Chemistry · doi:10.1080/10408347.2026.2701969 · Cited by 1 · see citing works
Liu, H., Li, R., Jiang, F., & Fengcui, L. I. (2026) · Applied Sciences · doi:10.3390/app16168102 · Cited by 1 · see citing works
Advances in metal-DOTA/DTPA complexes for mass cytometry
Wang, Y.-F., Wu, W., & Han, G. (2025) · The Analyst · doi:10.1039/d5an00485c
Modular lanthanide barcodes for high-throughput single-cell analysis by mass cytometry
Huang, P., Wei, C., Wang, W., & Li, M. (2026) · Applied Materials Today · doi:10.1016/j.apmt.2026.103256
Optical Cascade of a Flow Cytometer for Oncohematology Research
Крупко, К. (2025) · Електронний архів наукових та освітніх матеріалів КПІ ім. Ігоря Сікорського (КПІ ім. Ігоря Сікорського)
Jiao, Y., Xia, Y., Shi, Y., Jiang, G., Gu, Z., & Wang, H.-F. (2026) · Analytical Methods · doi:10.1039/d6ay01731b
- Authors
- Du, X., Chen, X., Gao, C., Wang, J., Huo, X., & Chen, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Huo, Xiaoye
- Chen, Jian
- Venue
- Biosensors
- Publisher
- MDPI AG
- Published
- 2025-03-02
- Volume
- 15
- Issue
- 3
- Pages
- 156
- ISSN
- 2079-6374 (ISSN-L)
- PMC
- PMC11940362
- OpenAlex
- W4408121523
- Cited by
- 10
- References
- 29
- Citations by year
- 2026: 5 · 2025: 5
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biosensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62331025, 62121003)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Microfluidic and Bio-sensing Technologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- flow cytometry, flow cytometry instrumentation, spectral flow cytometry, mass cytometry, imaging flow cytometry, nano-flow cytometry, single-cell analysis
- MeSH terms
- China, Flow Cytometry, Humans, Biosensing Techniques, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
