High throughput label-free three-dimensional image flow cytometry for characterizing biophysical properties of cells in large populations

Yanping He; Md Habibur Rahman; Yijin Wang; Yanqing Xu; Liting Duan; Yiping Ho; Renjie Zhou

doi:10.1117/12.2610465

2 March 2022 High throughput label-free three-dimensional image flow cytometry for characterizing biophysical properties of cells in large populations

Yanping He, Md Habibur Rahman, Yijin Wang, Yanqing Xu, Liting Duan, Yiping Ho, Renjie Zhou

Author Affiliations +

Proceedings Volume PC11971, High-Speed Biomedical Imaging and Spectroscopy VII; PC119710P (2022) https://doi.org/10.1117/12.2610465
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

We have recently demonstrated a high throughput three-dimensional (3D) image flow cytometry method, in which a machine-learning algorithm is used to retrieve the 3D refractive index maps of cells from one angle-multiplexing interferogram. Using this system, we have imaged flowing red blood cells and NIH/3T3 cells with a throughput of more than < 10,000 volumes/second. To further demonstrate its potential on cell phenotyping for clinical testing, we plan to apply this platform to image large populations of various cell types and extracting their morphological and biophysical parameters.

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Yanping He, Md Habibur Rahman, Yijin Wang, Yanqing Xu, Liting Duan, Yiping Ho, and Renjie Zhou "High throughput label-free three-dimensional image flow cytometry for characterizing biophysical properties of cells in large populations", Proc. SPIE PC11971, High-Speed Biomedical Imaging and Spectroscopy VII, PC119710P (2 March 2022); https://doi.org/10.1117/12.2610465

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KEYWORDS

3D image processing

Flow cytometry

Blood

Cancer

Imaging systems

Machine learning

Microfluidics

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