Virtual-freezing fluorescence imaging flow cytometry

• Published in: Nature communications Vol. 11; no. 1; p. 1162
• Main Authors: Mikami, Hideharu, Kawaguchi, Makoto, Huang, Chun-Jung, Matsumura, Hiroki, Sugimura, Takeaki, Huang, Kangrui, Lei, Cheng, Ueno, Shunnosuke, Miura, Taichi, Ito, Takuro, Nagasawa, Kazumichi, Maeno, Takanori, Watarai, Hiroshi, Yamagishi, Mai, Uemura, Sotaro, Ohnuki, Shinsuke, Ohya, Yoshikazu, Kurokawa, Hiromi, Matsusaka, Satoshi, Sun, Chia-Wei, Ozeki, Yasuyuki, Goda, Keisuke
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.03.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 14/63; 49/31; 631/1647/1407/1492; 631/1647/245/2225; 639/624/1111/55; Biology; Deep Learning; Dimensional analysis; Euglena gracilis; Feasibility Studies; Flow cytometry; Flow Cytometry - instrumentation; Flow Cytometry - methods; Fluorescence; Fluorescence microscopy; Freezing; Hematology; Hematology - instrumentation; Hematology - methods; High-Throughput Screening Assays - instrumentation; High-Throughput Screening Assays - methods; Humanities and Social Sciences; Humans; Image acquisition; Image classification; Image Processing, Computer-Assisted - instrumentation; Image Processing, Computer-Assisted - methods; Immunology; Jurkat Cells; Machine learning; Microbiological Techniques - instrumentation; Microbiology; Microscopy; Microscopy, Fluorescence - instrumentation; Microscopy, Fluorescence - methods; multidisciplinary; Science; Science (multidisciplinary); Sensitivity; Sensitivity and Specificity; Signal to noise ratio; Spatial resolution; Statistical analysis; Stem cells; Tradeoffs
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-14929-2

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s −1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology. High throughput imaging flow cytometry suffers from trade-offs between throughput, sensitivity and spatial resolution. Here the authors introduce a method to virtually freeze cells in the image acquisition window to enable 1000 times longer signal integration time and improve signal-to-noise ratio.